library(rstanarm)
Loading required package: Rcpp
Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
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  method           from         
  print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'xts':
  method     from
  as.zoo.xts zoo 
rstanarm (Version 2.18.2, packaged: 2018-11-08 22:19:38 UTC)
- Do not expect the default priors to remain the same in future rstanarm versions.
Thus, R scripts should specify priors explicitly, even if they are just the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
- Plotting theme set to bayesplot::theme_default().
library(rstan)
Loading required package: ggplot2
Loading required package: StanHeaders
rstan (Version 2.18.2, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
library(glmnet)
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
library(caret)
Loading required package: lattice
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: ‘caret’

The following objects are masked from ‘package:rstanarm’:

    compare_models, R2
randseed <- 12345
set.seed(randseed)
# load data
WORK_DIR <- "/Users/linyingzhang/LargeFiles/Hripcsak/deconfounder/"
DATA_PATH <- paste0(WORK_DIR, "data/simulation_multicause_data/")
# load causes
x_df <-  read.table(paste0(DATA_PATH, "simulated_causes.txt"))
names(x_df) <- seq(1, ncol(x_df), 1)
# load substitute confounders
T_hat_pmf <- read.table(paste0(DATA_PATH, "x_post_np_PMF_k450.txt"))
x_t_df_pmf <- as.data.frame(cbind(x_df, T_hat_pmf))
T_hat_def <- read.table(paste0(DATA_PATH, "x_post_np_DEF_2_2.txt"))
x_t_df_def <- as.data.frame(cbind(x_df, T_hat_def))
# load true confounders
C <- read.table(paste0(DATA_PATH, "simulated_multicause_conf.txt"))
x_c_df <- as.data.frame(cbind(x_df, C))
# load outcome
ys <- read.table(paste0(DATA_PATH, "simulated_outcomes_1592223649.txt"))
ys <- t(ys)
# load true coefficients
betas <- read.table(paste0(DATA_PATH, "simulated_true_coeffs_1592223649.txt"))
betas <- t(betas)
n_causes <- dim(x_df)[2]+1
n_sims = dim(ys)[2]
# Run outcome models
summary_stats <- array(NA, dim=c(n_sims, 4, 4))
for (sim in seq(1, n_sims, 1)){
  y <- ys[,sim]
  beta <- betas[,sim]
  #fit ridge models
  fitridge_no_control = stan_glm(y~., data = x_df, family = gaussian(), prior = normal(),
                                 algorithm = "meanfield", adapt_delta = NULL, QR = FALSE,
                                 sparse = FALSE, seed = randseed)
  fitridge_oracle = stan_glm(y~., data = x_c_df, family = gaussian(), prior = normal(),
                          algorithm = "meanfield", adapt_delta = NULL, QR = FALSE,
                          sparse = FALSE, seed = randseed)
  fitridge_pmf = stan_glm(y~., data = x_t_df_pmf, family = gaussian(), prior = normal(),
                          algorithm = "meanfield", adapt_delta = NULL, QR = FALSE,
                          sparse = FALSE, seed = randseed)
  fitridge_def = stan_glm(y~., data = x_t_df_def, family = gaussian(), prior = normal(),
                          algorithm = "meanfield", adapt_delta = NULL, QR = FALSE,
                          sparse = FALSE, seed = randseed)
  
  no_control_coefs <- fitridge_no_control$coefficients[2:n_causes]
  oracle_coefs <- fitridge_oracle$coefficients[2:n_causes]
  pmf_coefs <- fitridge_pmf$coefficients[2:n_causes]
  def_coefs <- fitridge_def$coefficients[2:n_causes]
  
  rmse_no_control <- sqrt(mean((beta - no_control_coefs)**2))
  rmse_oracle <- sqrt(mean((beta - oracle_coefs)**2))
  rmse_pmf <- sqrt(mean((beta - pmf_coefs)**2))
  rmse_def <- sqrt(mean((beta - def_coefs)**2))
  
  # CI
  ci95_no_control <- posterior_interval(fitridge_no_control, prob = 0.95)
  ci95_oracle <- posterior_interval(fitridge_oracle, prob = 0.95)
  ci95_pmf <- posterior_interval(fitridge_pmf, prob = 0.95)
  ci95_def <- posterior_interval(fitridge_def, prob = 0.95)
  # coverage: if the 95ci covers the true coefficients
  nc_coverage <-  (beta >=ci95_no_control[2:n_causes,1]) & (beta <= ci95_no_control[2:n_causes,2])
  oracle_coverage <-  (beta >=ci95_oracle[2:n_causes,1]) & (beta <= ci95_oracle[2:n_causes,2])
  pmf_coverage <-  (beta >=ci95_pmf[2:n_causes,1]) & (beta <= ci95_pmf[2:n_causes,2])
  def_coverage <-  (beta >=ci95_def[2:n_causes,1]) & (beta <= ci95_def[2:n_causes,2])
  
  truth <- as.factor(ifelse(beta != 0, 1, 0)) # factor of positive / negative cases
  
  oracle_all_coverage <- sum(oracle_coverage)/50
  nc_all_coverage <- sum(nc_coverage)/50
  pmf_all_coverage <- sum(pmf_coverage)/50
  def_all_coverage <- sum(def_coverage)/50
  
  oracle_causal_coverage <- sum(oracle_coverage[truth==1])/10
  nc_causal_coverage <- sum(nc_coverage[truth==1])/10
  pmf_causal_coverage <- sum(pmf_coverage[truth==1])/10
  def_causal_coverage <- sum(def_coverage[truth==1])/10
  
  oracle_noncausal_coverage <- sum(oracle_coverage[truth==0])/40
  nc_noncausal_coverage <- sum(nc_coverage[truth==0])/40
  pmf_noncausal_coverage <- sum(pmf_coverage[truth==0])/40
  def_noncausal_coverage <- sum(def_coverage[truth==0])/40
  
  summary_stats[sim,,] <- rbind(cbind(rmse_oracle, oracle_all_coverage, oracle_causal_coverage, oracle_noncausal_coverage),
                                cbind(rmse_no_control, nc_all_coverage, nc_causal_coverage, nc_noncausal_coverage),
                                cbind(rmse_pmf, pmf_all_coverage, pmf_causal_coverage, pmf_noncausal_coverage),
                                cbind(rmse_def, def_all_coverage, def_causal_coverage, def_noncausal_coverage))
}
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.008676 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 86.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49700.120             1.000            1.000
Chain 1:    200       -20897.885             1.189            1.378
Chain 1:    300       -18059.343             0.845            1.000
Chain 1:    400       -18870.918             0.645            1.000
Chain 1:    500       -16393.697             0.546            0.157
Chain 1:    600       -12632.725             0.505            0.298
Chain 1:    700       -14970.259             0.455            0.157
Chain 1:    800       -14506.094             0.402            0.157
Chain 1:    900       -11552.639             0.386            0.157
Chain 1:   1000       -19152.592             0.387            0.256
Chain 1:   1100       -18596.025             0.290            0.157
Chain 1:   1200       -11403.896             0.215            0.157
Chain 1:   1300       -13037.097             0.212            0.156
Chain 1:   1400       -11282.999             0.223            0.156
Chain 1:   1500       -11676.713             0.211            0.156
Chain 1:   1600       -11740.951             0.182            0.155
Chain 1:   1700       -12148.375             0.170            0.125
Chain 1:   1800       -11181.154             0.175            0.125
Chain 1:   1900       -11073.978             0.151            0.087
Chain 1:   2000       -12205.852             0.120            0.087
Chain 1:   2100       -11714.121             0.122            0.087
Chain 1:   2200       -11879.519             0.060            0.042
Chain 1:   2300       -18317.082             0.082            0.042
Chain 1:   2400       -10048.753             0.149            0.042
Chain 1:   2500       -10433.995             0.150            0.042
Chain 1:   2600       -10171.449             0.152            0.042
Chain 1:   2700       -10881.492             0.155            0.065
Chain 1:   2800       -17586.888             0.184            0.065
Chain 1:   2900       -11523.739             0.236            0.093
Chain 1:   3000       -17291.079             0.260            0.334
Chain 1:   3100       -10638.473             0.318            0.351
Chain 1:   3200        -9936.393             0.324            0.351
Chain 1:   3300       -10164.480             0.291            0.334
Chain 1:   3400       -13416.738             0.233            0.242
Chain 1:   3500       -14307.945             0.236            0.242
Chain 1:   3600       -10640.556             0.267            0.334
Chain 1:   3700        -9947.948             0.268            0.334
Chain 1:   3800       -11206.992             0.241            0.242
Chain 1:   3900       -14546.368             0.211            0.230
Chain 1:   4000       -10457.604             0.217            0.230
Chain 1:   4100       -11780.649             0.166            0.112
Chain 1:   4200       -11213.899             0.164            0.112
Chain 1:   4300        -9124.151             0.184            0.229
Chain 1:   4400        -8765.716             0.164            0.112
Chain 1:   4500        -9138.568             0.162            0.112
Chain 1:   4600        -8960.912             0.130            0.112
Chain 1:   4700       -15306.317             0.164            0.112
Chain 1:   4800        -9667.296             0.211            0.229
Chain 1:   4900       -11051.895             0.201            0.125
Chain 1:   5000       -10247.646             0.170            0.112
Chain 1:   5100        -9253.745             0.169            0.107
Chain 1:   5200        -9638.156             0.168            0.107
Chain 1:   5300       -12246.290             0.166            0.107
Chain 1:   5400        -8916.423             0.200            0.125
Chain 1:   5500        -9676.128             0.203            0.125
Chain 1:   5600       -10974.674             0.213            0.125
Chain 1:   5700       -14654.241             0.197            0.125
Chain 1:   5800        -9705.381             0.190            0.125
Chain 1:   5900       -14270.785             0.209            0.213
Chain 1:   6000       -11989.303             0.220            0.213
Chain 1:   6100        -9393.491             0.237            0.251
Chain 1:   6200       -11125.570             0.249            0.251
Chain 1:   6300        -9285.662             0.247            0.251
Chain 1:   6400       -13228.224             0.240            0.251
Chain 1:   6500        -9523.821             0.271            0.276
Chain 1:   6600        -9506.314             0.259            0.276
Chain 1:   6700        -8929.827             0.240            0.276
Chain 1:   6800        -9731.294             0.198            0.198
Chain 1:   6900       -10517.531             0.173            0.190
Chain 1:   7000        -8771.463             0.174            0.198
Chain 1:   7100       -13217.988             0.180            0.198
Chain 1:   7200       -10586.770             0.189            0.199
Chain 1:   7300        -9195.446             0.185            0.199
Chain 1:   7400       -11755.669             0.177            0.199
Chain 1:   7500        -9964.139             0.156            0.180
Chain 1:   7600        -9270.456             0.163            0.180
Chain 1:   7700        -8831.011             0.161            0.180
Chain 1:   7800        -8798.246             0.154            0.180
Chain 1:   7900        -8829.098             0.146            0.180
Chain 1:   8000        -8835.454             0.127            0.151
Chain 1:   8100        -9089.226             0.096            0.075
Chain 1:   8200        -8715.796             0.075            0.050
Chain 1:   8300        -8668.834             0.061            0.043
Chain 1:   8400        -9593.847             0.048            0.043
Chain 1:   8500       -11556.073             0.047            0.043
Chain 1:   8600        -8915.974             0.070            0.043
Chain 1:   8700        -8468.806             0.070            0.043
Chain 1:   8800        -8558.708             0.071            0.043
Chain 1:   8900        -9825.711             0.083            0.053
Chain 1:   9000        -9070.447             0.091            0.083
Chain 1:   9100       -10257.115             0.100            0.096
Chain 1:   9200       -10984.155             0.103            0.096
Chain 1:   9300        -9089.613             0.123            0.116
Chain 1:   9400        -8918.947             0.115            0.116
Chain 1:   9500        -9398.515             0.103            0.083
Chain 1:   9600       -10385.187             0.083            0.083
Chain 1:   9700        -9132.623             0.092            0.095
Chain 1:   9800        -9267.848             0.092            0.095
Chain 1:   9900       -11484.163             0.098            0.095
Chain 1:   10000        -8626.034             0.123            0.116
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001473 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59086.121             1.000            1.000
Chain 1:    200       -18393.965             1.606            2.212
Chain 1:    300        -9062.464             1.414            1.030
Chain 1:    400        -8235.888             1.086            1.030
Chain 1:    500        -8422.039             0.873            1.000
Chain 1:    600        -8348.637             0.729            1.000
Chain 1:    700        -9471.536             0.642            0.119
Chain 1:    800        -8460.911             0.576            0.119
Chain 1:    900        -7767.646             0.522            0.119
Chain 1:   1000        -8335.766             0.477            0.119
Chain 1:   1100        -8039.663             0.381            0.100
Chain 1:   1200        -7823.504             0.162            0.089
Chain 1:   1300        -7887.134             0.060            0.068
Chain 1:   1400        -7978.699             0.051            0.037
Chain 1:   1500        -7696.074             0.052            0.037
Chain 1:   1600        -8042.683             0.056            0.043
Chain 1:   1700        -7678.447             0.049            0.043
Chain 1:   1800        -7808.227             0.039            0.037
Chain 1:   1900        -7745.168             0.030            0.037
Chain 1:   2000        -7907.054             0.026            0.028
Chain 1:   2100        -7726.342             0.024            0.023
Chain 1:   2200        -7965.225             0.025            0.023
Chain 1:   2300        -7697.285             0.027            0.030
Chain 1:   2400        -7793.724             0.027            0.030
Chain 1:   2500        -7723.665             0.025            0.023
Chain 1:   2600        -7673.716             0.021            0.020
Chain 1:   2700        -7650.298             0.016            0.017
Chain 1:   2800        -7667.139             0.015            0.012
Chain 1:   2900        -7517.549             0.016            0.020
Chain 1:   3000        -7672.557             0.016            0.020
Chain 1:   3100        -7670.065             0.014            0.012
Chain 1:   3200        -7893.207             0.014            0.012
Chain 1:   3300        -7598.824             0.014            0.012
Chain 1:   3400        -7849.921             0.016            0.020
Chain 1:   3500        -7587.319             0.019            0.020
Chain 1:   3600        -7647.232             0.019            0.020
Chain 1:   3700        -7599.897             0.019            0.020
Chain 1:   3800        -7606.827             0.019            0.020
Chain 1:   3900        -7577.700             0.017            0.020
Chain 1:   4000        -7546.207             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003525 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86850.843             1.000            1.000
Chain 1:    200       -14153.942             3.068            5.136
Chain 1:    300       -10420.365             2.165            1.000
Chain 1:    400       -11696.772             1.651            1.000
Chain 1:    500        -9052.963             1.379            0.358
Chain 1:    600        -8871.892             1.153            0.358
Chain 1:    700        -8826.624             0.989            0.292
Chain 1:    800        -9145.714             0.870            0.292
Chain 1:    900        -9125.137             0.773            0.109
Chain 1:   1000        -9207.952             0.697            0.109
Chain 1:   1100        -8991.719             0.599            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8714.773             0.089            0.032
Chain 1:   1300        -9032.615             0.056            0.032
Chain 1:   1400        -9066.130             0.046            0.024
Chain 1:   1500        -8894.570             0.019            0.020
Chain 1:   1600        -9005.138             0.018            0.019
Chain 1:   1700        -9058.195             0.018            0.019
Chain 1:   1800        -8612.720             0.020            0.019
Chain 1:   1900        -8719.827             0.021            0.019
Chain 1:   2000        -8701.788             0.020            0.019
Chain 1:   2100        -8839.748             0.019            0.016
Chain 1:   2200        -8614.366             0.018            0.016
Chain 1:   2300        -8711.834             0.016            0.012
Chain 1:   2400        -8786.810             0.016            0.012
Chain 1:   2500        -8726.918             0.015            0.012
Chain 1:   2600        -8743.641             0.014            0.011
Chain 1:   2700        -8649.763             0.015            0.011
Chain 1:   2800        -8595.259             0.010            0.011
Chain 1:   2900        -8700.029             0.010            0.011
Chain 1:   3000        -8539.544             0.012            0.011
Chain 1:   3100        -8679.701             0.012            0.011
Chain 1:   3200        -8548.954             0.011            0.011
Chain 1:   3300        -8779.004             0.012            0.012
Chain 1:   3400        -8785.718             0.012            0.012
Chain 1:   3500        -8654.381             0.012            0.015
Chain 1:   3600        -8506.274             0.014            0.015
Chain 1:   3700        -8653.216             0.015            0.016
Chain 1:   3800        -8508.946             0.016            0.017
Chain 1:   3900        -8440.749             0.015            0.017
Chain 1:   4000        -8551.392             0.015            0.016
Chain 1:   4100        -8515.976             0.013            0.015
Chain 1:   4200        -8501.779             0.012            0.015
Chain 1:   4300        -8535.253             0.010            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003177 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411214.962             1.000            1.000
Chain 1:    200     -1587318.179             2.650            4.299
Chain 1:    300      -891666.459             2.026            1.000
Chain 1:    400      -458854.217             1.756            1.000
Chain 1:    500      -359097.187             1.460            0.943
Chain 1:    600      -233758.192             1.306            0.943
Chain 1:    700      -119909.278             1.255            0.943
Chain 1:    800       -87132.999             1.145            0.943
Chain 1:    900       -67470.208             1.050            0.780
Chain 1:   1000       -52277.027             0.974            0.780
Chain 1:   1100       -39759.083             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38940.194             0.478            0.376
Chain 1:   1300       -26888.682             0.445            0.376
Chain 1:   1400       -26609.629             0.352            0.315
Chain 1:   1500       -23195.573             0.339            0.315
Chain 1:   1600       -22412.850             0.288            0.291
Chain 1:   1700       -21284.694             0.199            0.291
Chain 1:   1800       -21228.988             0.161            0.147
Chain 1:   1900       -21555.748             0.134            0.053
Chain 1:   2000       -20064.946             0.112            0.053
Chain 1:   2100       -20303.380             0.082            0.035
Chain 1:   2200       -20530.563             0.081            0.035
Chain 1:   2300       -20146.950             0.038            0.019
Chain 1:   2400       -19918.796             0.038            0.019
Chain 1:   2500       -19720.970             0.024            0.015
Chain 1:   2600       -19350.436             0.023            0.015
Chain 1:   2700       -19307.115             0.018            0.012
Chain 1:   2800       -19023.794             0.019            0.015
Chain 1:   2900       -19305.326             0.019            0.015
Chain 1:   3000       -19291.372             0.011            0.012
Chain 1:   3100       -19376.538             0.011            0.011
Chain 1:   3200       -19066.769             0.011            0.015
Chain 1:   3300       -19271.827             0.010            0.011
Chain 1:   3400       -18746.035             0.012            0.015
Chain 1:   3500       -19359.073             0.014            0.015
Chain 1:   3600       -18664.158             0.016            0.015
Chain 1:   3700       -19052.182             0.018            0.016
Chain 1:   3800       -18009.541             0.022            0.020
Chain 1:   3900       -18005.635             0.021            0.020
Chain 1:   4000       -18122.912             0.021            0.020
Chain 1:   4100       -18036.626             0.021            0.020
Chain 1:   4200       -17852.296             0.021            0.020
Chain 1:   4300       -17991.069             0.020            0.020
Chain 1:   4400       -17947.456             0.018            0.010
Chain 1:   4500       -17849.923             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49364.995             1.000            1.000
Chain 1:    200       -16523.719             1.494            1.988
Chain 1:    300       -13936.577             1.058            1.000
Chain 1:    400       -18222.176             0.852            1.000
Chain 1:    500       -15694.346             0.714            0.235
Chain 1:    600       -11729.678             0.651            0.338
Chain 1:    700       -14331.165             0.584            0.235
Chain 1:    800       -13753.961             0.516            0.235
Chain 1:    900       -13806.735             0.459            0.186
Chain 1:   1000       -14661.751             0.419            0.186
Chain 1:   1100       -10802.471             0.355            0.186
Chain 1:   1200       -15945.288             0.189            0.186
Chain 1:   1300       -12602.179             0.196            0.235
Chain 1:   1400       -11512.600             0.182            0.182
Chain 1:   1500       -13045.486             0.178            0.182
Chain 1:   1600       -10392.907             0.170            0.182
Chain 1:   1700       -13018.028             0.172            0.202
Chain 1:   1800       -12235.781             0.174            0.202
Chain 1:   1900       -10192.650             0.194            0.202
Chain 1:   2000       -14506.376             0.218            0.255
Chain 1:   2100       -13188.756             0.192            0.202
Chain 1:   2200       -11166.346             0.178            0.200
Chain 1:   2300       -14115.805             0.172            0.200
Chain 1:   2400        -9930.102             0.205            0.202
Chain 1:   2500       -18233.374             0.239            0.209
Chain 1:   2600       -12166.689             0.263            0.209
Chain 1:   2700       -16021.102             0.267            0.241
Chain 1:   2800        -9766.639             0.324            0.297
Chain 1:   2900       -10653.445             0.313            0.297
Chain 1:   3000        -9459.307             0.296            0.241
Chain 1:   3100       -10926.485             0.299            0.241
Chain 1:   3200        -9521.019             0.296            0.241
Chain 1:   3300        -9609.074             0.276            0.241
Chain 1:   3400        -9537.895             0.234            0.148
Chain 1:   3500       -10033.526             0.194            0.134
Chain 1:   3600        -9824.239             0.146            0.126
Chain 1:   3700        -9835.705             0.122            0.083
Chain 1:   3800       -10529.627             0.065            0.066
Chain 1:   3900       -10610.244             0.057            0.049
Chain 1:   4000       -14604.577             0.072            0.049
Chain 1:   4100       -15377.338             0.063            0.049
Chain 1:   4200       -11023.576             0.088            0.049
Chain 1:   4300       -14479.071             0.111            0.050
Chain 1:   4400        -9350.410             0.165            0.066
Chain 1:   4500       -10049.826             0.167            0.070
Chain 1:   4600        -9344.879             0.173            0.075
Chain 1:   4700        -9485.736             0.174            0.075
Chain 1:   4800        -8975.216             0.173            0.075
Chain 1:   4900        -9201.318             0.175            0.075
Chain 1:   5000       -15883.903             0.189            0.075
Chain 1:   5100        -9475.944             0.252            0.239
Chain 1:   5200        -9312.795             0.214            0.075
Chain 1:   5300       -10137.979             0.199            0.075
Chain 1:   5400        -9409.684             0.151            0.075
Chain 1:   5500       -15649.741             0.184            0.077
Chain 1:   5600       -16048.430             0.179            0.077
Chain 1:   5700       -11446.358             0.218            0.081
Chain 1:   5800        -9211.586             0.237            0.243
Chain 1:   5900       -14777.564             0.272            0.377
Chain 1:   6000        -9131.979             0.292            0.377
Chain 1:   6100        -9620.963             0.229            0.243
Chain 1:   6200        -9661.185             0.228            0.243
Chain 1:   6300       -14792.649             0.254            0.347
Chain 1:   6400       -14914.334             0.247            0.347
Chain 1:   6500       -11822.826             0.234            0.261
Chain 1:   6600        -9120.570             0.261            0.296
Chain 1:   6700        -9862.303             0.228            0.261
Chain 1:   6800        -9132.991             0.212            0.261
Chain 1:   6900        -9103.816             0.174            0.080
Chain 1:   7000       -10985.966             0.130            0.080
Chain 1:   7100       -10041.436             0.134            0.094
Chain 1:   7200       -12227.521             0.152            0.171
Chain 1:   7300       -10575.598             0.132            0.156
Chain 1:   7400        -8658.716             0.154            0.171
Chain 1:   7500        -9452.910             0.136            0.156
Chain 1:   7600        -9435.785             0.107            0.094
Chain 1:   7700        -8802.875             0.106            0.094
Chain 1:   7800        -9164.323             0.102            0.094
Chain 1:   7900        -8807.510             0.106            0.094
Chain 1:   8000       -11561.622             0.113            0.094
Chain 1:   8100        -8722.282             0.136            0.156
Chain 1:   8200       -10984.821             0.138            0.156
Chain 1:   8300       -11519.892             0.128            0.084
Chain 1:   8400        -8832.243             0.136            0.084
Chain 1:   8500        -9945.641             0.139            0.112
Chain 1:   8600        -9344.017             0.145            0.112
Chain 1:   8700        -9263.182             0.139            0.112
Chain 1:   8800        -9297.682             0.135            0.112
Chain 1:   8900       -12689.702             0.158            0.206
Chain 1:   9000       -10369.852             0.156            0.206
Chain 1:   9100        -8828.442             0.141            0.175
Chain 1:   9200       -10578.735             0.137            0.165
Chain 1:   9300       -10052.718             0.138            0.165
Chain 1:   9400        -9682.092             0.111            0.112
Chain 1:   9500       -11436.534             0.115            0.153
Chain 1:   9600        -9210.665             0.133            0.165
Chain 1:   9700       -12225.045             0.157            0.175
Chain 1:   9800        -8920.856             0.193            0.224
Chain 1:   9900        -8947.246             0.167            0.175
Chain 1:   10000        -8673.228             0.148            0.165
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58869.123             1.000            1.000
Chain 1:    200       -18290.640             1.609            2.219
Chain 1:    300        -8941.529             1.421            1.046
Chain 1:    400        -8156.009             1.090            1.046
Chain 1:    500        -9247.626             0.896            1.000
Chain 1:    600        -8210.267             0.767            1.000
Chain 1:    700        -7818.451             0.665            0.126
Chain 1:    800        -8360.434             0.590            0.126
Chain 1:    900        -8151.787             0.527            0.118
Chain 1:   1000        -8004.713             0.476            0.118
Chain 1:   1100        -7783.492             0.379            0.096
Chain 1:   1200        -7579.317             0.160            0.065
Chain 1:   1300        -7889.244             0.059            0.050
Chain 1:   1400        -7658.497             0.053            0.039
Chain 1:   1500        -7572.256             0.042            0.030
Chain 1:   1600        -7794.757             0.032            0.029
Chain 1:   1700        -7599.778             0.030            0.028
Chain 1:   1800        -7644.890             0.024            0.027
Chain 1:   1900        -7567.699             0.022            0.027
Chain 1:   2000        -7691.989             0.022            0.027
Chain 1:   2100        -7645.228             0.020            0.026
Chain 1:   2200        -7837.515             0.020            0.025
Chain 1:   2300        -7600.894             0.019            0.025
Chain 1:   2400        -7624.112             0.016            0.016
Chain 1:   2500        -7627.258             0.015            0.016
Chain 1:   2600        -7558.357             0.013            0.010
Chain 1:   2700        -7471.914             0.012            0.010
Chain 1:   2800        -7655.369             0.014            0.012
Chain 1:   2900        -7406.611             0.016            0.016
Chain 1:   3000        -7568.595             0.016            0.021
Chain 1:   3100        -7549.726             0.016            0.021
Chain 1:   3200        -7770.074             0.017            0.021
Chain 1:   3300        -7473.905             0.017            0.021
Chain 1:   3400        -7724.679             0.020            0.024
Chain 1:   3500        -7466.136             0.024            0.028
Chain 1:   3600        -7528.995             0.024            0.028
Chain 1:   3700        -7482.347             0.023            0.028
Chain 1:   3800        -7481.369             0.021            0.028
Chain 1:   3900        -7437.611             0.018            0.021
Chain 1:   4000        -7431.064             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002927 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86661.830             1.000            1.000
Chain 1:    200       -14003.965             3.094            5.188
Chain 1:    300       -10334.333             2.181            1.000
Chain 1:    400       -11369.090             1.659            1.000
Chain 1:    500        -9322.375             1.371            0.355
Chain 1:    600        -8977.574             1.149            0.355
Chain 1:    700        -8839.308             0.987            0.220
Chain 1:    800        -9391.131             0.871            0.220
Chain 1:    900        -9159.096             0.777            0.091
Chain 1:   1000        -9089.133             0.700            0.091
Chain 1:   1100        -9031.718             0.601            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8790.347             0.085            0.038
Chain 1:   1300        -9021.921             0.052            0.027
Chain 1:   1400        -9047.106             0.043            0.026
Chain 1:   1500        -8892.947             0.023            0.025
Chain 1:   1600        -9006.859             0.020            0.017
Chain 1:   1700        -9084.106             0.019            0.017
Chain 1:   1800        -8661.912             0.018            0.017
Chain 1:   1900        -8762.279             0.017            0.013
Chain 1:   2000        -8736.757             0.016            0.013
Chain 1:   2100        -8861.928             0.017            0.014
Chain 1:   2200        -8666.410             0.017            0.014
Chain 1:   2300        -8757.065             0.015            0.013
Chain 1:   2400        -8826.040             0.016            0.013
Chain 1:   2500        -8772.291             0.015            0.011
Chain 1:   2600        -8773.437             0.013            0.010
Chain 1:   2700        -8690.226             0.013            0.010
Chain 1:   2800        -8650.440             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005762 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 57.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391889.677             1.000            1.000
Chain 1:    200     -1582144.781             2.652            4.304
Chain 1:    300      -891750.703             2.026            1.000
Chain 1:    400      -458643.574             1.756            1.000
Chain 1:    500      -359056.523             1.460            0.944
Chain 1:    600      -233875.582             1.306            0.944
Chain 1:    700      -119941.592             1.255            0.944
Chain 1:    800       -87067.775             1.145            0.944
Chain 1:    900       -67380.912             1.051            0.774
Chain 1:   1000       -52153.899             0.975            0.774
Chain 1:   1100       -39605.578             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38780.007             0.478            0.378
Chain 1:   1300       -26716.542             0.446            0.378
Chain 1:   1400       -26433.156             0.352            0.317
Chain 1:   1500       -23014.899             0.340            0.317
Chain 1:   1600       -22229.506             0.290            0.292
Chain 1:   1700       -21101.249             0.200            0.292
Chain 1:   1800       -21044.965             0.162            0.149
Chain 1:   1900       -21371.151             0.135            0.053
Chain 1:   2000       -19880.938             0.113            0.053
Chain 1:   2100       -20119.410             0.083            0.035
Chain 1:   2200       -20346.052             0.082            0.035
Chain 1:   2300       -19963.101             0.038            0.019
Chain 1:   2400       -19735.158             0.038            0.019
Chain 1:   2500       -19537.109             0.025            0.015
Chain 1:   2600       -19167.221             0.023            0.015
Chain 1:   2700       -19124.155             0.018            0.012
Chain 1:   2800       -18840.931             0.019            0.015
Chain 1:   2900       -19122.290             0.019            0.015
Chain 1:   3000       -19108.476             0.012            0.012
Chain 1:   3100       -19193.462             0.011            0.012
Chain 1:   3200       -18884.081             0.011            0.015
Chain 1:   3300       -19088.855             0.011            0.012
Chain 1:   3400       -18563.615             0.012            0.015
Chain 1:   3500       -19175.706             0.014            0.015
Chain 1:   3600       -18482.173             0.016            0.015
Chain 1:   3700       -18869.137             0.018            0.016
Chain 1:   3800       -17828.444             0.022            0.021
Chain 1:   3900       -17824.590             0.021            0.021
Chain 1:   4000       -17941.904             0.021            0.021
Chain 1:   4100       -17855.619             0.022            0.021
Chain 1:   4200       -17671.791             0.021            0.021
Chain 1:   4300       -17810.257             0.021            0.021
Chain 1:   4400       -17767.021             0.018            0.010
Chain 1:   4500       -17669.543             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001243 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48805.962             1.000            1.000
Chain 1:    200       -15101.698             1.616            2.232
Chain 1:    300       -17183.854             1.118            1.000
Chain 1:    400       -22334.530             0.896            1.000
Chain 1:    500       -13719.271             0.842            0.628
Chain 1:    600       -30694.144             0.794            0.628
Chain 1:    700       -15840.019             0.815            0.628
Chain 1:    800       -14210.393             0.727            0.628
Chain 1:    900       -11106.776             0.677            0.553
Chain 1:   1000       -29741.830             0.672            0.627
Chain 1:   1100       -14080.632             0.684            0.627   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11867.134             0.479            0.553   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300        -9905.381             0.487            0.553   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -13701.832             0.491            0.553   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -12631.274             0.437            0.279
Chain 1:   1600       -21841.622             0.424            0.279
Chain 1:   1700       -18965.045             0.345            0.277
Chain 1:   1800        -9684.907             0.430            0.279
Chain 1:   1900       -10035.610             0.405            0.277
Chain 1:   2000        -9144.447             0.352            0.198
Chain 1:   2100       -17431.624             0.289            0.198
Chain 1:   2200        -9388.941             0.356            0.277
Chain 1:   2300        -9705.151             0.339            0.277
Chain 1:   2400       -18170.780             0.358            0.422
Chain 1:   2500        -8782.433             0.456            0.466
Chain 1:   2600        -8823.037             0.415            0.466
Chain 1:   2700        -8857.528             0.400            0.466
Chain 1:   2800       -10666.042             0.321            0.170
Chain 1:   2900        -8885.055             0.338            0.200
Chain 1:   3000       -18017.555             0.378            0.466
Chain 1:   3100        -9217.536             0.426            0.466
Chain 1:   3200        -8592.569             0.348            0.200
Chain 1:   3300        -9514.373             0.354            0.200
Chain 1:   3400        -9558.938             0.308            0.170
Chain 1:   3500        -8748.709             0.211            0.097
Chain 1:   3600       -15061.672             0.252            0.170
Chain 1:   3700        -9085.478             0.318            0.200
Chain 1:   3800       -10336.034             0.313            0.200
Chain 1:   3900        -8503.544             0.314            0.215
Chain 1:   4000       -13763.737             0.302            0.215
Chain 1:   4100        -8678.525             0.265            0.215
Chain 1:   4200       -13362.529             0.293            0.351
Chain 1:   4300       -11589.336             0.298            0.351
Chain 1:   4400        -8565.826             0.333            0.353
Chain 1:   4500        -9062.806             0.329            0.353
Chain 1:   4600        -8920.432             0.289            0.351
Chain 1:   4700       -11847.576             0.248            0.247
Chain 1:   4800        -8340.058             0.278            0.351
Chain 1:   4900        -9108.187             0.265            0.351
Chain 1:   5000        -9269.838             0.228            0.247
Chain 1:   5100        -8890.381             0.174            0.153
Chain 1:   5200        -8452.206             0.144            0.084
Chain 1:   5300       -15201.744             0.173            0.084
Chain 1:   5400        -8449.540             0.218            0.084
Chain 1:   5500       -12439.622             0.244            0.247
Chain 1:   5600        -9113.532             0.279            0.321
Chain 1:   5700        -8567.508             0.261            0.321
Chain 1:   5800        -8288.152             0.222            0.084
Chain 1:   5900        -8890.095             0.221            0.068
Chain 1:   6000        -8500.311             0.223            0.068
Chain 1:   6100        -9414.397             0.229            0.097
Chain 1:   6200        -8844.535             0.230            0.097
Chain 1:   6300       -13055.957             0.218            0.097
Chain 1:   6400       -11440.644             0.152            0.097
Chain 1:   6500       -11428.516             0.120            0.068
Chain 1:   6600       -10493.627             0.093            0.068
Chain 1:   6700        -8688.289             0.107            0.089
Chain 1:   6800        -8070.182             0.111            0.089
Chain 1:   6900       -10291.580             0.126            0.097
Chain 1:   7000        -8225.920             0.147            0.141
Chain 1:   7100        -7917.486             0.141            0.141
Chain 1:   7200       -10443.573             0.159            0.208
Chain 1:   7300        -8864.365             0.144            0.178
Chain 1:   7400       -10258.122             0.144            0.178
Chain 1:   7500        -8914.219             0.159            0.178
Chain 1:   7600        -8055.301             0.160            0.178
Chain 1:   7700        -8147.830             0.141            0.151
Chain 1:   7800        -9020.646             0.143            0.151
Chain 1:   7900        -8013.509             0.134            0.136
Chain 1:   8000        -8712.297             0.117            0.126
Chain 1:   8100       -10651.011             0.131            0.136
Chain 1:   8200        -8584.733             0.131            0.136
Chain 1:   8300       -12349.788             0.143            0.136
Chain 1:   8400       -10394.319             0.149            0.151
Chain 1:   8500        -8091.322             0.162            0.182
Chain 1:   8600       -11358.962             0.180            0.188
Chain 1:   8700        -9689.858             0.196            0.188
Chain 1:   8800        -8208.698             0.205            0.188
Chain 1:   8900       -10194.738             0.212            0.195
Chain 1:   9000        -8039.294             0.230            0.241
Chain 1:   9100        -8077.489             0.213            0.241
Chain 1:   9200        -9365.062             0.202            0.195
Chain 1:   9300       -10175.467             0.180            0.188
Chain 1:   9400       -11642.770             0.174            0.180
Chain 1:   9500       -11121.376             0.150            0.172
Chain 1:   9600        -7993.006             0.160            0.172
Chain 1:   9700       -10082.215             0.164            0.180
Chain 1:   9800        -8475.552             0.165            0.190
Chain 1:   9900       -10065.340             0.161            0.158
Chain 1:   10000        -8441.054             0.153            0.158
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56660.572             1.000            1.000
Chain 1:    200       -17016.380             1.665            2.330
Chain 1:    300        -8436.901             1.449            1.017
Chain 1:    400        -7794.339             1.107            1.017
Chain 1:    500        -8275.042             0.897            1.000
Chain 1:    600        -7921.495             0.755            1.000
Chain 1:    700        -7660.047             0.652            0.082
Chain 1:    800        -7995.089             0.576            0.082
Chain 1:    900        -7831.980             0.514            0.058
Chain 1:   1000        -7534.223             0.467            0.058
Chain 1:   1100        -7568.452             0.367            0.045
Chain 1:   1200        -7648.353             0.135            0.042
Chain 1:   1300        -7584.862             0.034            0.040
Chain 1:   1400        -7723.494             0.028            0.034
Chain 1:   1500        -7564.092             0.024            0.021
Chain 1:   1600        -7484.147             0.021            0.021
Chain 1:   1700        -7437.977             0.018            0.018
Chain 1:   1800        -7512.339             0.015            0.011
Chain 1:   1900        -7536.674             0.013            0.010
Chain 1:   2000        -7518.824             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003067 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86092.376             1.000            1.000
Chain 1:    200       -13099.458             3.286            5.572
Chain 1:    300        -9541.622             2.315            1.000
Chain 1:    400       -10385.777             1.757            1.000
Chain 1:    500        -8450.564             1.451            0.373
Chain 1:    600        -8092.444             1.217            0.373
Chain 1:    700        -8146.393             1.044            0.229
Chain 1:    800        -8526.206             0.919            0.229
Chain 1:    900        -8441.393             0.818            0.081
Chain 1:   1000        -8142.769             0.740            0.081
Chain 1:   1100        -8392.678             0.643            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8046.194             0.090            0.044
Chain 1:   1300        -8284.226             0.055            0.043
Chain 1:   1400        -8270.584             0.047            0.037
Chain 1:   1500        -8163.988             0.026            0.030
Chain 1:   1600        -8264.722             0.023            0.029
Chain 1:   1700        -8353.990             0.023            0.029
Chain 1:   1800        -7964.226             0.023            0.029
Chain 1:   1900        -8066.486             0.024            0.029
Chain 1:   2000        -8036.603             0.020            0.013
Chain 1:   2100        -8166.645             0.019            0.013
Chain 1:   2200        -7952.998             0.017            0.013
Chain 1:   2300        -8095.654             0.016            0.013
Chain 1:   2400        -8108.952             0.016            0.013
Chain 1:   2500        -8076.539             0.015            0.013
Chain 1:   2600        -8077.208             0.014            0.013
Chain 1:   2700        -7984.914             0.014            0.013
Chain 1:   2800        -7959.964             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.010065 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 100.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8433208.743             1.000            1.000
Chain 1:    200     -1587313.775             2.656            4.313
Chain 1:    300      -889653.158             2.032            1.000
Chain 1:    400      -456670.595             1.761            1.000
Chain 1:    500      -356700.092             1.465            0.948
Chain 1:    600      -231830.659             1.311            0.948
Chain 1:    700      -118430.653             1.260            0.948
Chain 1:    800       -85723.236             1.150            0.948
Chain 1:    900       -66137.269             1.055            0.784
Chain 1:   1000       -50985.758             0.980            0.784
Chain 1:   1100       -38519.804             0.912            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37698.878             0.483            0.382
Chain 1:   1300       -25723.283             0.451            0.382
Chain 1:   1400       -25446.793             0.357            0.324
Chain 1:   1500       -22051.835             0.345            0.324
Chain 1:   1600       -21273.054             0.294            0.297
Chain 1:   1700       -20155.550             0.204            0.296
Chain 1:   1800       -20101.507             0.166            0.154
Chain 1:   1900       -20427.183             0.138            0.055
Chain 1:   2000       -18943.844             0.116            0.055
Chain 1:   2100       -19181.901             0.085            0.037
Chain 1:   2200       -19407.247             0.084            0.037
Chain 1:   2300       -19025.561             0.040            0.020
Chain 1:   2400       -18797.911             0.040            0.020
Chain 1:   2500       -18599.626             0.026            0.016
Chain 1:   2600       -18230.594             0.024            0.016
Chain 1:   2700       -18187.868             0.019            0.012
Chain 1:   2800       -17904.768             0.020            0.016
Chain 1:   2900       -18185.732             0.020            0.015
Chain 1:   3000       -18172.025             0.012            0.012
Chain 1:   3100       -18256.887             0.011            0.012
Chain 1:   3200       -17948.002             0.012            0.015
Chain 1:   3300       -18152.415             0.011            0.012
Chain 1:   3400       -17627.933             0.013            0.015
Chain 1:   3500       -18238.768             0.015            0.016
Chain 1:   3600       -17546.818             0.017            0.016
Chain 1:   3700       -17932.526             0.019            0.017
Chain 1:   3800       -16894.238             0.023            0.022
Chain 1:   3900       -16890.391             0.022            0.022
Chain 1:   4000       -17007.744             0.023            0.022
Chain 1:   4100       -16921.541             0.023            0.022
Chain 1:   4200       -16738.262             0.022            0.022
Chain 1:   4300       -16876.360             0.022            0.022
Chain 1:   4400       -16833.555             0.019            0.011
Chain 1:   4500       -16736.110             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12185.413             1.000            1.000
Chain 1:    200        -9097.688             0.670            1.000
Chain 1:    300        -8015.756             0.491            0.339
Chain 1:    400        -8132.492             0.372            0.339
Chain 1:    500        -7821.237             0.306            0.135
Chain 1:    600        -7843.286             0.255            0.135
Chain 1:    700        -7801.028             0.220            0.040
Chain 1:    800        -7813.595             0.192            0.040
Chain 1:    900        -7719.932             0.172            0.014
Chain 1:   1000        -7822.074             0.156            0.014
Chain 1:   1100        -7840.412             0.057            0.013
Chain 1:   1200        -7814.485             0.023            0.012
Chain 1:   1300        -7768.203             0.010            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001427 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57314.037             1.000            1.000
Chain 1:    200       -17312.591             1.655            2.311
Chain 1:    300        -8607.383             1.441            1.011
Chain 1:    400        -7996.596             1.100            1.011
Chain 1:    500        -8233.991             0.885            1.000
Chain 1:    600        -7992.344             0.743            1.000
Chain 1:    700        -7821.876             0.640            0.076
Chain 1:    800        -8108.138             0.564            0.076
Chain 1:    900        -7969.709             0.504            0.035
Chain 1:   1000        -7869.060             0.454            0.035
Chain 1:   1100        -7803.152             0.355            0.030
Chain 1:   1200        -7960.752             0.126            0.029
Chain 1:   1300        -7697.660             0.029            0.029
Chain 1:   1400        -8015.080             0.025            0.029
Chain 1:   1500        -7704.858             0.026            0.030
Chain 1:   1600        -7619.218             0.024            0.022
Chain 1:   1700        -7617.174             0.022            0.020
Chain 1:   1800        -7633.258             0.019            0.017
Chain 1:   1900        -7714.017             0.018            0.013
Chain 1:   2000        -7700.362             0.017            0.011
Chain 1:   2100        -7724.237             0.016            0.011
Chain 1:   2200        -7770.342             0.015            0.010
Chain 1:   2300        -7679.306             0.013            0.010
Chain 1:   2400        -7731.770             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003192 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86864.551             1.000            1.000
Chain 1:    200       -13212.201             3.287            5.575
Chain 1:    300        -9659.278             2.314            1.000
Chain 1:    400       -10470.226             1.755            1.000
Chain 1:    500        -8562.405             1.449            0.368
Chain 1:    600        -8262.491             1.213            0.368
Chain 1:    700        -8225.569             1.040            0.223
Chain 1:    800        -8461.266             0.914            0.223
Chain 1:    900        -8533.858             0.813            0.077
Chain 1:   1000        -8265.000             0.735            0.077
Chain 1:   1100        -8516.369             0.638            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8199.723             0.085            0.036
Chain 1:   1300        -8386.838             0.050            0.033
Chain 1:   1400        -8330.341             0.043            0.030
Chain 1:   1500        -8286.872             0.021            0.028
Chain 1:   1600        -8283.711             0.018            0.022
Chain 1:   1700        -8217.549             0.018            0.022
Chain 1:   1800        -8099.210             0.017            0.015
Chain 1:   1900        -8214.543             0.017            0.015
Chain 1:   2000        -8175.375             0.014            0.014
Chain 1:   2100        -8310.854             0.013            0.014
Chain 1:   2200        -8098.951             0.012            0.014
Chain 1:   2300        -8239.742             0.011            0.014
Chain 1:   2400        -8250.220             0.011            0.014
Chain 1:   2500        -8218.825             0.011            0.014
Chain 1:   2600        -8214.608             0.011            0.014
Chain 1:   2700        -8124.607             0.011            0.014
Chain 1:   2800        -8104.015             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406881.767             1.000            1.000
Chain 1:    200     -1588073.038             2.647            4.294
Chain 1:    300      -891439.987             2.025            1.000
Chain 1:    400      -457711.137             1.756            1.000
Chain 1:    500      -357531.181             1.461            0.948
Chain 1:    600      -232445.727             1.307            0.948
Chain 1:    700      -118749.071             1.257            0.948
Chain 1:    800       -85967.821             1.147            0.948
Chain 1:    900       -66340.400             1.053            0.781
Chain 1:   1000       -51155.150             0.977            0.781
Chain 1:   1100       -38656.967             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37833.006             0.482            0.381
Chain 1:   1300       -25830.147             0.451            0.381
Chain 1:   1400       -25550.879             0.357            0.323
Chain 1:   1500       -22148.381             0.344            0.323
Chain 1:   1600       -21366.882             0.294            0.297
Chain 1:   1700       -20246.323             0.204            0.296
Chain 1:   1800       -20191.450             0.166            0.154
Chain 1:   1900       -20516.932             0.138            0.055
Chain 1:   2000       -19032.052             0.116            0.055
Chain 1:   2100       -19270.394             0.085            0.037
Chain 1:   2200       -19495.782             0.084            0.037
Chain 1:   2300       -19114.040             0.040            0.020
Chain 1:   2400       -18886.376             0.040            0.020
Chain 1:   2500       -18688.155             0.026            0.016
Chain 1:   2600       -18319.286             0.024            0.016
Chain 1:   2700       -18276.526             0.019            0.012
Chain 1:   2800       -17993.520             0.020            0.016
Chain 1:   2900       -18274.454             0.020            0.015
Chain 1:   3000       -18260.773             0.012            0.012
Chain 1:   3100       -18345.629             0.011            0.012
Chain 1:   3200       -18036.813             0.012            0.015
Chain 1:   3300       -18241.140             0.011            0.012
Chain 1:   3400       -17716.856             0.013            0.015
Chain 1:   3500       -18327.462             0.015            0.016
Chain 1:   3600       -17635.781             0.017            0.016
Chain 1:   3700       -18021.312             0.019            0.017
Chain 1:   3800       -16983.507             0.023            0.021
Chain 1:   3900       -16979.655             0.022            0.021
Chain 1:   4000       -17097.008             0.022            0.021
Chain 1:   4100       -17010.844             0.023            0.021
Chain 1:   4200       -16827.638             0.022            0.021
Chain 1:   4300       -16965.680             0.022            0.021
Chain 1:   4400       -16922.959             0.019            0.011
Chain 1:   4500       -16825.516             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001423 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13291.750             1.000            1.000
Chain 1:    200       -10241.059             0.649            1.000
Chain 1:    300        -8553.485             0.498            0.298
Chain 1:    400        -8779.796             0.380            0.298
Chain 1:    500        -8686.861             0.306            0.197
Chain 1:    600        -8491.821             0.259            0.197
Chain 1:    700        -8379.219             0.224            0.026
Chain 1:    800        -8425.197             0.197            0.026
Chain 1:    900        -8511.846             0.176            0.023
Chain 1:   1000        -8445.643             0.159            0.023
Chain 1:   1100        -8418.390             0.059            0.013
Chain 1:   1200        -8410.446             0.030            0.011
Chain 1:   1300        -8349.311             0.011            0.010
Chain 1:   1400        -8381.594             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002805 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58132.904             1.000            1.000
Chain 1:    200       -18462.331             1.574            2.149
Chain 1:    300        -9255.234             1.381            1.000
Chain 1:    400        -8308.791             1.064            1.000
Chain 1:    500        -8874.743             0.864            0.995
Chain 1:    600        -9017.113             0.723            0.995
Chain 1:    700        -8532.115             0.628            0.114
Chain 1:    800        -8374.524             0.552            0.114
Chain 1:    900        -8324.046             0.491            0.064
Chain 1:   1000        -7893.597             0.447            0.064
Chain 1:   1100        -7576.897             0.352            0.057
Chain 1:   1200        -7664.327             0.138            0.055
Chain 1:   1300        -8203.431             0.045            0.055
Chain 1:   1400        -7696.183             0.040            0.055
Chain 1:   1500        -7621.746             0.035            0.042
Chain 1:   1600        -7867.847             0.036            0.042
Chain 1:   1700        -7743.682             0.032            0.031
Chain 1:   1800        -7718.281             0.031            0.031
Chain 1:   1900        -7672.206             0.031            0.031
Chain 1:   2000        -7774.645             0.026            0.016
Chain 1:   2100        -7579.770             0.025            0.016
Chain 1:   2200        -7966.344             0.029            0.026
Chain 1:   2300        -7632.509             0.026            0.026
Chain 1:   2400        -7615.255             0.020            0.016
Chain 1:   2500        -7443.456             0.021            0.023
Chain 1:   2600        -7627.388             0.021            0.023
Chain 1:   2700        -7586.148             0.020            0.023
Chain 1:   2800        -7544.400             0.020            0.023
Chain 1:   2900        -7573.335             0.020            0.023
Chain 1:   3000        -7578.698             0.018            0.023
Chain 1:   3100        -7580.138             0.016            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003029 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87583.579             1.000            1.000
Chain 1:    200       -14456.938             3.029            5.058
Chain 1:    300       -10642.012             2.139            1.000
Chain 1:    400       -12651.329             1.644            1.000
Chain 1:    500        -9052.086             1.395            0.398
Chain 1:    600        -9103.573             1.163            0.398
Chain 1:    700        -8845.977             1.001            0.358
Chain 1:    800        -9553.682             0.885            0.358
Chain 1:    900        -9354.408             0.789            0.159
Chain 1:   1000        -9632.849             0.713            0.159
Chain 1:   1100        -9341.114             0.616            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8816.018             0.116            0.060
Chain 1:   1300        -9238.177             0.085            0.046
Chain 1:   1400        -9161.413             0.070            0.031
Chain 1:   1500        -9078.169             0.031            0.029
Chain 1:   1600        -9120.113             0.031            0.029
Chain 1:   1700        -9205.390             0.029            0.029
Chain 1:   1800        -8738.909             0.027            0.029
Chain 1:   1900        -8853.495             0.026            0.029
Chain 1:   2000        -8870.581             0.024            0.013
Chain 1:   2100        -8965.244             0.022            0.011
Chain 1:   2200        -8731.275             0.018            0.011
Chain 1:   2300        -8902.509             0.016            0.011
Chain 1:   2400        -8760.458             0.016            0.013
Chain 1:   2500        -8821.825             0.016            0.013
Chain 1:   2600        -8728.562             0.017            0.013
Chain 1:   2700        -8764.012             0.016            0.013
Chain 1:   2800        -8722.098             0.011            0.011
Chain 1:   2900        -8830.825             0.011            0.011
Chain 1:   3000        -8738.834             0.012            0.011
Chain 1:   3100        -8706.144             0.012            0.011
Chain 1:   3200        -8675.226             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403134.862             1.000            1.000
Chain 1:    200     -1583704.122             2.653            4.306
Chain 1:    300      -890001.256             2.028            1.000
Chain 1:    400      -457329.280             1.758            1.000
Chain 1:    500      -357658.547             1.462            0.946
Chain 1:    600      -233041.403             1.307            0.946
Chain 1:    700      -119795.077             1.256            0.945
Chain 1:    800       -87131.833             1.146            0.945
Chain 1:    900       -67577.779             1.051            0.779
Chain 1:   1000       -52458.766             0.974            0.779
Chain 1:   1100       -39996.898             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39193.062             0.477            0.375
Chain 1:   1300       -27188.330             0.443            0.375
Chain 1:   1400       -26917.131             0.349            0.312
Chain 1:   1500       -23512.806             0.336            0.312
Chain 1:   1600       -22733.844             0.286            0.289
Chain 1:   1700       -21610.604             0.197            0.288
Chain 1:   1800       -21556.367             0.159            0.145
Chain 1:   1900       -21883.604             0.132            0.052
Chain 1:   2000       -20394.366             0.111            0.052
Chain 1:   2100       -20632.980             0.081            0.034
Chain 1:   2200       -20859.805             0.080            0.034
Chain 1:   2300       -20476.390             0.037            0.019
Chain 1:   2400       -20248.116             0.037            0.019
Chain 1:   2500       -20049.923             0.024            0.015
Chain 1:   2600       -19679.313             0.022            0.015
Chain 1:   2700       -19636.066             0.017            0.012
Chain 1:   2800       -19352.345             0.019            0.015
Chain 1:   2900       -19634.028             0.019            0.014
Chain 1:   3000       -19620.242             0.011            0.012
Chain 1:   3100       -19705.353             0.011            0.011
Chain 1:   3200       -19395.410             0.011            0.014
Chain 1:   3300       -19600.655             0.010            0.011
Chain 1:   3400       -19074.350             0.012            0.014
Chain 1:   3500       -19687.977             0.014            0.015
Chain 1:   3600       -18992.357             0.016            0.015
Chain 1:   3700       -19380.822             0.018            0.016
Chain 1:   3800       -18336.911             0.022            0.020
Chain 1:   3900       -18332.917             0.020            0.020
Chain 1:   4000       -18450.282             0.021            0.020
Chain 1:   4100       -18363.817             0.021            0.020
Chain 1:   4200       -18179.292             0.020            0.020
Chain 1:   4300       -18318.285             0.020            0.020
Chain 1:   4400       -18274.465             0.018            0.010
Chain 1:   4500       -18176.820             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48587.435             1.000            1.000
Chain 1:    200       -17264.644             1.407            1.814
Chain 1:    300       -20931.313             0.996            1.000
Chain 1:    400       -14294.341             0.863            1.000
Chain 1:    500       -29343.939             0.793            0.513
Chain 1:    600       -15933.365             0.801            0.842
Chain 1:    700       -14595.327             0.700            0.513
Chain 1:    800       -11144.730             0.651            0.513
Chain 1:    900       -14148.824             0.602            0.464
Chain 1:   1000       -11511.547             0.565            0.464
Chain 1:   1100       -11843.719             0.468            0.310
Chain 1:   1200       -10653.556             0.298            0.229
Chain 1:   1300        -9630.838             0.291            0.229
Chain 1:   1400       -10122.911             0.249            0.212
Chain 1:   1500       -10390.390             0.200            0.112
Chain 1:   1600       -11135.381             0.123            0.106
Chain 1:   1700       -12584.863             0.125            0.112
Chain 1:   1800        -9767.969             0.123            0.112
Chain 1:   1900        -9956.498             0.104            0.106
Chain 1:   2000       -10827.783             0.089            0.080
Chain 1:   2100       -10319.144             0.091            0.080
Chain 1:   2200       -10475.650             0.081            0.067
Chain 1:   2300       -10121.787             0.074            0.049
Chain 1:   2400        -9133.071             0.080            0.067
Chain 1:   2500       -13320.938             0.109            0.080
Chain 1:   2600        -9186.522             0.147            0.108
Chain 1:   2700       -10022.834             0.144            0.083
Chain 1:   2800        -9576.910             0.120            0.080
Chain 1:   2900        -9153.468             0.123            0.080
Chain 1:   3000        -9550.703             0.119            0.049
Chain 1:   3100        -9536.084             0.114            0.047
Chain 1:   3200        -9338.288             0.115            0.047
Chain 1:   3300        -8898.427             0.116            0.049
Chain 1:   3400       -13681.458             0.140            0.049
Chain 1:   3500        -9641.576             0.151            0.049
Chain 1:   3600       -10162.833             0.111            0.049
Chain 1:   3700        -9379.825             0.111            0.049
Chain 1:   3800        -8664.695             0.115            0.051
Chain 1:   3900       -13406.041             0.145            0.083
Chain 1:   4000        -9874.288             0.177            0.083
Chain 1:   4100        -8701.686             0.190            0.135
Chain 1:   4200        -9659.436             0.198            0.135
Chain 1:   4300       -12107.369             0.213            0.202
Chain 1:   4400        -8596.728             0.219            0.202
Chain 1:   4500        -8919.033             0.181            0.135
Chain 1:   4600       -13241.510             0.208            0.202
Chain 1:   4700       -13008.792             0.202            0.202
Chain 1:   4800        -8763.149             0.242            0.326
Chain 1:   4900       -14607.429             0.247            0.326
Chain 1:   5000        -9104.143             0.271            0.326
Chain 1:   5100        -8971.634             0.259            0.326
Chain 1:   5200       -10776.956             0.266            0.326
Chain 1:   5300       -10463.342             0.249            0.326
Chain 1:   5400        -8463.758             0.232            0.236
Chain 1:   5500       -11985.749             0.258            0.294
Chain 1:   5600       -11636.423             0.228            0.236
Chain 1:   5700        -9298.168             0.251            0.251
Chain 1:   5800        -8339.259             0.214            0.236
Chain 1:   5900       -13436.745             0.212            0.236
Chain 1:   6000        -9225.084             0.197            0.236
Chain 1:   6100        -8562.554             0.204            0.236
Chain 1:   6200        -8257.408             0.191            0.236
Chain 1:   6300       -12425.374             0.221            0.251
Chain 1:   6400        -8749.280             0.240            0.294
Chain 1:   6500       -10950.661             0.230            0.251
Chain 1:   6600        -9030.739             0.249            0.251
Chain 1:   6700       -13026.586             0.254            0.307
Chain 1:   6800        -8633.371             0.294            0.335
Chain 1:   6900       -12448.058             0.286            0.307
Chain 1:   7000        -8851.159             0.281            0.307
Chain 1:   7100        -8220.573             0.281            0.307
Chain 1:   7200        -9219.968             0.288            0.307
Chain 1:   7300       -13797.844             0.288            0.307
Chain 1:   7400        -8688.273             0.305            0.307
Chain 1:   7500       -12460.228             0.315            0.307
Chain 1:   7600        -8817.669             0.335            0.332
Chain 1:   7700        -8412.338             0.309            0.332
Chain 1:   7800       -11629.284             0.286            0.306
Chain 1:   7900        -8987.623             0.285            0.303
Chain 1:   8000        -8574.834             0.249            0.294
Chain 1:   8100        -8321.887             0.244            0.294
Chain 1:   8200        -9112.943             0.242            0.294
Chain 1:   8300        -9161.514             0.209            0.277
Chain 1:   8400        -8469.920             0.159            0.087
Chain 1:   8500        -8362.150             0.130            0.082
Chain 1:   8600        -8505.024             0.090            0.048
Chain 1:   8700        -9808.710             0.099            0.082
Chain 1:   8800        -8230.465             0.090            0.082
Chain 1:   8900        -9931.364             0.078            0.082
Chain 1:   9000        -8070.507             0.096            0.087
Chain 1:   9100       -10401.050             0.115            0.133
Chain 1:   9200        -8636.226             0.127            0.171
Chain 1:   9300        -8397.321             0.129            0.171
Chain 1:   9400        -9616.983             0.134            0.171
Chain 1:   9500        -8089.173             0.152            0.189
Chain 1:   9600       -10367.839             0.172            0.192
Chain 1:   9700        -8163.576             0.186            0.204
Chain 1:   9800        -8215.419             0.167            0.204
Chain 1:   9900        -8823.837             0.157            0.204
Chain 1:   10000        -8202.842             0.141            0.189
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56991.513             1.000            1.000
Chain 1:    200       -17243.610             1.653            2.305
Chain 1:    300        -8662.646             1.432            1.000
Chain 1:    400        -8190.024             1.088            1.000
Chain 1:    500        -8321.830             0.874            0.991
Chain 1:    600        -8216.679             0.730            0.991
Chain 1:    700        -8095.025             0.628            0.058
Chain 1:    800        -8029.530             0.551            0.058
Chain 1:    900        -7724.079             0.494            0.040
Chain 1:   1000        -7841.520             0.446            0.040
Chain 1:   1100        -7743.798             0.347            0.016
Chain 1:   1200        -7586.956             0.119            0.016
Chain 1:   1300        -7724.656             0.022            0.016
Chain 1:   1400        -7864.698             0.018            0.016
Chain 1:   1500        -7612.855             0.019            0.018
Chain 1:   1600        -7670.902             0.019            0.018
Chain 1:   1700        -7519.693             0.019            0.018
Chain 1:   1800        -7590.255             0.019            0.018
Chain 1:   1900        -7566.983             0.016            0.018
Chain 1:   2000        -7620.288             0.015            0.018
Chain 1:   2100        -7597.988             0.014            0.018
Chain 1:   2200        -7696.394             0.013            0.013
Chain 1:   2300        -7589.718             0.013            0.013
Chain 1:   2400        -7638.062             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002592 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85854.368             1.000            1.000
Chain 1:    200       -13338.824             3.218            5.436
Chain 1:    300        -9798.195             2.266            1.000
Chain 1:    400       -10713.388             1.721            1.000
Chain 1:    500        -8697.882             1.423            0.361
Chain 1:    600        -8424.476             1.191            0.361
Chain 1:    700        -8476.051             1.022            0.232
Chain 1:    800        -8831.316             0.899            0.232
Chain 1:    900        -8685.617             0.801            0.085
Chain 1:   1000        -8357.220             0.725            0.085
Chain 1:   1100        -8708.030             0.629            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8379.730             0.089            0.040
Chain 1:   1300        -8373.233             0.053            0.039
Chain 1:   1400        -8374.716             0.045            0.039
Chain 1:   1500        -8407.232             0.022            0.032
Chain 1:   1600        -8412.911             0.019            0.017
Chain 1:   1700        -8344.426             0.019            0.017
Chain 1:   1800        -8227.475             0.016            0.014
Chain 1:   1900        -8344.381             0.016            0.014
Chain 1:   2000        -8304.569             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002503 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389132.857             1.000            1.000
Chain 1:    200     -1581617.672             2.652            4.304
Chain 1:    300      -891344.061             2.026            1.000
Chain 1:    400      -458117.480             1.756            1.000
Chain 1:    500      -358768.302             1.460            0.946
Chain 1:    600      -233485.214             1.306            0.946
Chain 1:    700      -119373.725             1.256            0.946
Chain 1:    800       -86505.230             1.147            0.946
Chain 1:    900       -66776.335             1.052            0.774
Chain 1:   1000       -51514.699             0.977            0.774
Chain 1:   1100       -38943.866             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38108.050             0.481            0.380
Chain 1:   1300       -26025.196             0.450            0.380
Chain 1:   1400       -25737.163             0.356            0.323
Chain 1:   1500       -22315.532             0.344            0.323
Chain 1:   1600       -21528.611             0.294            0.296
Chain 1:   1700       -20398.351             0.204            0.295
Chain 1:   1800       -20341.123             0.166            0.153
Chain 1:   1900       -20666.723             0.138            0.055
Chain 1:   2000       -19176.957             0.116            0.055
Chain 1:   2100       -19415.157             0.085            0.037
Chain 1:   2200       -19641.732             0.084            0.037
Chain 1:   2300       -19258.996             0.040            0.020
Chain 1:   2400       -19031.298             0.040            0.020
Chain 1:   2500       -18833.558             0.025            0.016
Chain 1:   2600       -18464.090             0.024            0.016
Chain 1:   2700       -18421.112             0.018            0.012
Chain 1:   2800       -18138.396             0.020            0.016
Chain 1:   2900       -18419.404             0.020            0.015
Chain 1:   3000       -18405.543             0.012            0.012
Chain 1:   3100       -18490.497             0.011            0.012
Chain 1:   3200       -18181.477             0.012            0.015
Chain 1:   3300       -18385.950             0.011            0.012
Chain 1:   3400       -17861.563             0.013            0.015
Chain 1:   3500       -18472.491             0.015            0.016
Chain 1:   3600       -17780.416             0.017            0.016
Chain 1:   3700       -18166.379             0.019            0.017
Chain 1:   3800       -17128.074             0.023            0.021
Chain 1:   3900       -17124.322             0.022            0.021
Chain 1:   4000       -17241.565             0.022            0.021
Chain 1:   4100       -17155.497             0.022            0.021
Chain 1:   4200       -16972.150             0.022            0.021
Chain 1:   4300       -17110.212             0.021            0.021
Chain 1:   4400       -17067.398             0.019            0.011
Chain 1:   4500       -16970.051             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001454 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48832.371             1.000            1.000
Chain 1:    200       -16728.004             1.460            1.919
Chain 1:    300       -16078.536             0.987            1.000
Chain 1:    400       -14910.004             0.759            1.000
Chain 1:    500       -13432.432             0.630            0.110
Chain 1:    600       -14780.211             0.540            0.110
Chain 1:    700       -17628.050             0.486            0.110
Chain 1:    800       -15844.076             0.439            0.113
Chain 1:    900       -13597.086             0.409            0.113
Chain 1:   1000       -10397.735             0.399            0.162
Chain 1:   1100       -10656.624             0.301            0.113
Chain 1:   1200       -10753.438             0.110            0.110
Chain 1:   1300       -12611.490             0.121            0.113
Chain 1:   1400       -10808.974             0.130            0.147
Chain 1:   1500       -10633.824             0.120            0.147
Chain 1:   1600        -9682.620             0.121            0.147
Chain 1:   1700       -13756.586             0.134            0.147
Chain 1:   1800       -11099.633             0.147            0.165
Chain 1:   1900       -10236.659             0.139            0.147
Chain 1:   2000       -10399.358             0.110            0.098
Chain 1:   2100       -10239.640             0.109            0.098
Chain 1:   2200        -9961.388             0.111            0.098
Chain 1:   2300        -9216.305             0.104            0.084
Chain 1:   2400       -10386.250             0.099            0.084
Chain 1:   2500       -10160.733             0.099            0.084
Chain 1:   2600       -10495.265             0.093            0.081
Chain 1:   2700       -13119.726             0.083            0.081
Chain 1:   2800        -9752.489             0.094            0.081
Chain 1:   2900       -18590.176             0.133            0.081
Chain 1:   3000       -15624.819             0.150            0.113
Chain 1:   3100        -8979.771             0.223            0.190
Chain 1:   3200        -8749.854             0.222            0.190
Chain 1:   3300       -12462.140             0.244            0.200
Chain 1:   3400        -9081.580             0.270            0.298
Chain 1:   3500       -12578.827             0.296            0.298
Chain 1:   3600        -9569.744             0.324            0.314
Chain 1:   3700       -14502.639             0.338            0.340
Chain 1:   3800        -8436.773             0.375            0.340
Chain 1:   3900        -9442.009             0.338            0.314
Chain 1:   4000       -16645.140             0.363            0.340
Chain 1:   4100       -13486.430             0.312            0.314
Chain 1:   4200        -9816.315             0.347            0.340
Chain 1:   4300        -9737.078             0.318            0.340
Chain 1:   4400        -8865.907             0.291            0.314
Chain 1:   4500        -9244.151             0.267            0.314
Chain 1:   4600       -13339.554             0.266            0.307
Chain 1:   4700        -8540.976             0.288            0.307
Chain 1:   4800        -8800.510             0.219            0.234
Chain 1:   4900       -12396.900             0.238            0.290
Chain 1:   5000        -9600.593             0.224            0.290
Chain 1:   5100        -8536.628             0.213            0.290
Chain 1:   5200        -8630.811             0.176            0.125
Chain 1:   5300       -12579.601             0.207            0.290
Chain 1:   5400        -8603.771             0.243            0.291
Chain 1:   5500        -8321.448             0.243            0.291
Chain 1:   5600       -10877.374             0.235            0.290
Chain 1:   5700        -8419.895             0.208            0.290
Chain 1:   5800        -8844.634             0.210            0.290
Chain 1:   5900       -13567.742             0.216            0.291
Chain 1:   6000        -9876.937             0.224            0.292
Chain 1:   6100        -8344.566             0.230            0.292
Chain 1:   6200        -8291.041             0.230            0.292
Chain 1:   6300        -8437.158             0.200            0.235
Chain 1:   6400        -8487.112             0.154            0.184
Chain 1:   6500        -9223.706             0.159            0.184
Chain 1:   6600        -9863.187             0.142            0.080
Chain 1:   6700        -8334.106             0.131            0.080
Chain 1:   6800       -10780.936             0.149            0.183
Chain 1:   6900        -8946.959             0.135            0.183
Chain 1:   7000        -9321.587             0.101            0.080
Chain 1:   7100        -8278.427             0.096            0.080
Chain 1:   7200        -9078.158             0.104            0.088
Chain 1:   7300       -11749.875             0.125            0.126
Chain 1:   7400        -8097.000             0.169            0.183
Chain 1:   7500        -9744.426             0.178            0.183
Chain 1:   7600        -8585.154             0.185            0.183
Chain 1:   7700        -8148.597             0.172            0.169
Chain 1:   7800       -10610.598             0.173            0.169
Chain 1:   7900        -9943.728             0.159            0.135
Chain 1:   8000        -8294.915             0.175            0.169
Chain 1:   8100        -8250.244             0.163            0.169
Chain 1:   8200        -9080.387             0.163            0.169
Chain 1:   8300        -8129.810             0.152            0.135
Chain 1:   8400       -10507.617             0.130            0.135
Chain 1:   8500        -8376.846             0.138            0.135
Chain 1:   8600        -7942.592             0.130            0.117
Chain 1:   8700        -8240.422             0.128            0.117
Chain 1:   8800        -9504.883             0.118            0.117
Chain 1:   8900       -12324.344             0.135            0.133
Chain 1:   9000       -10974.346             0.127            0.123
Chain 1:   9100        -8165.032             0.161            0.133
Chain 1:   9200        -8238.316             0.153            0.133
Chain 1:   9300       -11798.380             0.171            0.226
Chain 1:   9400        -8100.009             0.194            0.229
Chain 1:   9500       -11565.785             0.199            0.229
Chain 1:   9600        -9664.006             0.213            0.229
Chain 1:   9700        -9349.024             0.213            0.229
Chain 1:   9800       -10903.941             0.214            0.229
Chain 1:   9900        -8413.276             0.220            0.296
Chain 1:   10000        -7936.063             0.214            0.296
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58186.026             1.000            1.000
Chain 1:    200       -17739.864             1.640            2.280
Chain 1:    300        -8688.551             1.441            1.042
Chain 1:    400        -8230.003             1.094            1.042
Chain 1:    500        -8454.047             0.881            1.000
Chain 1:    600        -7872.754             0.746            1.000
Chain 1:    700        -7836.018             0.640            0.074
Chain 1:    800        -8242.919             0.566            0.074
Chain 1:    900        -7953.292             0.508            0.056
Chain 1:   1000        -7819.997             0.459            0.056
Chain 1:   1100        -7652.949             0.361            0.049
Chain 1:   1200        -7583.242             0.134            0.036
Chain 1:   1300        -7746.301             0.032            0.027
Chain 1:   1400        -7815.311             0.027            0.022
Chain 1:   1500        -7609.532             0.027            0.022
Chain 1:   1600        -7863.363             0.023            0.022
Chain 1:   1700        -7514.555             0.027            0.027
Chain 1:   1800        -7620.698             0.023            0.022
Chain 1:   1900        -7485.744             0.022            0.021
Chain 1:   2000        -7595.409             0.021            0.021
Chain 1:   2100        -7595.120             0.019            0.018
Chain 1:   2200        -7720.623             0.020            0.018
Chain 1:   2300        -7575.046             0.020            0.018
Chain 1:   2400        -7641.467             0.020            0.018
Chain 1:   2500        -7574.611             0.018            0.016
Chain 1:   2600        -7515.775             0.015            0.014
Chain 1:   2700        -7514.562             0.011            0.014
Chain 1:   2800        -7577.003             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003111 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86627.323             1.000            1.000
Chain 1:    200       -13500.214             3.208            5.417
Chain 1:    300        -9801.662             2.265            1.000
Chain 1:    400       -10925.607             1.724            1.000
Chain 1:    500        -8791.964             1.428            0.377
Chain 1:    600        -8225.828             1.201            0.377
Chain 1:    700        -8386.446             1.033            0.243
Chain 1:    800        -8884.571             0.910            0.243
Chain 1:    900        -8560.228             0.814            0.103
Chain 1:   1000        -8680.149             0.734            0.103
Chain 1:   1100        -8386.411             0.637            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8182.502             0.098            0.056
Chain 1:   1300        -8482.131             0.064            0.038
Chain 1:   1400        -8432.792             0.054            0.035
Chain 1:   1500        -8324.146             0.031            0.035
Chain 1:   1600        -8430.442             0.025            0.025
Chain 1:   1700        -8505.099             0.024            0.025
Chain 1:   1800        -8072.711             0.024            0.025
Chain 1:   1900        -8176.918             0.022            0.014
Chain 1:   2000        -8152.206             0.020            0.013
Chain 1:   2100        -8129.189             0.017            0.013
Chain 1:   2200        -8094.798             0.015            0.013
Chain 1:   2300        -8224.508             0.013            0.013
Chain 1:   2400        -8079.500             0.014            0.013
Chain 1:   2500        -8148.445             0.014            0.013
Chain 1:   2600        -8067.601             0.014            0.010
Chain 1:   2700        -8096.885             0.013            0.010
Chain 1:   2800        -8057.922             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003336 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395505.843             1.000            1.000
Chain 1:    200     -1582606.521             2.652            4.305
Chain 1:    300      -890140.466             2.028            1.000
Chain 1:    400      -457714.681             1.757            1.000
Chain 1:    500      -357991.996             1.461            0.945
Chain 1:    600      -233072.787             1.307            0.945
Chain 1:    700      -119256.395             1.257            0.945
Chain 1:    800       -86467.824             1.147            0.945
Chain 1:    900       -66810.062             1.052            0.778
Chain 1:   1000       -51610.389             0.976            0.778
Chain 1:   1100       -39084.987             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38265.609             0.480            0.379
Chain 1:   1300       -26212.399             0.448            0.379
Chain 1:   1400       -25932.655             0.355            0.320
Chain 1:   1500       -22517.482             0.342            0.320
Chain 1:   1600       -21733.672             0.292            0.295
Chain 1:   1700       -20605.581             0.202            0.294
Chain 1:   1800       -20549.645             0.165            0.152
Chain 1:   1900       -20876.157             0.137            0.055
Chain 1:   2000       -19385.770             0.115            0.055
Chain 1:   2100       -19624.262             0.084            0.036
Chain 1:   2200       -19851.134             0.083            0.036
Chain 1:   2300       -19467.882             0.039            0.020
Chain 1:   2400       -19239.828             0.039            0.020
Chain 1:   2500       -19041.934             0.025            0.016
Chain 1:   2600       -18671.739             0.024            0.016
Chain 1:   2700       -18628.562             0.018            0.012
Chain 1:   2800       -18345.324             0.020            0.015
Chain 1:   2900       -18626.772             0.020            0.015
Chain 1:   3000       -18612.883             0.012            0.012
Chain 1:   3100       -18697.947             0.011            0.012
Chain 1:   3200       -18388.394             0.012            0.015
Chain 1:   3300       -18593.302             0.011            0.012
Chain 1:   3400       -18067.868             0.013            0.015
Chain 1:   3500       -18680.300             0.015            0.015
Chain 1:   3600       -17986.238             0.017            0.015
Chain 1:   3700       -18373.646             0.019            0.017
Chain 1:   3800       -17332.191             0.023            0.021
Chain 1:   3900       -17328.305             0.022            0.021
Chain 1:   4000       -17445.615             0.022            0.021
Chain 1:   4100       -17359.335             0.022            0.021
Chain 1:   4200       -17175.293             0.022            0.021
Chain 1:   4300       -17313.879             0.021            0.021
Chain 1:   4400       -17270.503             0.019            0.011
Chain 1:   4500       -17172.994             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12601.475             1.000            1.000
Chain 1:    200        -9399.279             0.670            1.000
Chain 1:    300        -7977.298             0.506            0.341
Chain 1:    400        -8040.578             0.382            0.341
Chain 1:    500        -7948.680             0.308            0.178
Chain 1:    600        -7896.736             0.257            0.178
Chain 1:    700        -7778.082             0.223            0.015
Chain 1:    800        -7780.071             0.195            0.015
Chain 1:    900        -7732.270             0.174            0.012
Chain 1:   1000        -7871.483             0.158            0.015
Chain 1:   1100        -7890.614             0.059            0.012
Chain 1:   1200        -7821.011             0.025            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57019.998             1.000            1.000
Chain 1:    200       -17628.640             1.617            2.235
Chain 1:    300        -8849.169             1.409            1.000
Chain 1:    400        -8388.574             1.070            1.000
Chain 1:    500        -8501.071             0.859            0.992
Chain 1:    600        -8685.833             0.719            0.992
Chain 1:    700        -7814.328             0.633            0.112
Chain 1:    800        -8099.479             0.558            0.112
Chain 1:    900        -7722.087             0.501            0.055
Chain 1:   1000        -7710.006             0.451            0.055
Chain 1:   1100        -7805.717             0.353            0.049
Chain 1:   1200        -7819.316             0.129            0.035
Chain 1:   1300        -7669.799             0.032            0.021
Chain 1:   1400        -7809.555             0.028            0.019
Chain 1:   1500        -7614.969             0.030            0.021
Chain 1:   1600        -7591.961             0.028            0.019
Chain 1:   1700        -7612.013             0.017            0.018
Chain 1:   1800        -7639.311             0.014            0.012
Chain 1:   1900        -7638.686             0.009            0.004   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003151 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87167.118             1.000            1.000
Chain 1:    200       -13641.720             3.195            5.390
Chain 1:    300        -9895.971             2.256            1.000
Chain 1:    400       -11415.909             1.725            1.000
Chain 1:    500        -8751.273             1.441            0.379
Chain 1:    600        -9222.448             1.209            0.379
Chain 1:    700        -8687.788             1.046            0.304
Chain 1:    800        -8173.355             0.923            0.304
Chain 1:    900        -8291.745             0.822            0.133
Chain 1:   1000        -8249.610             0.740            0.133
Chain 1:   1100        -8671.245             0.645            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8237.753             0.111            0.062
Chain 1:   1300        -8441.303             0.076            0.053
Chain 1:   1400        -8551.241             0.064            0.051
Chain 1:   1500        -8394.606             0.035            0.049
Chain 1:   1600        -8498.622             0.031            0.024
Chain 1:   1700        -8562.724             0.026            0.019
Chain 1:   1800        -8120.847             0.025            0.019
Chain 1:   1900        -8227.465             0.025            0.019
Chain 1:   2000        -8211.809             0.025            0.019
Chain 1:   2100        -8337.542             0.021            0.015
Chain 1:   2200        -8126.230             0.019            0.015
Chain 1:   2300        -8221.060             0.017            0.013
Chain 1:   2400        -8288.262             0.017            0.013
Chain 1:   2500        -8236.176             0.016            0.012
Chain 1:   2600        -8248.610             0.015            0.012
Chain 1:   2700        -8156.960             0.015            0.012
Chain 1:   2800        -8105.170             0.010            0.011
Chain 1:   2900        -8199.201             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003252 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8420824.065             1.000            1.000
Chain 1:    200     -1592949.336             2.643            4.286
Chain 1:    300      -892899.847             2.023            1.000
Chain 1:    400      -458433.873             1.755            1.000
Chain 1:    500      -357801.502             1.460            0.948
Chain 1:    600      -232624.922             1.306            0.948
Chain 1:    700      -119076.481             1.256            0.948
Chain 1:    800       -86328.672             1.146            0.948
Chain 1:    900       -66739.721             1.052            0.784
Chain 1:   1000       -51598.284             0.976            0.784
Chain 1:   1100       -39126.257             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38316.229             0.481            0.379
Chain 1:   1300       -26320.351             0.448            0.379
Chain 1:   1400       -26046.884             0.355            0.319
Chain 1:   1500       -22644.791             0.341            0.319
Chain 1:   1600       -21865.068             0.291            0.294
Chain 1:   1700       -20744.208             0.201            0.293
Chain 1:   1800       -20689.990             0.164            0.150
Chain 1:   1900       -21016.600             0.136            0.054
Chain 1:   2000       -19529.369             0.114            0.054
Chain 1:   2100       -19767.993             0.083            0.036
Chain 1:   2200       -19994.101             0.082            0.036
Chain 1:   2300       -19611.435             0.039            0.020
Chain 1:   2400       -19383.383             0.039            0.020
Chain 1:   2500       -19185.014             0.025            0.016
Chain 1:   2600       -18815.099             0.023            0.016
Chain 1:   2700       -18772.059             0.018            0.012
Chain 1:   2800       -18488.451             0.019            0.015
Chain 1:   2900       -18769.903             0.019            0.015
Chain 1:   3000       -18756.222             0.012            0.012
Chain 1:   3100       -18841.224             0.011            0.012
Chain 1:   3200       -18531.669             0.012            0.015
Chain 1:   3300       -18736.594             0.011            0.012
Chain 1:   3400       -18210.895             0.012            0.015
Chain 1:   3500       -18823.549             0.015            0.015
Chain 1:   3600       -18129.237             0.017            0.015
Chain 1:   3700       -18516.668             0.018            0.017
Chain 1:   3800       -17474.726             0.023            0.021
Chain 1:   3900       -17470.754             0.021            0.021
Chain 1:   4000       -17588.147             0.022            0.021
Chain 1:   4100       -17501.734             0.022            0.021
Chain 1:   4200       -17317.654             0.021            0.021
Chain 1:   4300       -17456.347             0.021            0.021
Chain 1:   4400       -17412.883             0.018            0.011
Chain 1:   4500       -17315.292             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12485.678             1.000            1.000
Chain 1:    200        -9403.574             0.664            1.000
Chain 1:    300        -8287.306             0.487            0.328
Chain 1:    400        -8270.093             0.366            0.328
Chain 1:    500        -8177.584             0.295            0.135
Chain 1:    600        -8083.999             0.248            0.135
Chain 1:    700        -7993.530             0.214            0.012
Chain 1:    800        -8035.968             0.188            0.012
Chain 1:    900        -8159.269             0.169            0.012
Chain 1:   1000        -8068.808             0.153            0.012
Chain 1:   1100        -8099.895             0.053            0.011
Chain 1:   1200        -8004.522             0.022            0.011
Chain 1:   1300        -7963.333             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58058.655             1.000            1.000
Chain 1:    200       -17711.065             1.639            2.278
Chain 1:    300        -8701.010             1.438            1.036
Chain 1:    400        -8123.871             1.096            1.036
Chain 1:    500        -8242.318             0.880            1.000
Chain 1:    600        -8112.612             0.736            1.000
Chain 1:    700        -7936.045             0.634            0.071
Chain 1:    800        -8300.159             0.560            0.071
Chain 1:    900        -7937.353             0.503            0.046
Chain 1:   1000        -7994.974             0.453            0.046
Chain 1:   1100        -7701.818             0.357            0.044
Chain 1:   1200        -7757.665             0.130            0.038
Chain 1:   1300        -7772.480             0.027            0.022
Chain 1:   1400        -7785.120             0.020            0.016
Chain 1:   1500        -7547.076             0.022            0.022
Chain 1:   1600        -7726.393             0.022            0.023
Chain 1:   1700        -7588.652             0.022            0.023
Chain 1:   1800        -7577.560             0.018            0.018
Chain 1:   1900        -7597.098             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86356.244             1.000            1.000
Chain 1:    200       -13549.041             3.187            5.374
Chain 1:    300        -9934.222             2.246            1.000
Chain 1:    400       -10753.237             1.703            1.000
Chain 1:    500        -8896.750             1.404            0.364
Chain 1:    600        -8580.690             1.177            0.364
Chain 1:    700        -8599.506             1.009            0.209
Chain 1:    800        -9081.943             0.889            0.209
Chain 1:    900        -8765.557             0.795            0.076
Chain 1:   1000        -8528.453             0.718            0.076
Chain 1:   1100        -8777.712             0.621            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8423.244             0.088            0.042
Chain 1:   1300        -8629.071             0.054            0.037
Chain 1:   1400        -8637.435             0.046            0.036
Chain 1:   1500        -8527.224             0.026            0.028
Chain 1:   1600        -8630.773             0.024            0.028
Chain 1:   1700        -8719.554             0.025            0.028
Chain 1:   1800        -8312.813             0.024            0.028
Chain 1:   1900        -8410.038             0.022            0.024
Chain 1:   2000        -8381.991             0.019            0.013
Chain 1:   2100        -8502.267             0.018            0.013
Chain 1:   2200        -8304.722             0.016            0.013
Chain 1:   2300        -8448.361             0.015            0.013
Chain 1:   2400        -8455.256             0.015            0.013
Chain 1:   2500        -8423.448             0.015            0.012
Chain 1:   2600        -8421.833             0.013            0.012
Chain 1:   2700        -8335.018             0.013            0.012
Chain 1:   2800        -8300.638             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004313 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407431.957             1.000            1.000
Chain 1:    200     -1586882.053             2.649            4.298
Chain 1:    300      -891879.171             2.026            1.000
Chain 1:    400      -457890.422             1.756            1.000
Chain 1:    500      -357848.672             1.461            0.948
Chain 1:    600      -232829.799             1.307            0.948
Chain 1:    700      -119171.184             1.256            0.948
Chain 1:    800       -86381.612             1.147            0.948
Chain 1:    900       -66754.153             1.052            0.779
Chain 1:   1000       -51570.187             0.976            0.779
Chain 1:   1100       -39061.661             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38242.557             0.481            0.380
Chain 1:   1300       -26218.851             0.449            0.380
Chain 1:   1400       -25939.741             0.355            0.320
Chain 1:   1500       -22531.264             0.342            0.320
Chain 1:   1600       -21748.906             0.292            0.294
Chain 1:   1700       -20625.263             0.202            0.294
Chain 1:   1800       -20570.063             0.164            0.151
Chain 1:   1900       -20896.000             0.137            0.054
Chain 1:   2000       -19408.804             0.115            0.054
Chain 1:   2100       -19647.198             0.084            0.036
Chain 1:   2200       -19873.157             0.083            0.036
Chain 1:   2300       -19490.857             0.039            0.020
Chain 1:   2400       -19263.055             0.039            0.020
Chain 1:   2500       -19064.885             0.025            0.016
Chain 1:   2600       -18695.425             0.023            0.016
Chain 1:   2700       -18652.554             0.018            0.012
Chain 1:   2800       -18369.336             0.020            0.015
Chain 1:   2900       -18650.535             0.019            0.015
Chain 1:   3000       -18636.836             0.012            0.012
Chain 1:   3100       -18721.735             0.011            0.012
Chain 1:   3200       -18412.575             0.012            0.015
Chain 1:   3300       -18617.213             0.011            0.012
Chain 1:   3400       -18092.319             0.013            0.015
Chain 1:   3500       -18703.791             0.015            0.015
Chain 1:   3600       -18011.090             0.017            0.015
Chain 1:   3700       -18397.370             0.018            0.017
Chain 1:   3800       -17357.909             0.023            0.021
Chain 1:   3900       -17354.066             0.021            0.021
Chain 1:   4000       -17471.412             0.022            0.021
Chain 1:   4100       -17385.119             0.022            0.021
Chain 1:   4200       -17201.634             0.021            0.021
Chain 1:   4300       -17339.879             0.021            0.021
Chain 1:   4400       -17296.863             0.019            0.011
Chain 1:   4500       -17199.421             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001278 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12137.487             1.000            1.000
Chain 1:    200        -8933.798             0.679            1.000
Chain 1:    300        -7901.168             0.496            0.359
Chain 1:    400        -8073.515             0.378            0.359
Chain 1:    500        -7929.909             0.306            0.131
Chain 1:    600        -7800.655             0.258            0.131
Chain 1:    700        -7728.783             0.222            0.021
Chain 1:    800        -7735.958             0.194            0.021
Chain 1:    900        -7650.391             0.174            0.018
Chain 1:   1000        -7828.810             0.159            0.021
Chain 1:   1100        -7840.953             0.059            0.018
Chain 1:   1200        -7744.763             0.024            0.017
Chain 1:   1300        -7707.388             0.012            0.012
Chain 1:   1400        -7725.509             0.010            0.011
Chain 1:   1500        -7817.216             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61496.763             1.000            1.000
Chain 1:    200       -17597.203             1.747            2.495
Chain 1:    300        -8743.454             1.502            1.013
Chain 1:    400        -9006.460             1.134            1.013
Chain 1:    500        -8330.921             0.924            1.000
Chain 1:    600        -8586.876             0.775            1.000
Chain 1:    700        -8010.840             0.674            0.081
Chain 1:    800        -8078.632             0.591            0.081
Chain 1:    900        -7891.486             0.528            0.072
Chain 1:   1000        -7766.851             0.477            0.072
Chain 1:   1100        -7680.551             0.378            0.030
Chain 1:   1200        -7609.815             0.129            0.029
Chain 1:   1300        -7679.370             0.029            0.024
Chain 1:   1400        -7687.346             0.026            0.016
Chain 1:   1500        -7636.677             0.019            0.011
Chain 1:   1600        -7604.505             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002529 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85528.494             1.000            1.000
Chain 1:    200       -13234.154             3.231            5.463
Chain 1:    300        -9658.865             2.278            1.000
Chain 1:    400       -10608.735             1.731            1.000
Chain 1:    500        -8580.758             1.432            0.370
Chain 1:    600        -8331.136             1.198            0.370
Chain 1:    700        -8285.836             1.028            0.236
Chain 1:    800        -8762.356             0.906            0.236
Chain 1:    900        -8534.722             0.808            0.090
Chain 1:   1000        -8246.078             0.731            0.090
Chain 1:   1100        -8522.964             0.634            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8292.389             0.091            0.035
Chain 1:   1300        -8352.796             0.054            0.032
Chain 1:   1400        -8375.292             0.046            0.030
Chain 1:   1500        -8246.571             0.024            0.028
Chain 1:   1600        -8356.630             0.022            0.027
Chain 1:   1700        -8444.618             0.023            0.027
Chain 1:   1800        -8042.719             0.022            0.027
Chain 1:   1900        -8142.732             0.021            0.016
Chain 1:   2000        -8113.939             0.018            0.013
Chain 1:   2100        -8233.905             0.016            0.013
Chain 1:   2200        -8023.434             0.016            0.013
Chain 1:   2300        -8174.140             0.017            0.015
Chain 1:   2400        -8055.504             0.018            0.015
Chain 1:   2500        -8118.312             0.017            0.015
Chain 1:   2600        -8139.635             0.016            0.015
Chain 1:   2700        -8058.731             0.016            0.015
Chain 1:   2800        -8032.748             0.011            0.012
Chain 1:   2900        -8088.132             0.011            0.010
Chain 1:   3000        -7972.356             0.012            0.015
Chain 1:   3100        -8110.110             0.012            0.015
Chain 1:   3200        -7990.084             0.011            0.015
Chain 1:   3300        -8011.560             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003211 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401794.730             1.000            1.000
Chain 1:    200     -1583453.675             2.653            4.306
Chain 1:    300      -889520.569             2.029            1.000
Chain 1:    400      -456705.642             1.758            1.000
Chain 1:    500      -357012.000             1.463            0.948
Chain 1:    600      -232265.687             1.308            0.948
Chain 1:    700      -118733.925             1.258            0.948
Chain 1:    800       -86024.081             1.148            0.948
Chain 1:    900       -66410.445             1.054            0.780
Chain 1:   1000       -51238.433             0.978            0.780
Chain 1:   1100       -38744.929             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37924.274             0.482            0.380
Chain 1:   1300       -25908.174             0.450            0.380
Chain 1:   1400       -25630.194             0.356            0.322
Chain 1:   1500       -22224.562             0.344            0.322
Chain 1:   1600       -21443.284             0.294            0.296
Chain 1:   1700       -20320.171             0.204            0.295
Chain 1:   1800       -20265.200             0.166            0.153
Chain 1:   1900       -20591.060             0.138            0.055
Chain 1:   2000       -19104.626             0.116            0.055
Chain 1:   2100       -19342.812             0.085            0.036
Chain 1:   2200       -19568.790             0.084            0.036
Chain 1:   2300       -19186.521             0.040            0.020
Chain 1:   2400       -18958.713             0.040            0.020
Chain 1:   2500       -18760.744             0.025            0.016
Chain 1:   2600       -18391.201             0.024            0.016
Chain 1:   2700       -18348.379             0.019            0.012
Chain 1:   2800       -18065.278             0.020            0.016
Chain 1:   2900       -18346.421             0.020            0.015
Chain 1:   3000       -18332.641             0.012            0.012
Chain 1:   3100       -18417.527             0.011            0.012
Chain 1:   3200       -18108.456             0.012            0.015
Chain 1:   3300       -18313.052             0.011            0.012
Chain 1:   3400       -17788.318             0.013            0.015
Chain 1:   3500       -18399.622             0.015            0.016
Chain 1:   3600       -17707.109             0.017            0.016
Chain 1:   3700       -18093.221             0.019            0.017
Chain 1:   3800       -17054.155             0.023            0.021
Chain 1:   3900       -17050.348             0.022            0.021
Chain 1:   4000       -17167.644             0.022            0.021
Chain 1:   4100       -17081.384             0.022            0.021
Chain 1:   4200       -16897.992             0.022            0.021
Chain 1:   4300       -17036.145             0.022            0.021
Chain 1:   4400       -16993.164             0.019            0.011
Chain 1:   4500       -16895.757             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49122.178             1.000            1.000
Chain 1:    200       -21180.989             1.160            1.319
Chain 1:    300       -15988.985             0.881            1.000
Chain 1:    400       -21441.759             0.725            1.000
Chain 1:    500       -12953.274             0.711            0.655
Chain 1:    600       -15145.841             0.616            0.655
Chain 1:    700       -11837.706             0.568            0.325
Chain 1:    800       -14356.658             0.519            0.325
Chain 1:    900       -11314.103             0.491            0.279
Chain 1:   1000       -13637.256             0.459            0.279
Chain 1:   1100       -11664.897             0.376            0.269
Chain 1:   1200       -16933.553             0.275            0.269
Chain 1:   1300       -12391.188             0.280            0.269
Chain 1:   1400       -11580.307             0.261            0.269
Chain 1:   1500       -10508.485             0.206            0.175
Chain 1:   1600        -9984.276             0.197            0.175
Chain 1:   1700       -12736.885             0.190            0.175
Chain 1:   1800       -10351.847             0.196            0.216
Chain 1:   1900       -11823.132             0.181            0.170
Chain 1:   2000       -11467.454             0.167            0.169
Chain 1:   2100       -10921.250             0.155            0.124
Chain 1:   2200       -10127.637             0.132            0.102
Chain 1:   2300        -9980.483             0.097            0.078
Chain 1:   2400        -9852.593             0.091            0.078
Chain 1:   2500       -14389.017             0.113            0.078
Chain 1:   2600       -18572.925             0.130            0.124
Chain 1:   2700        -9607.339             0.202            0.124
Chain 1:   2800        -9285.702             0.182            0.078
Chain 1:   2900        -9921.314             0.176            0.064
Chain 1:   3000       -16302.050             0.212            0.078
Chain 1:   3100        -9104.393             0.286            0.225
Chain 1:   3200       -16573.066             0.323            0.315
Chain 1:   3300       -15646.338             0.328            0.315
Chain 1:   3400       -10731.886             0.372            0.391
Chain 1:   3500       -10589.931             0.342            0.391
Chain 1:   3600       -10339.128             0.322            0.391
Chain 1:   3700       -20333.139             0.278            0.391
Chain 1:   3800       -10342.931             0.371            0.451
Chain 1:   3900       -11997.954             0.378            0.451
Chain 1:   4000        -9824.060             0.361            0.451
Chain 1:   4100        -9325.542             0.288            0.221
Chain 1:   4200       -13033.520             0.271            0.221
Chain 1:   4300       -10604.327             0.288            0.229
Chain 1:   4400        -8999.497             0.260            0.221
Chain 1:   4500        -9670.832             0.266            0.221
Chain 1:   4600       -13928.274             0.294            0.229
Chain 1:   4700       -10139.996             0.282            0.229
Chain 1:   4800        -9339.222             0.194            0.221
Chain 1:   4900        -9416.711             0.181            0.221
Chain 1:   5000       -14286.566             0.193            0.229
Chain 1:   5100        -9318.024             0.241            0.284
Chain 1:   5200       -16335.621             0.255            0.306
Chain 1:   5300       -12966.267             0.258            0.306
Chain 1:   5400        -9004.740             0.285            0.341
Chain 1:   5500       -11550.913             0.300            0.341
Chain 1:   5600        -9727.585             0.288            0.341
Chain 1:   5700        -8959.974             0.259            0.260
Chain 1:   5800        -9075.455             0.252            0.260
Chain 1:   5900       -10596.980             0.265            0.260
Chain 1:   6000       -12400.272             0.246            0.220
Chain 1:   6100        -8642.065             0.236            0.220
Chain 1:   6200       -11671.990             0.219            0.220
Chain 1:   6300       -11247.845             0.197            0.187
Chain 1:   6400       -12644.767             0.164            0.145
Chain 1:   6500       -10376.563             0.164            0.145
Chain 1:   6600        -9112.444             0.159            0.144
Chain 1:   6700        -9222.325             0.151            0.144
Chain 1:   6800        -9332.540             0.151            0.144
Chain 1:   6900       -13234.849             0.166            0.145
Chain 1:   7000        -8569.511             0.206            0.219
Chain 1:   7100        -8698.504             0.164            0.139
Chain 1:   7200        -8993.305             0.142            0.110
Chain 1:   7300        -8386.128             0.145            0.110
Chain 1:   7400       -14873.159             0.178            0.139
Chain 1:   7500        -8513.380             0.230            0.139
Chain 1:   7600       -11403.459             0.242            0.253
Chain 1:   7700        -8620.846             0.273            0.295
Chain 1:   7800        -9244.005             0.279            0.295
Chain 1:   7900        -8746.939             0.255            0.253
Chain 1:   8000        -9954.105             0.212            0.121
Chain 1:   8100        -8836.226             0.224            0.127
Chain 1:   8200       -10549.463             0.237            0.162
Chain 1:   8300        -8986.728             0.247            0.174
Chain 1:   8400       -12255.666             0.230            0.174
Chain 1:   8500        -8742.118             0.195            0.174
Chain 1:   8600        -9448.551             0.177            0.162
Chain 1:   8700        -9218.396             0.148            0.127
Chain 1:   8800        -9856.339             0.147            0.127
Chain 1:   8900        -9259.708             0.148            0.127
Chain 1:   9000       -11591.705             0.156            0.162
Chain 1:   9100       -12002.381             0.147            0.162
Chain 1:   9200        -9369.343             0.159            0.174
Chain 1:   9300        -8496.423             0.152            0.103
Chain 1:   9400        -8731.974             0.128            0.075
Chain 1:   9500        -9417.928             0.095            0.073
Chain 1:   9600        -8513.395             0.098            0.073
Chain 1:   9700        -8269.335             0.098            0.073
Chain 1:   9800       -12111.580             0.124            0.103
Chain 1:   9900       -10342.352             0.134            0.106
Chain 1:   10000        -8389.755             0.137            0.106
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62095.939             1.000            1.000
Chain 1:    200       -18256.626             1.701            2.401
Chain 1:    300        -9034.299             1.474            1.021
Chain 1:    400        -9467.281             1.117            1.021
Chain 1:    500        -7855.344             0.935            1.000
Chain 1:    600        -8800.634             0.797            1.000
Chain 1:    700        -8354.876             0.691            0.205
Chain 1:    800        -8227.165             0.606            0.205
Chain 1:    900        -7942.176             0.543            0.107
Chain 1:   1000        -7723.219             0.491            0.107
Chain 1:   1100        -7899.724             0.394            0.053
Chain 1:   1200        -7565.104             0.158            0.046
Chain 1:   1300        -7796.766             0.059            0.044
Chain 1:   1400        -7895.190             0.055            0.036
Chain 1:   1500        -7524.401             0.040            0.036
Chain 1:   1600        -7615.967             0.030            0.030
Chain 1:   1700        -7601.293             0.025            0.028
Chain 1:   1800        -7642.363             0.024            0.028
Chain 1:   1900        -7535.439             0.022            0.022
Chain 1:   2000        -7647.154             0.021            0.015
Chain 1:   2100        -7649.363             0.018            0.014
Chain 1:   2200        -7746.059             0.015            0.012
Chain 1:   2300        -7542.162             0.015            0.012
Chain 1:   2400        -7574.636             0.014            0.012
Chain 1:   2500        -7415.056             0.011            0.012
Chain 1:   2600        -7530.091             0.012            0.014
Chain 1:   2700        -7510.710             0.012            0.014
Chain 1:   2800        -7507.509             0.011            0.014
Chain 1:   2900        -7372.802             0.012            0.015
Chain 1:   3000        -7522.790             0.012            0.015
Chain 1:   3100        -7520.920             0.012            0.015
Chain 1:   3200        -7740.906             0.014            0.018
Chain 1:   3300        -7445.953             0.015            0.018
Chain 1:   3400        -7688.234             0.018            0.020
Chain 1:   3500        -7432.266             0.019            0.020
Chain 1:   3600        -7499.616             0.018            0.020
Chain 1:   3700        -7447.553             0.019            0.020
Chain 1:   3800        -7448.488             0.019            0.020
Chain 1:   3900        -7407.961             0.018            0.020
Chain 1:   4000        -7399.976             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003069 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86233.335             1.000            1.000
Chain 1:    200       -13892.578             3.104            5.207
Chain 1:    300       -10170.836             2.191            1.000
Chain 1:    400       -11567.115             1.673            1.000
Chain 1:    500        -9087.939             1.393            0.366
Chain 1:    600        -8938.252             1.164            0.366
Chain 1:    700        -8917.982             0.998            0.273
Chain 1:    800        -8679.998             0.877            0.273
Chain 1:    900        -8586.497             0.780            0.121
Chain 1:   1000        -8813.013             0.705            0.121
Chain 1:   1100        -8825.319             0.605            0.027   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8508.372             0.088            0.027
Chain 1:   1300        -8840.700             0.055            0.027
Chain 1:   1400        -8796.096             0.044            0.026
Chain 1:   1500        -8685.765             0.018            0.017
Chain 1:   1600        -8788.104             0.017            0.013
Chain 1:   1700        -8850.873             0.018            0.013
Chain 1:   1800        -8412.792             0.020            0.013
Chain 1:   1900        -8517.592             0.020            0.013
Chain 1:   2000        -8496.130             0.018            0.012
Chain 1:   2100        -8471.586             0.018            0.012
Chain 1:   2200        -8436.228             0.015            0.012
Chain 1:   2300        -8571.289             0.013            0.012
Chain 1:   2400        -8416.033             0.014            0.012
Chain 1:   2500        -8487.257             0.014            0.012
Chain 1:   2600        -8400.120             0.013            0.010
Chain 1:   2700        -8437.373             0.013            0.010
Chain 1:   2800        -8395.400             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003746 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8369948.180             1.000            1.000
Chain 1:    200     -1576207.096             2.655            4.310
Chain 1:    300      -889208.858             2.028            1.000
Chain 1:    400      -456947.327             1.757            1.000
Chain 1:    500      -358011.498             1.461            0.946
Chain 1:    600      -233435.058             1.306            0.946
Chain 1:    700      -119742.416             1.255            0.946
Chain 1:    800       -86953.667             1.146            0.946
Chain 1:    900       -67293.943             1.051            0.773
Chain 1:   1000       -52081.770             0.975            0.773
Chain 1:   1100       -39538.624             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38720.687             0.478            0.377
Chain 1:   1300       -26639.507             0.446            0.377
Chain 1:   1400       -26358.952             0.352            0.317
Chain 1:   1500       -22935.036             0.340            0.317
Chain 1:   1600       -22149.609             0.290            0.292
Chain 1:   1700       -21017.672             0.200            0.292
Chain 1:   1800       -20961.218             0.163            0.149
Chain 1:   1900       -21287.831             0.135            0.054
Chain 1:   2000       -19795.236             0.113            0.054
Chain 1:   2100       -20033.916             0.083            0.035
Chain 1:   2200       -20261.097             0.082            0.035
Chain 1:   2300       -19877.526             0.039            0.019
Chain 1:   2400       -19649.369             0.039            0.019
Chain 1:   2500       -19451.578             0.025            0.015
Chain 1:   2600       -19081.109             0.023            0.015
Chain 1:   2700       -19037.962             0.018            0.012
Chain 1:   2800       -18754.654             0.019            0.015
Chain 1:   2900       -19036.180             0.019            0.015
Chain 1:   3000       -19022.303             0.012            0.012
Chain 1:   3100       -19107.368             0.011            0.012
Chain 1:   3200       -18797.705             0.011            0.015
Chain 1:   3300       -19002.737             0.011            0.012
Chain 1:   3400       -18477.109             0.012            0.015
Chain 1:   3500       -19089.887             0.014            0.015
Chain 1:   3600       -18395.405             0.016            0.015
Chain 1:   3700       -18783.089             0.018            0.016
Chain 1:   3800       -17741.059             0.022            0.021
Chain 1:   3900       -17737.198             0.021            0.021
Chain 1:   4000       -17854.455             0.022            0.021
Chain 1:   4100       -17768.102             0.022            0.021
Chain 1:   4200       -17584.042             0.021            0.021
Chain 1:   4300       -17722.664             0.021            0.021
Chain 1:   4400       -17679.177             0.018            0.010
Chain 1:   4500       -17581.668             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001352 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48898.386             1.000            1.000
Chain 1:    200       -16205.276             1.509            2.017
Chain 1:    300       -23782.375             1.112            1.000
Chain 1:    400       -18672.312             0.902            1.000
Chain 1:    500       -15509.945             0.763            0.319
Chain 1:    600       -13013.885             0.668            0.319
Chain 1:    700       -12724.159             0.575            0.274
Chain 1:    800       -16244.158             0.531            0.274
Chain 1:    900       -14254.388             0.487            0.217
Chain 1:   1000       -12312.819             0.454            0.217
Chain 1:   1100       -11384.227             0.362            0.204
Chain 1:   1200       -11707.974             0.163            0.192
Chain 1:   1300       -11448.498             0.134            0.158
Chain 1:   1400       -12095.898             0.112            0.140
Chain 1:   1500        -9981.077             0.113            0.140
Chain 1:   1600       -10586.389             0.099            0.082
Chain 1:   1700        -9505.137             0.108            0.114
Chain 1:   1800       -10376.091             0.095            0.084
Chain 1:   1900        -9833.274             0.087            0.082
Chain 1:   2000       -11215.781             0.083            0.082
Chain 1:   2100        -9517.208             0.093            0.084
Chain 1:   2200        -9808.391             0.093            0.084
Chain 1:   2300       -11765.214             0.107            0.114
Chain 1:   2400        -9956.501             0.120            0.123
Chain 1:   2500       -10914.048             0.108            0.114
Chain 1:   2600       -10096.932             0.110            0.114
Chain 1:   2700       -12458.240             0.118            0.123
Chain 1:   2800       -10909.327             0.123            0.142
Chain 1:   2900        -9495.326             0.133            0.149
Chain 1:   3000       -12184.375             0.143            0.166
Chain 1:   3100       -11673.021             0.129            0.149
Chain 1:   3200       -17223.019             0.158            0.166
Chain 1:   3300       -16353.397             0.147            0.149
Chain 1:   3400        -8908.865             0.212            0.149
Chain 1:   3500       -10001.355             0.215            0.149
Chain 1:   3600       -10828.287             0.214            0.149
Chain 1:   3700       -14117.084             0.219            0.149
Chain 1:   3800       -14587.959             0.208            0.149
Chain 1:   3900       -14052.613             0.196            0.109
Chain 1:   4000       -12039.800             0.191            0.109
Chain 1:   4100        -9530.867             0.213            0.167
Chain 1:   4200       -13137.088             0.208            0.167
Chain 1:   4300       -13801.799             0.208            0.167
Chain 1:   4400       -10348.869             0.158            0.167
Chain 1:   4500        -8591.590             0.167            0.205
Chain 1:   4600        -9228.115             0.166            0.205
Chain 1:   4700        -9741.133             0.148            0.167
Chain 1:   4800        -8936.320             0.154            0.167
Chain 1:   4900        -8988.365             0.151            0.167
Chain 1:   5000        -9324.892             0.138            0.090
Chain 1:   5100        -8679.102             0.119            0.074
Chain 1:   5200        -8782.169             0.093            0.069
Chain 1:   5300       -14495.277             0.127            0.074
Chain 1:   5400       -12066.147             0.114            0.074
Chain 1:   5500        -8469.511             0.136            0.074
Chain 1:   5600       -10006.531             0.144            0.090
Chain 1:   5700       -12083.759             0.156            0.154
Chain 1:   5800       -10420.638             0.163            0.160
Chain 1:   5900        -9130.203             0.177            0.160
Chain 1:   6000        -9941.445             0.181            0.160
Chain 1:   6100        -8853.307             0.186            0.160
Chain 1:   6200        -8524.886             0.189            0.160
Chain 1:   6300        -9027.227             0.155            0.154
Chain 1:   6400        -9217.304             0.137            0.141
Chain 1:   6500       -10380.009             0.106            0.123
Chain 1:   6600       -10397.251             0.091            0.112
Chain 1:   6700       -10929.443             0.078            0.082
Chain 1:   6800        -8478.688             0.091            0.082
Chain 1:   6900        -8515.327             0.078            0.056
Chain 1:   7000        -9061.435             0.075            0.056
Chain 1:   7100        -8621.711             0.068            0.051
Chain 1:   7200        -8712.804             0.065            0.051
Chain 1:   7300        -9095.321             0.064            0.049
Chain 1:   7400       -11271.479             0.081            0.051
Chain 1:   7500        -9024.811             0.095            0.051
Chain 1:   7600        -8465.917             0.101            0.060
Chain 1:   7700        -8335.490             0.098            0.060
Chain 1:   7800       -10726.176             0.091            0.060
Chain 1:   7900        -8879.659             0.112            0.066
Chain 1:   8000       -10594.894             0.122            0.162
Chain 1:   8100        -8826.081             0.137            0.193
Chain 1:   8200        -8500.054             0.140            0.193
Chain 1:   8300       -10858.058             0.157            0.200
Chain 1:   8400        -8952.657             0.159            0.208
Chain 1:   8500        -9246.624             0.137            0.200
Chain 1:   8600        -8972.069             0.134            0.200
Chain 1:   8700        -8525.042             0.138            0.200
Chain 1:   8800        -8574.084             0.116            0.162
Chain 1:   8900       -11736.019             0.122            0.162
Chain 1:   9000        -8811.522             0.139            0.200
Chain 1:   9100        -9358.859             0.125            0.058
Chain 1:   9200        -8577.453             0.130            0.091
Chain 1:   9300       -10330.290             0.125            0.091
Chain 1:   9400        -9387.206             0.114            0.091
Chain 1:   9500        -8558.723             0.121            0.097
Chain 1:   9600       -10185.899             0.134            0.100
Chain 1:   9700       -11762.449             0.142            0.134
Chain 1:   9800        -8402.952             0.181            0.160
Chain 1:   9900        -8466.683             0.155            0.134
Chain 1:   10000        -8748.917             0.125            0.100
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57092.688             1.000            1.000
Chain 1:    200       -17483.510             1.633            2.266
Chain 1:    300        -8759.861             1.420            1.000
Chain 1:    400        -8387.079             1.076            1.000
Chain 1:    500        -7770.150             0.877            0.996
Chain 1:    600        -8876.222             0.752            0.996
Chain 1:    700        -8043.885             0.659            0.125
Chain 1:    800        -8164.993             0.579            0.125
Chain 1:    900        -8101.302             0.515            0.103
Chain 1:   1000        -7723.254             0.468            0.103
Chain 1:   1100        -7690.297             0.369            0.079
Chain 1:   1200        -7785.640             0.144            0.049
Chain 1:   1300        -7771.732             0.044            0.044
Chain 1:   1400        -7672.888             0.041            0.015
Chain 1:   1500        -7599.167             0.034            0.013
Chain 1:   1600        -7595.202             0.022            0.012
Chain 1:   1700        -7549.948             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002671 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87201.083             1.000            1.000
Chain 1:    200       -13526.469             3.223            5.447
Chain 1:    300        -9900.752             2.271            1.000
Chain 1:    400       -10803.043             1.724            1.000
Chain 1:    500        -8816.482             1.424            0.366
Chain 1:    600        -8452.338             1.194            0.366
Chain 1:    700        -8636.247             1.027            0.225
Chain 1:    800        -9392.752             0.908            0.225
Chain 1:    900        -8739.153             0.816            0.084
Chain 1:   1000        -8483.921             0.737            0.084
Chain 1:   1100        -8683.507             0.639            0.081   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8255.906             0.100            0.075
Chain 1:   1300        -8584.156             0.067            0.052
Chain 1:   1400        -8586.125             0.059            0.043
Chain 1:   1500        -8461.632             0.038            0.038
Chain 1:   1600        -8579.387             0.035            0.030
Chain 1:   1700        -8658.294             0.034            0.030
Chain 1:   1800        -8247.035             0.031            0.030
Chain 1:   1900        -8342.601             0.024            0.023
Chain 1:   2000        -8315.908             0.022            0.015
Chain 1:   2100        -8438.297             0.021            0.015
Chain 1:   2200        -8257.758             0.018            0.015
Chain 1:   2300        -8337.982             0.015            0.014
Chain 1:   2400        -8407.520             0.016            0.014
Chain 1:   2500        -8352.823             0.015            0.011
Chain 1:   2600        -8352.053             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002543 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413494.787             1.000            1.000
Chain 1:    200     -1586425.086             2.652            4.303
Chain 1:    300      -890178.648             2.029            1.000
Chain 1:    400      -456656.295             1.759            1.000
Chain 1:    500      -356959.110             1.463            0.949
Chain 1:    600      -232127.312             1.309            0.949
Chain 1:    700      -118801.276             1.258            0.949
Chain 1:    800       -86139.030             1.148            0.949
Chain 1:    900       -66570.648             1.053            0.782
Chain 1:   1000       -51437.045             0.977            0.782
Chain 1:   1100       -38977.076             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38161.174             0.481            0.379
Chain 1:   1300       -26178.470             0.449            0.379
Chain 1:   1400       -25903.786             0.355            0.320
Chain 1:   1500       -22506.392             0.342            0.320
Chain 1:   1600       -21727.659             0.292            0.294
Chain 1:   1700       -20608.373             0.202            0.294
Chain 1:   1800       -20554.211             0.164            0.151
Chain 1:   1900       -20880.360             0.136            0.054
Chain 1:   2000       -19395.109             0.115            0.054
Chain 1:   2100       -19633.460             0.084            0.036
Chain 1:   2200       -19859.262             0.083            0.036
Chain 1:   2300       -19476.992             0.039            0.020
Chain 1:   2400       -19249.112             0.039            0.020
Chain 1:   2500       -19050.898             0.025            0.016
Chain 1:   2600       -18681.485             0.023            0.016
Chain 1:   2700       -18638.530             0.018            0.012
Chain 1:   2800       -18355.317             0.020            0.015
Chain 1:   2900       -18636.425             0.019            0.015
Chain 1:   3000       -18622.705             0.012            0.012
Chain 1:   3100       -18707.695             0.011            0.012
Chain 1:   3200       -18398.493             0.012            0.015
Chain 1:   3300       -18603.107             0.011            0.012
Chain 1:   3400       -18078.161             0.013            0.015
Chain 1:   3500       -18689.810             0.015            0.015
Chain 1:   3600       -17996.678             0.017            0.015
Chain 1:   3700       -18383.297             0.018            0.017
Chain 1:   3800       -17343.352             0.023            0.021
Chain 1:   3900       -17339.431             0.021            0.021
Chain 1:   4000       -17456.782             0.022            0.021
Chain 1:   4100       -17370.569             0.022            0.021
Chain 1:   4200       -17186.849             0.021            0.021
Chain 1:   4300       -17325.264             0.021            0.021
Chain 1:   4400       -17282.141             0.019            0.011
Chain 1:   4500       -17184.606             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48348.065             1.000            1.000
Chain 1:    200       -20534.004             1.177            1.355
Chain 1:    300       -13681.088             0.952            1.000
Chain 1:    400       -19563.371             0.789            1.000
Chain 1:    500       -21262.295             0.647            0.501
Chain 1:    600       -11673.723             0.676            0.821
Chain 1:    700       -11087.129             0.587            0.501
Chain 1:    800       -14044.135             0.540            0.501
Chain 1:    900       -15756.470             0.492            0.301
Chain 1:   1000       -12725.574             0.467            0.301
Chain 1:   1100       -19546.422             0.402            0.301
Chain 1:   1200       -10592.044             0.351            0.301
Chain 1:   1300       -11536.521             0.309            0.238
Chain 1:   1400       -11704.546             0.280            0.211
Chain 1:   1500       -10760.593             0.281            0.211
Chain 1:   1600       -11999.855             0.209            0.109
Chain 1:   1700        -9558.227             0.229            0.211
Chain 1:   1800       -11626.500             0.226            0.178
Chain 1:   1900        -9609.265             0.236            0.210
Chain 1:   2000        -9436.067             0.214            0.178
Chain 1:   2100        -9188.827             0.182            0.103
Chain 1:   2200       -12649.902             0.125            0.103
Chain 1:   2300       -11445.988             0.127            0.105
Chain 1:   2400       -17444.154             0.160            0.178
Chain 1:   2500        -9363.896             0.238            0.210
Chain 1:   2600        -8948.062             0.232            0.210
Chain 1:   2700        -9183.194             0.209            0.178
Chain 1:   2800        -9720.480             0.197            0.105
Chain 1:   2900        -9344.453             0.180            0.055
Chain 1:   3000       -15078.456             0.216            0.105
Chain 1:   3100        -9378.492             0.274            0.274
Chain 1:   3200        -8533.936             0.257            0.105
Chain 1:   3300       -13627.669             0.284            0.344
Chain 1:   3400        -8656.462             0.307            0.374
Chain 1:   3500        -9911.904             0.233            0.127
Chain 1:   3600       -12708.502             0.250            0.220
Chain 1:   3700        -8604.272             0.295            0.374
Chain 1:   3800       -11075.545             0.312            0.374
Chain 1:   3900        -9535.781             0.324            0.374
Chain 1:   4000        -8513.865             0.298            0.223
Chain 1:   4100        -8550.555             0.238            0.220
Chain 1:   4200       -12583.247             0.260            0.223
Chain 1:   4300       -11301.113             0.234            0.220
Chain 1:   4400        -9102.701             0.201            0.220
Chain 1:   4500       -10588.754             0.202            0.220
Chain 1:   4600        -8437.477             0.206            0.223
Chain 1:   4700       -11172.679             0.182            0.223
Chain 1:   4800       -14399.770             0.183            0.224
Chain 1:   4900        -9402.231             0.220            0.242
Chain 1:   5000        -9061.833             0.211            0.242
Chain 1:   5100        -8789.493             0.214            0.242
Chain 1:   5200        -8539.014             0.185            0.224
Chain 1:   5300       -10570.520             0.193            0.224
Chain 1:   5400        -8369.025             0.195            0.224
Chain 1:   5500       -11090.738             0.205            0.245
Chain 1:   5600        -8465.862             0.211            0.245
Chain 1:   5700        -8448.359             0.187            0.224
Chain 1:   5800        -8423.255             0.165            0.192
Chain 1:   5900        -8223.953             0.114            0.038
Chain 1:   6000        -8332.910             0.111            0.031
Chain 1:   6100        -8938.926             0.115            0.068
Chain 1:   6200        -8365.890             0.119            0.068
Chain 1:   6300       -12139.326             0.131            0.068
Chain 1:   6400        -8888.505             0.141            0.068
Chain 1:   6500        -9195.342             0.120            0.068
Chain 1:   6600        -9112.814             0.090            0.033
Chain 1:   6700        -9814.853             0.097            0.068
Chain 1:   6800        -8553.445             0.111            0.068
Chain 1:   6900        -8287.918             0.112            0.068
Chain 1:   7000        -8523.735             0.113            0.068
Chain 1:   7100        -8158.348             0.111            0.068
Chain 1:   7200        -9674.328             0.120            0.072
Chain 1:   7300        -8386.515             0.104            0.072
Chain 1:   7400        -9440.494             0.079            0.072
Chain 1:   7500       -11179.170             0.091            0.112
Chain 1:   7600        -8304.805             0.125            0.147
Chain 1:   7700        -8297.758             0.118            0.147
Chain 1:   7800        -8382.387             0.104            0.112
Chain 1:   7900        -8198.704             0.103            0.112
Chain 1:   8000        -8291.885             0.101            0.112
Chain 1:   8100        -8204.749             0.098            0.112
Chain 1:   8200        -8124.526             0.083            0.022
Chain 1:   8300        -8133.577             0.068            0.011
Chain 1:   8400       -10648.232             0.080            0.011
Chain 1:   8500       -11427.288             0.072            0.011
Chain 1:   8600        -8406.666             0.073            0.011
Chain 1:   8700        -7936.235             0.079            0.022
Chain 1:   8800        -8017.380             0.079            0.022
Chain 1:   8900        -8335.709             0.080            0.038
Chain 1:   9000        -8319.966             0.079            0.038
Chain 1:   9100        -9820.088             0.094            0.059
Chain 1:   9200        -8660.695             0.106            0.068
Chain 1:   9300        -8301.655             0.110            0.068
Chain 1:   9400        -9492.322             0.099            0.068
Chain 1:   9500        -8815.131             0.100            0.077
Chain 1:   9600       -10634.765             0.081            0.077
Chain 1:   9700       -11328.696             0.081            0.077
Chain 1:   9800       -10549.991             0.088            0.077
Chain 1:   9900        -7890.882             0.118            0.125
Chain 1:   10000        -8366.977             0.123            0.125
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001553 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61372.086             1.000            1.000
Chain 1:    200       -17538.433             1.750            2.499
Chain 1:    300        -8670.862             1.507            1.023
Chain 1:    400        -8907.997             1.137            1.023
Chain 1:    500        -7880.467             0.936            1.000
Chain 1:    600        -8822.924             0.798            1.000
Chain 1:    700        -7828.879             0.702            0.130
Chain 1:    800        -8076.851             0.618            0.130
Chain 1:    900        -7605.552             0.556            0.127
Chain 1:   1000        -7702.085             0.502            0.127
Chain 1:   1100        -7687.643             0.402            0.107
Chain 1:   1200        -7593.028             0.153            0.062
Chain 1:   1300        -7576.104             0.051            0.031
Chain 1:   1400        -7679.844             0.050            0.031
Chain 1:   1500        -7573.053             0.038            0.014
Chain 1:   1600        -7518.444             0.028            0.014
Chain 1:   1700        -7461.782             0.016            0.013
Chain 1:   1800        -7522.642             0.014            0.012
Chain 1:   1900        -7522.414             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85732.830             1.000            1.000
Chain 1:    200       -13178.989             3.253            5.505
Chain 1:    300        -9646.929             2.290            1.000
Chain 1:    400       -10521.918             1.739            1.000
Chain 1:    500        -8527.670             1.438            0.366
Chain 1:    600        -8393.492             1.201            0.366
Chain 1:    700        -8550.349             1.032            0.234
Chain 1:    800        -8751.509             0.906            0.234
Chain 1:    900        -8543.603             0.808            0.083
Chain 1:   1000        -8215.572             0.731            0.083
Chain 1:   1100        -8511.678             0.634            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8189.838             0.088            0.039
Chain 1:   1300        -8390.882             0.054            0.035
Chain 1:   1400        -8407.616             0.046            0.024
Chain 1:   1500        -8296.652             0.023            0.024
Chain 1:   1600        -8384.021             0.023            0.024
Chain 1:   1700        -8486.153             0.022            0.024
Chain 1:   1800        -8100.188             0.025            0.024
Chain 1:   1900        -8201.623             0.024            0.024
Chain 1:   2000        -8171.208             0.020            0.013
Chain 1:   2100        -8310.682             0.018            0.013
Chain 1:   2200        -8091.802             0.017            0.013
Chain 1:   2300        -8233.978             0.016            0.013
Chain 1:   2400        -8118.544             0.017            0.014
Chain 1:   2500        -8177.628             0.017            0.014
Chain 1:   2600        -8192.062             0.016            0.014
Chain 1:   2700        -8114.429             0.016            0.014
Chain 1:   2800        -8095.893             0.011            0.012
Chain 1:   2900        -8108.086             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003275 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8374486.348             1.000            1.000
Chain 1:    200     -1579462.353             2.651            4.302
Chain 1:    300      -890390.631             2.025            1.000
Chain 1:    400      -457443.505             1.756            1.000
Chain 1:    500      -358012.152             1.460            0.946
Chain 1:    600      -233194.733             1.306            0.946
Chain 1:    700      -119175.505             1.256            0.946
Chain 1:    800       -86294.335             1.147            0.946
Chain 1:    900       -66591.034             1.052            0.774
Chain 1:   1000       -51333.435             0.977            0.774
Chain 1:   1100       -38764.500             0.909            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37932.133             0.481            0.381
Chain 1:   1300       -25854.238             0.450            0.381
Chain 1:   1400       -25567.907             0.357            0.324
Chain 1:   1500       -22145.622             0.345            0.324
Chain 1:   1600       -21358.364             0.295            0.297
Chain 1:   1700       -20228.609             0.205            0.296
Chain 1:   1800       -20171.582             0.167            0.155
Chain 1:   1900       -20497.046             0.139            0.056
Chain 1:   2000       -19007.446             0.117            0.056
Chain 1:   2100       -19245.985             0.086            0.037
Chain 1:   2200       -19472.165             0.085            0.037
Chain 1:   2300       -19089.749             0.040            0.020
Chain 1:   2400       -18862.008             0.040            0.020
Chain 1:   2500       -18664.150             0.026            0.016
Chain 1:   2600       -18294.935             0.024            0.016
Chain 1:   2700       -18252.075             0.019            0.012
Chain 1:   2800       -17969.218             0.020            0.016
Chain 1:   2900       -18250.242             0.020            0.015
Chain 1:   3000       -18236.524             0.012            0.012
Chain 1:   3100       -18321.379             0.011            0.012
Chain 1:   3200       -18012.488             0.012            0.015
Chain 1:   3300       -18216.884             0.011            0.012
Chain 1:   3400       -17692.592             0.013            0.015
Chain 1:   3500       -18303.336             0.015            0.016
Chain 1:   3600       -17611.537             0.017            0.016
Chain 1:   3700       -17997.204             0.019            0.017
Chain 1:   3800       -16959.302             0.023            0.021
Chain 1:   3900       -16955.521             0.022            0.021
Chain 1:   4000       -17072.800             0.023            0.021
Chain 1:   4100       -16986.645             0.023            0.021
Chain 1:   4200       -16803.445             0.022            0.021
Chain 1:   4300       -16941.458             0.022            0.021
Chain 1:   4400       -16898.716             0.019            0.011
Chain 1:   4500       -16801.328             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12857.533             1.000            1.000
Chain 1:    200        -9683.400             0.664            1.000
Chain 1:    300        -8567.771             0.486            0.328
Chain 1:    400        -8666.691             0.367            0.328
Chain 1:    500        -8625.409             0.295            0.130
Chain 1:    600        -8473.531             0.249            0.130
Chain 1:    700        -8391.707             0.215            0.018
Chain 1:    800        -8356.118             0.188            0.018
Chain 1:    900        -8403.859             0.168            0.011
Chain 1:   1000        -8472.660             0.152            0.011
Chain 1:   1100        -8533.853             0.053            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00171 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58399.693             1.000            1.000
Chain 1:    200       -18033.853             1.619            2.238
Chain 1:    300        -8870.086             1.424            1.033
Chain 1:    400        -8054.439             1.093            1.033
Chain 1:    500        -8849.776             0.893            1.000
Chain 1:    600        -9492.505             0.755            1.000
Chain 1:    700        -8297.004             0.668            0.144
Chain 1:    800        -8273.830             0.585            0.144
Chain 1:    900        -7948.104             0.524            0.101
Chain 1:   1000        -7759.046             0.474            0.101
Chain 1:   1100        -7743.685             0.374            0.090
Chain 1:   1200        -7718.624             0.151            0.068
Chain 1:   1300        -7616.277             0.049            0.041
Chain 1:   1400        -7939.643             0.043            0.041
Chain 1:   1500        -7610.744             0.038            0.041
Chain 1:   1600        -7760.514             0.033            0.024
Chain 1:   1700        -7594.154             0.021            0.022
Chain 1:   1800        -7605.060             0.021            0.022
Chain 1:   1900        -7624.691             0.017            0.019
Chain 1:   2000        -7685.235             0.016            0.013
Chain 1:   2100        -7597.180             0.017            0.013
Chain 1:   2200        -7836.267             0.019            0.019
Chain 1:   2300        -7561.189             0.022            0.022
Chain 1:   2400        -7562.904             0.018            0.019
Chain 1:   2500        -7627.172             0.014            0.012
Chain 1:   2600        -7549.859             0.013            0.010
Chain 1:   2700        -7476.630             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003196 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87131.396             1.000            1.000
Chain 1:    200       -13928.667             3.128            5.256
Chain 1:    300       -10346.883             2.201            1.000
Chain 1:    400       -11182.653             1.669            1.000
Chain 1:    500        -9298.823             1.376            0.346
Chain 1:    600        -8871.502             1.155            0.346
Chain 1:    700        -8771.321             0.991            0.203
Chain 1:    800        -9284.214             0.874            0.203
Chain 1:    900        -9162.644             0.779            0.075
Chain 1:   1000        -8941.759             0.703            0.075
Chain 1:   1100        -9161.829             0.606            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8840.257             0.084            0.048
Chain 1:   1300        -9057.474             0.051            0.036
Chain 1:   1400        -9041.654             0.044            0.025
Chain 1:   1500        -8938.870             0.025            0.024
Chain 1:   1600        -9042.600             0.021            0.024
Chain 1:   1700        -9131.312             0.021            0.024
Chain 1:   1800        -8730.206             0.020            0.024
Chain 1:   1900        -8829.967             0.020            0.024
Chain 1:   2000        -8801.278             0.018            0.011
Chain 1:   2100        -8921.174             0.017            0.011
Chain 1:   2200        -8706.650             0.016            0.011
Chain 1:   2300        -8860.734             0.015            0.011
Chain 1:   2400        -8742.320             0.016            0.013
Chain 1:   2500        -8805.546             0.016            0.013
Chain 1:   2600        -8826.873             0.015            0.013
Chain 1:   2700        -8745.957             0.015            0.013
Chain 1:   2800        -8720.230             0.011            0.011
Chain 1:   2900        -8775.576             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003202 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8423598.240             1.000            1.000
Chain 1:    200     -1587510.175             2.653            4.306
Chain 1:    300      -891701.932             2.029            1.000
Chain 1:    400      -458905.695             1.757            1.000
Chain 1:    500      -358785.725             1.462            0.943
Chain 1:    600      -233412.038             1.308            0.943
Chain 1:    700      -119555.955             1.257            0.943
Chain 1:    800       -86809.302             1.147            0.943
Chain 1:    900       -67144.270             1.052            0.780
Chain 1:   1000       -51948.018             0.976            0.780
Chain 1:   1100       -39445.714             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38617.284             0.479            0.377
Chain 1:   1300       -26595.597             0.446            0.377
Chain 1:   1400       -26315.794             0.353            0.317
Chain 1:   1500       -22909.891             0.340            0.317
Chain 1:   1600       -22128.400             0.290            0.293
Chain 1:   1700       -21004.693             0.200            0.293
Chain 1:   1800       -20949.330             0.163            0.149
Chain 1:   1900       -21275.189             0.135            0.053
Chain 1:   2000       -19788.427             0.113            0.053
Chain 1:   2100       -20026.566             0.083            0.035
Chain 1:   2200       -20252.774             0.082            0.035
Chain 1:   2300       -19870.225             0.038            0.019
Chain 1:   2400       -19642.401             0.038            0.019
Chain 1:   2500       -19444.519             0.025            0.015
Chain 1:   2600       -19074.949             0.023            0.015
Chain 1:   2700       -19031.945             0.018            0.012
Chain 1:   2800       -18749.032             0.019            0.015
Chain 1:   2900       -19030.025             0.019            0.015
Chain 1:   3000       -19016.228             0.012            0.012
Chain 1:   3100       -19101.236             0.011            0.012
Chain 1:   3200       -18792.088             0.011            0.015
Chain 1:   3300       -18996.627             0.011            0.012
Chain 1:   3400       -18471.957             0.012            0.015
Chain 1:   3500       -19083.312             0.014            0.015
Chain 1:   3600       -18390.531             0.016            0.015
Chain 1:   3700       -18776.933             0.018            0.016
Chain 1:   3800       -17737.643             0.022            0.021
Chain 1:   3900       -17733.787             0.021            0.021
Chain 1:   4000       -17851.077             0.022            0.021
Chain 1:   4100       -17764.976             0.022            0.021
Chain 1:   4200       -17581.351             0.021            0.021
Chain 1:   4300       -17719.628             0.021            0.021
Chain 1:   4400       -17676.605             0.018            0.010
Chain 1:   4500       -17579.154             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12528.725             1.000            1.000
Chain 1:    200        -9446.929             0.663            1.000
Chain 1:    300        -8089.372             0.498            0.326
Chain 1:    400        -8314.600             0.380            0.326
Chain 1:    500        -8212.058             0.307            0.168
Chain 1:    600        -8048.023             0.259            0.168
Chain 1:    700        -7941.235             0.224            0.027
Chain 1:    800        -7929.892             0.196            0.027
Chain 1:    900        -7897.526             0.175            0.020
Chain 1:   1000        -8074.259             0.159            0.022
Chain 1:   1100        -8082.829             0.060            0.020
Chain 1:   1200        -7964.442             0.028            0.015
Chain 1:   1300        -7935.743             0.012            0.013
Chain 1:   1400        -7939.595             0.009            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58854.270             1.000            1.000
Chain 1:    200       -17942.692             1.640            2.280
Chain 1:    300        -8784.358             1.441            1.043
Chain 1:    400        -8150.012             1.100            1.043
Chain 1:    500        -8644.522             0.892            1.000
Chain 1:    600        -8039.690             0.755            1.000
Chain 1:    700        -7902.766             0.650            0.078
Chain 1:    800        -8311.852             0.575            0.078
Chain 1:    900        -8067.357             0.514            0.075
Chain 1:   1000        -7909.723             0.465            0.075
Chain 1:   1100        -7612.505             0.369            0.057
Chain 1:   1200        -7768.687             0.143            0.049
Chain 1:   1300        -7775.578             0.039            0.039
Chain 1:   1400        -7950.359             0.033            0.030
Chain 1:   1500        -7551.083             0.033            0.030
Chain 1:   1600        -7747.461             0.028            0.025
Chain 1:   1700        -7542.713             0.029            0.027
Chain 1:   1800        -7565.476             0.024            0.025
Chain 1:   1900        -7596.518             0.021            0.022
Chain 1:   2000        -7629.265             0.020            0.022
Chain 1:   2100        -7589.812             0.016            0.020
Chain 1:   2200        -7718.961             0.016            0.017
Chain 1:   2300        -7572.268             0.018            0.019
Chain 1:   2400        -7636.678             0.017            0.017
Chain 1:   2500        -7565.624             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003331 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86144.426             1.000            1.000
Chain 1:    200       -13691.845             3.146            5.292
Chain 1:    300       -10005.745             2.220            1.000
Chain 1:    400       -11318.207             1.694            1.000
Chain 1:    500        -8992.275             1.407            0.368
Chain 1:    600        -8496.207             1.182            0.368
Chain 1:    700        -8630.822             1.016            0.259
Chain 1:    800        -8882.646             0.892            0.259
Chain 1:    900        -8812.186             0.794            0.116
Chain 1:   1000        -8773.232             0.715            0.116
Chain 1:   1100        -8602.613             0.617            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8387.445             0.090            0.028
Chain 1:   1300        -8685.738             0.057            0.028
Chain 1:   1400        -8638.361             0.046            0.026
Chain 1:   1500        -8527.907             0.021            0.020
Chain 1:   1600        -8635.187             0.017            0.016
Chain 1:   1700        -8708.581             0.016            0.013
Chain 1:   1800        -8276.371             0.018            0.013
Chain 1:   1900        -8380.534             0.019            0.013
Chain 1:   2000        -8355.862             0.019            0.013
Chain 1:   2100        -8328.107             0.017            0.012
Chain 1:   2200        -8297.860             0.015            0.012
Chain 1:   2300        -8428.101             0.013            0.012
Chain 1:   2400        -8283.316             0.014            0.012
Chain 1:   2500        -8352.151             0.014            0.012
Chain 1:   2600        -8271.446             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003274 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412366.111             1.000            1.000
Chain 1:    200     -1586631.415             2.651            4.302
Chain 1:    300      -891088.696             2.028            1.000
Chain 1:    400      -458363.170             1.757            1.000
Chain 1:    500      -358483.425             1.461            0.944
Chain 1:    600      -233307.933             1.307            0.944
Chain 1:    700      -119465.688             1.256            0.944
Chain 1:    800       -86664.688             1.147            0.944
Chain 1:    900       -67000.093             1.052            0.781
Chain 1:   1000       -51799.601             0.976            0.781
Chain 1:   1100       -39277.765             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38455.804             0.480            0.378
Chain 1:   1300       -26410.031             0.447            0.378
Chain 1:   1400       -26129.910             0.354            0.319
Chain 1:   1500       -22716.756             0.341            0.319
Chain 1:   1600       -21933.719             0.291            0.294
Chain 1:   1700       -20806.799             0.201            0.293
Chain 1:   1800       -20751.090             0.164            0.150
Chain 1:   1900       -21077.500             0.136            0.054
Chain 1:   2000       -19587.836             0.114            0.054
Chain 1:   2100       -19826.234             0.083            0.036
Chain 1:   2200       -20053.012             0.082            0.036
Chain 1:   2300       -19669.855             0.039            0.019
Chain 1:   2400       -19441.836             0.039            0.019
Chain 1:   2500       -19243.906             0.025            0.015
Chain 1:   2600       -18873.790             0.023            0.015
Chain 1:   2700       -18830.607             0.018            0.012
Chain 1:   2800       -18547.355             0.019            0.015
Chain 1:   2900       -18828.738             0.019            0.015
Chain 1:   3000       -18814.887             0.012            0.012
Chain 1:   3100       -18899.968             0.011            0.012
Chain 1:   3200       -18590.436             0.012            0.015
Chain 1:   3300       -18795.307             0.011            0.012
Chain 1:   3400       -18269.886             0.012            0.015
Chain 1:   3500       -18882.316             0.015            0.015
Chain 1:   3600       -18188.224             0.016            0.015
Chain 1:   3700       -18575.613             0.018            0.017
Chain 1:   3800       -17534.200             0.023            0.021
Chain 1:   3900       -17530.319             0.021            0.021
Chain 1:   4000       -17647.613             0.022            0.021
Chain 1:   4100       -17561.350             0.022            0.021
Chain 1:   4200       -17377.322             0.021            0.021
Chain 1:   4300       -17515.908             0.021            0.021
Chain 1:   4400       -17472.522             0.018            0.011
Chain 1:   4500       -17375.028             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49532.656             1.000            1.000
Chain 1:    200       -23046.233             1.075            1.149
Chain 1:    300       -16785.902             0.841            1.000
Chain 1:    400       -17527.600             0.641            1.000
Chain 1:    500       -12319.643             0.597            0.423
Chain 1:    600       -16102.625             0.537            0.423
Chain 1:    700       -20345.538             0.490            0.373
Chain 1:    800       -11824.533             0.519            0.423
Chain 1:    900       -16560.760             0.493            0.373
Chain 1:   1000       -12419.903             0.477            0.373
Chain 1:   1100       -20647.898             0.417            0.373
Chain 1:   1200       -15841.571             0.332            0.333
Chain 1:   1300       -11801.498             0.329            0.333
Chain 1:   1400       -11298.171             0.330            0.333
Chain 1:   1500       -13463.353             0.303            0.303
Chain 1:   1600       -12258.084             0.290            0.303
Chain 1:   1700       -15018.823             0.287            0.303
Chain 1:   1800       -10252.492             0.262            0.303
Chain 1:   1900       -10800.409             0.238            0.303
Chain 1:   2000       -10737.055             0.205            0.184
Chain 1:   2100       -10392.413             0.169            0.161
Chain 1:   2200       -18779.089             0.183            0.161
Chain 1:   2300       -17909.885             0.154            0.098
Chain 1:   2400        -9942.775             0.229            0.161
Chain 1:   2500       -14850.632             0.246            0.184
Chain 1:   2600        -9552.235             0.292            0.330
Chain 1:   2700        -9351.393             0.276            0.330
Chain 1:   2800        -9639.875             0.232            0.051
Chain 1:   2900        -9172.773             0.232            0.051
Chain 1:   3000        -9474.946             0.235            0.051
Chain 1:   3100        -9970.417             0.237            0.051
Chain 1:   3200       -11349.164             0.204            0.051
Chain 1:   3300       -10842.138             0.204            0.051
Chain 1:   3400       -13968.089             0.146            0.051
Chain 1:   3500        -9169.012             0.165            0.051
Chain 1:   3600        -9456.642             0.113            0.050
Chain 1:   3700        -9794.935             0.114            0.050
Chain 1:   3800        -8759.613             0.123            0.051
Chain 1:   3900        -9522.373             0.126            0.080
Chain 1:   4000        -9674.199             0.124            0.080
Chain 1:   4100        -9731.840             0.120            0.080
Chain 1:   4200       -16104.374             0.147            0.080
Chain 1:   4300       -10154.819             0.201            0.118
Chain 1:   4400       -16166.672             0.216            0.118
Chain 1:   4500       -17709.187             0.173            0.087
Chain 1:   4600       -12700.275             0.209            0.118
Chain 1:   4700       -10444.740             0.227            0.216
Chain 1:   4800        -9976.045             0.220            0.216
Chain 1:   4900        -9289.744             0.219            0.216
Chain 1:   5000       -13934.597             0.251            0.333
Chain 1:   5100       -14011.536             0.251            0.333
Chain 1:   5200        -9584.144             0.258            0.333
Chain 1:   5300       -11229.535             0.214            0.216
Chain 1:   5400       -16481.255             0.208            0.216
Chain 1:   5500       -11141.283             0.248            0.319
Chain 1:   5600        -9992.961             0.220            0.216
Chain 1:   5700       -10015.300             0.198            0.147
Chain 1:   5800        -8680.377             0.209            0.154
Chain 1:   5900        -9008.052             0.205            0.154
Chain 1:   6000       -11410.066             0.193            0.154
Chain 1:   6100       -10599.520             0.200            0.154
Chain 1:   6200       -10079.925             0.159            0.147
Chain 1:   6300       -13656.268             0.171            0.154
Chain 1:   6400       -10144.865             0.173            0.154
Chain 1:   6500        -9359.181             0.134            0.115
Chain 1:   6600        -8681.268             0.130            0.084
Chain 1:   6700        -8720.396             0.130            0.084
Chain 1:   6800       -13544.149             0.151            0.084
Chain 1:   6900        -8708.671             0.202            0.211
Chain 1:   7000        -9341.838             0.188            0.084
Chain 1:   7100        -9194.434             0.182            0.084
Chain 1:   7200        -8540.889             0.185            0.084
Chain 1:   7300        -8444.826             0.160            0.078
Chain 1:   7400        -8383.539             0.126            0.077
Chain 1:   7500        -9547.143             0.129            0.077
Chain 1:   7600        -8941.678             0.128            0.068
Chain 1:   7700        -8929.086             0.128            0.068
Chain 1:   7800       -11829.829             0.117            0.068
Chain 1:   7900        -8990.779             0.093            0.068
Chain 1:   8000        -8410.129             0.093            0.069
Chain 1:   8100        -9565.513             0.104            0.077
Chain 1:   8200        -8522.111             0.108            0.121
Chain 1:   8300       -13813.065             0.145            0.122
Chain 1:   8400       -10144.451             0.181            0.122
Chain 1:   8500        -8483.567             0.188            0.196
Chain 1:   8600       -11429.321             0.207            0.245
Chain 1:   8700        -9318.081             0.230            0.245
Chain 1:   8800        -9459.270             0.207            0.227
Chain 1:   8900        -9651.034             0.177            0.196
Chain 1:   9000        -8687.954             0.181            0.196
Chain 1:   9100        -8648.669             0.170            0.196
Chain 1:   9200        -8124.062             0.164            0.196
Chain 1:   9300       -10503.519             0.148            0.196
Chain 1:   9400        -8897.444             0.130            0.181
Chain 1:   9500        -8695.918             0.113            0.111
Chain 1:   9600       -10537.624             0.105            0.111
Chain 1:   9700        -8004.790             0.114            0.111
Chain 1:   9800       -13514.553             0.153            0.175
Chain 1:   9900       -11932.340             0.164            0.175
Chain 1:   10000        -9302.225             0.181            0.181
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46580.706             1.000            1.000
Chain 1:    200       -15818.371             1.472            1.945
Chain 1:    300        -8814.088             1.246            1.000
Chain 1:    400        -8150.168             0.955            1.000
Chain 1:    500        -7903.177             0.770            0.795
Chain 1:    600        -8208.239             0.648            0.795
Chain 1:    700        -7784.314             0.563            0.081
Chain 1:    800        -8207.777             0.499            0.081
Chain 1:    900        -7874.975             0.449            0.054
Chain 1:   1000        -7691.925             0.406            0.054
Chain 1:   1100        -7644.739             0.307            0.052
Chain 1:   1200        -7561.410             0.113            0.042
Chain 1:   1300        -7547.610             0.034            0.037
Chain 1:   1400        -7806.020             0.029            0.033
Chain 1:   1500        -7542.329             0.030            0.035
Chain 1:   1600        -7681.644             0.028            0.033
Chain 1:   1700        -7517.805             0.024            0.024
Chain 1:   1800        -7648.076             0.021            0.022
Chain 1:   1900        -7545.125             0.018            0.018
Chain 1:   2000        -7514.406             0.016            0.017
Chain 1:   2100        -7529.229             0.016            0.017
Chain 1:   2200        -7683.805             0.017            0.018
Chain 1:   2300        -7507.356             0.019            0.020
Chain 1:   2400        -7485.876             0.016            0.018
Chain 1:   2500        -7532.934             0.013            0.017
Chain 1:   2600        -7468.082             0.012            0.014
Chain 1:   2700        -7395.439             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002584 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87197.370             1.000            1.000
Chain 1:    200       -13792.639             3.161            5.322
Chain 1:    300       -10052.921             2.231            1.000
Chain 1:    400       -11248.050             1.700            1.000
Chain 1:    500        -8658.387             1.420            0.372
Chain 1:    600        -8396.103             1.188            0.372
Chain 1:    700        -8470.926             1.020            0.299
Chain 1:    800        -8691.571             0.896            0.299
Chain 1:    900        -8763.486             0.797            0.106
Chain 1:   1000        -8868.439             0.718            0.106
Chain 1:   1100        -8740.500             0.620            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8376.235             0.092            0.031
Chain 1:   1300        -8677.145             0.058            0.031
Chain 1:   1400        -8480.119             0.050            0.025
Chain 1:   1500        -8539.985             0.021            0.023
Chain 1:   1600        -8647.195             0.019            0.015
Chain 1:   1700        -8704.722             0.019            0.015
Chain 1:   1800        -8260.960             0.022            0.015
Chain 1:   1900        -8368.849             0.022            0.015
Chain 1:   2000        -8355.526             0.021            0.015
Chain 1:   2100        -8472.110             0.021            0.014
Chain 1:   2200        -8266.473             0.019            0.014
Chain 1:   2300        -8361.931             0.017            0.013
Chain 1:   2400        -8428.859             0.015            0.012
Chain 1:   2500        -8377.273             0.015            0.012
Chain 1:   2600        -8391.295             0.014            0.011
Chain 1:   2700        -8298.755             0.015            0.011
Chain 1:   2800        -8246.188             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002953 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8427715.595             1.000            1.000
Chain 1:    200     -1587636.329             2.654            4.308
Chain 1:    300      -890413.587             2.030            1.000
Chain 1:    400      -457723.929             1.759            1.000
Chain 1:    500      -357716.632             1.463            0.945
Chain 1:    600      -232795.685             1.309            0.945
Chain 1:    700      -119224.025             1.258            0.945
Chain 1:    800       -86536.702             1.148            0.945
Chain 1:    900       -66928.455             1.053            0.783
Chain 1:   1000       -51779.325             0.977            0.783
Chain 1:   1100       -39303.819             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38488.372             0.480            0.378
Chain 1:   1300       -26479.896             0.447            0.378
Chain 1:   1400       -26205.366             0.353            0.317
Chain 1:   1500       -22801.702             0.340            0.317
Chain 1:   1600       -22021.982             0.290            0.293
Chain 1:   1700       -20899.044             0.200            0.293
Chain 1:   1800       -20844.395             0.163            0.149
Chain 1:   1900       -21171.084             0.135            0.054
Chain 1:   2000       -19683.069             0.113            0.054
Chain 1:   2100       -19921.482             0.083            0.035
Chain 1:   2200       -20148.036             0.082            0.035
Chain 1:   2300       -19764.967             0.039            0.019
Chain 1:   2400       -19536.860             0.039            0.019
Chain 1:   2500       -19338.782             0.025            0.015
Chain 1:   2600       -18968.562             0.023            0.015
Chain 1:   2700       -18925.392             0.018            0.012
Chain 1:   2800       -18642.002             0.019            0.015
Chain 1:   2900       -18923.394             0.019            0.015
Chain 1:   3000       -18909.586             0.012            0.012
Chain 1:   3100       -18994.669             0.011            0.012
Chain 1:   3200       -18685.012             0.011            0.015
Chain 1:   3300       -18889.976             0.011            0.012
Chain 1:   3400       -18364.303             0.012            0.015
Chain 1:   3500       -18977.080             0.015            0.015
Chain 1:   3600       -18282.479             0.016            0.015
Chain 1:   3700       -18670.198             0.018            0.017
Chain 1:   3800       -17627.979             0.023            0.021
Chain 1:   3900       -17624.015             0.021            0.021
Chain 1:   4000       -17741.350             0.022            0.021
Chain 1:   4100       -17655.046             0.022            0.021
Chain 1:   4200       -17470.813             0.021            0.021
Chain 1:   4300       -17609.557             0.021            0.021
Chain 1:   4400       -17566.016             0.018            0.011
Chain 1:   4500       -17468.441             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002536 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48428.758             1.000            1.000
Chain 1:    200       -13853.399             1.748            2.496
Chain 1:    300       -12273.146             1.208            1.000
Chain 1:    400       -17225.661             0.978            1.000
Chain 1:    500       -17556.264             0.786            0.288
Chain 1:    600       -17902.966             0.658            0.288
Chain 1:    700       -10726.931             0.660            0.288
Chain 1:    800       -13701.962             0.605            0.288
Chain 1:    900       -17791.085             0.563            0.230
Chain 1:   1000       -13454.740             0.539            0.288
Chain 1:   1100       -17256.184             0.461            0.230
Chain 1:   1200       -13072.821             0.243            0.230
Chain 1:   1300        -9749.954             0.265            0.288
Chain 1:   1400       -11595.125             0.252            0.230
Chain 1:   1500       -10279.292             0.263            0.230
Chain 1:   1600        -9778.211             0.266            0.230
Chain 1:   1700        -9804.068             0.199            0.220
Chain 1:   1800        -9289.783             0.183            0.220
Chain 1:   1900       -14720.601             0.197            0.220
Chain 1:   2000       -11403.154             0.194            0.220
Chain 1:   2100        -9467.463             0.192            0.204
Chain 1:   2200       -14254.100             0.194            0.204
Chain 1:   2300        -9399.204             0.211            0.204
Chain 1:   2400        -8373.043             0.208            0.204
Chain 1:   2500       -14619.712             0.238            0.291
Chain 1:   2600        -8572.765             0.303            0.336
Chain 1:   2700       -10030.526             0.317            0.336
Chain 1:   2800        -8826.585             0.325            0.336
Chain 1:   2900       -12444.250             0.318            0.291
Chain 1:   3000       -11272.256             0.299            0.291
Chain 1:   3100        -9291.530             0.300            0.291
Chain 1:   3200       -13685.953             0.298            0.291
Chain 1:   3300       -13847.603             0.248            0.213
Chain 1:   3400        -9764.352             0.277            0.291
Chain 1:   3500        -9678.320             0.235            0.213
Chain 1:   3600       -10500.743             0.173            0.145
Chain 1:   3700        -8497.346             0.182            0.213
Chain 1:   3800       -10785.445             0.189            0.213
Chain 1:   3900       -13155.594             0.178            0.212
Chain 1:   4000        -8955.287             0.215            0.213
Chain 1:   4100        -8480.441             0.199            0.212
Chain 1:   4200       -12400.472             0.199            0.212
Chain 1:   4300        -8503.580             0.243            0.236
Chain 1:   4400        -8657.748             0.203            0.212
Chain 1:   4500        -9636.085             0.213            0.212
Chain 1:   4600        -9688.206             0.205            0.212
Chain 1:   4700        -8275.536             0.199            0.180
Chain 1:   4800        -8183.059             0.179            0.171
Chain 1:   4900        -8998.890             0.170            0.102
Chain 1:   5000       -14777.784             0.162            0.102
Chain 1:   5100       -13646.109             0.165            0.102
Chain 1:   5200       -12561.595             0.142            0.091
Chain 1:   5300       -13239.770             0.101            0.086
Chain 1:   5400        -8377.770             0.157            0.091
Chain 1:   5500       -12722.183             0.181            0.091
Chain 1:   5600       -11024.022             0.196            0.154
Chain 1:   5700        -8256.038             0.212            0.154
Chain 1:   5800        -8512.312             0.214            0.154
Chain 1:   5900       -11111.394             0.229            0.234
Chain 1:   6000       -10313.615             0.197            0.154
Chain 1:   6100        -8979.448             0.204            0.154
Chain 1:   6200        -8519.903             0.201            0.154
Chain 1:   6300       -13844.472             0.234            0.234
Chain 1:   6400       -12375.304             0.188            0.154
Chain 1:   6500        -9608.997             0.182            0.154
Chain 1:   6600        -9575.567             0.167            0.149
Chain 1:   6700        -9153.864             0.138            0.119
Chain 1:   6800       -11748.766             0.158            0.149
Chain 1:   6900        -8036.486             0.180            0.149
Chain 1:   7000        -8022.564             0.173            0.149
Chain 1:   7100        -7967.985             0.159            0.119
Chain 1:   7200        -8449.604             0.159            0.119
Chain 1:   7300       -11583.454             0.148            0.119
Chain 1:   7400       -11603.587             0.136            0.057
Chain 1:   7500       -10103.148             0.122            0.057
Chain 1:   7600        -7969.090             0.148            0.149
Chain 1:   7700        -9656.580             0.161            0.175
Chain 1:   7800        -8535.147             0.152            0.149
Chain 1:   7900        -7957.039             0.113            0.131
Chain 1:   8000        -9230.166             0.127            0.138
Chain 1:   8100        -8289.988             0.138            0.138
Chain 1:   8200        -8606.260             0.136            0.138
Chain 1:   8300        -7973.106             0.116            0.131
Chain 1:   8400       -11350.877             0.146            0.138
Chain 1:   8500        -7780.183             0.177            0.138
Chain 1:   8600       -11357.501             0.182            0.138
Chain 1:   8700        -8174.013             0.203            0.138
Chain 1:   8800        -8466.404             0.194            0.138
Chain 1:   8900       -10021.539             0.202            0.155
Chain 1:   9000        -9122.296             0.198            0.155
Chain 1:   9100        -8582.043             0.193            0.155
Chain 1:   9200        -7884.168             0.198            0.155
Chain 1:   9300        -8386.559             0.196            0.155
Chain 1:   9400        -8164.569             0.169            0.099
Chain 1:   9500       -10619.670             0.146            0.099
Chain 1:   9600        -9919.425             0.122            0.089
Chain 1:   9700        -7913.972             0.108            0.089
Chain 1:   9800        -8843.921             0.115            0.099
Chain 1:   9900       -10179.790             0.113            0.099
Chain 1:   10000        -8202.097             0.127            0.105
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001526 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61478.555             1.000            1.000
Chain 1:    200       -17413.019             1.765            2.531
Chain 1:    300        -8607.597             1.518            1.023
Chain 1:    400        -8154.981             1.152            1.023
Chain 1:    500        -8478.152             0.929            1.000
Chain 1:    600        -8070.988             0.783            1.000
Chain 1:    700        -7824.148             0.676            0.056
Chain 1:    800        -7966.811             0.593            0.056
Chain 1:    900        -7695.194             0.531            0.050
Chain 1:   1000        -7600.868             0.479            0.050
Chain 1:   1100        -7541.544             0.380            0.038
Chain 1:   1200        -7486.613             0.128            0.035
Chain 1:   1300        -7532.743             0.026            0.032
Chain 1:   1400        -7747.119             0.023            0.028
Chain 1:   1500        -7503.492             0.023            0.028
Chain 1:   1600        -7431.991             0.019            0.018
Chain 1:   1700        -7406.037             0.016            0.012
Chain 1:   1800        -7446.845             0.015            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003159 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85259.674             1.000            1.000
Chain 1:    200       -13048.532             3.267            5.534
Chain 1:    300        -9482.203             2.303            1.000
Chain 1:    400       -10230.286             1.746            1.000
Chain 1:    500        -8392.562             1.440            0.376
Chain 1:    600        -8019.988             1.208            0.376
Chain 1:    700        -8161.338             1.038            0.219
Chain 1:    800        -8675.637             0.916            0.219
Chain 1:    900        -8325.892             0.819            0.073
Chain 1:   1000        -8056.702             0.740            0.073
Chain 1:   1100        -8356.496             0.644            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7895.760             0.096            0.058
Chain 1:   1300        -8210.392             0.062            0.046
Chain 1:   1400        -8238.967             0.055            0.042
Chain 1:   1500        -8087.096             0.035            0.038
Chain 1:   1600        -8193.250             0.032            0.036
Chain 1:   1700        -8272.145             0.031            0.036
Chain 1:   1800        -7874.001             0.030            0.036
Chain 1:   1900        -7977.198             0.027            0.033
Chain 1:   2000        -7947.425             0.024            0.019
Chain 1:   2100        -8069.283             0.022            0.015
Chain 1:   2200        -7849.133             0.019            0.015
Chain 1:   2300        -8005.638             0.017            0.015
Chain 1:   2400        -8018.938             0.017            0.015
Chain 1:   2500        -7989.112             0.016            0.013
Chain 1:   2600        -7991.853             0.015            0.013
Chain 1:   2700        -7898.038             0.015            0.013
Chain 1:   2800        -7868.741             0.010            0.012
Chain 1:   2900        -7926.429             0.010            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003457 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386080.958             1.000            1.000
Chain 1:    200     -1578836.164             2.656            4.312
Chain 1:    300      -889449.596             2.029            1.000
Chain 1:    400      -457459.813             1.758            1.000
Chain 1:    500      -358282.219             1.462            0.944
Chain 1:    600      -233133.316             1.307            0.944
Chain 1:    700      -119045.432             1.258            0.944
Chain 1:    800       -86230.141             1.148            0.944
Chain 1:    900       -66497.348             1.053            0.775
Chain 1:   1000       -51244.123             0.978            0.775
Chain 1:   1100       -38679.410             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37843.747             0.481            0.381
Chain 1:   1300       -25757.688             0.451            0.381
Chain 1:   1400       -25470.581             0.357            0.325
Chain 1:   1500       -22049.084             0.345            0.325
Chain 1:   1600       -21262.658             0.295            0.298
Chain 1:   1700       -20131.322             0.205            0.297
Chain 1:   1800       -20074.139             0.167            0.155
Chain 1:   1900       -20399.955             0.139            0.056
Chain 1:   2000       -18909.485             0.117            0.056
Chain 1:   2100       -19147.545             0.086            0.037
Chain 1:   2200       -19374.551             0.085            0.037
Chain 1:   2300       -18991.432             0.040            0.020
Chain 1:   2400       -18763.607             0.040            0.020
Chain 1:   2500       -18566.056             0.026            0.016
Chain 1:   2600       -18196.154             0.024            0.016
Chain 1:   2700       -18153.050             0.019            0.012
Chain 1:   2800       -17870.331             0.020            0.016
Chain 1:   2900       -18151.443             0.020            0.015
Chain 1:   3000       -18137.487             0.012            0.012
Chain 1:   3100       -18222.512             0.011            0.012
Chain 1:   3200       -17913.294             0.012            0.015
Chain 1:   3300       -18117.914             0.011            0.012
Chain 1:   3400       -17593.271             0.013            0.015
Chain 1:   3500       -18204.647             0.015            0.016
Chain 1:   3600       -17511.949             0.017            0.016
Chain 1:   3700       -17898.391             0.019            0.017
Chain 1:   3800       -16859.189             0.024            0.022
Chain 1:   3900       -16855.436             0.022            0.022
Chain 1:   4000       -16972.660             0.023            0.022
Chain 1:   4100       -16886.576             0.023            0.022
Chain 1:   4200       -16703.006             0.022            0.022
Chain 1:   4300       -16841.199             0.022            0.022
Chain 1:   4400       -16798.194             0.019            0.011
Chain 1:   4500       -16700.840             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001345 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12540.727             1.000            1.000
Chain 1:    200        -9412.499             0.666            1.000
Chain 1:    300        -8142.569             0.496            0.332
Chain 1:    400        -8322.468             0.377            0.332
Chain 1:    500        -8292.028             0.303            0.156
Chain 1:    600        -8102.570             0.256            0.156
Chain 1:    700        -7993.266             0.222            0.023
Chain 1:    800        -8024.908             0.194            0.023
Chain 1:    900        -8119.398             0.174            0.022
Chain 1:   1000        -8075.030             0.157            0.022
Chain 1:   1100        -8119.269             0.058            0.014
Chain 1:   1200        -8011.044             0.026            0.014
Chain 1:   1300        -8104.110             0.011            0.012
Chain 1:   1400        -7993.564             0.011            0.012
Chain 1:   1500        -8090.700             0.011            0.012
Chain 1:   1600        -8025.815             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58672.752             1.000            1.000
Chain 1:    200       -17935.902             1.636            2.271
Chain 1:    300        -8984.284             1.423            1.000
Chain 1:    400        -8621.714             1.077            1.000
Chain 1:    500        -8662.436             0.863            0.996
Chain 1:    600        -8615.253             0.720            0.996
Chain 1:    700        -8420.521             0.620            0.042
Chain 1:    800        -8200.434             0.546            0.042
Chain 1:    900        -7889.535             0.490            0.039
Chain 1:   1000        -7936.294             0.442            0.039
Chain 1:   1100        -7871.421             0.342            0.027
Chain 1:   1200        -7754.410             0.117            0.023
Chain 1:   1300        -7897.833             0.019            0.018
Chain 1:   1400        -7911.140             0.015            0.015
Chain 1:   1500        -7670.465             0.018            0.018
Chain 1:   1600        -7842.782             0.019            0.022
Chain 1:   1700        -7635.053             0.020            0.022
Chain 1:   1800        -7737.484             0.018            0.018
Chain 1:   1900        -7733.969             0.014            0.015
Chain 1:   2000        -7664.915             0.015            0.015
Chain 1:   2100        -7734.937             0.015            0.015
Chain 1:   2200        -7780.924             0.014            0.013
Chain 1:   2300        -7643.576             0.014            0.013
Chain 1:   2400        -7720.871             0.015            0.013
Chain 1:   2500        -7687.376             0.012            0.010
Chain 1:   2600        -7602.621             0.011            0.010
Chain 1:   2700        -7624.173             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002882 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86183.444             1.000            1.000
Chain 1:    200       -13740.225             3.136            5.272
Chain 1:    300       -10057.598             2.213            1.000
Chain 1:    400       -11191.429             1.685            1.000
Chain 1:    500        -9029.183             1.396            0.366
Chain 1:    600        -8612.155             1.171            0.366
Chain 1:    700        -8466.801             1.006            0.239
Chain 1:    800        -8694.873             0.884            0.239
Chain 1:    900        -8950.580             0.789            0.101
Chain 1:   1000        -8545.162             0.715            0.101
Chain 1:   1100        -8862.957             0.618            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8455.601             0.096            0.048
Chain 1:   1300        -8765.133             0.063            0.047
Chain 1:   1400        -8718.971             0.053            0.036
Chain 1:   1500        -8573.209             0.031            0.035
Chain 1:   1600        -8688.871             0.027            0.029
Chain 1:   1700        -8761.871             0.027            0.029
Chain 1:   1800        -8328.975             0.029            0.035
Chain 1:   1900        -8433.352             0.028            0.035
Chain 1:   2000        -8409.384             0.023            0.017
Chain 1:   2100        -8544.237             0.021            0.016
Chain 1:   2200        -8339.100             0.019            0.016
Chain 1:   2300        -8482.096             0.017            0.016
Chain 1:   2400        -8336.788             0.018            0.017
Chain 1:   2500        -8408.115             0.017            0.016
Chain 1:   2600        -8322.038             0.017            0.016
Chain 1:   2700        -8353.843             0.016            0.016
Chain 1:   2800        -8316.747             0.012            0.012
Chain 1:   2900        -8407.117             0.012            0.011
Chain 1:   3000        -8231.925             0.013            0.016
Chain 1:   3100        -8397.970             0.014            0.017
Chain 1:   3200        -8271.330             0.013            0.015
Chain 1:   3300        -8283.463             0.011            0.011
Chain 1:   3400        -8421.209             0.011            0.011
Chain 1:   3500        -8405.666             0.011            0.011
Chain 1:   3600        -8229.569             0.012            0.015
Chain 1:   3700        -8370.429             0.013            0.016
Chain 1:   3800        -8236.572             0.014            0.016
Chain 1:   3900        -8172.256             0.014            0.016
Chain 1:   4000        -8247.301             0.013            0.016
Chain 1:   4100        -8236.770             0.011            0.015
Chain 1:   4200        -8225.242             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003492 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412961.409             1.000            1.000
Chain 1:    200     -1582313.498             2.658            4.317
Chain 1:    300      -891454.167             2.031            1.000
Chain 1:    400      -458185.911             1.759            1.000
Chain 1:    500      -358789.676             1.463            0.946
Chain 1:    600      -233569.097             1.308            0.946
Chain 1:    700      -119655.499             1.258            0.946
Chain 1:    800       -86835.123             1.148            0.946
Chain 1:    900       -67137.350             1.053            0.775
Chain 1:   1000       -51908.637             0.977            0.775
Chain 1:   1100       -39365.837             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38540.813             0.479            0.378
Chain 1:   1300       -26469.861             0.447            0.378
Chain 1:   1400       -26187.272             0.354            0.319
Chain 1:   1500       -22767.645             0.341            0.319
Chain 1:   1600       -21982.850             0.291            0.293
Chain 1:   1700       -20852.982             0.201            0.293
Chain 1:   1800       -20796.559             0.164            0.150
Chain 1:   1900       -21122.951             0.136            0.054
Chain 1:   2000       -19631.976             0.114            0.054
Chain 1:   2100       -19870.323             0.083            0.036
Chain 1:   2200       -20097.340             0.082            0.036
Chain 1:   2300       -19714.033             0.039            0.019
Chain 1:   2400       -19486.019             0.039            0.019
Chain 1:   2500       -19288.190             0.025            0.015
Chain 1:   2600       -18917.880             0.023            0.015
Chain 1:   2700       -18874.762             0.018            0.012
Chain 1:   2800       -18591.578             0.019            0.015
Chain 1:   2900       -18872.962             0.019            0.015
Chain 1:   3000       -18859.064             0.012            0.012
Chain 1:   3100       -18944.124             0.011            0.012
Chain 1:   3200       -18634.562             0.012            0.015
Chain 1:   3300       -18839.501             0.011            0.012
Chain 1:   3400       -18314.062             0.012            0.015
Chain 1:   3500       -18926.490             0.015            0.015
Chain 1:   3600       -18232.466             0.016            0.015
Chain 1:   3700       -18619.814             0.018            0.017
Chain 1:   3800       -17578.457             0.023            0.021
Chain 1:   3900       -17574.621             0.021            0.021
Chain 1:   4000       -17691.881             0.022            0.021
Chain 1:   4100       -17605.588             0.022            0.021
Chain 1:   4200       -17421.648             0.021            0.021
Chain 1:   4300       -17560.158             0.021            0.021
Chain 1:   4400       -17516.786             0.018            0.011
Chain 1:   4500       -17419.325             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001328 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12096.716             1.000            1.000
Chain 1:    200        -9028.110             0.670            1.000
Chain 1:    300        -7982.815             0.490            0.340
Chain 1:    400        -8075.229             0.371            0.340
Chain 1:    500        -7939.594             0.300            0.131
Chain 1:    600        -7815.866             0.253            0.131
Chain 1:    700        -7741.693             0.218            0.017
Chain 1:    800        -7772.547             0.191            0.017
Chain 1:    900        -7882.310             0.171            0.016
Chain 1:   1000        -7814.446             0.155            0.016
Chain 1:   1100        -7859.334             0.056            0.014
Chain 1:   1200        -7773.464             0.023            0.011
Chain 1:   1300        -7730.486             0.010            0.011
Chain 1:   1400        -7733.654             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57417.732             1.000            1.000
Chain 1:    200       -17314.557             1.658            2.316
Chain 1:    300        -8512.303             1.450            1.034
Chain 1:    400        -8119.479             1.100            1.034
Chain 1:    500        -8192.832             0.882            1.000
Chain 1:    600        -8994.687             0.749            1.000
Chain 1:    700        -7788.187             0.665            0.155
Chain 1:    800        -7928.745             0.584            0.155
Chain 1:    900        -7876.008             0.520            0.089
Chain 1:   1000        -7997.916             0.469            0.089
Chain 1:   1100        -7619.876             0.374            0.050
Chain 1:   1200        -7632.612             0.143            0.048
Chain 1:   1300        -7586.352             0.040            0.018
Chain 1:   1400        -7719.845             0.037            0.017
Chain 1:   1500        -7569.918             0.038            0.018
Chain 1:   1600        -7491.143             0.030            0.017
Chain 1:   1700        -7480.452             0.015            0.015
Chain 1:   1800        -7515.083             0.013            0.011
Chain 1:   1900        -7568.282             0.013            0.011
Chain 1:   2000        -7570.060             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003288 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85994.969             1.000            1.000
Chain 1:    200       -13154.549             3.269            5.537
Chain 1:    300        -9606.217             2.302            1.000
Chain 1:    400       -10413.633             1.746            1.000
Chain 1:    500        -8506.752             1.442            0.369
Chain 1:    600        -8160.988             1.208            0.369
Chain 1:    700        -8196.143             1.036            0.224
Chain 1:    800        -8408.218             0.910            0.224
Chain 1:    900        -8474.283             0.810            0.078
Chain 1:   1000        -8231.968             0.732            0.078
Chain 1:   1100        -8522.338             0.635            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8173.564             0.086            0.042
Chain 1:   1300        -8182.151             0.049            0.034
Chain 1:   1400        -8266.599             0.042            0.029
Chain 1:   1500        -8217.748             0.020            0.025
Chain 1:   1600        -8222.307             0.016            0.010
Chain 1:   1700        -8156.954             0.016            0.010
Chain 1:   1800        -8037.192             0.015            0.010
Chain 1:   1900        -8154.150             0.016            0.014
Chain 1:   2000        -8114.023             0.014            0.010
Chain 1:   2100        -8248.060             0.012            0.010
Chain 1:   2200        -8036.908             0.010            0.010
Chain 1:   2300        -8177.539             0.012            0.014
Chain 1:   2400        -8189.604             0.011            0.014
Chain 1:   2500        -8157.662             0.011            0.014
Chain 1:   2600        -8154.309             0.011            0.014
Chain 1:   2700        -8063.824             0.011            0.014
Chain 1:   2800        -8041.822             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003323 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8364797.186             1.000            1.000
Chain 1:    200     -1580686.091             2.646            4.292
Chain 1:    300      -891091.285             2.022            1.000
Chain 1:    400      -458342.854             1.752            1.000
Chain 1:    500      -358752.149             1.458            0.944
Chain 1:    600      -233535.476             1.304            0.944
Chain 1:    700      -119299.799             1.254            0.944
Chain 1:    800       -86391.087             1.145            0.944
Chain 1:    900       -66639.737             1.051            0.774
Chain 1:   1000       -51360.471             0.976            0.774
Chain 1:   1100       -38774.510             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37937.602             0.481            0.381
Chain 1:   1300       -25842.454             0.450            0.381
Chain 1:   1400       -25554.242             0.357            0.325
Chain 1:   1500       -22128.320             0.345            0.325
Chain 1:   1600       -21339.818             0.295            0.297
Chain 1:   1700       -20208.026             0.205            0.296
Chain 1:   1800       -20150.550             0.167            0.155
Chain 1:   1900       -20476.042             0.139            0.056
Chain 1:   2000       -18985.323             0.117            0.056
Chain 1:   2100       -19223.856             0.086            0.037
Chain 1:   2200       -19450.336             0.085            0.037
Chain 1:   2300       -19067.634             0.040            0.020
Chain 1:   2400       -18839.831             0.040            0.020
Chain 1:   2500       -18642.110             0.026            0.016
Chain 1:   2600       -18272.718             0.024            0.016
Chain 1:   2700       -18229.778             0.019            0.012
Chain 1:   2800       -17946.985             0.020            0.016
Chain 1:   2900       -18228.052             0.020            0.015
Chain 1:   3000       -18214.238             0.012            0.012
Chain 1:   3100       -18299.146             0.011            0.012
Chain 1:   3200       -17990.198             0.012            0.015
Chain 1:   3300       -18194.610             0.011            0.012
Chain 1:   3400       -17670.285             0.013            0.015
Chain 1:   3500       -18281.167             0.015            0.016
Chain 1:   3600       -17589.155             0.017            0.016
Chain 1:   3700       -17975.016             0.019            0.017
Chain 1:   3800       -16936.840             0.023            0.021
Chain 1:   3900       -16933.057             0.022            0.021
Chain 1:   4000       -17050.326             0.023            0.021
Chain 1:   4100       -16964.204             0.023            0.021
Chain 1:   4200       -16780.887             0.022            0.021
Chain 1:   4300       -16918.948             0.022            0.021
Chain 1:   4400       -16876.154             0.019            0.011
Chain 1:   4500       -16778.761             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001287 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12037.739             1.000            1.000
Chain 1:    200        -8887.954             0.677            1.000
Chain 1:    300        -7995.652             0.489            0.354
Chain 1:    400        -7995.918             0.367            0.354
Chain 1:    500        -7879.373             0.296            0.112
Chain 1:    600        -7817.495             0.248            0.112
Chain 1:    700        -7750.960             0.214            0.015
Chain 1:    800        -7788.675             0.188            0.015
Chain 1:    900        -7915.416             0.169            0.015
Chain 1:   1000        -7807.983             0.153            0.015
Chain 1:   1100        -7779.539             0.054            0.014
Chain 1:   1200        -7764.672             0.018            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56395.241             1.000            1.000
Chain 1:    200       -16948.505             1.664            2.327
Chain 1:    300        -8555.237             1.436            1.000
Chain 1:    400        -8751.482             1.083            1.000
Chain 1:    500        -8396.975             0.875            0.981
Chain 1:    600        -8656.960             0.734            0.981
Chain 1:    700        -8122.458             0.638            0.066
Chain 1:    800        -8136.870             0.559            0.066
Chain 1:    900        -7836.653             0.501            0.042
Chain 1:   1000        -7977.945             0.453            0.042
Chain 1:   1100        -7654.547             0.357            0.042
Chain 1:   1200        -7740.365             0.125            0.038
Chain 1:   1300        -7656.270             0.028            0.030
Chain 1:   1400        -7837.226             0.028            0.030
Chain 1:   1500        -7637.285             0.027            0.026
Chain 1:   1600        -7556.578             0.025            0.023
Chain 1:   1700        -7524.803             0.019            0.018
Chain 1:   1800        -7555.553             0.019            0.018
Chain 1:   1900        -7634.302             0.016            0.011
Chain 1:   2000        -7621.278             0.014            0.011
Chain 1:   2100        -7634.203             0.010            0.011
Chain 1:   2200        -7695.659             0.010            0.010
Chain 1:   2300        -7600.761             0.010            0.010
Chain 1:   2400        -7644.605             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00334 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85717.098             1.000            1.000
Chain 1:    200       -13110.039             3.269            5.538
Chain 1:    300        -9591.947             2.302            1.000
Chain 1:    400       -10443.592             1.747            1.000
Chain 1:    500        -8493.242             1.443            0.367
Chain 1:    600        -8165.364             1.209            0.367
Chain 1:    700        -8155.214             1.037            0.230
Chain 1:    800        -8458.149             0.912            0.230
Chain 1:    900        -8473.354             0.811            0.082
Chain 1:   1000        -8198.957             0.733            0.082
Chain 1:   1100        -8441.554             0.636            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8157.895             0.085            0.036
Chain 1:   1300        -8348.682             0.051            0.035
Chain 1:   1400        -8279.006             0.044            0.033
Chain 1:   1500        -8225.121             0.021            0.029
Chain 1:   1600        -8221.968             0.017            0.023
Chain 1:   1700        -8159.053             0.018            0.023
Chain 1:   1800        -8038.964             0.016            0.015
Chain 1:   1900        -8152.954             0.017            0.015
Chain 1:   2000        -8114.283             0.014            0.014
Chain 1:   2100        -8253.132             0.013            0.014
Chain 1:   2200        -8039.115             0.012            0.014
Chain 1:   2300        -8180.766             0.012            0.014
Chain 1:   2400        -8187.470             0.011            0.014
Chain 1:   2500        -8156.984             0.011            0.014
Chain 1:   2600        -8151.141             0.011            0.014
Chain 1:   2700        -8062.078             0.011            0.014
Chain 1:   2800        -8043.764             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003498 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408263.217             1.000            1.000
Chain 1:    200     -1585919.169             2.651            4.302
Chain 1:    300      -890293.316             2.028            1.000
Chain 1:    400      -456907.344             1.758            1.000
Chain 1:    500      -356888.537             1.462            0.949
Chain 1:    600      -232063.010             1.308            0.949
Chain 1:    700      -118545.643             1.258            0.949
Chain 1:    800       -85827.387             1.149            0.949
Chain 1:    900       -66215.673             1.054            0.781
Chain 1:   1000       -51046.917             0.978            0.781
Chain 1:   1100       -38559.512             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37734.395             0.483            0.381
Chain 1:   1300       -25735.614             0.451            0.381
Chain 1:   1400       -25456.394             0.357            0.324
Chain 1:   1500       -22055.894             0.345            0.324
Chain 1:   1600       -21275.100             0.295            0.297
Chain 1:   1700       -20154.840             0.204            0.296
Chain 1:   1800       -20100.029             0.167            0.154
Chain 1:   1900       -20425.492             0.139            0.056
Chain 1:   2000       -18941.130             0.117            0.056
Chain 1:   2100       -19179.186             0.085            0.037
Chain 1:   2200       -19404.685             0.084            0.037
Chain 1:   2300       -19022.947             0.040            0.020
Chain 1:   2400       -18795.351             0.040            0.020
Chain 1:   2500       -18597.240             0.026            0.016
Chain 1:   2600       -18228.278             0.024            0.016
Chain 1:   2700       -18185.567             0.019            0.012
Chain 1:   2800       -17902.673             0.020            0.016
Chain 1:   2900       -18183.535             0.020            0.015
Chain 1:   3000       -18169.832             0.012            0.012
Chain 1:   3100       -18254.666             0.011            0.012
Chain 1:   3200       -17945.887             0.012            0.015
Chain 1:   3300       -18150.209             0.011            0.012
Chain 1:   3400       -17626.011             0.013            0.015
Chain 1:   3500       -18236.491             0.015            0.016
Chain 1:   3600       -17545.028             0.017            0.016
Chain 1:   3700       -17930.398             0.019            0.017
Chain 1:   3800       -16892.918             0.023            0.021
Chain 1:   3900       -16889.126             0.022            0.021
Chain 1:   4000       -17006.446             0.023            0.021
Chain 1:   4100       -16920.306             0.023            0.021
Chain 1:   4200       -16737.207             0.022            0.021
Chain 1:   4300       -16875.155             0.022            0.021
Chain 1:   4400       -16832.474             0.019            0.011
Chain 1:   4500       -16735.098             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13061.639             1.000            1.000
Chain 1:    200        -9896.399             0.660            1.000
Chain 1:    300        -8575.442             0.491            0.320
Chain 1:    400        -8826.647             0.376            0.320
Chain 1:    500        -8666.164             0.304            0.154
Chain 1:    600        -8524.531             0.256            0.154
Chain 1:    700        -8422.194             0.221            0.028
Chain 1:    800        -8425.224             0.194            0.028
Chain 1:    900        -8361.793             0.173            0.019
Chain 1:   1000        -8549.966             0.158            0.022
Chain 1:   1100        -8566.934             0.058            0.019
Chain 1:   1200        -8440.364             0.028            0.017
Chain 1:   1300        -8407.946             0.013            0.015
Chain 1:   1400        -8416.377             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46700.654             1.000            1.000
Chain 1:    200       -16160.945             1.445            1.890
Chain 1:    300        -9015.107             1.227            1.000
Chain 1:    400        -8089.119             0.949            1.000
Chain 1:    500        -8766.804             0.775            0.793
Chain 1:    600        -8930.516             0.649            0.793
Chain 1:    700        -8153.700             0.570            0.114
Chain 1:    800        -8380.230             0.502            0.114
Chain 1:    900        -7985.515             0.452            0.095
Chain 1:   1000        -7786.116             0.409            0.095
Chain 1:   1100        -7783.220             0.309            0.077
Chain 1:   1200        -7768.294             0.120            0.049
Chain 1:   1300        -7760.910             0.041            0.027
Chain 1:   1400        -7673.534             0.031            0.026
Chain 1:   1500        -7554.261             0.025            0.018
Chain 1:   1600        -7745.253             0.025            0.025
Chain 1:   1700        -7619.000             0.017            0.017
Chain 1:   1800        -7716.601             0.016            0.016
Chain 1:   1900        -7547.644             0.013            0.016
Chain 1:   2000        -7668.072             0.012            0.016
Chain 1:   2100        -7636.613             0.013            0.016
Chain 1:   2200        -7767.365             0.014            0.016
Chain 1:   2300        -7512.478             0.017            0.017
Chain 1:   2400        -7709.563             0.019            0.017
Chain 1:   2500        -7495.042             0.020            0.022
Chain 1:   2600        -7538.072             0.018            0.017
Chain 1:   2700        -7513.276             0.017            0.017
Chain 1:   2800        -7520.424             0.016            0.017
Chain 1:   2900        -7355.357             0.016            0.017
Chain 1:   3000        -7512.010             0.016            0.021
Chain 1:   3100        -7510.443             0.016            0.021
Chain 1:   3200        -7713.094             0.017            0.022
Chain 1:   3300        -7391.079             0.018            0.022
Chain 1:   3400        -7629.370             0.018            0.022
Chain 1:   3500        -7418.948             0.018            0.022
Chain 1:   3600        -7489.568             0.019            0.022
Chain 1:   3700        -7445.638             0.019            0.022
Chain 1:   3800        -7416.316             0.019            0.022
Chain 1:   3900        -7389.175             0.017            0.021
Chain 1:   4000        -7384.545             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87333.370             1.000            1.000
Chain 1:    200       -14199.856             3.075            5.150
Chain 1:    300       -10488.405             2.168            1.000
Chain 1:    400       -11688.019             1.652            1.000
Chain 1:    500        -9493.730             1.368            0.354
Chain 1:    600        -9166.182             1.146            0.354
Chain 1:    700        -9087.770             0.983            0.231
Chain 1:    800        -9438.994             0.865            0.231
Chain 1:    900        -9258.122             0.771            0.103
Chain 1:   1000        -9192.848             0.695            0.103
Chain 1:   1100        -9229.493             0.595            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8766.785             0.085            0.037
Chain 1:   1300        -9302.706             0.056            0.037
Chain 1:   1400        -9101.600             0.048            0.036
Chain 1:   1500        -9019.289             0.025            0.022
Chain 1:   1600        -9129.693             0.023            0.020
Chain 1:   1700        -9196.642             0.023            0.020
Chain 1:   1800        -8761.798             0.024            0.020
Chain 1:   1900        -8866.256             0.023            0.012
Chain 1:   2000        -8841.829             0.023            0.012
Chain 1:   2100        -8960.370             0.024            0.013
Chain 1:   2200        -8772.367             0.021            0.013
Chain 1:   2300        -8929.092             0.017            0.013
Chain 1:   2400        -8772.521             0.016            0.013
Chain 1:   2500        -8841.107             0.016            0.013
Chain 1:   2600        -8754.789             0.016            0.013
Chain 1:   2700        -8786.773             0.016            0.013
Chain 1:   2800        -8748.103             0.011            0.012
Chain 1:   2900        -8839.821             0.011            0.010
Chain 1:   3000        -8665.109             0.013            0.013
Chain 1:   3100        -8829.787             0.013            0.018
Chain 1:   3200        -8702.932             0.012            0.015
Chain 1:   3300        -8712.337             0.011            0.010
Chain 1:   3400        -8863.259             0.011            0.010
Chain 1:   3500        -8851.266             0.010            0.010
Chain 1:   3600        -8660.370             0.011            0.015
Chain 1:   3700        -8803.028             0.013            0.016
Chain 1:   3800        -8667.250             0.014            0.016
Chain 1:   3900        -8602.573             0.013            0.016
Chain 1:   4000        -8676.920             0.012            0.016
Chain 1:   4100        -8667.726             0.011            0.015
Chain 1:   4200        -8656.182             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002958 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387271.673             1.000            1.000
Chain 1:    200     -1582519.669             2.650            4.300
Chain 1:    300      -891669.640             2.025            1.000
Chain 1:    400      -458599.959             1.755            1.000
Chain 1:    500      -359150.072             1.459            0.944
Chain 1:    600      -234021.095             1.305            0.944
Chain 1:    700      -120097.032             1.254            0.944
Chain 1:    800       -87261.047             1.144            0.944
Chain 1:    900       -67579.363             1.050            0.775
Chain 1:   1000       -52360.185             0.974            0.775
Chain 1:   1100       -39816.175             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38993.603             0.477            0.376
Chain 1:   1300       -26921.334             0.445            0.376
Chain 1:   1400       -26639.220             0.351            0.315
Chain 1:   1500       -23218.352             0.338            0.315
Chain 1:   1600       -22432.803             0.288            0.291
Chain 1:   1700       -21302.672             0.199            0.291
Chain 1:   1800       -21246.183             0.162            0.147
Chain 1:   1900       -21572.664             0.134            0.053
Chain 1:   2000       -20081.044             0.112            0.053
Chain 1:   2100       -20319.671             0.082            0.035
Chain 1:   2200       -20546.657             0.081            0.035
Chain 1:   2300       -20163.270             0.038            0.019
Chain 1:   2400       -19935.197             0.038            0.019
Chain 1:   2500       -19737.281             0.024            0.015
Chain 1:   2600       -19367.090             0.023            0.015
Chain 1:   2700       -19323.931             0.018            0.012
Chain 1:   2800       -19040.692             0.019            0.015
Chain 1:   2900       -19322.090             0.019            0.015
Chain 1:   3000       -19308.270             0.011            0.012
Chain 1:   3100       -19393.326             0.011            0.011
Chain 1:   3200       -19083.774             0.011            0.015
Chain 1:   3300       -19288.659             0.010            0.011
Chain 1:   3400       -18763.210             0.012            0.015
Chain 1:   3500       -19375.741             0.014            0.015
Chain 1:   3600       -18681.557             0.016            0.015
Chain 1:   3700       -19069.012             0.018            0.016
Chain 1:   3800       -18027.450             0.022            0.020
Chain 1:   3900       -18023.566             0.021            0.020
Chain 1:   4000       -18140.847             0.021            0.020
Chain 1:   4100       -18054.575             0.021            0.020
Chain 1:   4200       -17870.525             0.021            0.020
Chain 1:   4300       -18009.131             0.020            0.020
Chain 1:   4400       -17965.728             0.018            0.010
Chain 1:   4500       -17868.213             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001539 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49138.862             1.000            1.000
Chain 1:    200       -24228.724             1.014            1.028
Chain 1:    300       -14038.129             0.918            1.000
Chain 1:    400       -22063.091             0.779            1.000
Chain 1:    500       -18429.159             0.663            0.726
Chain 1:    600       -14819.462             0.593            0.726
Chain 1:    700       -13607.349             0.521            0.364
Chain 1:    800       -13763.295             0.457            0.364
Chain 1:    900       -11174.705             0.432            0.244
Chain 1:   1000       -11831.324             0.395            0.244
Chain 1:   1100       -15570.157             0.319            0.240
Chain 1:   1200       -17739.993             0.228            0.232
Chain 1:   1300       -12330.655             0.199            0.232
Chain 1:   1400       -10976.867             0.175            0.197
Chain 1:   1500       -11761.773             0.162            0.123
Chain 1:   1600       -11064.231             0.144            0.122
Chain 1:   1700       -12981.270             0.150            0.123
Chain 1:   1800       -10551.184             0.172            0.148
Chain 1:   1900       -11962.743             0.161            0.123
Chain 1:   2000       -13588.109             0.167            0.123
Chain 1:   2100       -13334.770             0.145            0.122
Chain 1:   2200       -10387.024             0.161            0.123
Chain 1:   2300       -16628.854             0.155            0.123
Chain 1:   2400        -9293.831             0.221            0.148
Chain 1:   2500       -11287.358             0.232            0.177
Chain 1:   2600        -9950.463             0.239            0.177
Chain 1:   2700       -14631.522             0.257            0.230
Chain 1:   2800        -9537.407             0.287            0.284
Chain 1:   2900        -9931.663             0.279            0.284
Chain 1:   3000        -9591.733             0.271            0.284
Chain 1:   3100        -8903.499             0.277            0.284
Chain 1:   3200       -15388.763             0.290            0.320
Chain 1:   3300        -9268.592             0.319            0.320
Chain 1:   3400       -13600.470             0.272            0.319
Chain 1:   3500        -9273.481             0.301            0.320
Chain 1:   3600        -9674.862             0.291            0.320
Chain 1:   3700       -10263.360             0.265            0.319
Chain 1:   3800       -14644.789             0.242            0.299
Chain 1:   3900       -10162.800             0.282            0.319
Chain 1:   4000       -11554.452             0.290            0.319
Chain 1:   4100       -11774.163             0.284            0.319
Chain 1:   4200       -11375.440             0.246            0.299
Chain 1:   4300        -9698.656             0.197            0.173
Chain 1:   4400       -11280.274             0.179            0.140
Chain 1:   4500        -9221.966             0.155            0.140
Chain 1:   4600       -13923.535             0.185            0.173
Chain 1:   4700        -9019.851             0.233            0.223
Chain 1:   4800        -8738.018             0.207            0.173
Chain 1:   4900        -8836.486             0.164            0.140
Chain 1:   5000        -9740.568             0.161            0.140
Chain 1:   5100       -12952.248             0.184            0.173
Chain 1:   5200       -16440.550             0.201            0.212
Chain 1:   5300       -13205.770             0.209            0.223
Chain 1:   5400       -11202.560             0.212            0.223
Chain 1:   5500       -14076.870             0.211            0.212
Chain 1:   5600       -15212.392             0.184            0.204
Chain 1:   5700       -13581.319             0.142            0.179
Chain 1:   5800        -9042.783             0.189            0.204
Chain 1:   5900        -9522.519             0.193            0.204
Chain 1:   6000       -11935.772             0.204            0.204
Chain 1:   6100        -9881.277             0.200            0.204
Chain 1:   6200        -8342.820             0.197            0.202
Chain 1:   6300        -8950.009             0.179            0.184
Chain 1:   6400       -13052.601             0.193            0.202
Chain 1:   6500        -8940.765             0.218            0.202
Chain 1:   6600        -9695.421             0.219            0.202
Chain 1:   6700        -8965.058             0.215            0.202
Chain 1:   6800        -9700.127             0.172            0.184
Chain 1:   6900       -11408.177             0.182            0.184
Chain 1:   7000        -8822.440             0.191            0.184
Chain 1:   7100       -14342.274             0.209            0.184
Chain 1:   7200       -11250.281             0.218            0.275
Chain 1:   7300        -8396.630             0.245            0.293
Chain 1:   7400       -11061.452             0.238            0.275
Chain 1:   7500        -8534.916             0.221            0.275
Chain 1:   7600        -8698.357             0.216            0.275
Chain 1:   7700        -8312.278             0.212            0.275
Chain 1:   7800       -10581.609             0.226            0.275
Chain 1:   7900        -8291.457             0.239            0.276
Chain 1:   8000        -8480.345             0.211            0.275
Chain 1:   8100       -10654.978             0.193            0.241
Chain 1:   8200        -9653.310             0.176            0.214
Chain 1:   8300        -9515.144             0.144            0.204
Chain 1:   8400        -8381.280             0.133            0.135
Chain 1:   8500        -8375.399             0.104            0.104
Chain 1:   8600       -12013.280             0.132            0.135
Chain 1:   8700        -8922.554             0.162            0.204
Chain 1:   8800        -8768.384             0.142            0.135
Chain 1:   8900        -9950.033             0.127            0.119
Chain 1:   9000        -8751.803             0.138            0.135
Chain 1:   9100        -9919.955             0.129            0.119
Chain 1:   9200        -8846.822             0.131            0.121
Chain 1:   9300        -9308.384             0.135            0.121
Chain 1:   9400       -12051.037             0.144            0.121
Chain 1:   9500        -8851.562             0.180            0.137
Chain 1:   9600        -9731.785             0.159            0.121
Chain 1:   9700        -9136.571             0.131            0.119
Chain 1:   9800       -11541.983             0.150            0.121
Chain 1:   9900        -8679.166             0.171            0.137
Chain 1:   10000        -8680.917             0.157            0.121
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001425 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47528.667             1.000            1.000
Chain 1:    200       -15880.744             1.496            1.993
Chain 1:    300        -8607.446             1.279            1.000
Chain 1:    400        -8480.948             0.963            1.000
Chain 1:    500        -8681.489             0.775            0.845
Chain 1:    600        -8268.679             0.654            0.845
Chain 1:    700        -7744.604             0.570            0.068
Chain 1:    800        -8212.891             0.506            0.068
Chain 1:    900        -8078.741             0.452            0.057
Chain 1:   1000        -7711.876             0.411            0.057
Chain 1:   1100        -7753.888             0.312            0.050
Chain 1:   1200        -7567.834             0.115            0.048
Chain 1:   1300        -7758.203             0.033            0.025
Chain 1:   1400        -7888.912             0.033            0.025
Chain 1:   1500        -7577.446             0.035            0.041
Chain 1:   1600        -7790.173             0.033            0.027
Chain 1:   1700        -7505.815             0.030            0.027
Chain 1:   1800        -7569.725             0.025            0.025
Chain 1:   1900        -7591.064             0.024            0.025
Chain 1:   2000        -7632.030             0.019            0.025
Chain 1:   2100        -7582.990             0.020            0.025
Chain 1:   2200        -7696.160             0.019            0.017
Chain 1:   2300        -7550.478             0.018            0.017
Chain 1:   2400        -7625.646             0.017            0.015
Chain 1:   2500        -7606.363             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86764.697             1.000            1.000
Chain 1:    200       -13577.414             3.195            5.390
Chain 1:    300        -9960.895             2.251            1.000
Chain 1:    400       -10667.751             1.705            1.000
Chain 1:    500        -8944.528             1.402            0.363
Chain 1:    600        -8483.418             1.178            0.363
Chain 1:    700        -8588.177             1.011            0.193
Chain 1:    800        -9258.265             0.894            0.193
Chain 1:    900        -8842.355             0.800            0.072
Chain 1:   1000        -8513.780             0.724            0.072
Chain 1:   1100        -8823.319             0.627            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8328.131             0.094            0.059
Chain 1:   1300        -8664.701             0.062            0.054
Chain 1:   1400        -8665.275             0.055            0.047
Chain 1:   1500        -8543.879             0.037            0.039
Chain 1:   1600        -8650.554             0.033            0.039
Chain 1:   1700        -8735.057             0.033            0.039
Chain 1:   1800        -8326.762             0.030            0.039
Chain 1:   1900        -8423.137             0.027            0.035
Chain 1:   2000        -8395.499             0.023            0.014
Chain 1:   2100        -8516.542             0.021            0.014
Chain 1:   2200        -8429.255             0.016            0.012
Chain 1:   2300        -8464.468             0.013            0.011
Chain 1:   2400        -8358.667             0.014            0.012
Chain 1:   2500        -8399.905             0.013            0.011
Chain 1:   2600        -8425.103             0.012            0.010
Chain 1:   2700        -8343.260             0.012            0.010
Chain 1:   2800        -8311.715             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411581.475             1.000            1.000
Chain 1:    200     -1586873.580             2.650            4.301
Chain 1:    300      -891073.837             2.027            1.000
Chain 1:    400      -457275.707             1.758            1.000
Chain 1:    500      -357214.998             1.462            0.949
Chain 1:    600      -232398.945             1.308            0.949
Chain 1:    700      -119001.632             1.257            0.949
Chain 1:    800       -86254.578             1.147            0.949
Chain 1:    900       -66676.510             1.053            0.781
Chain 1:   1000       -51521.492             0.977            0.781
Chain 1:   1100       -39040.451             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38224.107             0.481            0.380
Chain 1:   1300       -26231.587             0.448            0.380
Chain 1:   1400       -25955.174             0.355            0.320
Chain 1:   1500       -22554.484             0.342            0.320
Chain 1:   1600       -21774.167             0.292            0.294
Chain 1:   1700       -20654.523             0.202            0.294
Chain 1:   1800       -20600.133             0.164            0.151
Chain 1:   1900       -20926.114             0.136            0.054
Chain 1:   2000       -19440.731             0.114            0.054
Chain 1:   2100       -19679.140             0.084            0.036
Chain 1:   2200       -19904.737             0.083            0.036
Chain 1:   2300       -19522.731             0.039            0.020
Chain 1:   2400       -19294.928             0.039            0.020
Chain 1:   2500       -19096.534             0.025            0.016
Chain 1:   2600       -18727.300             0.023            0.016
Chain 1:   2700       -18684.466             0.018            0.012
Chain 1:   2800       -18401.163             0.019            0.015
Chain 1:   2900       -18682.334             0.019            0.015
Chain 1:   3000       -18668.664             0.012            0.012
Chain 1:   3100       -18753.545             0.011            0.012
Chain 1:   3200       -18444.442             0.012            0.015
Chain 1:   3300       -18649.026             0.011            0.012
Chain 1:   3400       -18124.114             0.012            0.015
Chain 1:   3500       -18735.558             0.015            0.015
Chain 1:   3600       -18042.867             0.017            0.015
Chain 1:   3700       -18429.129             0.018            0.017
Chain 1:   3800       -17389.621             0.023            0.021
Chain 1:   3900       -17385.731             0.021            0.021
Chain 1:   4000       -17503.120             0.022            0.021
Chain 1:   4100       -17416.813             0.022            0.021
Chain 1:   4200       -17233.286             0.021            0.021
Chain 1:   4300       -17371.595             0.021            0.021
Chain 1:   4400       -17328.582             0.018            0.011
Chain 1:   4500       -17231.072             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001259 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12338.769             1.000            1.000
Chain 1:    200        -9331.869             0.661            1.000
Chain 1:    300        -8041.249             0.494            0.322
Chain 1:    400        -8266.668             0.377            0.322
Chain 1:    500        -8149.703             0.305            0.161
Chain 1:    600        -8001.142             0.257            0.161
Chain 1:    700        -7911.871             0.222            0.027
Chain 1:    800        -7920.880             0.194            0.027
Chain 1:    900        -7845.395             0.174            0.019
Chain 1:   1000        -8023.088             0.159            0.022
Chain 1:   1100        -8050.776             0.059            0.019
Chain 1:   1200        -7946.557             0.028            0.014
Chain 1:   1300        -7892.180             0.013            0.013
Chain 1:   1400        -7909.411             0.010            0.011
Chain 1:   1500        -7995.969             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001457 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55595.002             1.000            1.000
Chain 1:    200       -17217.744             1.614            2.229
Chain 1:    300        -8687.570             1.404            1.000
Chain 1:    400        -8484.448             1.059            1.000
Chain 1:    500        -8043.087             0.858            0.982
Chain 1:    600        -8430.834             0.723            0.982
Chain 1:    700        -8042.729             0.626            0.055
Chain 1:    800        -8176.253             0.550            0.055
Chain 1:    900        -8391.552             0.492            0.048
Chain 1:   1000        -8030.605             0.447            0.048
Chain 1:   1100        -7758.120             0.351            0.046
Chain 1:   1200        -7703.171             0.128            0.045
Chain 1:   1300        -7812.676             0.032            0.035
Chain 1:   1400        -7784.930             0.030            0.035
Chain 1:   1500        -7570.517             0.027            0.028
Chain 1:   1600        -7541.401             0.023            0.026
Chain 1:   1700        -7570.984             0.018            0.016
Chain 1:   1800        -7627.556             0.017            0.014
Chain 1:   1900        -7633.259             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003036 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86387.767             1.000            1.000
Chain 1:    200       -13485.963             3.203            5.406
Chain 1:    300        -9860.020             2.258            1.000
Chain 1:    400       -10687.477             1.713            1.000
Chain 1:    500        -8825.918             1.412            0.368
Chain 1:    600        -8428.027             1.185            0.368
Chain 1:    700        -8299.042             1.018            0.211
Chain 1:    800        -9198.887             0.903            0.211
Chain 1:    900        -8645.272             0.810            0.098
Chain 1:   1000        -8510.759             0.730            0.098
Chain 1:   1100        -8569.965             0.631            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8377.279             0.093            0.064
Chain 1:   1300        -8561.718             0.058            0.047
Chain 1:   1400        -8574.767             0.050            0.023
Chain 1:   1500        -8438.962             0.031            0.022
Chain 1:   1600        -8550.416             0.028            0.016
Chain 1:   1700        -8636.593             0.027            0.016
Chain 1:   1800        -8228.242             0.022            0.016
Chain 1:   1900        -8324.175             0.017            0.016
Chain 1:   2000        -8296.689             0.016            0.013
Chain 1:   2100        -8417.920             0.016            0.014
Chain 1:   2200        -8248.718             0.016            0.014
Chain 1:   2300        -8323.628             0.015            0.013
Chain 1:   2400        -8388.624             0.016            0.013
Chain 1:   2500        -8334.304             0.015            0.012
Chain 1:   2600        -8332.895             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003307 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387595.197             1.000            1.000
Chain 1:    200     -1583318.257             2.649            4.297
Chain 1:    300      -891587.761             2.024            1.000
Chain 1:    400      -458106.642             1.755            1.000
Chain 1:    500      -358579.438             1.459            0.946
Chain 1:    600      -233413.675             1.306            0.946
Chain 1:    700      -119434.249             1.255            0.946
Chain 1:    800       -86568.492             1.146            0.946
Chain 1:    900       -66874.695             1.051            0.776
Chain 1:   1000       -51637.462             0.976            0.776
Chain 1:   1100       -39082.986             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38255.960             0.480            0.380
Chain 1:   1300       -26187.799             0.449            0.380
Chain 1:   1400       -25903.553             0.355            0.321
Chain 1:   1500       -22484.427             0.343            0.321
Chain 1:   1600       -21698.547             0.293            0.295
Chain 1:   1700       -20569.920             0.203            0.294
Chain 1:   1800       -20513.356             0.165            0.152
Chain 1:   1900       -20839.349             0.137            0.055
Chain 1:   2000       -19349.469             0.115            0.055
Chain 1:   2100       -19587.913             0.084            0.036
Chain 1:   2200       -19814.421             0.083            0.036
Chain 1:   2300       -19431.638             0.039            0.020
Chain 1:   2400       -19203.779             0.039            0.020
Chain 1:   2500       -19005.809             0.025            0.016
Chain 1:   2600       -18636.104             0.024            0.016
Chain 1:   2700       -18593.137             0.018            0.012
Chain 1:   2800       -18310.039             0.020            0.015
Chain 1:   2900       -18591.310             0.020            0.015
Chain 1:   3000       -18577.480             0.012            0.012
Chain 1:   3100       -18662.416             0.011            0.012
Chain 1:   3200       -18353.198             0.012            0.015
Chain 1:   3300       -18557.871             0.011            0.012
Chain 1:   3400       -18032.943             0.013            0.015
Chain 1:   3500       -18644.562             0.015            0.015
Chain 1:   3600       -17951.674             0.017            0.015
Chain 1:   3700       -18338.165             0.019            0.017
Chain 1:   3800       -17298.442             0.023            0.021
Chain 1:   3900       -17294.622             0.022            0.021
Chain 1:   4000       -17411.922             0.022            0.021
Chain 1:   4100       -17325.677             0.022            0.021
Chain 1:   4200       -17142.081             0.022            0.021
Chain 1:   4300       -17280.356             0.021            0.021
Chain 1:   4400       -17237.302             0.019            0.011
Chain 1:   4500       -17139.874             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12310.729             1.000            1.000
Chain 1:    200        -9211.430             0.668            1.000
Chain 1:    300        -7898.690             0.501            0.336
Chain 1:    400        -8071.451             0.381            0.336
Chain 1:    500        -7931.545             0.308            0.166
Chain 1:    600        -7854.643             0.259            0.166
Chain 1:    700        -7758.231             0.223            0.021
Chain 1:    800        -7781.775             0.196            0.021
Chain 1:    900        -7869.112             0.175            0.018
Chain 1:   1000        -7809.901             0.159            0.018
Chain 1:   1100        -7865.252             0.059            0.012
Chain 1:   1200        -7766.743             0.027            0.012
Chain 1:   1300        -7726.428             0.011            0.011
Chain 1:   1400        -7754.204             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00141 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56657.995             1.000            1.000
Chain 1:    200       -17312.776             1.636            2.273
Chain 1:    300        -8628.674             1.426            1.006
Chain 1:    400        -8295.248             1.080            1.006
Chain 1:    500        -8170.083             0.867            1.000
Chain 1:    600        -8837.783             0.735            1.000
Chain 1:    700        -8131.194             0.642            0.087
Chain 1:    800        -8045.750             0.563            0.087
Chain 1:    900        -7903.750             0.503            0.076
Chain 1:   1000        -7728.476             0.455            0.076
Chain 1:   1100        -7677.526             0.355            0.040
Chain 1:   1200        -7613.853             0.129            0.023
Chain 1:   1300        -7653.834             0.029            0.018
Chain 1:   1400        -7560.489             0.026            0.015
Chain 1:   1500        -7518.427             0.025            0.012
Chain 1:   1600        -7679.168             0.020            0.012
Chain 1:   1700        -7428.911             0.014            0.012
Chain 1:   1800        -7512.296             0.014            0.012
Chain 1:   1900        -7474.700             0.013            0.011
Chain 1:   2000        -7516.813             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002556 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86471.296             1.000            1.000
Chain 1:    200       -13370.456             3.234            5.467
Chain 1:    300        -9735.946             2.280            1.000
Chain 1:    400       -10564.600             1.730            1.000
Chain 1:    500        -8710.332             1.426            0.373
Chain 1:    600        -8221.162             1.199            0.373
Chain 1:    700        -8345.913             1.029            0.213
Chain 1:    800        -8988.497             0.910            0.213
Chain 1:    900        -8536.294             0.815            0.078
Chain 1:   1000        -8350.550             0.735            0.078
Chain 1:   1100        -8607.703             0.638            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8199.041             0.097            0.060
Chain 1:   1300        -8349.969             0.061            0.053
Chain 1:   1400        -8408.471             0.054            0.050
Chain 1:   1500        -8310.609             0.034            0.030
Chain 1:   1600        -8417.098             0.029            0.022
Chain 1:   1700        -8502.957             0.029            0.022
Chain 1:   1800        -8090.576             0.027            0.022
Chain 1:   1900        -8186.781             0.022            0.018
Chain 1:   2000        -8159.729             0.021            0.013
Chain 1:   2100        -8282.221             0.019            0.013
Chain 1:   2200        -8101.977             0.016            0.013
Chain 1:   2300        -8181.758             0.015            0.012
Chain 1:   2400        -8251.332             0.016            0.012
Chain 1:   2500        -8196.643             0.015            0.012
Chain 1:   2600        -8195.922             0.014            0.010
Chain 1:   2700        -8113.252             0.014            0.010
Chain 1:   2800        -8077.176             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002601 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400039.469             1.000            1.000
Chain 1:    200     -1584709.217             2.650            4.301
Chain 1:    300      -891113.543             2.026            1.000
Chain 1:    400      -457876.109             1.756            1.000
Chain 1:    500      -357867.715             1.461            0.946
Chain 1:    600      -232935.010             1.307            0.946
Chain 1:    700      -119121.802             1.257            0.946
Chain 1:    800       -86310.463             1.147            0.946
Chain 1:    900       -66657.664             1.052            0.778
Chain 1:   1000       -51454.024             0.977            0.778
Chain 1:   1100       -38928.870             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38107.035             0.481            0.380
Chain 1:   1300       -26063.084             0.449            0.380
Chain 1:   1400       -25782.929             0.356            0.322
Chain 1:   1500       -22369.296             0.343            0.322
Chain 1:   1600       -21585.462             0.293            0.295
Chain 1:   1700       -20459.154             0.203            0.295
Chain 1:   1800       -20403.298             0.165            0.153
Chain 1:   1900       -20729.406             0.137            0.055
Chain 1:   2000       -19240.457             0.116            0.055
Chain 1:   2100       -19479.016             0.085            0.036
Chain 1:   2200       -19705.324             0.084            0.036
Chain 1:   2300       -19322.635             0.039            0.020
Chain 1:   2400       -19094.702             0.040            0.020
Chain 1:   2500       -18896.668             0.025            0.016
Chain 1:   2600       -18526.974             0.024            0.016
Chain 1:   2700       -18483.993             0.018            0.012
Chain 1:   2800       -18200.806             0.020            0.016
Chain 1:   2900       -18482.070             0.020            0.015
Chain 1:   3000       -18468.341             0.012            0.012
Chain 1:   3100       -18553.271             0.011            0.012
Chain 1:   3200       -18244.004             0.012            0.015
Chain 1:   3300       -18448.699             0.011            0.012
Chain 1:   3400       -17923.670             0.013            0.015
Chain 1:   3500       -18535.429             0.015            0.016
Chain 1:   3600       -17842.301             0.017            0.016
Chain 1:   3700       -18228.935             0.019            0.017
Chain 1:   3800       -17188.871             0.023            0.021
Chain 1:   3900       -17184.999             0.022            0.021
Chain 1:   4000       -17302.342             0.022            0.021
Chain 1:   4100       -17216.054             0.022            0.021
Chain 1:   4200       -17032.373             0.022            0.021
Chain 1:   4300       -17170.739             0.021            0.021
Chain 1:   4400       -17127.616             0.019            0.011
Chain 1:   4500       -17030.124             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11840.419             1.000            1.000
Chain 1:    200        -8854.961             0.669            1.000
Chain 1:    300        -7854.968             0.488            0.337
Chain 1:    400        -7969.069             0.370            0.337
Chain 1:    500        -7766.096             0.301            0.127
Chain 1:    600        -7704.831             0.252            0.127
Chain 1:    700        -7650.327             0.217            0.026
Chain 1:    800        -7629.952             0.190            0.026
Chain 1:    900        -7579.895             0.170            0.014
Chain 1:   1000        -7695.606             0.154            0.015
Chain 1:   1100        -7742.580             0.055            0.014
Chain 1:   1200        -7657.067             0.022            0.011
Chain 1:   1300        -7618.558             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001481 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56381.710             1.000            1.000
Chain 1:    200       -16839.666             1.674            2.348
Chain 1:    300        -8480.429             1.445            1.000
Chain 1:    400        -8590.159             1.087            1.000
Chain 1:    500        -8055.303             0.883            0.986
Chain 1:    600        -8902.148             0.751            0.986
Chain 1:    700        -8117.703             0.658            0.097
Chain 1:    800        -8028.540             0.577            0.097
Chain 1:    900        -7900.055             0.515            0.095
Chain 1:   1000        -7683.883             0.466            0.095
Chain 1:   1100        -7647.335             0.367            0.066
Chain 1:   1200        -7804.267             0.134            0.028
Chain 1:   1300        -7710.911             0.036            0.020
Chain 1:   1400        -7778.476             0.036            0.020
Chain 1:   1500        -7608.181             0.032            0.020
Chain 1:   1600        -7523.289             0.023            0.016
Chain 1:   1700        -7494.302             0.014            0.012
Chain 1:   1800        -7563.873             0.014            0.012
Chain 1:   1900        -7532.017             0.012            0.011
Chain 1:   2000        -7586.466             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003698 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85876.823             1.000            1.000
Chain 1:    200       -12914.680             3.325            5.650
Chain 1:    300        -9437.175             2.339            1.000
Chain 1:    400       -10189.454             1.773            1.000
Chain 1:    500        -8282.288             1.464            0.368
Chain 1:    600        -8059.372             1.225            0.368
Chain 1:    700        -8349.626             1.055            0.230
Chain 1:    800        -8488.464             0.925            0.230
Chain 1:    900        -8337.565             0.824            0.074
Chain 1:   1000        -8082.427             0.745            0.074
Chain 1:   1100        -8237.455             0.647            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8082.174             0.084            0.032
Chain 1:   1300        -8235.771             0.049            0.028
Chain 1:   1400        -8149.543             0.043            0.019
Chain 1:   1500        -8111.705             0.020            0.019
Chain 1:   1600        -8107.210             0.017            0.019
Chain 1:   1700        -8056.914             0.014            0.018
Chain 1:   1800        -7935.752             0.014            0.018
Chain 1:   1900        -8045.654             0.014            0.015
Chain 1:   2000        -8011.248             0.011            0.014
Chain 1:   2100        -8159.473             0.011            0.014
Chain 1:   2200        -7937.383             0.012            0.014
Chain 1:   2300        -8018.929             0.011            0.011
Chain 1:   2400        -8084.838             0.011            0.010
Chain 1:   2500        -8046.947             0.011            0.010
Chain 1:   2600        -8040.091             0.011            0.010
Chain 1:   2700        -7952.525             0.011            0.011
Chain 1:   2800        -7940.056             0.010            0.010
Chain 1:   2900        -7943.306             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003721 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400075.097             1.000            1.000
Chain 1:    200     -1585309.204             2.649            4.299
Chain 1:    300      -890954.059             2.026            1.000
Chain 1:    400      -457152.832             1.757            1.000
Chain 1:    500      -357235.640             1.461            0.949
Chain 1:    600      -232233.292             1.307            0.949
Chain 1:    700      -118542.934             1.258            0.949
Chain 1:    800       -85740.693             1.148            0.949
Chain 1:    900       -66099.150             1.054            0.779
Chain 1:   1000       -50894.278             0.978            0.779
Chain 1:   1100       -38376.688             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37547.141             0.483            0.383
Chain 1:   1300       -25529.422             0.452            0.383
Chain 1:   1400       -25246.070             0.359            0.326
Chain 1:   1500       -21840.371             0.346            0.326
Chain 1:   1600       -21057.421             0.296            0.299
Chain 1:   1700       -19935.534             0.206            0.297
Chain 1:   1800       -19880.039             0.168            0.156
Chain 1:   1900       -20205.174             0.140            0.056
Chain 1:   2000       -18720.474             0.118            0.056
Chain 1:   2100       -18958.565             0.086            0.037
Chain 1:   2200       -19183.915             0.085            0.037
Chain 1:   2300       -18802.386             0.040            0.020
Chain 1:   2400       -18574.903             0.040            0.020
Chain 1:   2500       -18376.767             0.026            0.016
Chain 1:   2600       -18008.106             0.024            0.016
Chain 1:   2700       -17965.488             0.019            0.013
Chain 1:   2800       -17682.691             0.020            0.016
Chain 1:   2900       -17963.490             0.020            0.016
Chain 1:   3000       -17949.789             0.012            0.013
Chain 1:   3100       -18034.563             0.011            0.012
Chain 1:   3200       -17725.975             0.012            0.016
Chain 1:   3300       -17930.172             0.011            0.012
Chain 1:   3400       -17406.290             0.013            0.016
Chain 1:   3500       -18016.261             0.015            0.016
Chain 1:   3600       -17325.537             0.017            0.016
Chain 1:   3700       -17710.357             0.019            0.017
Chain 1:   3800       -16673.959             0.024            0.022
Chain 1:   3900       -16670.217             0.022            0.022
Chain 1:   4000       -16787.526             0.023            0.022
Chain 1:   4100       -16701.403             0.023            0.022
Chain 1:   4200       -16518.581             0.022            0.022
Chain 1:   4300       -16656.343             0.022            0.022
Chain 1:   4400       -16613.880             0.019            0.011
Chain 1:   4500       -16516.545             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48274.761             1.000            1.000
Chain 1:    200       -16822.833             1.435            1.870
Chain 1:    300       -15046.159             0.996            1.000
Chain 1:    400       -15989.509             0.762            1.000
Chain 1:    500       -15785.359             0.612            0.118
Chain 1:    600       -10716.815             0.589            0.473
Chain 1:    700       -11644.590             0.516            0.118
Chain 1:    800       -14278.633             0.475            0.184
Chain 1:    900       -15272.411             0.429            0.118
Chain 1:   1000       -13288.122             0.401            0.149
Chain 1:   1100       -14182.543             0.307            0.118
Chain 1:   1200       -10483.845             0.156            0.118
Chain 1:   1300       -16064.514             0.179            0.149
Chain 1:   1400       -21680.921             0.199            0.184
Chain 1:   1500       -19165.384             0.211            0.184
Chain 1:   1600        -9300.686             0.269            0.184
Chain 1:   1700       -11187.258             0.278            0.184
Chain 1:   1800       -10672.509             0.265            0.169
Chain 1:   1900       -10357.694             0.261            0.169
Chain 1:   2000        -9807.071             0.252            0.169
Chain 1:   2100       -10514.256             0.252            0.169
Chain 1:   2200       -11133.259             0.222            0.131
Chain 1:   2300        -9007.673             0.211            0.131
Chain 1:   2400        -9518.163             0.191            0.067
Chain 1:   2500       -11528.179             0.195            0.067
Chain 1:   2600        -9602.004             0.109            0.067
Chain 1:   2700       -11234.575             0.107            0.067
Chain 1:   2800        -9795.837             0.117            0.145
Chain 1:   2900       -15464.237             0.150            0.147
Chain 1:   3000       -13678.047             0.158            0.147
Chain 1:   3100        -9093.496             0.201            0.174
Chain 1:   3200        -9350.323             0.199            0.174
Chain 1:   3300       -15879.674             0.216            0.174
Chain 1:   3400        -9527.865             0.277            0.201
Chain 1:   3500        -9316.266             0.262            0.201
Chain 1:   3600        -9872.578             0.248            0.147
Chain 1:   3700        -8542.762             0.249            0.156
Chain 1:   3800        -8545.597             0.234            0.156
Chain 1:   3900        -8841.647             0.201            0.131
Chain 1:   4000        -9247.153             0.192            0.056
Chain 1:   4100       -12614.659             0.168            0.056
Chain 1:   4200        -9076.623             0.205            0.156
Chain 1:   4300       -10180.299             0.174            0.108
Chain 1:   4400       -10664.253             0.112            0.056
Chain 1:   4500        -8534.667             0.135            0.108
Chain 1:   4600       -13660.351             0.167            0.156
Chain 1:   4700        -8617.214             0.210            0.250
Chain 1:   4800        -8211.442             0.215            0.250
Chain 1:   4900        -8828.929             0.218            0.250
Chain 1:   5000       -11839.835             0.239            0.254
Chain 1:   5100        -8400.557             0.254            0.254
Chain 1:   5200       -10251.048             0.233            0.250
Chain 1:   5300       -12759.386             0.242            0.250
Chain 1:   5400       -12854.525             0.238            0.250
Chain 1:   5500        -8749.395             0.260            0.254
Chain 1:   5600        -8580.546             0.224            0.197
Chain 1:   5700        -8614.510             0.166            0.181
Chain 1:   5800        -8278.978             0.165            0.181
Chain 1:   5900       -10108.302             0.176            0.181
Chain 1:   6000       -10826.281             0.157            0.181
Chain 1:   6100        -8100.990             0.150            0.181
Chain 1:   6200       -14306.386             0.175            0.181
Chain 1:   6300        -9233.158             0.211            0.181
Chain 1:   6400       -10688.469             0.224            0.181
Chain 1:   6500        -9919.165             0.184            0.136
Chain 1:   6600       -11511.259             0.196            0.138
Chain 1:   6700        -9735.887             0.214            0.181
Chain 1:   6800        -8457.399             0.225            0.181
Chain 1:   6900        -9164.131             0.215            0.151
Chain 1:   7000       -11858.292             0.231            0.182
Chain 1:   7100        -8800.798             0.232            0.182
Chain 1:   7200        -8273.514             0.195            0.151
Chain 1:   7300       -10740.963             0.163            0.151
Chain 1:   7400        -8088.454             0.182            0.182
Chain 1:   7500        -8033.362             0.175            0.182
Chain 1:   7600        -8295.904             0.165            0.182
Chain 1:   7700        -8405.203             0.148            0.151
Chain 1:   7800        -9922.842             0.148            0.153
Chain 1:   7900        -8018.413             0.164            0.227
Chain 1:   8000       -10670.709             0.166            0.230
Chain 1:   8100        -7997.884             0.165            0.230
Chain 1:   8200       -10510.849             0.182            0.238
Chain 1:   8300        -7943.524             0.191            0.239
Chain 1:   8400       -11123.494             0.187            0.239
Chain 1:   8500        -8250.770             0.221            0.249
Chain 1:   8600        -9178.094             0.228            0.249
Chain 1:   8700        -8180.531             0.239            0.249
Chain 1:   8800        -8450.802             0.227            0.249
Chain 1:   8900        -9995.269             0.219            0.249
Chain 1:   9000       -10060.896             0.195            0.239
Chain 1:   9100        -7892.535             0.189            0.239
Chain 1:   9200        -8013.419             0.166            0.155
Chain 1:   9300        -8353.180             0.138            0.122
Chain 1:   9400       -11214.297             0.135            0.122
Chain 1:   9500       -11608.482             0.104            0.101
Chain 1:   9600        -8569.296             0.129            0.122
Chain 1:   9700       -10581.918             0.136            0.155
Chain 1:   9800        -8012.756             0.165            0.190
Chain 1:   9900        -8343.069             0.153            0.190
Chain 1:   10000        -7866.666             0.159            0.190
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45712.268             1.000            1.000
Chain 1:    200       -15076.546             1.516            2.032
Chain 1:    300        -8482.151             1.270            1.000
Chain 1:    400        -8298.255             0.958            1.000
Chain 1:    500        -8130.499             0.770            0.777
Chain 1:    600        -7858.796             0.648            0.777
Chain 1:    700        -7962.349             0.557            0.035
Chain 1:    800        -7519.839             0.495            0.059
Chain 1:    900        -7878.428             0.445            0.046
Chain 1:   1000        -7726.542             0.402            0.046
Chain 1:   1100        -7708.425             0.303            0.035
Chain 1:   1200        -7560.166             0.101            0.022
Chain 1:   1300        -7674.846             0.025            0.021
Chain 1:   1400        -7843.736             0.025            0.021
Chain 1:   1500        -7590.089             0.026            0.022
Chain 1:   1600        -7503.781             0.024            0.020
Chain 1:   1700        -7485.684             0.023            0.020
Chain 1:   1800        -7524.572             0.018            0.020
Chain 1:   1900        -7578.216             0.014            0.015
Chain 1:   2000        -7575.801             0.012            0.012
Chain 1:   2100        -7589.499             0.012            0.012
Chain 1:   2200        -7646.128             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003148 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86258.591             1.000            1.000
Chain 1:    200       -13062.541             3.302            5.604
Chain 1:    300        -9552.259             2.324            1.000
Chain 1:    400       -10347.137             1.762            1.000
Chain 1:    500        -8420.539             1.455            0.367
Chain 1:    600        -8175.078             1.218            0.367
Chain 1:    700        -8213.372             1.044            0.229
Chain 1:    800        -8580.569             0.919            0.229
Chain 1:    900        -8394.733             0.820            0.077
Chain 1:   1000        -8184.675             0.740            0.077
Chain 1:   1100        -8445.482             0.643            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8152.361             0.087            0.036
Chain 1:   1300        -8327.112             0.052            0.031
Chain 1:   1400        -8316.522             0.044            0.030
Chain 1:   1500        -8231.271             0.022            0.026
Chain 1:   1600        -8313.476             0.020            0.022
Chain 1:   1700        -8418.542             0.021            0.022
Chain 1:   1800        -8036.532             0.022            0.022
Chain 1:   1900        -8134.894             0.021            0.021
Chain 1:   2000        -8105.158             0.019            0.012
Chain 1:   2100        -8250.211             0.017            0.012
Chain 1:   2200        -8027.967             0.016            0.012
Chain 1:   2300        -8163.401             0.016            0.012
Chain 1:   2400        -8052.014             0.017            0.014
Chain 1:   2500        -8112.032             0.017            0.014
Chain 1:   2600        -8125.190             0.016            0.014
Chain 1:   2700        -8046.055             0.016            0.014
Chain 1:   2800        -8031.114             0.011            0.012
Chain 1:   2900        -8019.974             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002757 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395974.803             1.000            1.000
Chain 1:    200     -1585066.382             2.648            4.297
Chain 1:    300      -890951.950             2.025            1.000
Chain 1:    400      -457294.370             1.756            1.000
Chain 1:    500      -357569.995             1.461            0.948
Chain 1:    600      -232507.878             1.307            0.948
Chain 1:    700      -118751.613             1.257            0.948
Chain 1:    800       -85935.854             1.148            0.948
Chain 1:    900       -66283.782             1.053            0.779
Chain 1:   1000       -51074.596             0.978            0.779
Chain 1:   1100       -38552.360             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37720.952             0.483            0.382
Chain 1:   1300       -25696.607             0.451            0.382
Chain 1:   1400       -25412.861             0.358            0.325
Chain 1:   1500       -22005.349             0.345            0.325
Chain 1:   1600       -21221.748             0.295            0.298
Chain 1:   1700       -20099.124             0.205            0.296
Chain 1:   1800       -20043.409             0.167            0.155
Chain 1:   1900       -20368.756             0.139            0.056
Chain 1:   2000       -18882.981             0.117            0.056
Chain 1:   2100       -19121.239             0.086            0.037
Chain 1:   2200       -19346.812             0.085            0.037
Chain 1:   2300       -18964.969             0.040            0.020
Chain 1:   2400       -18737.397             0.040            0.020
Chain 1:   2500       -18539.239             0.026            0.016
Chain 1:   2600       -18170.478             0.024            0.016
Chain 1:   2700       -18127.728             0.019            0.012
Chain 1:   2800       -17844.917             0.020            0.016
Chain 1:   2900       -18125.719             0.020            0.015
Chain 1:   3000       -18112.040             0.012            0.012
Chain 1:   3100       -18196.887             0.011            0.012
Chain 1:   3200       -17888.172             0.012            0.015
Chain 1:   3300       -18092.393             0.011            0.012
Chain 1:   3400       -17568.339             0.013            0.015
Chain 1:   3500       -18178.666             0.015            0.016
Chain 1:   3600       -17487.375             0.017            0.016
Chain 1:   3700       -17872.667             0.019            0.017
Chain 1:   3800       -16835.470             0.024            0.022
Chain 1:   3900       -16831.671             0.022            0.022
Chain 1:   4000       -16948.989             0.023            0.022
Chain 1:   4100       -16862.909             0.023            0.022
Chain 1:   4200       -16679.818             0.022            0.022
Chain 1:   4300       -16817.765             0.022            0.022
Chain 1:   4400       -16775.163             0.019            0.011
Chain 1:   4500       -16677.769             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001268 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12276.099             1.000            1.000
Chain 1:    200        -9201.038             0.667            1.000
Chain 1:    300        -7958.564             0.497            0.334
Chain 1:    400        -8133.768             0.378            0.334
Chain 1:    500        -7986.986             0.306            0.156
Chain 1:    600        -7911.947             0.257            0.156
Chain 1:    700        -7828.551             0.221            0.022
Chain 1:    800        -7868.180             0.194            0.022
Chain 1:    900        -7993.762             0.175            0.018
Chain 1:   1000        -7874.394             0.159            0.018
Chain 1:   1100        -7913.039             0.059            0.016
Chain 1:   1200        -7857.696             0.026            0.015
Chain 1:   1300        -7807.248             0.011            0.011
Chain 1:   1400        -7816.541             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001525 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57865.469             1.000            1.000
Chain 1:    200       -17534.121             1.650            2.300
Chain 1:    300        -8587.785             1.447            1.042
Chain 1:    400        -8165.528             1.098            1.042
Chain 1:    500        -8400.010             0.884            1.000
Chain 1:    600        -8562.440             0.740            1.000
Chain 1:    700        -8521.881             0.635            0.052
Chain 1:    800        -8131.354             0.562            0.052
Chain 1:    900        -7752.866             0.505            0.049
Chain 1:   1000        -7875.540             0.456            0.049
Chain 1:   1100        -7767.424             0.357            0.048
Chain 1:   1200        -7779.450             0.127            0.028
Chain 1:   1300        -7690.996             0.024            0.019
Chain 1:   1400        -7789.144             0.020            0.016
Chain 1:   1500        -7572.625             0.020            0.016
Chain 1:   1600        -7627.721             0.019            0.014
Chain 1:   1700        -7495.629             0.021            0.016
Chain 1:   1800        -7580.907             0.017            0.014
Chain 1:   1900        -7432.826             0.014            0.014
Chain 1:   2000        -7539.301             0.014            0.014
Chain 1:   2100        -7473.434             0.013            0.013
Chain 1:   2200        -7669.158             0.016            0.014
Chain 1:   2300        -7544.381             0.016            0.017
Chain 1:   2400        -7581.531             0.015            0.017
Chain 1:   2500        -7580.882             0.013            0.014
Chain 1:   2600        -7490.817             0.013            0.014
Chain 1:   2700        -7546.802             0.012            0.012
Chain 1:   2800        -7455.397             0.012            0.012
Chain 1:   2900        -7372.230             0.011            0.012
Chain 1:   3000        -7497.526             0.012            0.012
Chain 1:   3100        -7484.187             0.011            0.012
Chain 1:   3200        -7663.103             0.011            0.012
Chain 1:   3300        -7437.338             0.012            0.012
Chain 1:   3400        -7603.426             0.014            0.012
Chain 1:   3500        -7407.658             0.016            0.017
Chain 1:   3600        -7460.802             0.016            0.017
Chain 1:   3700        -7416.282             0.016            0.017
Chain 1:   3800        -7432.436             0.015            0.017
Chain 1:   3900        -7416.675             0.014            0.017
Chain 1:   4000        -7385.539             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002946 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86618.487             1.000            1.000
Chain 1:    200       -13343.632             3.246            5.491
Chain 1:    300        -9756.148             2.286            1.000
Chain 1:    400       -10650.790             1.736            1.000
Chain 1:    500        -8674.883             1.434            0.368
Chain 1:    600        -8410.473             1.200            0.368
Chain 1:    700        -8567.346             1.032            0.228
Chain 1:    800        -8975.577             0.908            0.228
Chain 1:    900        -8578.345             0.812            0.084
Chain 1:   1000        -8276.424             0.735            0.084
Chain 1:   1100        -8642.817             0.639            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8258.066             0.095            0.046
Chain 1:   1300        -8447.798             0.060            0.045
Chain 1:   1400        -8451.333             0.052            0.042
Chain 1:   1500        -8350.817             0.030            0.036
Chain 1:   1600        -8455.776             0.028            0.036
Chain 1:   1700        -8545.477             0.028            0.036
Chain 1:   1800        -8145.376             0.028            0.036
Chain 1:   1900        -8245.783             0.024            0.022
Chain 1:   2000        -8216.655             0.021            0.012
Chain 1:   2100        -8336.918             0.018            0.012
Chain 1:   2200        -8115.018             0.016            0.012
Chain 1:   2300        -8275.336             0.016            0.012
Chain 1:   2400        -8157.171             0.018            0.014
Chain 1:   2500        -8220.888             0.017            0.014
Chain 1:   2600        -8242.163             0.016            0.014
Chain 1:   2700        -8161.518             0.016            0.014
Chain 1:   2800        -8136.048             0.011            0.012
Chain 1:   2900        -8191.102             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003207 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398528.910             1.000            1.000
Chain 1:    200     -1583740.794             2.651            4.303
Chain 1:    300      -889480.578             2.028            1.000
Chain 1:    400      -457216.938             1.757            1.000
Chain 1:    500      -357544.581             1.462            0.945
Chain 1:    600      -232649.688             1.307            0.945
Chain 1:    700      -118981.601             1.257            0.945
Chain 1:    800       -86203.925             1.148            0.945
Chain 1:    900       -66563.954             1.053            0.781
Chain 1:   1000       -51366.538             0.977            0.781
Chain 1:   1100       -38855.421             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38031.885             0.481            0.380
Chain 1:   1300       -26006.995             0.449            0.380
Chain 1:   1400       -25726.813             0.356            0.322
Chain 1:   1500       -22318.680             0.343            0.322
Chain 1:   1600       -21536.172             0.293            0.296
Chain 1:   1700       -20412.361             0.203            0.295
Chain 1:   1800       -20357.003             0.165            0.153
Chain 1:   1900       -20682.897             0.138            0.055
Chain 1:   2000       -19195.783             0.116            0.055
Chain 1:   2100       -19434.180             0.085            0.036
Chain 1:   2200       -19660.145             0.084            0.036
Chain 1:   2300       -19277.806             0.039            0.020
Chain 1:   2400       -19049.985             0.040            0.020
Chain 1:   2500       -18851.929             0.025            0.016
Chain 1:   2600       -18482.543             0.024            0.016
Chain 1:   2700       -18439.637             0.018            0.012
Chain 1:   2800       -18156.549             0.020            0.016
Chain 1:   2900       -18437.683             0.020            0.015
Chain 1:   3000       -18423.901             0.012            0.012
Chain 1:   3100       -18508.838             0.011            0.012
Chain 1:   3200       -18199.779             0.012            0.015
Chain 1:   3300       -18404.307             0.011            0.012
Chain 1:   3400       -17879.619             0.013            0.015
Chain 1:   3500       -18490.899             0.015            0.016
Chain 1:   3600       -17798.338             0.017            0.016
Chain 1:   3700       -18184.558             0.019            0.017
Chain 1:   3800       -17145.430             0.023            0.021
Chain 1:   3900       -17141.579             0.022            0.021
Chain 1:   4000       -17258.904             0.022            0.021
Chain 1:   4100       -17172.694             0.022            0.021
Chain 1:   4200       -16989.197             0.022            0.021
Chain 1:   4300       -17127.433             0.021            0.021
Chain 1:   4400       -17084.478             0.019            0.011
Chain 1:   4500       -16987.007             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001324 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12431.010             1.000            1.000
Chain 1:    200        -9184.370             0.677            1.000
Chain 1:    300        -8149.640             0.493            0.353
Chain 1:    400        -8225.340             0.372            0.353
Chain 1:    500        -8215.460             0.298            0.127
Chain 1:    600        -8003.385             0.253            0.127
Chain 1:    700        -7955.334             0.218            0.026
Chain 1:    800        -7953.344             0.190            0.026
Chain 1:    900        -8017.272             0.170            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00143 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51594.487             1.000            1.000
Chain 1:    200       -16181.521             1.594            2.188
Chain 1:    300        -8694.981             1.350            1.000
Chain 1:    400        -8053.096             1.032            1.000
Chain 1:    500        -8488.490             0.836            0.861
Chain 1:    600        -9079.579             0.708            0.861
Chain 1:    700        -7983.944             0.626            0.137
Chain 1:    800        -8063.815             0.549            0.137
Chain 1:    900        -7996.093             0.489            0.080
Chain 1:   1000        -7855.889             0.442            0.080
Chain 1:   1100        -7731.076             0.344            0.065
Chain 1:   1200        -7751.220             0.125            0.051
Chain 1:   1300        -7752.174             0.039            0.018
Chain 1:   1400        -7715.023             0.031            0.016
Chain 1:   1500        -7633.757             0.027            0.011
Chain 1:   1600        -7820.557             0.023            0.011
Chain 1:   1700        -7550.138             0.013            0.011
Chain 1:   1800        -7646.874             0.013            0.013
Chain 1:   1900        -7652.396             0.013            0.013
Chain 1:   2000        -7661.592             0.011            0.011
Chain 1:   2100        -7687.739             0.010            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003145 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85857.222             1.000            1.000
Chain 1:    200       -13354.891             3.214            5.429
Chain 1:    300        -9817.390             2.263            1.000
Chain 1:    400       -10623.932             1.716            1.000
Chain 1:    500        -8743.155             1.416            0.360
Chain 1:    600        -8364.139             1.188            0.360
Chain 1:    700        -8696.933             1.023            0.215
Chain 1:    800        -8888.089             0.898            0.215
Chain 1:    900        -8654.081             0.801            0.076
Chain 1:   1000        -8407.290             0.724            0.076
Chain 1:   1100        -8713.963             0.628            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8383.197             0.089            0.039
Chain 1:   1300        -8384.818             0.053            0.038
Chain 1:   1400        -8387.429             0.045            0.035
Chain 1:   1500        -8420.073             0.024            0.029
Chain 1:   1600        -8426.683             0.020            0.027
Chain 1:   1700        -8356.283             0.017            0.022
Chain 1:   1800        -8240.978             0.016            0.014
Chain 1:   1900        -8358.063             0.015            0.014
Chain 1:   2000        -8318.281             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003557 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403858.247             1.000            1.000
Chain 1:    200     -1584354.940             2.652            4.304
Chain 1:    300      -890980.366             2.027            1.000
Chain 1:    400      -457687.511             1.757            1.000
Chain 1:    500      -358028.635             1.462            0.947
Chain 1:    600      -232948.045             1.307            0.947
Chain 1:    700      -119099.963             1.257            0.947
Chain 1:    800       -86293.458             1.148            0.947
Chain 1:    900       -66622.338             1.053            0.778
Chain 1:   1000       -51409.129             0.977            0.778
Chain 1:   1100       -38882.437             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38050.103             0.481            0.380
Chain 1:   1300       -26014.709             0.450            0.380
Chain 1:   1400       -25730.986             0.356            0.322
Chain 1:   1500       -22321.265             0.343            0.322
Chain 1:   1600       -21537.472             0.293            0.296
Chain 1:   1700       -20413.126             0.203            0.295
Chain 1:   1800       -20357.138             0.166            0.153
Chain 1:   1900       -20682.710             0.138            0.055
Chain 1:   2000       -19195.818             0.116            0.055
Chain 1:   2100       -19433.986             0.085            0.036
Chain 1:   2200       -19660.002             0.084            0.036
Chain 1:   2300       -19277.740             0.039            0.020
Chain 1:   2400       -19050.060             0.040            0.020
Chain 1:   2500       -18852.037             0.025            0.016
Chain 1:   2600       -18482.899             0.024            0.016
Chain 1:   2700       -18439.995             0.018            0.012
Chain 1:   2800       -18157.183             0.020            0.016
Chain 1:   2900       -18438.067             0.020            0.015
Chain 1:   3000       -18424.335             0.012            0.012
Chain 1:   3100       -18509.248             0.011            0.012
Chain 1:   3200       -18200.319             0.012            0.015
Chain 1:   3300       -18404.681             0.011            0.012
Chain 1:   3400       -17880.344             0.013            0.015
Chain 1:   3500       -18491.151             0.015            0.016
Chain 1:   3600       -17799.180             0.017            0.016
Chain 1:   3700       -18185.008             0.019            0.017
Chain 1:   3800       -17146.835             0.023            0.021
Chain 1:   3900       -17143.016             0.022            0.021
Chain 1:   4000       -17260.318             0.022            0.021
Chain 1:   4100       -17174.246             0.022            0.021
Chain 1:   4200       -16990.892             0.022            0.021
Chain 1:   4300       -17129.001             0.021            0.021
Chain 1:   4400       -17086.200             0.019            0.011
Chain 1:   4500       -16988.794             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001301 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12477.649             1.000            1.000
Chain 1:    200        -9425.642             0.662            1.000
Chain 1:    300        -8116.733             0.495            0.324
Chain 1:    400        -8287.866             0.376            0.324
Chain 1:    500        -8110.256             0.306            0.161
Chain 1:    600        -8044.129             0.256            0.161
Chain 1:    700        -7954.012             0.221            0.022
Chain 1:    800        -7962.338             0.194            0.022
Chain 1:    900        -7929.907             0.172            0.021
Chain 1:   1000        -8066.354             0.157            0.021
Chain 1:   1100        -8064.720             0.057            0.017
Chain 1:   1200        -7977.861             0.026            0.011
Chain 1:   1300        -7928.492             0.010            0.011
Chain 1:   1400        -7945.677             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58074.333             1.000            1.000
Chain 1:    200       -17781.605             1.633            2.266
Chain 1:    300        -8709.402             1.436            1.042
Chain 1:    400        -8126.242             1.095            1.042
Chain 1:    500        -8581.679             0.886            1.000
Chain 1:    600        -8052.294             0.750            1.000
Chain 1:    700        -8112.970             0.644            0.072
Chain 1:    800        -8171.189             0.564            0.072
Chain 1:    900        -7894.736             0.505            0.066
Chain 1:   1000        -7791.618             0.456            0.066
Chain 1:   1100        -7824.182             0.357            0.053
Chain 1:   1200        -7564.157             0.133            0.035
Chain 1:   1300        -7743.211             0.032            0.034
Chain 1:   1400        -7630.353             0.026            0.023
Chain 1:   1500        -7568.250             0.021            0.015
Chain 1:   1600        -7533.669             0.015            0.013
Chain 1:   1700        -7536.562             0.015            0.013
Chain 1:   1800        -7576.064             0.014            0.013
Chain 1:   1900        -7577.381             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86654.943             1.000            1.000
Chain 1:    200       -13578.919             3.191            5.382
Chain 1:    300        -9934.447             2.249            1.000
Chain 1:    400       -10813.603             1.707            1.000
Chain 1:    500        -8917.140             1.408            0.367
Chain 1:    600        -8808.354             1.176            0.367
Chain 1:    700        -8741.443             1.009            0.213
Chain 1:    800        -8740.101             0.883            0.213
Chain 1:    900        -8734.750             0.785            0.081
Chain 1:   1000        -8614.562             0.708            0.081
Chain 1:   1100        -8716.519             0.609            0.014   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8405.626             0.074            0.014
Chain 1:   1300        -8634.435             0.040            0.014
Chain 1:   1400        -8641.836             0.032            0.012
Chain 1:   1500        -8496.792             0.013            0.012
Chain 1:   1600        -8609.796             0.013            0.013
Chain 1:   1700        -8691.985             0.013            0.013
Chain 1:   1800        -8276.808             0.018            0.014
Chain 1:   1900        -8373.890             0.019            0.014
Chain 1:   2000        -8347.465             0.018            0.013
Chain 1:   2100        -8470.603             0.018            0.015
Chain 1:   2200        -8289.534             0.017            0.015
Chain 1:   2300        -8368.558             0.015            0.013
Chain 1:   2400        -8438.286             0.016            0.013
Chain 1:   2500        -8383.954             0.015            0.012
Chain 1:   2600        -8383.754             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003236 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422093.113             1.000            1.000
Chain 1:    200     -1589296.888             2.650            4.299
Chain 1:    300      -892713.314             2.027            1.000
Chain 1:    400      -458731.284             1.756            1.000
Chain 1:    500      -358551.632             1.461            0.946
Chain 1:    600      -233142.740             1.307            0.946
Chain 1:    700      -119290.477             1.257            0.946
Chain 1:    800       -86509.083             1.147            0.946
Chain 1:    900       -66842.018             1.052            0.780
Chain 1:   1000       -51641.002             0.976            0.780
Chain 1:   1100       -39125.810             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38302.163             0.481            0.379
Chain 1:   1300       -26265.629             0.448            0.379
Chain 1:   1400       -25985.562             0.355            0.320
Chain 1:   1500       -22575.246             0.342            0.320
Chain 1:   1600       -21792.732             0.292            0.294
Chain 1:   1700       -20667.182             0.202            0.294
Chain 1:   1800       -20611.599             0.164            0.151
Chain 1:   1900       -20937.682             0.136            0.054
Chain 1:   2000       -19449.541             0.115            0.054
Chain 1:   2100       -19687.760             0.084            0.036
Chain 1:   2200       -19914.211             0.083            0.036
Chain 1:   2300       -19531.448             0.039            0.020
Chain 1:   2400       -19303.541             0.039            0.020
Chain 1:   2500       -19105.619             0.025            0.016
Chain 1:   2600       -18735.755             0.023            0.016
Chain 1:   2700       -18692.732             0.018            0.012
Chain 1:   2800       -18409.609             0.019            0.015
Chain 1:   2900       -18690.856             0.019            0.015
Chain 1:   3000       -18677.019             0.012            0.012
Chain 1:   3100       -18762.009             0.011            0.012
Chain 1:   3200       -18452.690             0.012            0.015
Chain 1:   3300       -18657.431             0.011            0.012
Chain 1:   3400       -18132.365             0.012            0.015
Chain 1:   3500       -18744.208             0.015            0.015
Chain 1:   3600       -18050.943             0.017            0.015
Chain 1:   3700       -18437.669             0.018            0.017
Chain 1:   3800       -17397.482             0.023            0.021
Chain 1:   3900       -17393.641             0.021            0.021
Chain 1:   4000       -17510.943             0.022            0.021
Chain 1:   4100       -17424.695             0.022            0.021
Chain 1:   4200       -17240.976             0.021            0.021
Chain 1:   4300       -17379.334             0.021            0.021
Chain 1:   4400       -17336.162             0.018            0.011
Chain 1:   4500       -17238.718             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12865.654             1.000            1.000
Chain 1:    200        -9772.240             0.658            1.000
Chain 1:    300        -8343.204             0.496            0.317
Chain 1:    400        -8567.476             0.379            0.317
Chain 1:    500        -8431.782             0.306            0.171
Chain 1:    600        -8283.372             0.258            0.171
Chain 1:    700        -8359.608             0.222            0.026
Chain 1:    800        -8221.282             0.197            0.026
Chain 1:    900        -8383.030             0.177            0.019
Chain 1:   1000        -8260.568             0.161            0.019
Chain 1:   1100        -8305.775             0.061            0.018
Chain 1:   1200        -8213.909             0.031            0.017
Chain 1:   1300        -8152.763             0.014            0.016
Chain 1:   1400        -8161.797             0.012            0.015
Chain 1:   1500        -8251.341             0.011            0.011
Chain 1:   1600        -8170.640             0.011            0.011
Chain 1:   1700        -8137.294             0.010            0.011
Chain 1:   1800        -8109.656             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59221.520             1.000            1.000
Chain 1:    200       -18206.699             1.626            2.253
Chain 1:    300        -9088.083             1.419            1.003
Chain 1:    400        -8379.927             1.085            1.003
Chain 1:    500        -8526.872             0.872            1.000
Chain 1:    600        -8505.197             0.727            1.000
Chain 1:    700        -8110.831             0.630            0.085
Chain 1:    800        -8121.765             0.551            0.085
Chain 1:    900        -8090.546             0.490            0.049
Chain 1:   1000        -7781.755             0.445            0.049
Chain 1:   1100        -7749.438             0.346            0.040
Chain 1:   1200        -7616.928             0.122            0.017
Chain 1:   1300        -7824.465             0.025            0.017
Chain 1:   1400        -7949.251             0.018            0.017
Chain 1:   1500        -7614.954             0.020            0.017
Chain 1:   1600        -7774.927             0.022            0.021
Chain 1:   1700        -7475.764             0.021            0.021
Chain 1:   1800        -7609.953             0.023            0.021
Chain 1:   1900        -7583.162             0.023            0.021
Chain 1:   2000        -7670.614             0.020            0.018
Chain 1:   2100        -7512.976             0.022            0.021
Chain 1:   2200        -7636.015             0.022            0.021
Chain 1:   2300        -7571.390             0.020            0.018
Chain 1:   2400        -7607.189             0.019            0.018
Chain 1:   2500        -7631.792             0.015            0.016
Chain 1:   2600        -7515.300             0.014            0.016
Chain 1:   2700        -7482.788             0.011            0.011
Chain 1:   2800        -7518.260             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003295 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87368.904             1.000            1.000
Chain 1:    200       -14074.397             3.104            5.208
Chain 1:    300       -10309.909             2.191            1.000
Chain 1:    400       -12023.684             1.679            1.000
Chain 1:    500        -8882.227             1.414            0.365
Chain 1:    600        -8729.301             1.181            0.365
Chain 1:    700        -8826.990             1.014            0.354
Chain 1:    800        -9178.934             0.892            0.354
Chain 1:    900        -9072.218             0.794            0.143
Chain 1:   1000        -9195.266             0.716            0.143
Chain 1:   1100        -9094.257             0.617            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8623.037             0.102            0.038
Chain 1:   1300        -8941.736             0.069            0.036
Chain 1:   1400        -8748.986             0.057            0.022
Chain 1:   1500        -8796.567             0.022            0.018
Chain 1:   1600        -8904.082             0.022            0.013
Chain 1:   1700        -8957.344             0.021            0.013
Chain 1:   1800        -8508.308             0.022            0.013
Chain 1:   1900        -8616.289             0.023            0.013
Chain 1:   2000        -8602.376             0.021            0.013
Chain 1:   2100        -8721.637             0.022            0.014
Chain 1:   2200        -8509.828             0.019            0.014
Chain 1:   2300        -8670.783             0.017            0.014
Chain 1:   2400        -8508.579             0.017            0.014
Chain 1:   2500        -8587.778             0.017            0.014
Chain 1:   2600        -8522.968             0.017            0.014
Chain 1:   2700        -8531.417             0.016            0.014
Chain 1:   2800        -8486.888             0.011            0.013
Chain 1:   2900        -8596.407             0.011            0.013
Chain 1:   3000        -8546.191             0.012            0.013
Chain 1:   3100        -8478.042             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003255 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409091.198             1.000            1.000
Chain 1:    200     -1584414.037             2.654            4.307
Chain 1:    300      -891995.497             2.028            1.000
Chain 1:    400      -458593.180             1.757            1.000
Chain 1:    500      -358898.369             1.461            0.945
Chain 1:    600      -233641.449             1.307            0.945
Chain 1:    700      -119862.652             1.256            0.945
Chain 1:    800       -87052.609             1.146            0.945
Chain 1:    900       -67394.359             1.051            0.776
Chain 1:   1000       -52197.464             0.975            0.776
Chain 1:   1100       -39673.111             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38858.742             0.478            0.377
Chain 1:   1300       -26804.745             0.445            0.377
Chain 1:   1400       -26526.330             0.352            0.316
Chain 1:   1500       -23110.093             0.339            0.316
Chain 1:   1600       -22326.686             0.289            0.292
Chain 1:   1700       -21198.559             0.199            0.291
Chain 1:   1800       -21142.893             0.162            0.148
Chain 1:   1900       -21469.648             0.134            0.053
Chain 1:   2000       -19978.816             0.113            0.053
Chain 1:   2100       -20217.330             0.082            0.035
Chain 1:   2200       -20444.285             0.081            0.035
Chain 1:   2300       -20060.928             0.038            0.019
Chain 1:   2400       -19832.793             0.038            0.019
Chain 1:   2500       -19634.744             0.024            0.015
Chain 1:   2600       -19264.234             0.023            0.015
Chain 1:   2700       -19221.122             0.018            0.012
Chain 1:   2800       -18937.585             0.019            0.015
Chain 1:   2900       -19219.257             0.019            0.015
Chain 1:   3000       -19205.333             0.012            0.012
Chain 1:   3100       -19290.376             0.011            0.012
Chain 1:   3200       -18980.655             0.011            0.015
Chain 1:   3300       -19185.774             0.010            0.012
Chain 1:   3400       -18659.869             0.012            0.015
Chain 1:   3500       -19272.906             0.014            0.015
Chain 1:   3600       -18578.191             0.016            0.015
Chain 1:   3700       -18965.988             0.018            0.016
Chain 1:   3800       -17923.400             0.022            0.020
Chain 1:   3900       -17919.506             0.021            0.020
Chain 1:   4000       -18036.818             0.021            0.020
Chain 1:   4100       -17950.383             0.021            0.020
Chain 1:   4200       -17766.207             0.021            0.020
Chain 1:   4300       -17904.906             0.021            0.020
Chain 1:   4400       -17861.334             0.018            0.010
Chain 1:   4500       -17763.801             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001291 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12407.553             1.000            1.000
Chain 1:    200        -9180.435             0.676            1.000
Chain 1:    300        -7950.686             0.502            0.352
Chain 1:    400        -8179.515             0.384            0.352
Chain 1:    500        -8062.426             0.310            0.155
Chain 1:    600        -8069.146             0.258            0.155
Chain 1:    700        -7871.810             0.225            0.028
Chain 1:    800        -7821.186             0.198            0.028
Chain 1:    900        -7774.384             0.176            0.025
Chain 1:   1000        -7935.413             0.161            0.025
Chain 1:   1100        -8012.433             0.062            0.020
Chain 1:   1200        -7875.077             0.028            0.017
Chain 1:   1300        -7810.203             0.014            0.015
Chain 1:   1400        -7850.217             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57039.766             1.000            1.000
Chain 1:    200       -17360.087             1.643            2.286
Chain 1:    300        -8753.152             1.423            1.000
Chain 1:    400        -8327.158             1.080            1.000
Chain 1:    500        -8579.932             0.870            0.983
Chain 1:    600        -8658.863             0.726            0.983
Chain 1:    700        -7816.939             0.638            0.108
Chain 1:    800        -8153.343             0.563            0.108
Chain 1:    900        -8004.330             0.503            0.051
Chain 1:   1000        -7879.918             0.454            0.051
Chain 1:   1100        -7881.151             0.354            0.041
Chain 1:   1200        -7627.689             0.129            0.033
Chain 1:   1300        -7791.997             0.033            0.029
Chain 1:   1400        -7875.973             0.029            0.021
Chain 1:   1500        -7654.391             0.029            0.021
Chain 1:   1600        -7844.582             0.030            0.024
Chain 1:   1700        -7577.086             0.023            0.024
Chain 1:   1800        -7632.288             0.020            0.021
Chain 1:   1900        -7647.676             0.018            0.021
Chain 1:   2000        -7673.508             0.017            0.021
Chain 1:   2100        -7670.635             0.017            0.021
Chain 1:   2200        -7745.873             0.014            0.011
Chain 1:   2300        -7814.105             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86619.772             1.000            1.000
Chain 1:    200       -13472.045             3.215            5.430
Chain 1:    300        -9843.503             2.266            1.000
Chain 1:    400       -10877.348             1.723            1.000
Chain 1:    500        -8679.759             1.429            0.369
Chain 1:    600        -8317.377             1.198            0.369
Chain 1:    700        -8330.674             1.027            0.253
Chain 1:    800        -9052.207             0.909            0.253
Chain 1:    900        -8642.021             0.813            0.095
Chain 1:   1000        -8551.367             0.733            0.095
Chain 1:   1100        -8680.308             0.634            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8193.590             0.097            0.059
Chain 1:   1300        -8535.998             0.065            0.047
Chain 1:   1400        -8534.932             0.055            0.044
Chain 1:   1500        -8400.734             0.031            0.040
Chain 1:   1600        -8518.214             0.028            0.016
Chain 1:   1700        -8591.668             0.029            0.016
Chain 1:   1800        -8178.078             0.026            0.016
Chain 1:   1900        -8274.329             0.023            0.015
Chain 1:   2000        -8247.718             0.022            0.015
Chain 1:   2100        -8370.995             0.022            0.015
Chain 1:   2200        -8189.970             0.018            0.015
Chain 1:   2300        -8268.948             0.015            0.014
Chain 1:   2400        -8338.645             0.016            0.014
Chain 1:   2500        -8284.300             0.015            0.012
Chain 1:   2600        -8284.063             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003114 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8440532.579             1.000            1.000
Chain 1:    200     -1592337.315             2.650            4.301
Chain 1:    300      -891495.682             2.029            1.000
Chain 1:    400      -457634.986             1.759            1.000
Chain 1:    500      -357171.907             1.463            0.948
Chain 1:    600      -231951.015             1.309            0.948
Chain 1:    700      -118607.730             1.259            0.948
Chain 1:    800       -85970.058             1.149            0.948
Chain 1:    900       -66418.988             1.054            0.786
Chain 1:   1000       -51313.026             0.978            0.786
Chain 1:   1100       -38882.190             0.910            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38067.588             0.482            0.380
Chain 1:   1300       -26112.433             0.449            0.380
Chain 1:   1400       -25840.102             0.355            0.320
Chain 1:   1500       -22450.798             0.342            0.320
Chain 1:   1600       -21674.636             0.292            0.294
Chain 1:   1700       -20558.825             0.202            0.294
Chain 1:   1800       -20505.467             0.164            0.151
Chain 1:   1900       -20831.634             0.136            0.054
Chain 1:   2000       -19348.315             0.115            0.054
Chain 1:   2100       -19586.339             0.084            0.036
Chain 1:   2200       -19812.014             0.083            0.036
Chain 1:   2300       -19429.890             0.039            0.020
Chain 1:   2400       -19202.091             0.039            0.020
Chain 1:   2500       -19003.820             0.025            0.016
Chain 1:   2600       -18634.339             0.023            0.016
Chain 1:   2700       -18591.412             0.018            0.012
Chain 1:   2800       -18308.192             0.020            0.015
Chain 1:   2900       -18589.233             0.019            0.015
Chain 1:   3000       -18575.524             0.012            0.012
Chain 1:   3100       -18660.525             0.011            0.012
Chain 1:   3200       -18351.288             0.012            0.015
Chain 1:   3300       -18555.931             0.011            0.012
Chain 1:   3400       -18030.907             0.013            0.015
Chain 1:   3500       -18642.609             0.015            0.015
Chain 1:   3600       -17949.417             0.017            0.015
Chain 1:   3700       -18336.045             0.019            0.017
Chain 1:   3800       -17295.975             0.023            0.021
Chain 1:   3900       -17292.068             0.022            0.021
Chain 1:   4000       -17409.415             0.022            0.021
Chain 1:   4100       -17323.205             0.022            0.021
Chain 1:   4200       -17139.478             0.022            0.021
Chain 1:   4300       -17277.886             0.021            0.021
Chain 1:   4400       -17234.725             0.019            0.011
Chain 1:   4500       -17137.228             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001447 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12753.420             1.000            1.000
Chain 1:    200        -9549.743             0.668            1.000
Chain 1:    300        -8166.054             0.502            0.335
Chain 1:    400        -8407.054             0.383            0.335
Chain 1:    500        -8015.271             0.316            0.169
Chain 1:    600        -8152.784             0.267            0.169
Chain 1:    700        -8220.842             0.230            0.049
Chain 1:    800        -8056.197             0.204            0.049
Chain 1:    900        -8175.930             0.183            0.029
Chain 1:   1000        -8118.745             0.165            0.029
Chain 1:   1100        -8155.319             0.065            0.020
Chain 1:   1200        -8061.620             0.033            0.017
Chain 1:   1300        -8151.019             0.017            0.015
Chain 1:   1400        -8041.168             0.016            0.014
Chain 1:   1500        -8145.870             0.012            0.013
Chain 1:   1600        -8067.971             0.011            0.012
Chain 1:   1700        -8022.182             0.011            0.012
Chain 1:   1800        -7996.601             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57131.884             1.000            1.000
Chain 1:    200       -17765.176             1.608            2.216
Chain 1:    300        -8860.944             1.407            1.005
Chain 1:    400        -8248.560             1.074            1.005
Chain 1:    500        -9089.188             0.878            1.000
Chain 1:    600        -8777.656             0.737            1.000
Chain 1:    700        -8769.568             0.632            0.092
Chain 1:    800        -8024.932             0.565            0.093
Chain 1:    900        -7681.064             0.507            0.092
Chain 1:   1000        -7750.024             0.457            0.092
Chain 1:   1100        -7830.871             0.358            0.074
Chain 1:   1200        -7969.181             0.138            0.045
Chain 1:   1300        -7498.265             0.044            0.045
Chain 1:   1400        -8001.817             0.043            0.045
Chain 1:   1500        -7544.126             0.040            0.045
Chain 1:   1600        -7743.135             0.039            0.045
Chain 1:   1700        -7541.395             0.041            0.045
Chain 1:   1800        -7602.821             0.033            0.027
Chain 1:   1900        -7492.255             0.030            0.026
Chain 1:   2000        -7653.070             0.031            0.026
Chain 1:   2100        -7530.056             0.032            0.026
Chain 1:   2200        -7721.504             0.032            0.026
Chain 1:   2300        -7501.146             0.029            0.026
Chain 1:   2400        -7530.778             0.023            0.025
Chain 1:   2500        -7557.825             0.017            0.021
Chain 1:   2600        -7479.474             0.016            0.016
Chain 1:   2700        -7397.613             0.014            0.015
Chain 1:   2800        -7453.352             0.014            0.015
Chain 1:   2900        -7371.362             0.014            0.011
Chain 1:   3000        -7496.552             0.013            0.011
Chain 1:   3100        -7483.387             0.012            0.011
Chain 1:   3200        -7681.846             0.012            0.011
Chain 1:   3300        -7398.453             0.013            0.011
Chain 1:   3400        -7636.367             0.016            0.011
Chain 1:   3500        -7384.489             0.019            0.017
Chain 1:   3600        -7450.208             0.019            0.017
Chain 1:   3700        -7400.110             0.018            0.017
Chain 1:   3800        -7400.433             0.017            0.017
Chain 1:   3900        -7361.323             0.017            0.017
Chain 1:   4000        -7353.125             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87831.088             1.000            1.000
Chain 1:    200       -13829.150             3.176            5.351
Chain 1:    300       -10120.808             2.239            1.000
Chain 1:    400       -11288.221             1.705            1.000
Chain 1:    500        -9130.885             1.411            0.366
Chain 1:    600        -8534.398             1.188            0.366
Chain 1:    700        -8561.730             1.019            0.236
Chain 1:    800        -8896.816             0.896            0.236
Chain 1:    900        -8970.443             0.797            0.103
Chain 1:   1000        -8722.940             0.720            0.103
Chain 1:   1100        -8718.669             0.621            0.070   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8496.054             0.088            0.038
Chain 1:   1300        -8767.975             0.054            0.031
Chain 1:   1400        -8789.389             0.044            0.028
Chain 1:   1500        -8636.602             0.023            0.026
Chain 1:   1600        -8745.762             0.017            0.018
Chain 1:   1700        -8816.767             0.017            0.018
Chain 1:   1800        -8381.644             0.019            0.018
Chain 1:   1900        -8486.279             0.019            0.018
Chain 1:   2000        -8461.986             0.017            0.012
Chain 1:   2100        -8599.320             0.018            0.016
Chain 1:   2200        -8393.370             0.018            0.016
Chain 1:   2300        -8551.489             0.017            0.016
Chain 1:   2400        -8390.665             0.018            0.018
Chain 1:   2500        -8460.740             0.017            0.016
Chain 1:   2600        -8372.973             0.017            0.016
Chain 1:   2700        -8406.499             0.017            0.016
Chain 1:   2800        -8366.878             0.012            0.012
Chain 1:   2900        -8459.799             0.012            0.011
Chain 1:   3000        -8290.743             0.014            0.016
Chain 1:   3100        -8449.239             0.014            0.018
Chain 1:   3200        -8321.514             0.013            0.015
Chain 1:   3300        -8329.343             0.011            0.011
Chain 1:   3400        -8486.669             0.011            0.011
Chain 1:   3500        -8490.271             0.010            0.011
Chain 1:   3600        -8278.208             0.012            0.015
Chain 1:   3700        -8423.311             0.013            0.017
Chain 1:   3800        -8284.849             0.014            0.017
Chain 1:   3900        -8219.608             0.014            0.017
Chain 1:   4000        -8294.425             0.013            0.017
Chain 1:   4100        -8285.473             0.011            0.015
Chain 1:   4200        -8274.962             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 45.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8438870.201             1.000            1.000
Chain 1:    200     -1596919.332             2.642            4.284
Chain 1:    300      -893348.854             2.024            1.000
Chain 1:    400      -458280.624             1.755            1.000
Chain 1:    500      -357277.953             1.461            0.949
Chain 1:    600      -231907.400             1.307            0.949
Chain 1:    700      -118786.873             1.257            0.949
Chain 1:    800       -86118.652             1.147            0.949
Chain 1:    900       -66612.148             1.052            0.788
Chain 1:   1000       -51557.490             0.976            0.788
Chain 1:   1100       -39164.703             0.908            0.541   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38366.161             0.481            0.379
Chain 1:   1300       -26458.629             0.448            0.379
Chain 1:   1400       -26193.198             0.354            0.316
Chain 1:   1500       -22813.489             0.340            0.316
Chain 1:   1600       -22040.436             0.290            0.293
Chain 1:   1700       -20930.450             0.200            0.292
Chain 1:   1800       -20878.719             0.162            0.148
Chain 1:   1900       -21205.213             0.134            0.053
Chain 1:   2000       -19723.754             0.113            0.053
Chain 1:   2100       -19962.158             0.082            0.035
Chain 1:   2200       -20187.155             0.081            0.035
Chain 1:   2300       -19805.495             0.038            0.019
Chain 1:   2400       -19577.568             0.038            0.019
Chain 1:   2500       -19378.760             0.025            0.015
Chain 1:   2600       -19009.395             0.023            0.015
Chain 1:   2700       -18966.622             0.018            0.012
Chain 1:   2800       -18682.804             0.019            0.015
Chain 1:   2900       -18964.148             0.019            0.015
Chain 1:   3000       -18950.556             0.012            0.012
Chain 1:   3100       -19035.476             0.011            0.012
Chain 1:   3200       -18726.164             0.011            0.015
Chain 1:   3300       -18930.963             0.011            0.012
Chain 1:   3400       -18405.397             0.012            0.015
Chain 1:   3500       -19017.652             0.014            0.015
Chain 1:   3600       -18323.908             0.016            0.015
Chain 1:   3700       -18710.778             0.018            0.017
Chain 1:   3800       -17669.597             0.023            0.021
Chain 1:   3900       -17665.602             0.021            0.021
Chain 1:   4000       -17783.063             0.022            0.021
Chain 1:   4100       -17696.584             0.022            0.021
Chain 1:   4200       -17512.747             0.021            0.021
Chain 1:   4300       -17651.333             0.021            0.021
Chain 1:   4400       -17608.015             0.018            0.010
Chain 1:   4500       -17510.391             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001625 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49264.358             1.000            1.000
Chain 1:    200       -18111.041             1.360            1.720
Chain 1:    300       -16445.977             0.940            1.000
Chain 1:    400       -17261.910             0.717            1.000
Chain 1:    500       -15028.968             0.603            0.149
Chain 1:    600       -14227.834             0.512            0.149
Chain 1:    700       -16033.666             0.455            0.113
Chain 1:    800       -13471.656             0.422            0.149
Chain 1:    900       -12230.731             0.386            0.113
Chain 1:   1000       -12888.482             0.353            0.113
Chain 1:   1100       -11275.872             0.267            0.113
Chain 1:   1200       -12248.891             0.103            0.101
Chain 1:   1300       -17901.404             0.125            0.113
Chain 1:   1400       -11296.439             0.178            0.143
Chain 1:   1500       -10914.125             0.167            0.113
Chain 1:   1600       -11209.772             0.164            0.113
Chain 1:   1700       -11388.071             0.154            0.101
Chain 1:   1800       -11199.349             0.137            0.079
Chain 1:   1900       -10590.861             0.133            0.057
Chain 1:   2000       -20592.057             0.176            0.079
Chain 1:   2100       -11318.964             0.244            0.079
Chain 1:   2200       -11848.749             0.240            0.057
Chain 1:   2300        -9386.375             0.235            0.057
Chain 1:   2400        -9687.523             0.179            0.045
Chain 1:   2500        -9765.724             0.177            0.045
Chain 1:   2600        -9925.819             0.176            0.045
Chain 1:   2700       -10510.631             0.180            0.056
Chain 1:   2800       -10413.364             0.179            0.056
Chain 1:   2900        -9743.433             0.180            0.056
Chain 1:   3000       -10700.016             0.140            0.056
Chain 1:   3100        -9468.514             0.072            0.056
Chain 1:   3200       -12640.645             0.092            0.069
Chain 1:   3300       -16928.409             0.091            0.069
Chain 1:   3400       -13195.932             0.116            0.089
Chain 1:   3500        -9701.894             0.152            0.130
Chain 1:   3600       -11279.204             0.164            0.140
Chain 1:   3700       -17935.782             0.196            0.251
Chain 1:   3800        -9073.300             0.292            0.253
Chain 1:   3900       -10988.444             0.303            0.253
Chain 1:   4000        -8878.827             0.318            0.253
Chain 1:   4100       -11386.608             0.327            0.253
Chain 1:   4200        -9589.590             0.320            0.253
Chain 1:   4300       -14214.395             0.328            0.283
Chain 1:   4400       -13891.144             0.302            0.238
Chain 1:   4500       -11391.954             0.288            0.220
Chain 1:   4600        -8564.894             0.307            0.238
Chain 1:   4700        -8696.413             0.271            0.220
Chain 1:   4800        -8661.200             0.174            0.219
Chain 1:   4900        -9253.938             0.163            0.219
Chain 1:   5000       -13794.653             0.172            0.219
Chain 1:   5100       -10961.522             0.176            0.219
Chain 1:   5200        -8870.218             0.180            0.236
Chain 1:   5300        -9753.798             0.157            0.219
Chain 1:   5400        -8511.589             0.169            0.219
Chain 1:   5500       -12600.870             0.180            0.236
Chain 1:   5600       -10588.338             0.166            0.190
Chain 1:   5700       -10242.562             0.168            0.190
Chain 1:   5800        -8464.641             0.188            0.210
Chain 1:   5900       -14017.345             0.221            0.236
Chain 1:   6000        -8569.437             0.252            0.236
Chain 1:   6100        -8564.445             0.226            0.210
Chain 1:   6200        -8464.899             0.204            0.190
Chain 1:   6300        -8421.127             0.195            0.190
Chain 1:   6400       -14500.772             0.223            0.210
Chain 1:   6500       -10487.332             0.229            0.210
Chain 1:   6600       -12655.033             0.227            0.210
Chain 1:   6700        -8615.735             0.270            0.383
Chain 1:   6800        -8945.776             0.253            0.383
Chain 1:   6900       -11297.543             0.234            0.208
Chain 1:   7000        -8854.301             0.198            0.208
Chain 1:   7100        -8839.808             0.198            0.208
Chain 1:   7200        -8716.761             0.198            0.208
Chain 1:   7300        -8930.828             0.200            0.208
Chain 1:   7400        -8633.680             0.162            0.171
Chain 1:   7500       -10391.448             0.140            0.169
Chain 1:   7600        -8589.966             0.144            0.169
Chain 1:   7700        -8720.221             0.099            0.037
Chain 1:   7800        -8447.024             0.098            0.034
Chain 1:   7900        -8167.282             0.081            0.034
Chain 1:   8000       -12669.079             0.089            0.034
Chain 1:   8100       -10802.143             0.106            0.034
Chain 1:   8200        -8351.561             0.134            0.169
Chain 1:   8300       -10205.127             0.150            0.173
Chain 1:   8400        -8552.303             0.166            0.182
Chain 1:   8500       -11992.440             0.177            0.193
Chain 1:   8600        -8550.789             0.197            0.193
Chain 1:   8700        -8460.877             0.196            0.193
Chain 1:   8800        -8380.723             0.194            0.193
Chain 1:   8900        -8546.808             0.193            0.193
Chain 1:   9000       -10530.374             0.176            0.188
Chain 1:   9100        -8176.096             0.187            0.193
Chain 1:   9200        -8423.720             0.161            0.188
Chain 1:   9300        -8831.938             0.147            0.188
Chain 1:   9400       -12376.312             0.157            0.188
Chain 1:   9500        -9385.621             0.160            0.188
Chain 1:   9600       -10326.026             0.129            0.091
Chain 1:   9700       -12813.575             0.147            0.188
Chain 1:   9800        -8359.341             0.199            0.194
Chain 1:   9900       -10313.787             0.216            0.194
Chain 1:   10000        -8545.880             0.218            0.207
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001524 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58178.241             1.000            1.000
Chain 1:    200       -17793.276             1.635            2.270
Chain 1:    300        -8731.283             1.436            1.038
Chain 1:    400        -8108.720             1.096            1.038
Chain 1:    500        -8941.878             0.896            1.000
Chain 1:    600        -9420.724             0.755            1.000
Chain 1:    700        -7719.971             0.678            0.220
Chain 1:    800        -8037.529             0.599            0.220
Chain 1:    900        -7690.455             0.537            0.093
Chain 1:   1000        -8037.792             0.488            0.093
Chain 1:   1100        -7759.440             0.391            0.077
Chain 1:   1200        -7706.711             0.165            0.051
Chain 1:   1300        -7735.639             0.062            0.045
Chain 1:   1400        -7659.495             0.055            0.043
Chain 1:   1500        -7549.846             0.047            0.040
Chain 1:   1600        -7676.336             0.044            0.036
Chain 1:   1700        -7566.999             0.023            0.016
Chain 1:   1800        -7593.311             0.019            0.015
Chain 1:   1900        -7593.650             0.015            0.014
Chain 1:   2000        -7672.106             0.012            0.010
Chain 1:   2100        -7589.337             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00332 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86132.908             1.000            1.000
Chain 1:    200       -13636.566             3.158            5.316
Chain 1:    300        -9944.247             2.229            1.000
Chain 1:    400       -10970.099             1.695            1.000
Chain 1:    500        -8933.615             1.402            0.371
Chain 1:    600        -8427.311             1.178            0.371
Chain 1:    700        -8381.822             1.011            0.228
Chain 1:    800        -8603.180             0.888            0.228
Chain 1:    900        -8695.443             0.790            0.094
Chain 1:   1000        -8796.510             0.712            0.094
Chain 1:   1100        -8599.276             0.615            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8356.361             0.086            0.029
Chain 1:   1300        -8638.040             0.052            0.029
Chain 1:   1400        -8591.210             0.043            0.026
Chain 1:   1500        -8486.045             0.022            0.023
Chain 1:   1600        -8594.145             0.017            0.013
Chain 1:   1700        -8670.239             0.017            0.013
Chain 1:   1800        -8240.385             0.020            0.013
Chain 1:   1900        -8344.166             0.020            0.013
Chain 1:   2000        -8319.252             0.019            0.013
Chain 1:   2100        -8450.891             0.018            0.013
Chain 1:   2200        -8246.973             0.018            0.013
Chain 1:   2300        -8342.138             0.016            0.012
Chain 1:   2400        -8407.549             0.016            0.012
Chain 1:   2500        -8352.847             0.015            0.012
Chain 1:   2600        -8356.710             0.014            0.011
Chain 1:   2700        -8272.179             0.014            0.011
Chain 1:   2800        -8229.320             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003523 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388735.020             1.000            1.000
Chain 1:    200     -1586229.427             2.644            4.288
Chain 1:    300      -891656.448             2.022            1.000
Chain 1:    400      -457857.089             1.754            1.000
Chain 1:    500      -358126.614             1.459            0.947
Chain 1:    600      -233141.850             1.305            0.947
Chain 1:    700      -119372.808             1.255            0.947
Chain 1:    800       -86560.363             1.145            0.947
Chain 1:    900       -66912.535             1.051            0.779
Chain 1:   1000       -51720.291             0.975            0.779
Chain 1:   1100       -39199.426             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38381.318             0.480            0.379
Chain 1:   1300       -26339.680             0.448            0.379
Chain 1:   1400       -26060.789             0.354            0.319
Chain 1:   1500       -22647.034             0.341            0.319
Chain 1:   1600       -21863.408             0.291            0.294
Chain 1:   1700       -20737.329             0.202            0.294
Chain 1:   1800       -20681.782             0.164            0.151
Chain 1:   1900       -21008.106             0.136            0.054
Chain 1:   2000       -19518.633             0.114            0.054
Chain 1:   2100       -19757.337             0.084            0.036
Chain 1:   2200       -19983.716             0.083            0.036
Chain 1:   2300       -19600.883             0.039            0.020
Chain 1:   2400       -19372.856             0.039            0.020
Chain 1:   2500       -19174.736             0.025            0.016
Chain 1:   2600       -18804.919             0.023            0.016
Chain 1:   2700       -18761.863             0.018            0.012
Chain 1:   2800       -18478.491             0.019            0.015
Chain 1:   2900       -18759.893             0.019            0.015
Chain 1:   3000       -18746.152             0.012            0.012
Chain 1:   3100       -18831.131             0.011            0.012
Chain 1:   3200       -18521.723             0.012            0.015
Chain 1:   3300       -18726.519             0.011            0.012
Chain 1:   3400       -18201.180             0.012            0.015
Chain 1:   3500       -18813.414             0.015            0.015
Chain 1:   3600       -18119.645             0.017            0.015
Chain 1:   3700       -18506.738             0.018            0.017
Chain 1:   3800       -17465.716             0.023            0.021
Chain 1:   3900       -17461.800             0.021            0.021
Chain 1:   4000       -17579.149             0.022            0.021
Chain 1:   4100       -17492.812             0.022            0.021
Chain 1:   4200       -17308.921             0.021            0.021
Chain 1:   4300       -17447.454             0.021            0.021
Chain 1:   4400       -17404.150             0.018            0.011
Chain 1:   4500       -17306.625             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001526 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49029.652             1.000            1.000
Chain 1:    200       -22570.746             1.086            1.172
Chain 1:    300       -17298.005             0.826            1.000
Chain 1:    400       -18739.225             0.638            1.000
Chain 1:    500       -12921.423             0.601            0.450
Chain 1:    600       -11905.239             0.515            0.450
Chain 1:    700       -14958.008             0.471            0.305
Chain 1:    800       -16332.576             0.422            0.305
Chain 1:    900       -15410.370             0.382            0.204
Chain 1:   1000       -11264.962             0.381            0.305
Chain 1:   1100        -9973.374             0.294            0.204
Chain 1:   1200       -13018.972             0.200            0.204
Chain 1:   1300       -13246.799             0.171            0.130
Chain 1:   1400       -11118.517             0.182            0.191
Chain 1:   1500       -11681.673             0.142            0.130
Chain 1:   1600       -10488.212             0.145            0.130
Chain 1:   1700       -11094.875             0.130            0.114
Chain 1:   1800       -13170.444             0.137            0.130
Chain 1:   1900       -12452.278             0.137            0.130
Chain 1:   2000        -9744.927             0.128            0.130
Chain 1:   2100       -10983.638             0.127            0.114
Chain 1:   2200       -11180.495             0.105            0.113
Chain 1:   2300       -10456.654             0.110            0.113
Chain 1:   2400       -11010.274             0.096            0.069
Chain 1:   2500       -14206.031             0.114            0.113
Chain 1:   2600       -10721.956             0.135            0.113
Chain 1:   2700       -11847.404             0.139            0.113
Chain 1:   2800       -10262.805             0.138            0.113
Chain 1:   2900       -11941.357             0.147            0.141
Chain 1:   3000        -9162.711             0.149            0.141
Chain 1:   3100       -10064.002             0.147            0.141
Chain 1:   3200       -16072.648             0.183            0.154
Chain 1:   3300       -19203.946             0.192            0.163
Chain 1:   3400       -14716.510             0.217            0.225
Chain 1:   3500        -9017.683             0.258            0.303
Chain 1:   3600        -9241.565             0.228            0.163
Chain 1:   3700        -9803.864             0.224            0.163
Chain 1:   3800       -13839.893             0.238            0.292
Chain 1:   3900        -9450.973             0.270            0.303
Chain 1:   4000       -11169.075             0.255            0.292
Chain 1:   4100        -9686.154             0.262            0.292
Chain 1:   4200        -8766.602             0.235            0.163
Chain 1:   4300       -10181.309             0.233            0.154
Chain 1:   4400        -9197.251             0.213            0.153
Chain 1:   4500        -9199.403             0.150            0.139
Chain 1:   4600       -13105.282             0.177            0.153
Chain 1:   4700       -11070.209             0.190            0.154
Chain 1:   4800        -8756.047             0.187            0.154
Chain 1:   4900       -10552.777             0.157            0.154
Chain 1:   5000        -8551.717             0.165            0.170
Chain 1:   5100        -8844.698             0.153            0.170
Chain 1:   5200       -12985.128             0.175            0.184
Chain 1:   5300        -8318.733             0.217            0.234
Chain 1:   5400        -8355.816             0.207            0.234
Chain 1:   5500        -8621.913             0.210            0.234
Chain 1:   5600        -8487.571             0.182            0.184
Chain 1:   5700       -13901.307             0.202            0.234
Chain 1:   5800       -14719.585             0.181            0.170
Chain 1:   5900       -13051.992             0.177            0.128
Chain 1:   6000        -8723.043             0.203            0.128
Chain 1:   6100        -9607.108             0.209            0.128
Chain 1:   6200        -8284.672             0.193            0.128
Chain 1:   6300       -10742.350             0.160            0.128
Chain 1:   6400       -12020.785             0.170            0.128
Chain 1:   6500       -12349.513             0.170            0.128
Chain 1:   6600        -8527.652             0.213            0.160
Chain 1:   6700       -12272.448             0.205            0.160
Chain 1:   6800        -8335.386             0.246            0.229
Chain 1:   6900        -8446.971             0.235            0.229
Chain 1:   7000       -10156.597             0.202            0.168
Chain 1:   7100       -16436.451             0.231            0.229
Chain 1:   7200        -8360.920             0.312            0.305
Chain 1:   7300       -10357.554             0.308            0.305
Chain 1:   7400        -8184.866             0.324            0.305
Chain 1:   7500       -10722.142             0.345            0.305
Chain 1:   7600        -8713.149             0.323            0.265
Chain 1:   7700        -8364.433             0.297            0.237
Chain 1:   7800        -8743.853             0.254            0.231
Chain 1:   7900        -8191.223             0.259            0.231
Chain 1:   8000        -8303.395             0.244            0.231
Chain 1:   8100        -9062.962             0.214            0.193
Chain 1:   8200        -9001.637             0.118            0.084
Chain 1:   8300        -8690.570             0.103            0.067
Chain 1:   8400       -12572.375             0.107            0.067
Chain 1:   8500        -8048.823             0.139            0.067
Chain 1:   8600        -8312.271             0.119            0.043
Chain 1:   8700        -8625.877             0.119            0.043
Chain 1:   8800        -8445.073             0.117            0.036
Chain 1:   8900        -8959.172             0.116            0.036
Chain 1:   9000       -11237.713             0.135            0.057
Chain 1:   9100        -8156.448             0.164            0.057
Chain 1:   9200        -8987.272             0.173            0.092
Chain 1:   9300        -9347.730             0.173            0.092
Chain 1:   9400        -8119.212             0.157            0.092
Chain 1:   9500        -8433.463             0.105            0.057
Chain 1:   9600        -8486.445             0.102            0.057
Chain 1:   9700        -8764.143             0.102            0.057
Chain 1:   9800        -9641.648             0.109            0.091
Chain 1:   9900        -8046.577             0.123            0.092
Chain 1:   10000        -8239.446             0.105            0.091
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58128.951             1.000            1.000
Chain 1:    200       -17874.895             1.626            2.252
Chain 1:    300        -8773.647             1.430            1.037
Chain 1:    400        -8212.436             1.089            1.037
Chain 1:    500        -8512.405             0.879            1.000
Chain 1:    600        -8446.212             0.733            1.000
Chain 1:    700        -8188.812             0.633            0.068
Chain 1:    800        -8375.752             0.557            0.068
Chain 1:    900        -8004.659             0.500            0.046
Chain 1:   1000        -7667.625             0.454            0.046
Chain 1:   1100        -7678.609             0.355            0.044
Chain 1:   1200        -8161.546             0.135            0.044
Chain 1:   1300        -7813.849             0.036            0.044
Chain 1:   1400        -7924.331             0.031            0.035
Chain 1:   1500        -7632.026             0.031            0.038
Chain 1:   1600        -7613.548             0.030            0.038
Chain 1:   1700        -7582.469             0.028            0.038
Chain 1:   1800        -7624.006             0.026            0.038
Chain 1:   1900        -7635.038             0.021            0.014
Chain 1:   2000        -7618.868             0.017            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86924.226             1.000            1.000
Chain 1:    200       -13661.565             3.181            5.363
Chain 1:    300        -9898.002             2.248            1.000
Chain 1:    400       -11188.756             1.715            1.000
Chain 1:    500        -8939.890             1.422            0.380
Chain 1:    600        -8337.326             1.197            0.380
Chain 1:    700        -8333.286             1.026            0.252
Chain 1:    800        -8571.613             0.901            0.252
Chain 1:    900        -8632.395             0.802            0.115
Chain 1:   1000        -8704.304             0.723            0.115
Chain 1:   1100        -8628.977             0.623            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8239.752             0.092            0.047
Chain 1:   1300        -8534.621             0.057            0.035
Chain 1:   1400        -8410.485             0.047            0.028
Chain 1:   1500        -8398.091             0.022            0.015
Chain 1:   1600        -8507.841             0.016            0.013
Chain 1:   1700        -8562.156             0.017            0.013
Chain 1:   1800        -8118.841             0.020            0.013
Chain 1:   1900        -8223.722             0.020            0.013
Chain 1:   2000        -8206.901             0.020            0.013
Chain 1:   2100        -8331.664             0.020            0.015
Chain 1:   2200        -8118.690             0.018            0.015
Chain 1:   2300        -8219.821             0.016            0.013
Chain 1:   2400        -8282.621             0.015            0.013
Chain 1:   2500        -8232.379             0.016            0.013
Chain 1:   2600        -8245.777             0.014            0.012
Chain 1:   2700        -8152.707             0.015            0.012
Chain 1:   2800        -8098.542             0.010            0.011
Chain 1:   2900        -8199.833             0.010            0.011
Chain 1:   3000        -8040.985             0.012            0.012
Chain 1:   3100        -8183.662             0.012            0.012
Chain 1:   3200        -8053.110             0.011            0.012
Chain 1:   3300        -8090.779             0.010            0.011
Chain 1:   3400        -8238.885             0.011            0.012
Chain 1:   3500        -8231.382             0.011            0.012
Chain 1:   3600        -8007.823             0.014            0.016
Chain 1:   3700        -8161.127             0.014            0.017
Chain 1:   3800        -8012.341             0.015            0.018
Chain 1:   3900        -7945.346             0.015            0.018
Chain 1:   4000        -8055.760             0.014            0.017
Chain 1:   4100        -8020.137             0.013            0.016
Chain 1:   4200        -8006.028             0.012            0.014
Chain 1:   4300        -8039.458             0.012            0.014
Chain 1:   4400        -7996.246             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003314 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397389.603             1.000            1.000
Chain 1:    200     -1588761.163             2.643            4.285
Chain 1:    300      -892383.796             2.022            1.000
Chain 1:    400      -457797.188             1.754            1.000
Chain 1:    500      -357602.747             1.459            0.949
Chain 1:    600      -232518.935             1.306            0.949
Chain 1:    700      -119086.738             1.255            0.949
Chain 1:    800       -86306.307             1.146            0.949
Chain 1:    900       -66735.925             1.051            0.780
Chain 1:   1000       -51611.068             0.975            0.780
Chain 1:   1100       -39140.843             0.907            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38336.949             0.481            0.380
Chain 1:   1300       -26343.938             0.448            0.380
Chain 1:   1400       -26071.915             0.354            0.319
Chain 1:   1500       -22669.094             0.341            0.319
Chain 1:   1600       -21889.317             0.291            0.293
Chain 1:   1700       -20768.856             0.201            0.293
Chain 1:   1800       -20714.790             0.163            0.150
Chain 1:   1900       -21041.542             0.136            0.054
Chain 1:   2000       -19553.999             0.114            0.054
Chain 1:   2100       -19792.834             0.083            0.036
Chain 1:   2200       -20018.841             0.082            0.036
Chain 1:   2300       -19636.243             0.039            0.019
Chain 1:   2400       -19408.164             0.039            0.019
Chain 1:   2500       -19209.648             0.025            0.016
Chain 1:   2600       -18839.823             0.023            0.016
Chain 1:   2700       -18796.836             0.018            0.012
Chain 1:   2800       -18513.076             0.019            0.015
Chain 1:   2900       -18794.626             0.019            0.015
Chain 1:   3000       -18781.003             0.012            0.012
Chain 1:   3100       -18865.960             0.011            0.012
Chain 1:   3200       -18556.407             0.012            0.015
Chain 1:   3300       -18761.375             0.011            0.012
Chain 1:   3400       -18235.539             0.012            0.015
Chain 1:   3500       -18848.306             0.015            0.015
Chain 1:   3600       -18153.954             0.017            0.015
Chain 1:   3700       -18541.406             0.018            0.017
Chain 1:   3800       -17499.266             0.023            0.021
Chain 1:   3900       -17495.290             0.021            0.021
Chain 1:   4000       -17612.715             0.022            0.021
Chain 1:   4100       -17526.224             0.022            0.021
Chain 1:   4200       -17342.161             0.021            0.021
Chain 1:   4300       -17480.876             0.021            0.021
Chain 1:   4400       -17437.413             0.018            0.011
Chain 1:   4500       -17339.794             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001305 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49301.047             1.000            1.000
Chain 1:    200       -23164.854             1.064            1.128
Chain 1:    300       -17574.901             0.815            1.000
Chain 1:    400       -16781.326             0.623            1.000
Chain 1:    500       -17716.484             0.509            0.318
Chain 1:    600       -12155.232             0.501            0.458
Chain 1:    700       -17467.622             0.473            0.318
Chain 1:    800       -14350.204             0.441            0.318
Chain 1:    900       -12430.890             0.409            0.304
Chain 1:   1000       -12541.962             0.369            0.304
Chain 1:   1100       -11228.514             0.281            0.217
Chain 1:   1200       -25081.303             0.223            0.217
Chain 1:   1300       -10673.995             0.326            0.217
Chain 1:   1400       -12559.828             0.336            0.217
Chain 1:   1500       -12511.744             0.332            0.217
Chain 1:   1600       -15625.146             0.306            0.199
Chain 1:   1700       -10260.766             0.328            0.199
Chain 1:   1800       -11814.334             0.319            0.154
Chain 1:   1900       -10674.986             0.314            0.150
Chain 1:   2000       -11943.850             0.324            0.150
Chain 1:   2100       -10548.532             0.325            0.150
Chain 1:   2200       -19661.101             0.317            0.150
Chain 1:   2300        -9895.996             0.280            0.150
Chain 1:   2400       -12866.280             0.288            0.199
Chain 1:   2500       -10929.112             0.306            0.199
Chain 1:   2600       -10014.944             0.295            0.177
Chain 1:   2700        -9415.373             0.249            0.132
Chain 1:   2800       -11664.673             0.255            0.177
Chain 1:   2900       -10188.113             0.259            0.177
Chain 1:   3000        -9385.506             0.257            0.177
Chain 1:   3100       -10816.150             0.257            0.177
Chain 1:   3200       -11619.279             0.217            0.145
Chain 1:   3300       -13896.075             0.135            0.145
Chain 1:   3400       -12471.866             0.123            0.132
Chain 1:   3500       -10850.531             0.121            0.132
Chain 1:   3600       -10192.528             0.118            0.132
Chain 1:   3700        -9393.482             0.120            0.132
Chain 1:   3800        -9176.104             0.103            0.114
Chain 1:   3900        -9832.286             0.095            0.086
Chain 1:   4000        -9892.494             0.087            0.085
Chain 1:   4100        -9107.352             0.083            0.085
Chain 1:   4200       -12595.936             0.104            0.086
Chain 1:   4300       -14268.958             0.099            0.086
Chain 1:   4400        -8939.996             0.147            0.086
Chain 1:   4500       -11044.410             0.151            0.086
Chain 1:   4600        -9373.055             0.163            0.117
Chain 1:   4700        -8799.550             0.161            0.117
Chain 1:   4800       -13935.783             0.195            0.178
Chain 1:   4900       -10263.744             0.224            0.191
Chain 1:   5000       -10660.306             0.227            0.191
Chain 1:   5100        -9536.937             0.231            0.191
Chain 1:   5200       -16158.036             0.244            0.191
Chain 1:   5300       -14780.660             0.241            0.191
Chain 1:   5400        -9157.555             0.243            0.191
Chain 1:   5500       -10075.105             0.233            0.178
Chain 1:   5600        -9525.971             0.221            0.118
Chain 1:   5700        -9780.816             0.217            0.118
Chain 1:   5800        -9563.769             0.183            0.093
Chain 1:   5900        -9650.943             0.148            0.091
Chain 1:   6000       -10476.093             0.152            0.091
Chain 1:   6100       -14573.652             0.168            0.091
Chain 1:   6200        -9095.342             0.188            0.091
Chain 1:   6300       -10605.979             0.193            0.091
Chain 1:   6400        -9859.459             0.139            0.079
Chain 1:   6500        -9570.495             0.133            0.076
Chain 1:   6600        -8853.345             0.135            0.079
Chain 1:   6700        -8746.274             0.134            0.079
Chain 1:   6800        -8846.700             0.132            0.079
Chain 1:   6900       -13788.827             0.167            0.081
Chain 1:   7000       -12655.786             0.168            0.090
Chain 1:   7100        -8727.152             0.185            0.090
Chain 1:   7200        -8595.242             0.127            0.081
Chain 1:   7300       -11863.030             0.140            0.081
Chain 1:   7400       -14944.728             0.153            0.090
Chain 1:   7500       -10028.065             0.199            0.206
Chain 1:   7600        -8769.192             0.205            0.206
Chain 1:   7700       -10196.416             0.218            0.206
Chain 1:   7800       -11882.527             0.231            0.206
Chain 1:   7900        -8941.189             0.228            0.206
Chain 1:   8000        -9161.159             0.222            0.206
Chain 1:   8100        -9278.273             0.178            0.144
Chain 1:   8200       -10181.035             0.185            0.144
Chain 1:   8300        -8567.334             0.176            0.144
Chain 1:   8400        -8572.481             0.156            0.142
Chain 1:   8500       -10151.449             0.122            0.142
Chain 1:   8600       -13300.814             0.132            0.142
Chain 1:   8700        -9281.986             0.161            0.156
Chain 1:   8800        -8700.085             0.154            0.156
Chain 1:   8900       -10659.096             0.139            0.156
Chain 1:   9000        -9129.204             0.153            0.168
Chain 1:   9100        -8948.086             0.154            0.168
Chain 1:   9200       -10398.439             0.159            0.168
Chain 1:   9300        -8586.037             0.161            0.168
Chain 1:   9400        -8859.189             0.165            0.168
Chain 1:   9500        -9934.784             0.160            0.168
Chain 1:   9600       -11535.887             0.150            0.139
Chain 1:   9700       -10686.166             0.115            0.139
Chain 1:   9800       -10805.862             0.109            0.139
Chain 1:   9900        -9887.361             0.100            0.108
Chain 1:   10000        -9116.347             0.092            0.093
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001422 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47849.987             1.000            1.000
Chain 1:    200       -16140.497             1.482            1.965
Chain 1:    300        -8801.514             1.266            1.000
Chain 1:    400        -8651.550             0.954            1.000
Chain 1:    500        -8845.312             0.768            0.834
Chain 1:    600        -9157.837             0.645            0.834
Chain 1:    700        -8164.362             0.570            0.122
Chain 1:    800        -8350.426             0.502            0.122
Chain 1:    900        -8116.085             0.449            0.034
Chain 1:   1000        -8040.818             0.405            0.034
Chain 1:   1100        -7857.298             0.308            0.029
Chain 1:   1200        -8002.870             0.113            0.023
Chain 1:   1300        -7663.213             0.034            0.023
Chain 1:   1400        -8042.530             0.037            0.029
Chain 1:   1500        -7722.862             0.039            0.034
Chain 1:   1600        -7886.799             0.038            0.029
Chain 1:   1700        -7657.638             0.029            0.029
Chain 1:   1800        -7716.970             0.027            0.029
Chain 1:   1900        -7701.957             0.024            0.023
Chain 1:   2000        -7787.870             0.025            0.023
Chain 1:   2100        -7704.131             0.023            0.021
Chain 1:   2200        -7840.616             0.023            0.021
Chain 1:   2300        -7701.574             0.021            0.018
Chain 1:   2400        -7761.112             0.017            0.017
Chain 1:   2500        -7700.165             0.013            0.011
Chain 1:   2600        -7646.921             0.012            0.011
Chain 1:   2700        -7572.746             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002553 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85765.443             1.000            1.000
Chain 1:    200       -13882.804             3.089            5.178
Chain 1:    300       -10235.473             2.178            1.000
Chain 1:    400       -11070.121             1.652            1.000
Chain 1:    500        -9092.833             1.365            0.356
Chain 1:    600        -8679.501             1.146            0.356
Chain 1:    700        -8746.038             0.983            0.217
Chain 1:    800        -9259.608             0.867            0.217
Chain 1:    900        -8923.899             0.775            0.075
Chain 1:   1000        -8779.106             0.699            0.075
Chain 1:   1100        -9064.972             0.602            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8671.546             0.089            0.048
Chain 1:   1300        -8989.275             0.057            0.045
Chain 1:   1400        -8938.851             0.050            0.038
Chain 1:   1500        -8781.769             0.030            0.035
Chain 1:   1600        -8893.397             0.027            0.032
Chain 1:   1700        -8974.073             0.027            0.032
Chain 1:   1800        -8550.518             0.026            0.032
Chain 1:   1900        -8651.489             0.024            0.018
Chain 1:   2000        -8626.074             0.022            0.018
Chain 1:   2100        -8751.392             0.020            0.014
Chain 1:   2200        -8554.602             0.018            0.014
Chain 1:   2300        -8646.319             0.016            0.013
Chain 1:   2400        -8715.124             0.016            0.013
Chain 1:   2500        -8661.395             0.015            0.012
Chain 1:   2600        -8662.761             0.014            0.011
Chain 1:   2700        -8579.454             0.014            0.011
Chain 1:   2800        -8539.357             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003175 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378877.428             1.000            1.000
Chain 1:    200     -1581020.526             2.650            4.300
Chain 1:    300      -890529.006             2.025            1.000
Chain 1:    400      -457940.297             1.755            1.000
Chain 1:    500      -358790.733             1.459            0.945
Chain 1:    600      -233823.516             1.305            0.945
Chain 1:    700      -119888.465             1.254            0.945
Chain 1:    800       -87034.509             1.145            0.945
Chain 1:    900       -67331.496             1.050            0.775
Chain 1:   1000       -52092.688             0.974            0.775
Chain 1:   1100       -39532.505             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38706.139             0.478            0.377
Chain 1:   1300       -26623.400             0.446            0.377
Chain 1:   1400       -26339.224             0.353            0.318
Chain 1:   1500       -22915.922             0.340            0.318
Chain 1:   1600       -22129.434             0.290            0.293
Chain 1:   1700       -20998.365             0.201            0.293
Chain 1:   1800       -20941.531             0.163            0.149
Chain 1:   1900       -21267.832             0.135            0.054
Chain 1:   2000       -19776.220             0.114            0.054
Chain 1:   2100       -20014.763             0.083            0.036
Chain 1:   2200       -20241.673             0.082            0.036
Chain 1:   2300       -19858.446             0.039            0.019
Chain 1:   2400       -19630.422             0.039            0.019
Chain 1:   2500       -19432.589             0.025            0.015
Chain 1:   2600       -19062.514             0.023            0.015
Chain 1:   2700       -19019.434             0.018            0.012
Chain 1:   2800       -18736.250             0.019            0.015
Chain 1:   2900       -19017.667             0.019            0.015
Chain 1:   3000       -19003.796             0.012            0.012
Chain 1:   3100       -19088.787             0.011            0.012
Chain 1:   3200       -18779.395             0.011            0.015
Chain 1:   3300       -18984.199             0.011            0.012
Chain 1:   3400       -18458.965             0.012            0.015
Chain 1:   3500       -19071.142             0.014            0.015
Chain 1:   3600       -18377.494             0.016            0.015
Chain 1:   3700       -18764.545             0.018            0.016
Chain 1:   3800       -17723.758             0.022            0.021
Chain 1:   3900       -17719.923             0.021            0.021
Chain 1:   4000       -17837.201             0.022            0.021
Chain 1:   4100       -17750.916             0.022            0.021
Chain 1:   4200       -17567.079             0.021            0.021
Chain 1:   4300       -17705.528             0.021            0.021
Chain 1:   4400       -17662.268             0.018            0.010
Chain 1:   4500       -17564.806             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001305 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12790.573             1.000            1.000
Chain 1:    200        -9696.548             0.660            1.000
Chain 1:    300        -8288.568             0.496            0.319
Chain 1:    400        -8488.181             0.378            0.319
Chain 1:    500        -8381.091             0.305            0.170
Chain 1:    600        -8216.771             0.258            0.170
Chain 1:    700        -8089.326             0.223            0.024
Chain 1:    800        -8071.943             0.195            0.024
Chain 1:    900        -8271.554             0.176            0.024
Chain 1:   1000        -8199.185             0.160            0.024
Chain 1:   1100        -8165.742             0.060            0.020
Chain 1:   1200        -8162.722             0.028            0.016
Chain 1:   1300        -8057.254             0.012            0.013
Chain 1:   1400        -8063.319             0.010            0.013
Chain 1:   1500        -8178.072             0.010            0.013
Chain 1:   1600        -8103.462             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59020.265             1.000            1.000
Chain 1:    200       -18253.463             1.617            2.233
Chain 1:    300        -9102.341             1.413            1.005
Chain 1:    400        -8355.904             1.082            1.005
Chain 1:    500        -7846.871             0.879            1.000
Chain 1:    600        -8582.074             0.746            1.000
Chain 1:    700        -8163.872             0.647            0.089
Chain 1:    800        -8339.253             0.569            0.089
Chain 1:    900        -7724.670             0.514            0.086
Chain 1:   1000        -7644.596             0.464            0.086
Chain 1:   1100        -7685.819             0.365            0.080
Chain 1:   1200        -7528.769             0.143            0.065
Chain 1:   1300        -7779.211             0.046            0.051
Chain 1:   1400        -7871.033             0.038            0.032
Chain 1:   1500        -7550.256             0.036            0.032
Chain 1:   1600        -7716.947             0.030            0.022
Chain 1:   1700        -7517.243             0.027            0.022
Chain 1:   1800        -7569.687             0.026            0.022
Chain 1:   1900        -7560.332             0.018            0.021
Chain 1:   2000        -7617.911             0.018            0.021
Chain 1:   2100        -7586.720             0.018            0.021
Chain 1:   2200        -7844.912             0.019            0.022
Chain 1:   2300        -7597.310             0.019            0.022
Chain 1:   2400        -7508.705             0.019            0.022
Chain 1:   2500        -7400.736             0.016            0.015
Chain 1:   2600        -7516.159             0.015            0.015
Chain 1:   2700        -7489.420             0.013            0.012
Chain 1:   2800        -7510.000             0.013            0.012
Chain 1:   2900        -7360.334             0.015            0.015
Chain 1:   3000        -7519.633             0.016            0.015
Chain 1:   3100        -7512.665             0.016            0.015
Chain 1:   3200        -7738.093             0.015            0.015
Chain 1:   3300        -7409.738             0.016            0.015
Chain 1:   3400        -7687.861             0.019            0.020
Chain 1:   3500        -7432.007             0.021            0.021
Chain 1:   3600        -7490.403             0.020            0.021
Chain 1:   3700        -7441.722             0.020            0.021
Chain 1:   3800        -7450.785             0.020            0.021
Chain 1:   3900        -7418.272             0.019            0.021
Chain 1:   4000        -7387.199             0.017            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002561 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86305.240             1.000            1.000
Chain 1:    200       -14022.598             3.077            5.155
Chain 1:    300       -10266.488             2.174            1.000
Chain 1:    400       -12037.315             1.667            1.000
Chain 1:    500        -8867.192             1.405            0.366
Chain 1:    600        -9865.814             1.188            0.366
Chain 1:    700        -8755.943             1.036            0.358
Chain 1:    800        -9353.640             0.915            0.358
Chain 1:    900        -9058.365             0.817            0.147
Chain 1:   1000        -9188.657             0.736            0.147
Chain 1:   1100        -8978.438             0.639            0.127   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8558.676             0.128            0.101
Chain 1:   1300        -8884.940             0.095            0.064
Chain 1:   1400        -8828.464             0.081            0.049
Chain 1:   1500        -8742.608             0.046            0.037
Chain 1:   1600        -8841.429             0.037            0.033
Chain 1:   1700        -8899.399             0.025            0.023
Chain 1:   1800        -8446.966             0.024            0.023
Chain 1:   1900        -8555.179             0.022            0.014
Chain 1:   2000        -8554.939             0.021            0.013
Chain 1:   2100        -8723.522             0.021            0.013
Chain 1:   2200        -8451.215             0.019            0.013
Chain 1:   2300        -8631.705             0.017            0.013
Chain 1:   2400        -8450.902             0.019            0.019
Chain 1:   2500        -8527.649             0.019            0.019
Chain 1:   2600        -8438.685             0.019            0.019
Chain 1:   2700        -8471.755             0.018            0.019
Chain 1:   2800        -8423.513             0.014            0.013
Chain 1:   2900        -8534.941             0.014            0.013
Chain 1:   3000        -8473.181             0.014            0.013
Chain 1:   3100        -8415.788             0.013            0.011
Chain 1:   3200        -8388.875             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003587 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416397.601             1.000            1.000
Chain 1:    200     -1584929.568             2.655            4.310
Chain 1:    300      -892198.990             2.029            1.000
Chain 1:    400      -459073.515             1.758            1.000
Chain 1:    500      -359447.978             1.461            0.943
Chain 1:    600      -234205.416             1.307            0.943
Chain 1:    700      -120100.955             1.256            0.943
Chain 1:    800       -87257.185             1.146            0.943
Chain 1:    900       -67524.028             1.051            0.776
Chain 1:   1000       -52279.887             0.975            0.776
Chain 1:   1100       -39717.831             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38892.385             0.478            0.376
Chain 1:   1300       -26796.871             0.445            0.376
Chain 1:   1400       -26513.789             0.352            0.316
Chain 1:   1500       -23088.064             0.339            0.316
Chain 1:   1600       -22301.848             0.289            0.292
Chain 1:   1700       -21168.657             0.200            0.292
Chain 1:   1800       -21111.655             0.162            0.148
Chain 1:   1900       -21438.445             0.135            0.054
Chain 1:   2000       -19944.870             0.113            0.054
Chain 1:   2100       -20183.440             0.083            0.035
Chain 1:   2200       -20411.033             0.082            0.035
Chain 1:   2300       -20027.092             0.038            0.019
Chain 1:   2400       -19798.858             0.038            0.019
Chain 1:   2500       -19601.148             0.025            0.015
Chain 1:   2600       -19230.353             0.023            0.015
Chain 1:   2700       -19186.999             0.018            0.012
Chain 1:   2800       -18903.684             0.019            0.015
Chain 1:   2900       -19185.318             0.019            0.015
Chain 1:   3000       -19171.347             0.012            0.012
Chain 1:   3100       -19256.498             0.011            0.012
Chain 1:   3200       -18946.616             0.011            0.015
Chain 1:   3300       -19151.774             0.011            0.012
Chain 1:   3400       -18625.838             0.012            0.015
Chain 1:   3500       -19239.067             0.014            0.015
Chain 1:   3600       -18543.961             0.016            0.015
Chain 1:   3700       -18932.164             0.018            0.016
Chain 1:   3800       -17889.158             0.022            0.021
Chain 1:   3900       -17885.264             0.021            0.021
Chain 1:   4000       -18002.538             0.021            0.021
Chain 1:   4100       -17916.194             0.022            0.021
Chain 1:   4200       -17731.824             0.021            0.021
Chain 1:   4300       -17870.613             0.021            0.021
Chain 1:   4400       -17826.946             0.018            0.010
Chain 1:   4500       -17729.413             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001492 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48679.645             1.000            1.000
Chain 1:    200       -17890.084             1.361            1.721
Chain 1:    300       -35163.073             1.071            1.000
Chain 1:    400       -15921.216             1.105            1.209
Chain 1:    500       -17536.639             0.903            1.000
Chain 1:    600       -15264.277             0.777            1.000
Chain 1:    700       -15027.253             0.668            0.491
Chain 1:    800       -16193.452             0.594            0.491
Chain 1:    900       -11415.657             0.574            0.419
Chain 1:   1000       -13465.539             0.532            0.419
Chain 1:   1100       -15964.155             0.448            0.157
Chain 1:   1200       -13237.353             0.296            0.157
Chain 1:   1300       -12555.376             0.252            0.152
Chain 1:   1400       -10392.241             0.152            0.152
Chain 1:   1500       -18052.447             0.186            0.157
Chain 1:   1600        -9818.974             0.255            0.206
Chain 1:   1700        -9411.922             0.257            0.206
Chain 1:   1800       -11501.025             0.268            0.206
Chain 1:   1900       -10162.712             0.240            0.182
Chain 1:   2000       -12738.784             0.245            0.202
Chain 1:   2100       -19099.644             0.262            0.206
Chain 1:   2200       -10869.447             0.317            0.208
Chain 1:   2300        -9899.484             0.322            0.208
Chain 1:   2400        -9747.501             0.303            0.202
Chain 1:   2500        -9895.340             0.262            0.182
Chain 1:   2600       -10111.882             0.180            0.132
Chain 1:   2700       -15226.116             0.209            0.182
Chain 1:   2800        -8910.666             0.262            0.202
Chain 1:   2900        -9031.335             0.250            0.202
Chain 1:   3000        -9839.514             0.238            0.098
Chain 1:   3100        -9788.287             0.205            0.082
Chain 1:   3200        -9861.152             0.130            0.021
Chain 1:   3300        -8785.243             0.133            0.021
Chain 1:   3400        -9438.900             0.138            0.069
Chain 1:   3500        -8974.254             0.142            0.069
Chain 1:   3600        -9691.224             0.147            0.074
Chain 1:   3700        -9644.385             0.114            0.069
Chain 1:   3800        -8519.485             0.056            0.069
Chain 1:   3900       -10102.445             0.071            0.074
Chain 1:   4000       -10242.262             0.064            0.069
Chain 1:   4100        -9736.017             0.068            0.069
Chain 1:   4200       -11497.998             0.083            0.074
Chain 1:   4300       -15526.372             0.097            0.074
Chain 1:   4400        -8926.583             0.164            0.132
Chain 1:   4500        -9465.266             0.164            0.132
Chain 1:   4600        -9053.718             0.161            0.132
Chain 1:   4700       -12813.558             0.190            0.153
Chain 1:   4800        -8493.632             0.228            0.157
Chain 1:   4900        -8918.630             0.217            0.153
Chain 1:   5000       -13516.824             0.250            0.259
Chain 1:   5100        -8461.976             0.304            0.293
Chain 1:   5200        -8769.473             0.292            0.293
Chain 1:   5300        -9443.144             0.274            0.293
Chain 1:   5400       -10413.511             0.209            0.093
Chain 1:   5500        -9173.786             0.217            0.135
Chain 1:   5600        -8288.658             0.223            0.135
Chain 1:   5700       -14270.046             0.235            0.135
Chain 1:   5800        -8429.422             0.254            0.135
Chain 1:   5900        -9689.908             0.262            0.135
Chain 1:   6000        -8315.598             0.245            0.135
Chain 1:   6100       -11651.908             0.214            0.135
Chain 1:   6200        -8588.108             0.246            0.165
Chain 1:   6300       -10103.595             0.254            0.165
Chain 1:   6400        -8195.813             0.268            0.233
Chain 1:   6500        -8902.972             0.262            0.233
Chain 1:   6600        -8269.788             0.259            0.233
Chain 1:   6700        -8769.603             0.223            0.165
Chain 1:   6800        -8065.138             0.162            0.150
Chain 1:   6900        -8170.892             0.150            0.150
Chain 1:   7000        -9409.521             0.147            0.132
Chain 1:   7100        -8108.467             0.134            0.132
Chain 1:   7200       -11292.480             0.127            0.132
Chain 1:   7300        -9962.396             0.125            0.132
Chain 1:   7400        -8176.613             0.124            0.132
Chain 1:   7500        -8172.770             0.116            0.132
Chain 1:   7600        -8712.297             0.115            0.132
Chain 1:   7700       -10173.449             0.123            0.134
Chain 1:   7800        -8077.539             0.140            0.144
Chain 1:   7900        -8097.631             0.139            0.144
Chain 1:   8000        -9628.284             0.142            0.159
Chain 1:   8100        -9344.836             0.129            0.144
Chain 1:   8200        -7985.526             0.118            0.144
Chain 1:   8300       -10953.647             0.132            0.159
Chain 1:   8400        -9476.174             0.125            0.156
Chain 1:   8500        -8049.078             0.143            0.159
Chain 1:   8600        -8180.983             0.139            0.159
Chain 1:   8700        -7921.881             0.127            0.159
Chain 1:   8800        -8925.770             0.113            0.156
Chain 1:   8900       -12439.609             0.141            0.159
Chain 1:   9000       -10278.581             0.146            0.170
Chain 1:   9100        -8100.164             0.170            0.177
Chain 1:   9200       -11629.026             0.183            0.210
Chain 1:   9300        -8139.153             0.199            0.210
Chain 1:   9400        -8579.887             0.188            0.210
Chain 1:   9500        -7826.879             0.180            0.210
Chain 1:   9600        -8270.915             0.184            0.210
Chain 1:   9700        -9561.023             0.194            0.210
Chain 1:   9800        -8176.110             0.200            0.210
Chain 1:   9900       -10676.778             0.195            0.210
Chain 1:   10000        -7861.942             0.210            0.234
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58103.627             1.000            1.000
Chain 1:    200       -17538.694             1.656            2.313
Chain 1:    300        -8581.058             1.452            1.044
Chain 1:    400        -8152.208             1.102            1.044
Chain 1:    500        -8064.200             0.884            1.000
Chain 1:    600        -8619.276             0.747            1.000
Chain 1:    700        -8121.557             0.649            0.064
Chain 1:    800        -8146.216             0.569            0.064
Chain 1:    900        -7710.392             0.512            0.061
Chain 1:   1000        -7751.757             0.461            0.061
Chain 1:   1100        -7666.481             0.362            0.057
Chain 1:   1200        -7550.889             0.132            0.053
Chain 1:   1300        -7603.653             0.029            0.015
Chain 1:   1400        -7776.477             0.026            0.015
Chain 1:   1500        -7544.107             0.028            0.022
Chain 1:   1600        -7603.335             0.022            0.015
Chain 1:   1700        -7479.803             0.018            0.015
Chain 1:   1800        -7513.189             0.018            0.015
Chain 1:   1900        -7529.230             0.012            0.011
Chain 1:   2000        -7560.752             0.012            0.011
Chain 1:   2100        -7569.812             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86055.445             1.000            1.000
Chain 1:    200       -13302.288             3.235            5.469
Chain 1:    300        -9668.345             2.282            1.000
Chain 1:    400       -10676.777             1.735            1.000
Chain 1:    500        -8443.479             1.441            0.376
Chain 1:    600        -8138.245             1.207            0.376
Chain 1:    700        -8368.165             1.038            0.264
Chain 1:    800        -8524.751             0.911            0.264
Chain 1:    900        -8506.102             0.810            0.094
Chain 1:   1000        -8210.905             0.733            0.094
Chain 1:   1100        -8465.661             0.636            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.779             0.093            0.038
Chain 1:   1300        -8329.017             0.058            0.036
Chain 1:   1400        -8353.929             0.049            0.030
Chain 1:   1500        -8226.600             0.024            0.027
Chain 1:   1600        -8332.178             0.022            0.026
Chain 1:   1700        -8413.917             0.020            0.018
Chain 1:   1800        -7995.263             0.023            0.026
Chain 1:   1900        -8093.843             0.024            0.026
Chain 1:   2000        -8067.594             0.021            0.015
Chain 1:   2100        -8191.686             0.019            0.015
Chain 1:   2200        -8005.039             0.017            0.015
Chain 1:   2300        -8088.337             0.016            0.013
Chain 1:   2400        -8157.808             0.016            0.013
Chain 1:   2500        -8103.718             0.015            0.012
Chain 1:   2600        -8104.060             0.014            0.010
Chain 1:   2700        -8021.247             0.014            0.010
Chain 1:   2800        -7982.782             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003466 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412200.597             1.000            1.000
Chain 1:    200     -1588173.848             2.648            4.297
Chain 1:    300      -890874.036             2.026            1.000
Chain 1:    400      -457098.936             1.757            1.000
Chain 1:    500      -357217.203             1.462            0.949
Chain 1:    600      -232028.211             1.308            0.949
Chain 1:    700      -118627.456             1.258            0.949
Chain 1:    800       -85935.213             1.148            0.949
Chain 1:    900       -66354.707             1.053            0.783
Chain 1:   1000       -51229.410             0.977            0.783
Chain 1:   1100       -38768.157             0.910            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37954.354             0.482            0.380
Chain 1:   1300       -25971.169             0.450            0.380
Chain 1:   1400       -25695.708             0.356            0.321
Chain 1:   1500       -22299.072             0.343            0.321
Chain 1:   1600       -21520.602             0.293            0.295
Chain 1:   1700       -20401.538             0.203            0.295
Chain 1:   1800       -20347.526             0.165            0.152
Chain 1:   1900       -20673.788             0.137            0.055
Chain 1:   2000       -19188.604             0.115            0.055
Chain 1:   2100       -19426.695             0.085            0.036
Chain 1:   2200       -19652.671             0.084            0.036
Chain 1:   2300       -19270.321             0.039            0.020
Chain 1:   2400       -19042.506             0.039            0.020
Chain 1:   2500       -18844.243             0.025            0.016
Chain 1:   2600       -18474.606             0.024            0.016
Chain 1:   2700       -18431.656             0.018            0.012
Chain 1:   2800       -18148.371             0.020            0.016
Chain 1:   2900       -18429.598             0.020            0.015
Chain 1:   3000       -18415.788             0.012            0.012
Chain 1:   3100       -18500.791             0.011            0.012
Chain 1:   3200       -18191.477             0.012            0.015
Chain 1:   3300       -18396.229             0.011            0.012
Chain 1:   3400       -17871.051             0.013            0.015
Chain 1:   3500       -18482.940             0.015            0.016
Chain 1:   3600       -17789.639             0.017            0.016
Chain 1:   3700       -18176.390             0.019            0.017
Chain 1:   3800       -17136.014             0.023            0.021
Chain 1:   3900       -17132.149             0.022            0.021
Chain 1:   4000       -17249.489             0.022            0.021
Chain 1:   4100       -17163.218             0.022            0.021
Chain 1:   4200       -16979.485             0.022            0.021
Chain 1:   4300       -17117.897             0.021            0.021
Chain 1:   4400       -17074.709             0.019            0.011
Chain 1:   4500       -16977.235             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001274 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48899.295             1.000            1.000
Chain 1:    200       -20048.235             1.220            1.439
Chain 1:    300       -19960.186             0.814            1.000
Chain 1:    400       -16754.825             0.659            1.000
Chain 1:    500       -11367.453             0.622            0.474
Chain 1:    600       -12284.119             0.531            0.474
Chain 1:    700       -14226.696             0.474            0.191
Chain 1:    800       -12946.783             0.427            0.191
Chain 1:    900       -13903.351             0.388            0.137
Chain 1:   1000       -12481.240             0.360            0.137
Chain 1:   1100       -10227.310             0.282            0.137
Chain 1:   1200       -11996.116             0.153            0.137
Chain 1:   1300       -10747.665             0.164            0.137
Chain 1:   1400       -12151.711             0.157            0.116
Chain 1:   1500       -10881.053             0.121            0.116
Chain 1:   1600       -21034.170             0.162            0.117
Chain 1:   1700       -17484.056             0.168            0.117
Chain 1:   1800       -11884.716             0.206            0.147
Chain 1:   1900        -9950.898             0.218            0.194
Chain 1:   2000        -9418.561             0.212            0.194
Chain 1:   2100        -9144.367             0.193            0.147
Chain 1:   2200        -9322.750             0.181            0.117
Chain 1:   2300        -9087.541             0.172            0.117
Chain 1:   2400        -9772.723             0.167            0.117
Chain 1:   2500       -10855.277             0.165            0.100
Chain 1:   2600       -15336.861             0.146            0.100
Chain 1:   2700       -10494.155             0.172            0.100
Chain 1:   2800        -9212.112             0.139            0.100
Chain 1:   2900       -15580.546             0.160            0.100
Chain 1:   3000        -8640.241             0.235            0.139
Chain 1:   3100        -9181.309             0.238            0.139
Chain 1:   3200        -8806.852             0.240            0.139
Chain 1:   3300        -9988.945             0.249            0.139
Chain 1:   3400        -8775.228             0.256            0.139
Chain 1:   3500        -8779.270             0.246            0.139
Chain 1:   3600       -15736.299             0.261            0.139
Chain 1:   3700        -9777.923             0.276            0.139
Chain 1:   3800        -8800.090             0.273            0.138
Chain 1:   3900        -9635.053             0.241            0.118
Chain 1:   4000        -9142.341             0.166            0.111
Chain 1:   4100        -8628.303             0.166            0.111
Chain 1:   4200       -10241.310             0.178            0.118
Chain 1:   4300        -8577.709             0.185            0.138
Chain 1:   4400        -9069.703             0.177            0.111
Chain 1:   4500        -8862.087             0.179            0.111
Chain 1:   4600        -8504.843             0.139            0.087
Chain 1:   4700       -14687.576             0.120            0.087
Chain 1:   4800        -8438.278             0.183            0.087
Chain 1:   4900       -13449.955             0.212            0.158
Chain 1:   5000        -8304.255             0.268            0.194
Chain 1:   5100        -8932.013             0.270            0.194
Chain 1:   5200        -9257.929             0.257            0.194
Chain 1:   5300        -9121.687             0.239            0.070
Chain 1:   5400        -8255.642             0.244            0.105
Chain 1:   5500       -11534.855             0.271            0.284
Chain 1:   5600        -9412.029             0.289            0.284
Chain 1:   5700        -8909.197             0.252            0.226
Chain 1:   5800        -8400.207             0.184            0.105
Chain 1:   5900       -10709.905             0.169            0.105
Chain 1:   6000        -8740.106             0.129            0.105
Chain 1:   6100        -8174.091             0.129            0.105
Chain 1:   6200       -10759.643             0.150            0.216
Chain 1:   6300       -10805.997             0.149            0.216
Chain 1:   6400        -9133.108             0.156            0.216
Chain 1:   6500        -8194.598             0.140            0.183
Chain 1:   6600        -8742.037             0.123            0.115
Chain 1:   6700        -8224.833             0.124            0.115
Chain 1:   6800       -15115.971             0.163            0.183
Chain 1:   6900        -9111.475             0.208            0.183
Chain 1:   7000        -8430.288             0.193            0.115
Chain 1:   7100        -8121.015             0.190            0.115
Chain 1:   7200        -8996.616             0.176            0.097
Chain 1:   7300       -10731.821             0.192            0.115
Chain 1:   7400        -9554.400             0.186            0.115
Chain 1:   7500        -8453.464             0.187            0.123
Chain 1:   7600        -9462.216             0.192            0.123
Chain 1:   7700        -8256.711             0.200            0.130
Chain 1:   7800        -8528.589             0.157            0.123
Chain 1:   7900        -9918.761             0.106            0.123
Chain 1:   8000        -8455.555             0.115            0.130
Chain 1:   8100        -8478.255             0.111            0.130
Chain 1:   8200       -11283.337             0.126            0.140
Chain 1:   8300        -8059.217             0.150            0.140
Chain 1:   8400        -9605.906             0.154            0.146
Chain 1:   8500        -8319.180             0.156            0.155
Chain 1:   8600        -8788.087             0.151            0.155
Chain 1:   8700        -8459.343             0.140            0.155
Chain 1:   8800        -7950.917             0.144            0.155
Chain 1:   8900       -10502.094             0.154            0.161
Chain 1:   9000        -8537.789             0.160            0.161
Chain 1:   9100        -8466.512             0.160            0.161
Chain 1:   9200        -9149.872             0.143            0.155
Chain 1:   9300       -10745.411             0.118            0.148
Chain 1:   9400        -9668.896             0.113            0.111
Chain 1:   9500       -10430.571             0.105            0.075
Chain 1:   9600        -8319.511             0.125            0.111
Chain 1:   9700       -10297.027             0.140            0.148
Chain 1:   9800        -8096.907             0.161            0.192
Chain 1:   9900        -8214.285             0.138            0.148
Chain 1:   10000        -8324.895             0.116            0.111
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61551.496             1.000            1.000
Chain 1:    200       -17672.707             1.741            2.483
Chain 1:    300        -8789.502             1.498            1.011
Chain 1:    400        -8290.708             1.138            1.011
Chain 1:    500        -8130.545             0.915            1.000
Chain 1:    600        -8690.932             0.773            1.000
Chain 1:    700        -7775.191             0.679            0.118
Chain 1:    800        -8500.836             0.605            0.118
Chain 1:    900        -7803.215             0.548            0.089
Chain 1:   1000        -7747.708             0.494            0.089
Chain 1:   1100        -7716.156             0.394            0.085
Chain 1:   1200        -7561.172             0.148            0.064
Chain 1:   1300        -7727.459             0.049            0.060
Chain 1:   1400        -7810.212             0.044            0.022
Chain 1:   1500        -7596.390             0.045            0.028
Chain 1:   1600        -7655.797             0.039            0.022
Chain 1:   1700        -7509.387             0.029            0.020
Chain 1:   1800        -7575.211             0.022            0.019
Chain 1:   1900        -7546.148             0.013            0.011
Chain 1:   2000        -7631.801             0.014            0.011
Chain 1:   2100        -7592.043             0.014            0.011
Chain 1:   2200        -7673.962             0.013            0.011
Chain 1:   2300        -7591.254             0.012            0.011
Chain 1:   2400        -7629.308             0.011            0.011
Chain 1:   2500        -7556.365             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003579 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86875.740             1.000            1.000
Chain 1:    200       -13321.906             3.261            5.521
Chain 1:    300        -9681.514             2.299            1.000
Chain 1:    400       -10571.848             1.745            1.000
Chain 1:    500        -8650.075             1.441            0.376
Chain 1:    600        -8104.335             1.212            0.376
Chain 1:    700        -8137.948             1.039            0.222
Chain 1:    800        -8572.677             0.916            0.222
Chain 1:    900        -8526.219             0.815            0.084
Chain 1:   1000        -8120.495             0.738            0.084
Chain 1:   1100        -8426.086             0.642            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8023.298             0.095            0.051
Chain 1:   1300        -8369.048             0.061            0.050
Chain 1:   1400        -8170.205             0.055            0.050
Chain 1:   1500        -8208.787             0.033            0.041
Chain 1:   1600        -8196.294             0.027            0.036
Chain 1:   1700        -8098.978             0.028            0.036
Chain 1:   1800        -7997.737             0.024            0.024
Chain 1:   1900        -8120.551             0.025            0.024
Chain 1:   2000        -8084.313             0.020            0.015
Chain 1:   2100        -8215.421             0.018            0.015
Chain 1:   2200        -8031.270             0.016            0.015
Chain 1:   2300        -8109.652             0.012            0.013
Chain 1:   2400        -8179.184             0.011            0.012
Chain 1:   2500        -8124.388             0.011            0.012
Chain 1:   2600        -8123.683             0.011            0.012
Chain 1:   2700        -8041.130             0.011            0.010
Chain 1:   2800        -8003.751             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8429270.425             1.000            1.000
Chain 1:    200     -1587991.734             2.654            4.308
Chain 1:    300      -891208.547             2.030            1.000
Chain 1:    400      -457364.129             1.760            1.000
Chain 1:    500      -357355.531             1.464            0.949
Chain 1:    600      -232358.282             1.309            0.949
Chain 1:    700      -118821.140             1.259            0.949
Chain 1:    800       -86077.399             1.149            0.949
Chain 1:    900       -66466.910             1.054            0.782
Chain 1:   1000       -51300.913             0.978            0.782
Chain 1:   1100       -38812.538             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37994.614             0.482            0.380
Chain 1:   1300       -25987.622             0.450            0.380
Chain 1:   1400       -25710.301             0.356            0.322
Chain 1:   1500       -22306.811             0.343            0.322
Chain 1:   1600       -21526.175             0.293            0.296
Chain 1:   1700       -20404.344             0.203            0.295
Chain 1:   1800       -20349.563             0.165            0.153
Chain 1:   1900       -20675.692             0.137            0.055
Chain 1:   2000       -19189.297             0.116            0.055
Chain 1:   2100       -19427.555             0.085            0.036
Chain 1:   2200       -19653.548             0.084            0.036
Chain 1:   2300       -19271.195             0.039            0.020
Chain 1:   2400       -19043.344             0.040            0.020
Chain 1:   2500       -18845.159             0.025            0.016
Chain 1:   2600       -18475.546             0.024            0.016
Chain 1:   2700       -18432.643             0.018            0.012
Chain 1:   2800       -18149.379             0.020            0.016
Chain 1:   2900       -18430.625             0.020            0.015
Chain 1:   3000       -18416.852             0.012            0.012
Chain 1:   3100       -18501.786             0.011            0.012
Chain 1:   3200       -18192.546             0.012            0.015
Chain 1:   3300       -18397.257             0.011            0.012
Chain 1:   3400       -17872.195             0.013            0.015
Chain 1:   3500       -18483.910             0.015            0.016
Chain 1:   3600       -17790.861             0.017            0.016
Chain 1:   3700       -18177.399             0.019            0.017
Chain 1:   3800       -17137.405             0.023            0.021
Chain 1:   3900       -17133.537             0.022            0.021
Chain 1:   4000       -17250.880             0.022            0.021
Chain 1:   4100       -17164.583             0.022            0.021
Chain 1:   4200       -16980.951             0.022            0.021
Chain 1:   4300       -17119.291             0.021            0.021
Chain 1:   4400       -17076.180             0.019            0.011
Chain 1:   4500       -16978.703             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001333 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12992.330             1.000            1.000
Chain 1:    200        -9891.546             0.657            1.000
Chain 1:    300        -8484.429             0.493            0.313
Chain 1:    400        -8636.854             0.374            0.313
Chain 1:    500        -8353.908             0.306            0.166
Chain 1:    600        -8398.394             0.256            0.166
Chain 1:    700        -8310.798             0.221            0.034
Chain 1:    800        -8323.596             0.194            0.034
Chain 1:    900        -8345.223             0.172            0.018
Chain 1:   1000        -8413.863             0.156            0.018
Chain 1:   1100        -8448.372             0.056            0.011
Chain 1:   1200        -8342.794             0.026            0.011
Chain 1:   1300        -8279.035             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46528.491             1.000            1.000
Chain 1:    200       -16139.861             1.441            1.883
Chain 1:    300        -9097.887             1.219            1.000
Chain 1:    400        -8724.061             0.925            1.000
Chain 1:    500        -9129.148             0.749            0.774
Chain 1:    600        -8402.628             0.638            0.774
Chain 1:    700        -8000.458             0.554            0.086
Chain 1:    800        -8129.665             0.487            0.086
Chain 1:    900        -8016.800             0.435            0.050
Chain 1:   1000        -8197.439             0.393            0.050
Chain 1:   1100        -8033.278             0.295            0.044
Chain 1:   1200        -7732.371             0.111            0.043
Chain 1:   1300        -8030.339             0.037            0.039
Chain 1:   1400        -8143.116             0.034            0.037
Chain 1:   1500        -7799.581             0.034            0.037
Chain 1:   1600        -7949.548             0.028            0.022
Chain 1:   1700        -7815.372             0.024            0.020
Chain 1:   1800        -7850.508             0.023            0.020
Chain 1:   1900        -7819.627             0.022            0.020
Chain 1:   2000        -7907.582             0.021            0.019
Chain 1:   2100        -7813.913             0.020            0.017
Chain 1:   2200        -7976.742             0.018            0.017
Chain 1:   2300        -7818.597             0.017            0.017
Chain 1:   2400        -7800.783             0.015            0.017
Chain 1:   2500        -7834.260             0.011            0.012
Chain 1:   2600        -7745.830             0.011            0.011
Chain 1:   2700        -7663.367             0.010            0.011
Chain 1:   2800        -7844.040             0.012            0.011
Chain 1:   2900        -7598.709             0.015            0.012
Chain 1:   3000        -7755.184             0.016            0.020
Chain 1:   3100        -7738.560             0.015            0.020
Chain 1:   3200        -7955.481             0.015            0.020
Chain 1:   3300        -7665.093             0.017            0.020
Chain 1:   3400        -7907.640             0.020            0.023
Chain 1:   3500        -7653.983             0.023            0.027
Chain 1:   3600        -7718.896             0.023            0.027
Chain 1:   3700        -7670.196             0.022            0.027
Chain 1:   3800        -7669.856             0.020            0.027
Chain 1:   3900        -7629.507             0.017            0.020
Chain 1:   4000        -7621.360             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003175 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87501.618             1.000            1.000
Chain 1:    200       -14130.957             3.096            5.192
Chain 1:    300       -10389.872             2.184            1.000
Chain 1:    400       -11881.638             1.669            1.000
Chain 1:    500        -9269.333             1.392            0.360
Chain 1:    600        -9717.109             1.168            0.360
Chain 1:    700        -9129.275             1.010            0.282
Chain 1:    800        -8673.682             0.890            0.282
Chain 1:    900        -8730.813             0.792            0.126
Chain 1:   1000        -9129.340             0.717            0.126
Chain 1:   1100        -9202.298             0.618            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8759.043             0.104            0.053
Chain 1:   1300        -9033.397             0.071            0.051
Chain 1:   1400        -9009.504             0.059            0.046
Chain 1:   1500        -8914.160             0.032            0.044
Chain 1:   1600        -9016.151             0.028            0.030
Chain 1:   1700        -9078.789             0.022            0.011
Chain 1:   1800        -8641.259             0.022            0.011
Chain 1:   1900        -8746.176             0.023            0.012
Chain 1:   2000        -8723.731             0.019            0.011
Chain 1:   2100        -8700.576             0.018            0.011
Chain 1:   2200        -8667.749             0.013            0.011
Chain 1:   2300        -8801.727             0.012            0.011
Chain 1:   2400        -8644.101             0.013            0.011
Chain 1:   2500        -8715.993             0.013            0.011
Chain 1:   2600        -8629.345             0.013            0.010
Chain 1:   2700        -8665.903             0.013            0.010
Chain 1:   2800        -8624.461             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003275 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393870.922             1.000            1.000
Chain 1:    200     -1583809.216             2.650            4.300
Chain 1:    300      -891894.138             2.025            1.000
Chain 1:    400      -458488.884             1.755            1.000
Chain 1:    500      -358702.375             1.460            0.945
Chain 1:    600      -233777.149             1.306            0.945
Chain 1:    700      -119960.820             1.255            0.945
Chain 1:    800       -87115.904             1.145            0.945
Chain 1:    900       -67466.144             1.050            0.776
Chain 1:   1000       -52262.315             0.974            0.776
Chain 1:   1100       -39726.465             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38910.073             0.478            0.377
Chain 1:   1300       -26848.758             0.445            0.377
Chain 1:   1400       -26568.616             0.352            0.316
Chain 1:   1500       -23149.522             0.339            0.316
Chain 1:   1600       -22364.519             0.289            0.291
Chain 1:   1700       -21235.902             0.199            0.291
Chain 1:   1800       -21179.751             0.162            0.148
Chain 1:   1900       -21506.348             0.134            0.053
Chain 1:   2000       -20015.192             0.112            0.053
Chain 1:   2100       -20253.915             0.082            0.035
Chain 1:   2200       -20480.669             0.081            0.035
Chain 1:   2300       -20097.482             0.038            0.019
Chain 1:   2400       -19869.407             0.038            0.019
Chain 1:   2500       -19671.295             0.024            0.015
Chain 1:   2600       -19301.120             0.023            0.015
Chain 1:   2700       -19258.051             0.018            0.012
Chain 1:   2800       -18974.608             0.019            0.015
Chain 1:   2900       -19256.138             0.019            0.015
Chain 1:   3000       -19242.387             0.012            0.012
Chain 1:   3100       -19327.358             0.011            0.011
Chain 1:   3200       -19017.788             0.011            0.015
Chain 1:   3300       -19222.747             0.010            0.011
Chain 1:   3400       -18697.099             0.012            0.015
Chain 1:   3500       -19309.750             0.014            0.015
Chain 1:   3600       -18615.557             0.016            0.015
Chain 1:   3700       -19002.973             0.018            0.016
Chain 1:   3800       -17961.168             0.022            0.020
Chain 1:   3900       -17957.275             0.021            0.020
Chain 1:   4000       -18074.613             0.021            0.020
Chain 1:   4100       -17988.209             0.021            0.020
Chain 1:   4200       -17804.199             0.021            0.020
Chain 1:   4300       -17942.810             0.021            0.020
Chain 1:   4400       -17899.391             0.018            0.010
Chain 1:   4500       -17801.863             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001284 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12841.378             1.000            1.000
Chain 1:    200        -9816.549             0.654            1.000
Chain 1:    300        -8484.444             0.488            0.308
Chain 1:    400        -8619.662             0.370            0.308
Chain 1:    500        -8619.588             0.296            0.157
Chain 1:    600        -8398.805             0.251            0.157
Chain 1:    700        -8305.543             0.217            0.026
Chain 1:    800        -8328.911             0.190            0.026
Chain 1:    900        -8444.828             0.171            0.016
Chain 1:   1000        -8346.763             0.155            0.016
Chain 1:   1100        -8378.864             0.055            0.014
Chain 1:   1200        -8348.133             0.025            0.012
Chain 1:   1300        -8264.107             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58710.469             1.000            1.000
Chain 1:    200       -18177.868             1.615            2.230
Chain 1:    300        -8916.381             1.423            1.039
Chain 1:    400        -8134.074             1.091            1.039
Chain 1:    500        -8767.588             0.887            1.000
Chain 1:    600        -8550.414             0.744            1.000
Chain 1:    700        -7795.477             0.651            0.097
Chain 1:    800        -8142.870             0.575            0.097
Chain 1:    900        -7902.852             0.515            0.096
Chain 1:   1000        -7924.315             0.463            0.096
Chain 1:   1100        -7871.826             0.364            0.072
Chain 1:   1200        -7825.696             0.142            0.043
Chain 1:   1300        -7890.714             0.039            0.030
Chain 1:   1400        -7919.589             0.029            0.025
Chain 1:   1500        -7612.004             0.026            0.025
Chain 1:   1600        -7816.379             0.026            0.026
Chain 1:   1700        -7629.814             0.019            0.024
Chain 1:   1800        -7659.643             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003493 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86716.324             1.000            1.000
Chain 1:    200       -13975.061             3.103            5.205
Chain 1:    300       -10309.691             2.187            1.000
Chain 1:    400       -11437.052             1.665            1.000
Chain 1:    500        -9292.868             1.378            0.356
Chain 1:    600        -8889.275             1.156            0.356
Chain 1:    700        -8865.493             0.991            0.231
Chain 1:    800        -9505.883             0.876            0.231
Chain 1:    900        -9097.708             0.783            0.099
Chain 1:   1000        -9034.645             0.706            0.099
Chain 1:   1100        -9129.641             0.607            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8646.168             0.092            0.056
Chain 1:   1300        -8997.307             0.060            0.045
Chain 1:   1400        -8998.329             0.050            0.045
Chain 1:   1500        -8866.556             0.029            0.039
Chain 1:   1600        -8974.877             0.025            0.015
Chain 1:   1700        -9051.997             0.026            0.015
Chain 1:   1800        -8627.955             0.024            0.015
Chain 1:   1900        -8728.736             0.021            0.012
Chain 1:   2000        -8703.363             0.020            0.012
Chain 1:   2100        -8829.008             0.021            0.014
Chain 1:   2200        -8631.403             0.018            0.014
Chain 1:   2300        -8723.658             0.015            0.012
Chain 1:   2400        -8792.364             0.015            0.012
Chain 1:   2500        -8738.668             0.015            0.012
Chain 1:   2600        -8740.148             0.013            0.011
Chain 1:   2700        -8656.783             0.014            0.011
Chain 1:   2800        -8616.514             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003464 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401727.369             1.000            1.000
Chain 1:    200     -1585430.286             2.650            4.299
Chain 1:    300      -892309.303             2.025            1.000
Chain 1:    400      -459064.489             1.755            1.000
Chain 1:    500      -359098.708             1.460            0.944
Chain 1:    600      -233858.935             1.306            0.944
Chain 1:    700      -119871.613             1.255            0.944
Chain 1:    800       -87047.857             1.145            0.944
Chain 1:    900       -67355.352             1.050            0.777
Chain 1:   1000       -52127.067             0.975            0.777
Chain 1:   1100       -39583.913             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38759.606             0.479            0.377
Chain 1:   1300       -26692.861             0.446            0.377
Chain 1:   1400       -26411.276             0.353            0.317
Chain 1:   1500       -22992.347             0.340            0.317
Chain 1:   1600       -22207.598             0.290            0.292
Chain 1:   1700       -21078.137             0.200            0.292
Chain 1:   1800       -21021.749             0.163            0.149
Chain 1:   1900       -21347.992             0.135            0.054
Chain 1:   2000       -19857.333             0.113            0.054
Chain 1:   2100       -20095.863             0.083            0.035
Chain 1:   2200       -20322.689             0.082            0.035
Chain 1:   2300       -19939.525             0.038            0.019
Chain 1:   2400       -19711.512             0.038            0.019
Chain 1:   2500       -19513.716             0.025            0.015
Chain 1:   2600       -19143.674             0.023            0.015
Chain 1:   2700       -19100.517             0.018            0.012
Chain 1:   2800       -18817.387             0.019            0.015
Chain 1:   2900       -19098.738             0.019            0.015
Chain 1:   3000       -19084.885             0.012            0.012
Chain 1:   3100       -19169.928             0.011            0.012
Chain 1:   3200       -18860.481             0.011            0.015
Chain 1:   3300       -19065.297             0.011            0.012
Chain 1:   3400       -18540.090             0.012            0.015
Chain 1:   3500       -19152.211             0.014            0.015
Chain 1:   3600       -18458.544             0.016            0.015
Chain 1:   3700       -18845.638             0.018            0.016
Chain 1:   3800       -17804.881             0.022            0.021
Chain 1:   3900       -17801.017             0.021            0.021
Chain 1:   4000       -17918.312             0.022            0.021
Chain 1:   4100       -17832.069             0.022            0.021
Chain 1:   4200       -17648.183             0.021            0.021
Chain 1:   4300       -17786.650             0.021            0.021
Chain 1:   4400       -17743.391             0.018            0.010
Chain 1:   4500       -17645.909             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001284 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12101.170             1.000            1.000
Chain 1:    200        -9101.503             0.665            1.000
Chain 1:    300        -7794.915             0.499            0.330
Chain 1:    400        -7931.835             0.379            0.330
Chain 1:    500        -7822.174             0.306            0.168
Chain 1:    600        -7746.294             0.256            0.168
Chain 1:    700        -7658.458             0.221            0.017
Chain 1:    800        -7702.138             0.194            0.017
Chain 1:    900        -7821.828             0.175            0.015
Chain 1:   1000        -7722.965             0.158            0.015
Chain 1:   1100        -7709.306             0.059            0.014
Chain 1:   1200        -7669.161             0.026            0.013
Chain 1:   1300        -7626.975             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001398 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56361.486             1.000            1.000
Chain 1:    200       -17119.646             1.646            2.292
Chain 1:    300        -8605.142             1.427            1.000
Chain 1:    400        -9115.821             1.084            1.000
Chain 1:    500        -8629.401             0.879            0.989
Chain 1:    600        -8490.538             0.735            0.989
Chain 1:    700        -7809.027             0.643            0.087
Chain 1:    800        -8091.209             0.567            0.087
Chain 1:    900        -7688.196             0.509            0.056
Chain 1:   1000        -7868.278             0.461            0.056
Chain 1:   1100        -7673.503             0.363            0.056
Chain 1:   1200        -7587.791             0.135            0.052
Chain 1:   1300        -7721.117             0.038            0.035
Chain 1:   1400        -7867.027             0.034            0.025
Chain 1:   1500        -7631.215             0.032            0.025
Chain 1:   1600        -7575.608             0.031            0.025
Chain 1:   1700        -7516.199             0.023            0.023
Chain 1:   1800        -7587.516             0.020            0.019
Chain 1:   1900        -7631.852             0.016            0.017
Chain 1:   2000        -7633.815             0.013            0.011
Chain 1:   2100        -7657.425             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003141 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86672.284             1.000            1.000
Chain 1:    200       -13212.534             3.280            5.560
Chain 1:    300        -9600.956             2.312            1.000
Chain 1:    400       -10323.161             1.751            1.000
Chain 1:    500        -8541.510             1.443            0.376
Chain 1:    600        -8188.528             1.210            0.376
Chain 1:    700        -8230.429             1.038            0.209
Chain 1:    800        -8831.333             0.916            0.209
Chain 1:    900        -8340.971             0.821            0.070
Chain 1:   1000        -8186.266             0.741            0.070
Chain 1:   1100        -8447.559             0.644            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8081.872             0.092            0.059
Chain 1:   1300        -8293.883             0.057            0.045
Chain 1:   1400        -8294.893             0.050            0.043
Chain 1:   1500        -8183.202             0.031            0.031
Chain 1:   1600        -8287.963             0.028            0.026
Chain 1:   1700        -8376.351             0.028            0.026
Chain 1:   1800        -7970.396             0.027            0.026
Chain 1:   1900        -8067.971             0.022            0.019
Chain 1:   2000        -8039.725             0.021            0.014
Chain 1:   2100        -8159.914             0.019            0.014
Chain 1:   2200        -7953.686             0.017            0.014
Chain 1:   2300        -8104.075             0.016            0.014
Chain 1:   2400        -8110.966             0.016            0.014
Chain 1:   2500        -8081.477             0.015            0.013
Chain 1:   2600        -8080.215             0.014            0.012
Chain 1:   2700        -7992.621             0.014            0.012
Chain 1:   2800        -7958.583             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003509 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397444.290             1.000            1.000
Chain 1:    200     -1583120.107             2.652            4.304
Chain 1:    300      -890781.049             2.027            1.000
Chain 1:    400      -457205.946             1.757            1.000
Chain 1:    500      -357818.003             1.462            0.948
Chain 1:    600      -232756.097             1.307            0.948
Chain 1:    700      -119018.719             1.257            0.948
Chain 1:    800       -86189.683             1.148            0.948
Chain 1:    900       -66522.709             1.053            0.777
Chain 1:   1000       -51305.257             0.977            0.777
Chain 1:   1100       -38767.742             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37941.852             0.481            0.381
Chain 1:   1300       -25890.554             0.450            0.381
Chain 1:   1400       -25607.079             0.357            0.323
Chain 1:   1500       -22192.049             0.344            0.323
Chain 1:   1600       -21407.475             0.294            0.297
Chain 1:   1700       -20280.935             0.204            0.296
Chain 1:   1800       -20224.824             0.166            0.154
Chain 1:   1900       -20550.786             0.138            0.056
Chain 1:   2000       -19062.005             0.116            0.056
Chain 1:   2100       -19300.462             0.085            0.037
Chain 1:   2200       -19526.695             0.084            0.037
Chain 1:   2300       -19144.129             0.040            0.020
Chain 1:   2400       -18916.314             0.040            0.020
Chain 1:   2500       -18718.224             0.026            0.016
Chain 1:   2600       -18348.714             0.024            0.016
Chain 1:   2700       -18305.796             0.019            0.012
Chain 1:   2800       -18022.697             0.020            0.016
Chain 1:   2900       -18303.897             0.020            0.015
Chain 1:   3000       -18290.059             0.012            0.012
Chain 1:   3100       -18375.019             0.011            0.012
Chain 1:   3200       -18065.857             0.012            0.015
Chain 1:   3300       -18270.484             0.011            0.012
Chain 1:   3400       -17745.644             0.013            0.015
Chain 1:   3500       -18357.121             0.015            0.016
Chain 1:   3600       -17664.366             0.017            0.016
Chain 1:   3700       -18050.772             0.019            0.017
Chain 1:   3800       -17011.275             0.023            0.021
Chain 1:   3900       -17007.446             0.022            0.021
Chain 1:   4000       -17124.746             0.022            0.021
Chain 1:   4100       -17038.510             0.023            0.021
Chain 1:   4200       -16854.975             0.022            0.021
Chain 1:   4300       -16993.243             0.022            0.021
Chain 1:   4400       -16950.243             0.019            0.011
Chain 1:   4500       -16852.777             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001246 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13369.228             1.000            1.000
Chain 1:    200       -10008.249             0.668            1.000
Chain 1:    300        -8591.896             0.500            0.336
Chain 1:    400        -8334.243             0.383            0.336
Chain 1:    500        -8381.462             0.307            0.165
Chain 1:    600        -8224.054             0.259            0.165
Chain 1:    700        -8130.249             0.224            0.031
Chain 1:    800        -8378.502             0.200            0.031
Chain 1:    900        -8118.961             0.181            0.031
Chain 1:   1000        -8145.249             0.163            0.031
Chain 1:   1100        -8230.855             0.064            0.030
Chain 1:   1200        -8136.592             0.032            0.019
Chain 1:   1300        -8130.330             0.015            0.012
Chain 1:   1400        -8122.469             0.012            0.012
Chain 1:   1500        -8209.169             0.013            0.012
Chain 1:   1600        -8159.716             0.012            0.011
Chain 1:   1700        -8102.810             0.011            0.010
Chain 1:   1800        -8077.982             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47613.483             1.000            1.000
Chain 1:    200       -16075.334             1.481            1.962
Chain 1:    300        -8718.945             1.269            1.000
Chain 1:    400        -8559.974             0.956            1.000
Chain 1:    500        -8538.562             0.765            0.844
Chain 1:    600        -8409.218             0.640            0.844
Chain 1:    700        -8049.890             0.555            0.045
Chain 1:    800        -7987.689             0.487            0.045
Chain 1:    900        -7837.432             0.435            0.019
Chain 1:   1000        -7973.618             0.393            0.019
Chain 1:   1100        -7617.537             0.298            0.019
Chain 1:   1200        -7903.426             0.105            0.019
Chain 1:   1300        -7855.660             0.021            0.019
Chain 1:   1400        -7734.807             0.021            0.017
Chain 1:   1500        -7641.451             0.022            0.017
Chain 1:   1600        -7794.209             0.023            0.019
Chain 1:   1700        -7572.847             0.021            0.019
Chain 1:   1800        -7624.800             0.021            0.019
Chain 1:   1900        -7627.713             0.019            0.017
Chain 1:   2000        -7704.150             0.018            0.016
Chain 1:   2100        -7624.610             0.015            0.012
Chain 1:   2200        -7753.451             0.013            0.012
Chain 1:   2300        -7606.091             0.014            0.016
Chain 1:   2400        -7676.133             0.013            0.012
Chain 1:   2500        -7593.970             0.013            0.011
Chain 1:   2600        -7554.409             0.012            0.010
Chain 1:   2700        -7497.529             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003549 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86611.166             1.000            1.000
Chain 1:    200       -13788.578             3.141            5.281
Chain 1:    300       -10125.242             2.214            1.000
Chain 1:    400       -10974.671             1.680            1.000
Chain 1:    500        -9126.072             1.385            0.362
Chain 1:    600        -8884.513             1.158            0.362
Chain 1:    700        -8575.183             0.998            0.203
Chain 1:    800        -9125.054             0.881            0.203
Chain 1:    900        -8880.443             0.786            0.077
Chain 1:   1000        -8666.933             0.710            0.077
Chain 1:   1100        -8913.992             0.613            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8467.141             0.090            0.053
Chain 1:   1300        -8677.869             0.056            0.036
Chain 1:   1400        -8814.029             0.050            0.028
Chain 1:   1500        -8686.182             0.031            0.028
Chain 1:   1600        -8804.883             0.030            0.028
Chain 1:   1700        -8875.379             0.027            0.025
Chain 1:   1800        -8455.461             0.026            0.025
Chain 1:   1900        -8554.203             0.024            0.024
Chain 1:   2000        -8528.541             0.022            0.015
Chain 1:   2100        -8653.525             0.021            0.015
Chain 1:   2200        -8459.811             0.018            0.015
Chain 1:   2300        -8549.011             0.016            0.014
Chain 1:   2400        -8618.115             0.016            0.013
Chain 1:   2500        -8564.302             0.015            0.012
Chain 1:   2600        -8565.220             0.013            0.010
Chain 1:   2700        -8482.126             0.014            0.010
Chain 1:   2800        -8442.676             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397353.624             1.000            1.000
Chain 1:    200     -1585727.396             2.648            4.296
Chain 1:    300      -891519.831             2.025            1.000
Chain 1:    400      -457966.975             1.755            1.000
Chain 1:    500      -358078.143             1.460            0.947
Chain 1:    600      -233110.284             1.306            0.947
Chain 1:    700      -119479.635             1.255            0.947
Chain 1:    800       -86648.531             1.146            0.947
Chain 1:    900       -67022.485             1.051            0.779
Chain 1:   1000       -51834.659             0.975            0.779
Chain 1:   1100       -39321.740             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38504.109             0.480            0.379
Chain 1:   1300       -26477.959             0.447            0.379
Chain 1:   1400       -26198.901             0.354            0.318
Chain 1:   1500       -22788.758             0.341            0.318
Chain 1:   1600       -22005.829             0.291            0.293
Chain 1:   1700       -20882.102             0.201            0.293
Chain 1:   1800       -20826.898             0.163            0.150
Chain 1:   1900       -21153.123             0.135            0.054
Chain 1:   2000       -19665.099             0.114            0.054
Chain 1:   2100       -19903.687             0.083            0.036
Chain 1:   2200       -20129.701             0.082            0.036
Chain 1:   2300       -19747.255             0.039            0.019
Chain 1:   2400       -19519.338             0.039            0.019
Chain 1:   2500       -19321.018             0.025            0.015
Chain 1:   2600       -18951.423             0.023            0.015
Chain 1:   2700       -18908.557             0.018            0.012
Chain 1:   2800       -18625.120             0.019            0.015
Chain 1:   2900       -18906.494             0.019            0.015
Chain 1:   3000       -18892.775             0.012            0.012
Chain 1:   3100       -18977.666             0.011            0.012
Chain 1:   3200       -18668.432             0.011            0.015
Chain 1:   3300       -18873.148             0.011            0.012
Chain 1:   3400       -18347.932             0.012            0.015
Chain 1:   3500       -18959.871             0.015            0.015
Chain 1:   3600       -18266.611             0.016            0.015
Chain 1:   3700       -18653.275             0.018            0.017
Chain 1:   3800       -17612.872             0.023            0.021
Chain 1:   3900       -17608.993             0.021            0.021
Chain 1:   4000       -17726.358             0.022            0.021
Chain 1:   4100       -17639.971             0.022            0.021
Chain 1:   4200       -17456.295             0.021            0.021
Chain 1:   4300       -17594.705             0.021            0.021
Chain 1:   4400       -17551.546             0.018            0.011
Chain 1:   4500       -17454.032             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12362.475             1.000            1.000
Chain 1:    200        -9154.341             0.675            1.000
Chain 1:    300        -8096.470             0.494            0.350
Chain 1:    400        -8063.397             0.371            0.350
Chain 1:    500        -7962.939             0.300            0.131
Chain 1:    600        -7897.741             0.251            0.131
Chain 1:    700        -7808.870             0.217            0.013
Chain 1:    800        -7817.082             0.190            0.013
Chain 1:    900        -7716.593             0.170            0.013
Chain 1:   1000        -7874.352             0.155            0.013
Chain 1:   1100        -7844.481             0.056            0.013
Chain 1:   1200        -7835.139             0.021            0.011
Chain 1:   1300        -7779.444             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -60017.108             1.000            1.000
Chain 1:    200       -17827.530             1.683            2.367
Chain 1:    300        -8659.722             1.475            1.059
Chain 1:    400        -8301.581             1.117            1.059
Chain 1:    500        -8057.390             0.900            1.000
Chain 1:    600        -8418.679             0.757            1.000
Chain 1:    700        -7883.869             0.658            0.068
Chain 1:    800        -8201.390             0.581            0.068
Chain 1:    900        -7888.239             0.521            0.043
Chain 1:   1000        -7801.866             0.470            0.043
Chain 1:   1100        -7709.516             0.371            0.043
Chain 1:   1200        -7567.922             0.136            0.040
Chain 1:   1300        -7638.984             0.031            0.039
Chain 1:   1400        -7903.550             0.030            0.033
Chain 1:   1500        -7559.191             0.032            0.039
Chain 1:   1600        -7537.879             0.028            0.033
Chain 1:   1700        -7490.358             0.022            0.019
Chain 1:   1800        -7523.916             0.018            0.012
Chain 1:   1900        -7525.516             0.014            0.011
Chain 1:   2000        -7554.322             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00313 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86092.239             1.000            1.000
Chain 1:    200       -13385.622             3.216            5.432
Chain 1:    300        -9772.301             2.267            1.000
Chain 1:    400       -10493.743             1.718            1.000
Chain 1:    500        -8744.889             1.414            0.370
Chain 1:    600        -8260.281             1.188            0.370
Chain 1:    700        -8330.785             1.020            0.200
Chain 1:    800        -9020.881             0.902            0.200
Chain 1:    900        -8540.853             0.808            0.076
Chain 1:   1000        -8396.181             0.729            0.076
Chain 1:   1100        -8595.841             0.631            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8302.272             0.091            0.059
Chain 1:   1300        -8494.235             0.057            0.056
Chain 1:   1400        -8495.259             0.050            0.035
Chain 1:   1500        -8345.837             0.032            0.023
Chain 1:   1600        -8456.740             0.027            0.023
Chain 1:   1700        -8545.760             0.027            0.023
Chain 1:   1800        -8138.440             0.025            0.023
Chain 1:   1900        -8234.265             0.020            0.018
Chain 1:   2000        -8206.682             0.019            0.018
Chain 1:   2100        -8327.471             0.018            0.015
Chain 1:   2200        -8155.358             0.016            0.015
Chain 1:   2300        -8271.787             0.016            0.014
Chain 1:   2400        -8283.328             0.016            0.014
Chain 1:   2500        -8244.970             0.014            0.013
Chain 1:   2600        -8244.827             0.013            0.012
Chain 1:   2700        -8159.978             0.013            0.012
Chain 1:   2800        -8124.674             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003458 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412238.546             1.000            1.000
Chain 1:    200     -1583500.999             2.656            4.312
Chain 1:    300      -891288.883             2.030            1.000
Chain 1:    400      -458178.714             1.759            1.000
Chain 1:    500      -358646.469             1.462            0.945
Chain 1:    600      -233351.364             1.308            0.945
Chain 1:    700      -119333.488             1.258            0.945
Chain 1:    800       -86497.790             1.148            0.945
Chain 1:    900       -66783.028             1.053            0.777
Chain 1:   1000       -51540.165             0.977            0.777
Chain 1:   1100       -38989.238             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38158.623             0.481            0.380
Chain 1:   1300       -26089.745             0.449            0.380
Chain 1:   1400       -25804.811             0.356            0.322
Chain 1:   1500       -22386.634             0.343            0.322
Chain 1:   1600       -21601.540             0.293            0.296
Chain 1:   1700       -20472.531             0.203            0.295
Chain 1:   1800       -20415.951             0.166            0.153
Chain 1:   1900       -20741.938             0.138            0.055
Chain 1:   2000       -19252.149             0.116            0.055
Chain 1:   2100       -19490.380             0.085            0.036
Chain 1:   2200       -19717.088             0.084            0.036
Chain 1:   2300       -19334.129             0.039            0.020
Chain 1:   2400       -19106.273             0.040            0.020
Chain 1:   2500       -18908.456             0.025            0.016
Chain 1:   2600       -18538.593             0.024            0.016
Chain 1:   2700       -18495.541             0.018            0.012
Chain 1:   2800       -18212.569             0.020            0.016
Chain 1:   2900       -18493.798             0.020            0.015
Chain 1:   3000       -18479.850             0.012            0.012
Chain 1:   3100       -18564.868             0.011            0.012
Chain 1:   3200       -18255.585             0.012            0.015
Chain 1:   3300       -18460.291             0.011            0.012
Chain 1:   3400       -17935.378             0.013            0.015
Chain 1:   3500       -18547.038             0.015            0.016
Chain 1:   3600       -17853.989             0.017            0.016
Chain 1:   3700       -18240.660             0.019            0.017
Chain 1:   3800       -17200.798             0.023            0.021
Chain 1:   3900       -17196.985             0.022            0.021
Chain 1:   4000       -17314.253             0.022            0.021
Chain 1:   4100       -17228.066             0.022            0.021
Chain 1:   4200       -17044.390             0.022            0.021
Chain 1:   4300       -17182.692             0.021            0.021
Chain 1:   4400       -17139.611             0.019            0.011
Chain 1:   4500       -17042.170             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001351 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12896.397             1.000            1.000
Chain 1:    200        -9784.884             0.659            1.000
Chain 1:    300        -8645.335             0.483            0.318
Chain 1:    400        -8293.676             0.373            0.318
Chain 1:    500        -8473.334             0.303            0.132
Chain 1:    600        -8184.945             0.258            0.132
Chain 1:    700        -8041.076             0.224            0.042
Chain 1:    800        -8085.972             0.197            0.042
Chain 1:    900        -8180.650             0.176            0.035
Chain 1:   1000        -8229.812             0.159            0.035
Chain 1:   1100        -8177.190             0.060            0.021
Chain 1:   1200        -8084.208             0.029            0.018
Chain 1:   1300        -8022.767             0.017            0.012
Chain 1:   1400        -8045.419             0.013            0.012
Chain 1:   1500        -8075.196             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001502 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63973.343             1.000            1.000
Chain 1:    200       -18849.612             1.697            2.394
Chain 1:    300        -9061.763             1.491            1.080
Chain 1:    400       -10048.308             1.143            1.080
Chain 1:    500        -9091.712             0.935            1.000
Chain 1:    600        -9218.800             0.782            1.000
Chain 1:    700        -8665.166             0.679            0.105
Chain 1:    800        -8394.252             0.598            0.105
Chain 1:    900        -7911.882             0.539            0.098
Chain 1:   1000        -7834.396             0.486            0.098
Chain 1:   1100        -7677.686             0.388            0.064
Chain 1:   1200        -7557.014             0.150            0.061
Chain 1:   1300        -7905.724             0.046            0.044
Chain 1:   1400        -7595.742             0.041            0.041
Chain 1:   1500        -7509.438             0.031            0.032
Chain 1:   1600        -7730.300             0.033            0.032
Chain 1:   1700        -7765.905             0.027            0.029
Chain 1:   1800        -7739.862             0.024            0.020
Chain 1:   1900        -7567.441             0.020            0.020
Chain 1:   2000        -7548.551             0.019            0.020
Chain 1:   2100        -7521.161             0.018            0.016
Chain 1:   2200        -7765.668             0.019            0.023
Chain 1:   2300        -7562.233             0.018            0.023
Chain 1:   2400        -7668.382             0.015            0.014
Chain 1:   2500        -7568.719             0.015            0.014
Chain 1:   2600        -7493.744             0.013            0.013
Chain 1:   2700        -7531.986             0.013            0.013
Chain 1:   2800        -7599.591             0.014            0.013
Chain 1:   2900        -7318.956             0.015            0.013
Chain 1:   3000        -7493.712             0.017            0.014
Chain 1:   3100        -7476.914             0.017            0.014
Chain 1:   3200        -7687.023             0.017            0.014
Chain 1:   3300        -7368.797             0.019            0.014
Chain 1:   3400        -7616.145             0.020            0.023
Chain 1:   3500        -7392.629             0.022            0.027
Chain 1:   3600        -7455.395             0.022            0.027
Chain 1:   3700        -7412.747             0.022            0.027
Chain 1:   3800        -7382.635             0.022            0.027
Chain 1:   3900        -7355.039             0.018            0.023
Chain 1:   4000        -7351.216             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003078 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86863.060             1.000            1.000
Chain 1:    200       -14087.547             3.083            5.166
Chain 1:    300       -10285.142             2.179            1.000
Chain 1:    400       -12239.138             1.674            1.000
Chain 1:    500        -8678.620             1.421            0.410
Chain 1:    600        -8973.479             1.190            0.410
Chain 1:    700        -8573.463             1.026            0.370
Chain 1:    800        -8872.315             0.902            0.370
Chain 1:    900        -9013.331             0.804            0.160
Chain 1:   1000        -9024.402             0.724            0.160
Chain 1:   1100        -8946.607             0.624            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8396.536             0.114            0.047
Chain 1:   1300        -8862.153             0.083            0.047
Chain 1:   1400        -8760.878             0.068            0.034
Chain 1:   1500        -8716.092             0.027            0.033
Chain 1:   1600        -8834.127             0.025            0.016
Chain 1:   1700        -8872.422             0.021            0.013
Chain 1:   1800        -8409.623             0.023            0.013
Chain 1:   1900        -8520.304             0.023            0.013
Chain 1:   2000        -8540.364             0.023            0.013
Chain 1:   2100        -8622.917             0.023            0.013
Chain 1:   2200        -8402.547             0.019            0.013
Chain 1:   2300        -8615.859             0.017            0.013
Chain 1:   2400        -8411.131             0.018            0.013
Chain 1:   2500        -8488.217             0.018            0.013
Chain 1:   2600        -8395.498             0.018            0.013
Chain 1:   2700        -8433.108             0.018            0.013
Chain 1:   2800        -8385.571             0.013            0.011
Chain 1:   2900        -8499.084             0.013            0.011
Chain 1:   3000        -8407.716             0.014            0.011
Chain 1:   3100        -8375.619             0.013            0.011
Chain 1:   3200        -8346.142             0.011            0.011
Chain 1:   3300        -8611.647             0.012            0.011
Chain 1:   3400        -8660.001             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8435073.711             1.000            1.000
Chain 1:    200     -1585660.889             2.660            4.320
Chain 1:    300      -890598.278             2.033            1.000
Chain 1:    400      -457909.684             1.761            1.000
Chain 1:    500      -358021.705             1.465            0.945
Chain 1:    600      -233062.368             1.310            0.945
Chain 1:    700      -119552.472             1.259            0.945
Chain 1:    800       -86857.625             1.148            0.945
Chain 1:    900       -67257.670             1.053            0.780
Chain 1:   1000       -52114.140             0.977            0.780
Chain 1:   1100       -39640.235             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38831.014             0.478            0.376
Chain 1:   1300       -26817.900             0.445            0.376
Chain 1:   1400       -26544.917             0.352            0.315
Chain 1:   1500       -23139.863             0.338            0.315
Chain 1:   1600       -22360.691             0.288            0.291
Chain 1:   1700       -21236.717             0.199            0.291
Chain 1:   1800       -21182.315             0.161            0.147
Chain 1:   1900       -21509.350             0.134            0.053
Chain 1:   2000       -20020.410             0.112            0.053
Chain 1:   2100       -20258.709             0.082            0.035
Chain 1:   2200       -20485.621             0.081            0.035
Chain 1:   2300       -20102.249             0.038            0.019
Chain 1:   2400       -19874.057             0.038            0.019
Chain 1:   2500       -19675.984             0.024            0.015
Chain 1:   2600       -19305.229             0.023            0.015
Chain 1:   2700       -19262.014             0.018            0.012
Chain 1:   2800       -18978.390             0.019            0.015
Chain 1:   2900       -19260.070             0.019            0.015
Chain 1:   3000       -19246.160             0.012            0.012
Chain 1:   3100       -19331.276             0.011            0.011
Chain 1:   3200       -19021.337             0.011            0.015
Chain 1:   3300       -19226.603             0.010            0.011
Chain 1:   3400       -18700.375             0.012            0.015
Chain 1:   3500       -19313.861             0.014            0.015
Chain 1:   3600       -18618.452             0.016            0.015
Chain 1:   3700       -19006.757             0.018            0.016
Chain 1:   3800       -17963.151             0.022            0.020
Chain 1:   3900       -17959.205             0.021            0.020
Chain 1:   4000       -18076.534             0.021            0.020
Chain 1:   4100       -17990.090             0.021            0.020
Chain 1:   4200       -17805.653             0.021            0.020
Chain 1:   4300       -17944.538             0.021            0.020
Chain 1:   4400       -17900.761             0.018            0.010
Chain 1:   4500       -17803.189             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48479.941             1.000            1.000
Chain 1:    200       -19822.309             1.223            1.446
Chain 1:    300       -17785.476             0.853            1.000
Chain 1:    400       -12857.553             0.736            1.000
Chain 1:    500       -12110.503             0.601            0.383
Chain 1:    600       -15469.379             0.537            0.383
Chain 1:    700       -14633.238             0.468            0.217
Chain 1:    800       -19932.862             0.443            0.266
Chain 1:    900       -14906.289             0.431            0.266
Chain 1:   1000       -12376.194             0.409            0.266
Chain 1:   1100       -11355.591             0.318            0.217
Chain 1:   1200       -15222.907             0.199            0.217
Chain 1:   1300       -12142.984             0.212            0.254
Chain 1:   1400       -10256.790             0.192            0.217
Chain 1:   1500        -9788.266             0.191            0.217
Chain 1:   1600       -10809.520             0.179            0.204
Chain 1:   1700        -9751.227             0.184            0.204
Chain 1:   1800        -9688.162             0.158            0.184
Chain 1:   1900       -10349.353             0.131            0.109
Chain 1:   2000       -17601.481             0.151            0.109
Chain 1:   2100        -9259.255             0.233            0.184
Chain 1:   2200       -12715.476             0.234            0.184
Chain 1:   2300        -9304.860             0.246            0.184
Chain 1:   2400       -10119.036             0.235            0.109
Chain 1:   2500       -17858.421             0.274            0.272
Chain 1:   2600       -10161.305             0.340            0.367
Chain 1:   2700        -9051.153             0.342            0.367
Chain 1:   2800       -10943.311             0.358            0.367
Chain 1:   2900        -8818.739             0.376            0.367
Chain 1:   3000        -9143.352             0.338            0.272
Chain 1:   3100       -15010.586             0.287            0.272
Chain 1:   3200        -9325.253             0.321            0.367
Chain 1:   3300       -17711.038             0.332            0.391
Chain 1:   3400        -9886.087             0.403            0.433
Chain 1:   3500       -13395.000             0.386            0.391
Chain 1:   3600        -9840.927             0.346            0.361
Chain 1:   3700        -8947.918             0.344            0.361
Chain 1:   3800        -9080.391             0.328            0.361
Chain 1:   3900        -8945.285             0.305            0.361
Chain 1:   4000       -10280.143             0.315            0.361
Chain 1:   4100        -8690.758             0.294            0.262
Chain 1:   4200       -13276.655             0.268            0.262
Chain 1:   4300        -8868.991             0.270            0.262
Chain 1:   4400        -8828.775             0.191            0.183
Chain 1:   4500        -8626.101             0.167            0.130
Chain 1:   4600       -12355.570             0.161            0.130
Chain 1:   4700       -10736.611             0.167            0.151
Chain 1:   4800        -8526.714             0.191            0.183
Chain 1:   4900        -8662.299             0.191            0.183
Chain 1:   5000        -9308.423             0.185            0.183
Chain 1:   5100        -9147.293             0.168            0.151
Chain 1:   5200       -14283.521             0.170            0.151
Chain 1:   5300        -9410.913             0.172            0.151
Chain 1:   5400        -8559.577             0.181            0.151
Chain 1:   5500        -8450.023             0.180            0.151
Chain 1:   5600        -9390.756             0.160            0.100
Chain 1:   5700        -8776.281             0.152            0.099
Chain 1:   5800       -13049.853             0.159            0.099
Chain 1:   5900        -9231.835             0.199            0.100
Chain 1:   6000       -12065.524             0.215            0.235
Chain 1:   6100       -11469.137             0.219            0.235
Chain 1:   6200        -9916.799             0.198            0.157
Chain 1:   6300       -10771.456             0.155            0.100
Chain 1:   6400       -12020.341             0.155            0.104
Chain 1:   6500        -8533.151             0.195            0.157
Chain 1:   6600        -8370.059             0.187            0.157
Chain 1:   6700        -8499.060             0.181            0.157
Chain 1:   6800        -8384.993             0.150            0.104
Chain 1:   6900       -11012.647             0.132            0.104
Chain 1:   7000       -11910.602             0.116            0.079
Chain 1:   7100        -9118.913             0.142            0.104
Chain 1:   7200       -10285.213             0.137            0.104
Chain 1:   7300       -10814.515             0.134            0.104
Chain 1:   7400        -8582.046             0.150            0.113
Chain 1:   7500        -9618.513             0.120            0.108
Chain 1:   7600        -8406.273             0.132            0.113
Chain 1:   7700       -12085.500             0.161            0.144
Chain 1:   7800        -8759.261             0.198            0.239
Chain 1:   7900       -11006.415             0.194            0.204
Chain 1:   8000        -8233.756             0.221            0.260
Chain 1:   8100        -8839.337             0.197            0.204
Chain 1:   8200        -8321.827             0.192            0.204
Chain 1:   8300        -8230.614             0.188            0.204
Chain 1:   8400        -9778.686             0.178            0.158
Chain 1:   8500       -10087.506             0.170            0.158
Chain 1:   8600        -8941.320             0.168            0.158
Chain 1:   8700        -8403.009             0.144            0.128
Chain 1:   8800        -8271.892             0.108            0.069
Chain 1:   8900        -8477.902             0.090            0.064
Chain 1:   9000        -8394.147             0.057            0.062
Chain 1:   9100        -8234.393             0.052            0.031
Chain 1:   9200       -11829.460             0.077            0.031
Chain 1:   9300        -8094.663             0.122            0.064
Chain 1:   9400        -8178.249             0.107            0.031
Chain 1:   9500        -8039.739             0.105            0.024
Chain 1:   9600        -8689.596             0.100            0.024
Chain 1:   9700        -8640.911             0.094            0.019
Chain 1:   9800        -8428.113             0.095            0.024
Chain 1:   9900        -8693.410             0.096            0.025
Chain 1:   10000        -8172.890             0.101            0.031
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56821.559             1.000            1.000
Chain 1:    200       -17230.714             1.649            2.298
Chain 1:    300        -8691.330             1.427            1.000
Chain 1:    400        -8278.692             1.083            1.000
Chain 1:    500        -8413.913             0.869            0.983
Chain 1:    600        -8496.769             0.726            0.983
Chain 1:    700        -7945.201             0.632            0.069
Chain 1:    800        -8090.333             0.555            0.069
Chain 1:    900        -7970.997             0.495            0.050
Chain 1:   1000        -7885.597             0.447            0.050
Chain 1:   1100        -7911.298             0.347            0.018
Chain 1:   1200        -7752.070             0.120            0.018
Chain 1:   1300        -7776.503             0.022            0.016
Chain 1:   1400        -7928.018             0.019            0.016
Chain 1:   1500        -7699.522             0.020            0.018
Chain 1:   1600        -7655.800             0.019            0.018
Chain 1:   1700        -7597.252             0.013            0.015
Chain 1:   1800        -7657.985             0.012            0.011
Chain 1:   1900        -7631.657             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003637 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86349.962             1.000            1.000
Chain 1:    200       -13276.509             3.252            5.504
Chain 1:    300        -9756.004             2.288            1.000
Chain 1:    400       -10568.039             1.735            1.000
Chain 1:    500        -8614.556             1.434            0.361
Chain 1:    600        -8333.987             1.200            0.361
Chain 1:    700        -8730.554             1.035            0.227
Chain 1:    800        -8806.466             0.907            0.227
Chain 1:    900        -8628.641             0.809            0.077
Chain 1:   1000        -8359.096             0.731            0.077
Chain 1:   1100        -8586.507             0.634            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8322.386             0.086            0.034
Chain 1:   1300        -8355.578             0.051            0.032
Chain 1:   1400        -8350.089             0.043            0.032
Chain 1:   1500        -8383.950             0.021            0.026
Chain 1:   1600        -8389.361             0.017            0.021
Chain 1:   1700        -8324.988             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003092 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419135.992             1.000            1.000
Chain 1:    200     -1587471.386             2.652            4.303
Chain 1:    300      -891510.007             2.028            1.000
Chain 1:    400      -457542.075             1.758            1.000
Chain 1:    500      -357461.005             1.463            0.948
Chain 1:    600      -232385.455             1.308            0.948
Chain 1:    700      -118791.900             1.258            0.948
Chain 1:    800       -86025.403             1.148            0.948
Chain 1:    900       -66407.971             1.054            0.781
Chain 1:   1000       -51227.825             0.978            0.781
Chain 1:   1100       -38732.029             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37906.483             0.482            0.381
Chain 1:   1300       -25905.896             0.450            0.381
Chain 1:   1400       -25625.197             0.357            0.323
Chain 1:   1500       -22224.188             0.344            0.323
Chain 1:   1600       -21443.002             0.294            0.296
Chain 1:   1700       -20322.933             0.204            0.295
Chain 1:   1800       -20267.985             0.166            0.153
Chain 1:   1900       -20593.425             0.138            0.055
Chain 1:   2000       -19109.124             0.116            0.055
Chain 1:   2100       -19347.175             0.085            0.036
Chain 1:   2200       -19572.632             0.084            0.036
Chain 1:   2300       -19190.934             0.040            0.020
Chain 1:   2400       -18963.371             0.040            0.020
Chain 1:   2500       -18765.151             0.025            0.016
Chain 1:   2600       -18396.256             0.024            0.016
Chain 1:   2700       -18353.539             0.018            0.012
Chain 1:   2800       -18070.616             0.020            0.016
Chain 1:   2900       -18351.500             0.020            0.015
Chain 1:   3000       -18337.787             0.012            0.012
Chain 1:   3100       -18422.628             0.011            0.012
Chain 1:   3200       -18113.833             0.012            0.015
Chain 1:   3300       -18318.173             0.011            0.012
Chain 1:   3400       -17793.929             0.013            0.015
Chain 1:   3500       -18404.412             0.015            0.016
Chain 1:   3600       -17712.980             0.017            0.016
Chain 1:   3700       -18098.336             0.019            0.017
Chain 1:   3800       -17060.828             0.023            0.021
Chain 1:   3900       -17057.034             0.022            0.021
Chain 1:   4000       -17174.369             0.022            0.021
Chain 1:   4100       -17088.212             0.022            0.021
Chain 1:   4200       -16905.114             0.022            0.021
Chain 1:   4300       -17043.074             0.021            0.021
Chain 1:   4400       -17000.412             0.019            0.011
Chain 1:   4500       -16903.022             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001341 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50037.111             1.000            1.000
Chain 1:    200       -20992.048             1.192            1.384
Chain 1:    300       -18959.761             0.830            1.000
Chain 1:    400       -21773.815             0.655            1.000
Chain 1:    500       -14926.097             0.616            0.459
Chain 1:    600       -13749.186             0.527            0.459
Chain 1:    700       -18560.377             0.489            0.259
Chain 1:    800       -11922.218             0.498            0.459
Chain 1:    900       -15401.494             0.467            0.259
Chain 1:   1000       -13790.527             0.432            0.259
Chain 1:   1100       -13252.643             0.336            0.226
Chain 1:   1200       -12900.935             0.201            0.129
Chain 1:   1300       -12772.392             0.191            0.129
Chain 1:   1400       -11277.943             0.191            0.133
Chain 1:   1500       -11644.301             0.149            0.117
Chain 1:   1600       -12260.408             0.145            0.117
Chain 1:   1700       -11013.658             0.130            0.113
Chain 1:   1800       -18283.168             0.115            0.113
Chain 1:   1900       -10543.792             0.165            0.113
Chain 1:   2000       -20057.666             0.201            0.113
Chain 1:   2100       -10417.937             0.290            0.133
Chain 1:   2200       -10950.050             0.292            0.133
Chain 1:   2300       -17477.085             0.328            0.373
Chain 1:   2400        -9860.575             0.392            0.398
Chain 1:   2500       -10103.921             0.391            0.398
Chain 1:   2600        -9814.828             0.389            0.398
Chain 1:   2700       -10598.414             0.385            0.398
Chain 1:   2800        -9985.233             0.352            0.373
Chain 1:   2900       -15545.342             0.314            0.358
Chain 1:   3000       -13584.613             0.281            0.144
Chain 1:   3100       -10865.781             0.214            0.144
Chain 1:   3200       -14117.476             0.232            0.230
Chain 1:   3300       -16420.447             0.208            0.144
Chain 1:   3400        -9497.918             0.204            0.144
Chain 1:   3500       -10174.086             0.208            0.144
Chain 1:   3600        -9659.067             0.211            0.144
Chain 1:   3700        -9714.142             0.204            0.144
Chain 1:   3800       -12294.556             0.219            0.210
Chain 1:   3900       -10630.276             0.199            0.157
Chain 1:   4000       -10132.144             0.189            0.157
Chain 1:   4100        -9599.846             0.170            0.140
Chain 1:   4200       -11444.036             0.163            0.140
Chain 1:   4300       -11997.985             0.153            0.066
Chain 1:   4400        -9959.591             0.101            0.066
Chain 1:   4500        -9406.487             0.100            0.059
Chain 1:   4600        -9450.563             0.095            0.059
Chain 1:   4700       -10283.957             0.103            0.081
Chain 1:   4800       -15243.144             0.114            0.081
Chain 1:   4900        -9617.974             0.157            0.081
Chain 1:   5000       -17451.828             0.197            0.161
Chain 1:   5100        -9043.422             0.285            0.205
Chain 1:   5200        -9187.342             0.270            0.205
Chain 1:   5300       -12230.314             0.290            0.249
Chain 1:   5400        -9214.052             0.303            0.325
Chain 1:   5500        -9105.246             0.298            0.325
Chain 1:   5600        -9447.349             0.301            0.325
Chain 1:   5700        -9921.076             0.298            0.325
Chain 1:   5800        -9037.638             0.275            0.249
Chain 1:   5900       -14677.744             0.255            0.249
Chain 1:   6000       -15044.996             0.212            0.098
Chain 1:   6100        -9892.802             0.171            0.098
Chain 1:   6200        -9911.348             0.170            0.098
Chain 1:   6300        -9448.650             0.150            0.049
Chain 1:   6400       -11452.799             0.135            0.049
Chain 1:   6500        -9681.837             0.152            0.098
Chain 1:   6600        -9792.779             0.150            0.098
Chain 1:   6700        -8950.760             0.154            0.098
Chain 1:   6800        -9930.875             0.154            0.099
Chain 1:   6900        -8765.688             0.129            0.099
Chain 1:   7000       -10101.550             0.140            0.132
Chain 1:   7100        -8838.717             0.102            0.132
Chain 1:   7200        -9431.968             0.108            0.132
Chain 1:   7300        -9100.131             0.107            0.132
Chain 1:   7400        -9284.315             0.091            0.099
Chain 1:   7500       -11437.061             0.092            0.099
Chain 1:   7600        -9177.561             0.115            0.132
Chain 1:   7700       -14877.217             0.144            0.133
Chain 1:   7800       -13564.880             0.144            0.133
Chain 1:   7900        -9579.454             0.172            0.143
Chain 1:   8000        -8985.822             0.166            0.143
Chain 1:   8100        -9326.112             0.155            0.097
Chain 1:   8200       -11247.410             0.166            0.171
Chain 1:   8300        -8843.236             0.190            0.188
Chain 1:   8400        -9517.118             0.195            0.188
Chain 1:   8500        -8832.476             0.184            0.171
Chain 1:   8600        -9012.702             0.161            0.097
Chain 1:   8700        -8762.979             0.125            0.078
Chain 1:   8800       -10715.154             0.134            0.078
Chain 1:   8900       -11822.936             0.102            0.078
Chain 1:   9000       -12368.193             0.100            0.078
Chain 1:   9100        -9097.697             0.132            0.094
Chain 1:   9200        -9735.024             0.121            0.078
Chain 1:   9300        -9719.950             0.094            0.071
Chain 1:   9400        -9440.794             0.090            0.065
Chain 1:   9500        -8850.998             0.089            0.065
Chain 1:   9600        -9538.897             0.094            0.067
Chain 1:   9700        -9355.067             0.093            0.067
Chain 1:   9800        -9037.268             0.079            0.065
Chain 1:   9900       -12897.369             0.099            0.065
Chain 1:   10000        -9063.051             0.137            0.067
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001481 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -64419.336             1.000            1.000
Chain 1:    200       -19034.432             1.692            2.384
Chain 1:    300        -9191.403             1.485            1.071
Chain 1:    400        -8168.314             1.145            1.071
Chain 1:    500        -8348.280             0.920            1.000
Chain 1:    600        -8116.684             0.772            1.000
Chain 1:    700        -9135.161             0.677            0.125
Chain 1:    800        -8589.319             0.601            0.125
Chain 1:    900        -8025.537             0.542            0.111
Chain 1:   1000        -8154.863             0.489            0.111
Chain 1:   1100        -7851.469             0.393            0.070
Chain 1:   1200        -7714.942             0.156            0.064
Chain 1:   1300        -7848.140             0.051            0.039
Chain 1:   1400        -7803.554             0.039            0.029
Chain 1:   1500        -7583.844             0.040            0.029
Chain 1:   1600        -7761.912             0.039            0.029
Chain 1:   1700        -7708.217             0.029            0.023
Chain 1:   1800        -7711.083             0.022            0.018
Chain 1:   1900        -7607.966             0.017            0.017
Chain 1:   2000        -7571.896             0.016            0.017
Chain 1:   2100        -7626.547             0.013            0.014
Chain 1:   2200        -7877.115             0.014            0.014
Chain 1:   2300        -7647.993             0.015            0.014
Chain 1:   2400        -7596.908             0.015            0.014
Chain 1:   2500        -7642.507             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003825 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86229.843             1.000            1.000
Chain 1:    200       -14274.244             3.020            5.041
Chain 1:    300       -10554.474             2.131            1.000
Chain 1:    400       -11887.366             1.626            1.000
Chain 1:    500        -9542.001             1.350            0.352
Chain 1:    600        -9243.732             1.131            0.352
Chain 1:    700        -9625.875             0.975            0.246
Chain 1:    800        -8919.811             0.863            0.246
Chain 1:    900        -8819.124             0.768            0.112
Chain 1:   1000        -9580.290             0.699            0.112
Chain 1:   1100        -9157.716             0.604            0.079   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9395.085             0.102            0.079
Chain 1:   1300        -8925.321             0.072            0.053
Chain 1:   1400        -8973.698             0.062            0.046
Chain 1:   1500        -8946.526             0.037            0.040
Chain 1:   1600        -8949.993             0.034            0.040
Chain 1:   1700        -8830.389             0.032            0.025
Chain 1:   1800        -8887.639             0.024            0.014
Chain 1:   1900        -8766.999             0.025            0.014
Chain 1:   2000        -8831.103             0.017            0.014
Chain 1:   2100        -8985.875             0.014            0.014
Chain 1:   2200        -8762.499             0.015            0.014
Chain 1:   2300        -8892.781             0.011            0.014
Chain 1:   2400        -8761.893             0.012            0.014
Chain 1:   2500        -8831.191             0.012            0.014
Chain 1:   2600        -8748.775             0.013            0.014
Chain 1:   2700        -8777.070             0.012            0.014
Chain 1:   2800        -8731.377             0.012            0.014
Chain 1:   2900        -8833.008             0.012            0.012
Chain 1:   3000        -8730.128             0.012            0.012
Chain 1:   3100        -8718.665             0.011            0.012
Chain 1:   3200        -8693.981             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8423262.417             1.000            1.000
Chain 1:    200     -1586678.972             2.654            4.309
Chain 1:    300      -890287.738             2.030            1.000
Chain 1:    400      -457883.276             1.759            1.000
Chain 1:    500      -358031.804             1.463            0.944
Chain 1:    600      -233199.971             1.308            0.944
Chain 1:    700      -119727.763             1.257            0.944
Chain 1:    800       -87036.217             1.147            0.944
Chain 1:    900       -67440.573             1.051            0.782
Chain 1:   1000       -52296.276             0.975            0.782
Chain 1:   1100       -39821.453             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39008.624             0.478            0.376
Chain 1:   1300       -26996.358             0.444            0.376
Chain 1:   1400       -26721.884             0.351            0.313
Chain 1:   1500       -23317.353             0.337            0.313
Chain 1:   1600       -22537.653             0.287            0.291
Chain 1:   1700       -21414.050             0.198            0.290
Chain 1:   1800       -21359.475             0.161            0.146
Chain 1:   1900       -21686.285             0.133            0.052
Chain 1:   2000       -20197.933             0.111            0.052
Chain 1:   2100       -20436.191             0.081            0.035
Chain 1:   2200       -20662.923             0.080            0.035
Chain 1:   2300       -20279.740             0.038            0.019
Chain 1:   2400       -20051.654             0.038            0.019
Chain 1:   2500       -19853.638             0.024            0.015
Chain 1:   2600       -19483.239             0.023            0.015
Chain 1:   2700       -19440.069             0.018            0.012
Chain 1:   2800       -19156.634             0.019            0.015
Chain 1:   2900       -19438.111             0.019            0.014
Chain 1:   3000       -19424.287             0.011            0.012
Chain 1:   3100       -19509.361             0.011            0.011
Chain 1:   3200       -19199.661             0.011            0.014
Chain 1:   3300       -19404.702             0.010            0.011
Chain 1:   3400       -18878.894             0.012            0.014
Chain 1:   3500       -19491.818             0.014            0.015
Chain 1:   3600       -18797.108             0.016            0.015
Chain 1:   3700       -19184.913             0.018            0.016
Chain 1:   3800       -18142.464             0.022            0.020
Chain 1:   3900       -18138.550             0.021            0.020
Chain 1:   4000       -18255.870             0.021            0.020
Chain 1:   4100       -18169.519             0.021            0.020
Chain 1:   4200       -17985.309             0.021            0.020
Chain 1:   4300       -18124.036             0.020            0.020
Chain 1:   4400       -18080.454             0.018            0.010
Chain 1:   4500       -17982.930             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001772 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12276.873             1.000            1.000
Chain 1:    200        -9138.272             0.672            1.000
Chain 1:    300        -7949.284             0.498            0.343
Chain 1:    400        -8086.097             0.377            0.343
Chain 1:    500        -8075.790             0.302            0.150
Chain 1:    600        -7860.803             0.256            0.150
Chain 1:    700        -7766.079             0.222            0.027
Chain 1:    800        -7808.489             0.195            0.027
Chain 1:    900        -7932.519             0.175            0.017
Chain 1:   1000        -7810.055             0.159            0.017
Chain 1:   1100        -7852.142             0.059            0.016
Chain 1:   1200        -7791.106             0.026            0.016
Chain 1:   1300        -7728.892             0.012            0.012
Chain 1:   1400        -7768.859             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61777.239             1.000            1.000
Chain 1:    200       -17726.900             1.742            2.485
Chain 1:    300        -8843.409             1.496            1.005
Chain 1:    400        -8353.271             1.137            1.005
Chain 1:    500        -8320.110             0.910            1.000
Chain 1:    600        -8135.334             0.762            1.000
Chain 1:    700        -8103.370             0.654            0.059
Chain 1:    800        -7988.713             0.574            0.059
Chain 1:    900        -7691.539             0.515            0.039
Chain 1:   1000        -7934.015             0.466            0.039
Chain 1:   1100        -7829.068             0.368            0.031
Chain 1:   1200        -7693.237             0.121            0.023
Chain 1:   1300        -7735.843             0.021            0.018
Chain 1:   1400        -7678.659             0.016            0.014
Chain 1:   1500        -7571.614             0.017            0.014
Chain 1:   1600        -7615.786             0.015            0.014
Chain 1:   1700        -7530.937             0.016            0.014
Chain 1:   1800        -7602.525             0.015            0.013
Chain 1:   1900        -7632.443             0.012            0.011
Chain 1:   2000        -7612.569             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86705.230             1.000            1.000
Chain 1:    200       -13417.311             3.231            5.462
Chain 1:    300        -9769.847             2.279            1.000
Chain 1:    400       -10837.970             1.734            1.000
Chain 1:    500        -8744.009             1.435            0.373
Chain 1:    600        -8399.393             1.202            0.373
Chain 1:    700        -8212.111             1.034            0.239
Chain 1:    800        -9036.817             0.916            0.239
Chain 1:    900        -8621.750             0.820            0.099
Chain 1:   1000        -8500.564             0.739            0.099
Chain 1:   1100        -8601.457             0.640            0.091   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.998             0.100            0.061
Chain 1:   1300        -8450.759             0.067            0.048
Chain 1:   1400        -8428.245             0.057            0.041
Chain 1:   1500        -8336.147             0.034            0.040
Chain 1:   1600        -8443.112             0.032            0.023
Chain 1:   1700        -8513.138             0.030            0.014
Chain 1:   1800        -8095.720             0.026            0.014
Chain 1:   1900        -8193.304             0.022            0.013
Chain 1:   2000        -8167.581             0.021            0.012
Chain 1:   2100        -8292.046             0.022            0.013
Chain 1:   2200        -8102.733             0.018            0.013
Chain 1:   2300        -8188.263             0.015            0.012
Chain 1:   2400        -8257.620             0.016            0.012
Chain 1:   2500        -8203.624             0.015            0.012
Chain 1:   2600        -8204.154             0.014            0.010
Chain 1:   2700        -8121.264             0.014            0.010
Chain 1:   2800        -8082.427             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8444997.290             1.000            1.000
Chain 1:    200     -1592936.509             2.651            4.302
Chain 1:    300      -891638.405             2.029            1.000
Chain 1:    400      -457513.009             1.759            1.000
Chain 1:    500      -356860.707             1.464            0.949
Chain 1:    600      -231684.673             1.310            0.949
Chain 1:    700      -118449.977             1.259            0.949
Chain 1:    800       -85826.041             1.149            0.949
Chain 1:    900       -66297.697             1.054            0.787
Chain 1:   1000       -51211.702             0.978            0.787
Chain 1:   1100       -38797.738             0.910            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37989.543             0.482            0.380
Chain 1:   1300       -26049.015             0.450            0.380
Chain 1:   1400       -25779.648             0.356            0.320
Chain 1:   1500       -22393.589             0.343            0.320
Chain 1:   1600       -21618.839             0.292            0.295
Chain 1:   1700       -20504.325             0.202            0.295
Chain 1:   1800       -20451.538             0.164            0.151
Chain 1:   1900       -20777.762             0.136            0.054
Chain 1:   2000       -19295.058             0.115            0.054
Chain 1:   2100       -19533.144             0.084            0.036
Chain 1:   2200       -19758.671             0.083            0.036
Chain 1:   2300       -19376.661             0.039            0.020
Chain 1:   2400       -19148.829             0.039            0.020
Chain 1:   2500       -18950.510             0.025            0.016
Chain 1:   2600       -18581.008             0.024            0.016
Chain 1:   2700       -18538.109             0.018            0.012
Chain 1:   2800       -18254.759             0.020            0.016
Chain 1:   2900       -18535.881             0.020            0.015
Chain 1:   3000       -18522.181             0.012            0.012
Chain 1:   3100       -18607.174             0.011            0.012
Chain 1:   3200       -18297.903             0.012            0.015
Chain 1:   3300       -18502.612             0.011            0.012
Chain 1:   3400       -17977.478             0.013            0.015
Chain 1:   3500       -18589.285             0.015            0.016
Chain 1:   3600       -17895.974             0.017            0.016
Chain 1:   3700       -18282.652             0.019            0.017
Chain 1:   3800       -17242.383             0.023            0.021
Chain 1:   3900       -17238.461             0.022            0.021
Chain 1:   4000       -17355.820             0.022            0.021
Chain 1:   4100       -17269.555             0.022            0.021
Chain 1:   4200       -17085.822             0.022            0.021
Chain 1:   4300       -17224.248             0.021            0.021
Chain 1:   4400       -17181.055             0.019            0.011
Chain 1:   4500       -17083.540             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001916 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12334.340             1.000            1.000
Chain 1:    200        -9127.771             0.676            1.000
Chain 1:    300        -7994.152             0.498            0.351
Chain 1:    400        -8097.544             0.376            0.351
Chain 1:    500        -8173.859             0.303            0.142
Chain 1:    600        -7897.832             0.258            0.142
Chain 1:    700        -7856.479             0.222            0.035
Chain 1:    800        -7824.578             0.195            0.035
Chain 1:    900        -7803.172             0.174            0.013
Chain 1:   1000        -7863.283             0.157            0.013
Chain 1:   1100        -7964.733             0.058            0.013
Chain 1:   1200        -7914.280             0.024            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001495 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63144.473             1.000            1.000
Chain 1:    200       -18083.416             1.746            2.492
Chain 1:    300        -8703.796             1.523            1.078
Chain 1:    400        -8457.478             1.150            1.078
Chain 1:    500        -8341.054             0.923            1.000
Chain 1:    600        -8516.352             0.772            1.000
Chain 1:    700        -7674.909             0.678            0.110
Chain 1:    800        -8138.646             0.600            0.110
Chain 1:    900        -7842.104             0.538            0.057
Chain 1:   1000        -7713.774             0.485            0.057
Chain 1:   1100        -7630.753             0.387            0.038
Chain 1:   1200        -7808.934             0.140            0.029
Chain 1:   1300        -7707.012             0.033            0.023
Chain 1:   1400        -7571.215             0.032            0.021
Chain 1:   1500        -7536.913             0.031            0.021
Chain 1:   1600        -7713.013             0.031            0.023
Chain 1:   1700        -7459.334             0.024            0.023
Chain 1:   1800        -7540.315             0.019            0.018
Chain 1:   1900        -7512.957             0.016            0.017
Chain 1:   2000        -7552.622             0.015            0.013
Chain 1:   2100        -7516.954             0.014            0.013
Chain 1:   2200        -7627.647             0.013            0.013
Chain 1:   2300        -7541.256             0.013            0.011
Chain 1:   2400        -7599.109             0.012            0.011
Chain 1:   2500        -7450.684             0.013            0.011
Chain 1:   2600        -7506.571             0.012            0.011
Chain 1:   2700        -7450.619             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004175 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86246.114             1.000            1.000
Chain 1:    200       -13377.567             3.224            5.447
Chain 1:    300        -9775.826             2.272            1.000
Chain 1:    400       -10605.805             1.723            1.000
Chain 1:    500        -8741.315             1.421            0.368
Chain 1:    600        -8263.538             1.194            0.368
Chain 1:    700        -8345.800             1.025            0.213
Chain 1:    800        -9031.661             0.906            0.213
Chain 1:    900        -8567.978             0.812            0.078
Chain 1:   1000        -8392.664             0.733            0.078
Chain 1:   1100        -8607.508             0.635            0.076   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8259.561             0.095            0.058
Chain 1:   1300        -8465.344             0.060            0.054
Chain 1:   1400        -8469.496             0.052            0.042
Chain 1:   1500        -8357.187             0.032            0.025
Chain 1:   1600        -8461.754             0.028            0.024
Chain 1:   1700        -8550.088             0.028            0.024
Chain 1:   1800        -8143.560             0.025            0.024
Chain 1:   1900        -8240.781             0.021            0.021
Chain 1:   2000        -8212.707             0.019            0.013
Chain 1:   2100        -8333.001             0.018            0.013
Chain 1:   2200        -8134.902             0.016            0.013
Chain 1:   2300        -8279.035             0.016            0.013
Chain 1:   2400        -8285.823             0.016            0.013
Chain 1:   2500        -8254.184             0.015            0.012
Chain 1:   2600        -8252.575             0.014            0.012
Chain 1:   2700        -8165.744             0.014            0.012
Chain 1:   2800        -8131.358             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003158 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8428295.459             1.000            1.000
Chain 1:    200     -1586890.296             2.656            4.311
Chain 1:    300      -890152.124             2.031            1.000
Chain 1:    400      -457107.772             1.760            1.000
Chain 1:    500      -357069.346             1.464            0.947
Chain 1:    600      -232218.678             1.310            0.947
Chain 1:    700      -118728.723             1.259            0.947
Chain 1:    800       -86040.608             1.149            0.947
Chain 1:    900       -66447.358             1.054            0.783
Chain 1:   1000       -51299.792             0.979            0.783
Chain 1:   1100       -38831.820             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38011.350             0.482            0.380
Chain 1:   1300       -26025.270             0.449            0.380
Chain 1:   1400       -25749.466             0.356            0.321
Chain 1:   1500       -22352.013             0.343            0.321
Chain 1:   1600       -21573.054             0.293            0.295
Chain 1:   1700       -20453.718             0.203            0.295
Chain 1:   1800       -20399.434             0.165            0.152
Chain 1:   1900       -20725.380             0.137            0.055
Chain 1:   2000       -19240.445             0.115            0.055
Chain 1:   2100       -19478.706             0.084            0.036
Chain 1:   2200       -19704.446             0.083            0.036
Chain 1:   2300       -19322.283             0.039            0.020
Chain 1:   2400       -19094.479             0.039            0.020
Chain 1:   2500       -18896.298             0.025            0.016
Chain 1:   2600       -18526.967             0.024            0.016
Chain 1:   2700       -18484.024             0.018            0.012
Chain 1:   2800       -18200.921             0.020            0.016
Chain 1:   2900       -18481.935             0.020            0.015
Chain 1:   3000       -18468.201             0.012            0.012
Chain 1:   3100       -18553.190             0.011            0.012
Chain 1:   3200       -18244.040             0.012            0.015
Chain 1:   3300       -18448.586             0.011            0.012
Chain 1:   3400       -17923.819             0.013            0.015
Chain 1:   3500       -18535.219             0.015            0.016
Chain 1:   3600       -17842.371             0.017            0.016
Chain 1:   3700       -18228.799             0.019            0.017
Chain 1:   3800       -17189.308             0.023            0.021
Chain 1:   3900       -17185.397             0.022            0.021
Chain 1:   4000       -17302.734             0.022            0.021
Chain 1:   4100       -17216.583             0.022            0.021
Chain 1:   4200       -17032.931             0.022            0.021
Chain 1:   4300       -17171.273             0.021            0.021
Chain 1:   4400       -17128.230             0.019            0.011
Chain 1:   4500       -17030.722             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001483 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12895.279             1.000            1.000
Chain 1:    200        -9774.407             0.660            1.000
Chain 1:    300        -8343.941             0.497            0.319
Chain 1:    400        -8579.795             0.380            0.319
Chain 1:    500        -8482.310             0.306            0.171
Chain 1:    600        -8444.641             0.256            0.171
Chain 1:    700        -8160.094             0.224            0.035
Chain 1:    800        -8162.736             0.196            0.035
Chain 1:    900        -8254.060             0.176            0.027
Chain 1:   1000        -8195.185             0.159            0.027
Chain 1:   1100        -8261.589             0.060            0.011
Chain 1:   1200        -8184.995             0.029            0.011
Chain 1:   1300        -8141.058             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001541 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57370.223             1.000            1.000
Chain 1:    200       -18047.971             1.589            2.179
Chain 1:    300        -8979.431             1.396            1.010
Chain 1:    400        -8205.043             1.071            1.010
Chain 1:    500        -9260.328             0.879            1.000
Chain 1:    600        -8753.292             0.742            1.000
Chain 1:    700        -8322.895             0.644            0.114
Chain 1:    800        -8268.844             0.564            0.114
Chain 1:    900        -7875.233             0.507            0.094
Chain 1:   1000        -7567.590             0.460            0.094
Chain 1:   1100        -7557.724             0.361            0.058
Chain 1:   1200        -8304.980             0.152            0.058
Chain 1:   1300        -7925.413             0.055            0.052
Chain 1:   1400        -7871.524             0.047            0.050
Chain 1:   1500        -7559.802             0.039            0.048
Chain 1:   1600        -7606.835             0.034            0.041
Chain 1:   1700        -7516.565             0.030            0.041
Chain 1:   1800        -7590.093             0.031            0.041
Chain 1:   1900        -7596.209             0.026            0.012
Chain 1:   2000        -7681.573             0.023            0.011
Chain 1:   2100        -7597.653             0.024            0.011
Chain 1:   2200        -7838.568             0.018            0.011
Chain 1:   2300        -7580.952             0.016            0.011
Chain 1:   2400        -7695.996             0.017            0.012
Chain 1:   2500        -7684.003             0.013            0.011
Chain 1:   2600        -7553.836             0.014            0.012
Chain 1:   2700        -7460.655             0.014            0.012
Chain 1:   2800        -7430.948             0.014            0.012
Chain 1:   2900        -7407.922             0.014            0.012
Chain 1:   3000        -7571.834             0.015            0.015
Chain 1:   3100        -7530.892             0.015            0.015
Chain 1:   3200        -7764.326             0.014            0.015
Chain 1:   3300        -7465.494             0.015            0.015
Chain 1:   3400        -7712.940             0.017            0.017
Chain 1:   3500        -7461.084             0.020            0.022
Chain 1:   3600        -7520.184             0.019            0.022
Chain 1:   3700        -7474.037             0.018            0.022
Chain 1:   3800        -7482.341             0.018            0.022
Chain 1:   3900        -7444.603             0.018            0.022
Chain 1:   4000        -7418.511             0.017            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003056 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87243.828             1.000            1.000
Chain 1:    200       -14045.946             3.106            5.211
Chain 1:    300       -10295.951             2.192            1.000
Chain 1:    400       -11842.892             1.677            1.000
Chain 1:    500        -9179.878             1.399            0.364
Chain 1:    600        -9538.643             1.172            0.364
Chain 1:    700        -9102.918             1.012            0.290
Chain 1:    800        -9123.713             0.886            0.290
Chain 1:    900        -9140.854             0.787            0.131
Chain 1:   1000        -8761.959             0.713            0.131
Chain 1:   1100        -8926.279             0.615            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8609.075             0.097            0.043
Chain 1:   1300        -9000.883             0.065            0.043
Chain 1:   1400        -8705.091             0.056            0.038
Chain 1:   1500        -8783.530             0.027            0.037
Chain 1:   1600        -8895.117             0.025            0.034
Chain 1:   1700        -8953.678             0.021            0.018
Chain 1:   1800        -8510.847             0.026            0.034
Chain 1:   1900        -8613.969             0.027            0.034
Chain 1:   2000        -8596.445             0.023            0.018
Chain 1:   2100        -8730.138             0.022            0.015
Chain 1:   2200        -8510.455             0.021            0.015
Chain 1:   2300        -8616.622             0.018            0.013
Chain 1:   2400        -8675.475             0.015            0.012
Chain 1:   2500        -8623.026             0.015            0.012
Chain 1:   2600        -8637.336             0.014            0.012
Chain 1:   2700        -8544.564             0.014            0.012
Chain 1:   2800        -8490.445             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003583 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399763.036             1.000            1.000
Chain 1:    200     -1584973.308             2.650            4.300
Chain 1:    300      -891693.262             2.026            1.000
Chain 1:    400      -458477.304             1.756            1.000
Chain 1:    500      -358801.090             1.460            0.945
Chain 1:    600      -233814.851             1.306            0.945
Chain 1:    700      -119930.864             1.255            0.945
Chain 1:    800       -87104.304             1.145            0.945
Chain 1:    900       -67437.040             1.050            0.777
Chain 1:   1000       -52223.130             0.974            0.777
Chain 1:   1100       -39680.673             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38861.317             0.478            0.377
Chain 1:   1300       -26788.674             0.445            0.377
Chain 1:   1400       -26507.743             0.352            0.316
Chain 1:   1500       -23086.894             0.339            0.316
Chain 1:   1600       -22301.688             0.289            0.292
Chain 1:   1700       -21171.509             0.200            0.291
Chain 1:   1800       -21115.042             0.162            0.148
Chain 1:   1900       -21441.827             0.134            0.053
Chain 1:   2000       -19949.697             0.113            0.053
Chain 1:   2100       -20188.334             0.082            0.035
Chain 1:   2200       -20415.512             0.081            0.035
Chain 1:   2300       -20031.928             0.038            0.019
Chain 1:   2400       -19803.776             0.038            0.019
Chain 1:   2500       -19605.826             0.025            0.015
Chain 1:   2600       -19235.350             0.023            0.015
Chain 1:   2700       -19192.119             0.018            0.012
Chain 1:   2800       -18908.732             0.019            0.015
Chain 1:   2900       -19190.321             0.019            0.015
Chain 1:   3000       -19176.462             0.012            0.012
Chain 1:   3100       -19261.521             0.011            0.012
Chain 1:   3200       -18951.783             0.011            0.015
Chain 1:   3300       -19156.851             0.010            0.012
Chain 1:   3400       -18631.016             0.012            0.015
Chain 1:   3500       -19244.018             0.014            0.015
Chain 1:   3600       -18549.303             0.016            0.015
Chain 1:   3700       -18937.161             0.018            0.016
Chain 1:   3800       -17894.624             0.022            0.020
Chain 1:   3900       -17890.722             0.021            0.020
Chain 1:   4000       -18008.034             0.021            0.020
Chain 1:   4100       -17921.665             0.021            0.020
Chain 1:   4200       -17737.426             0.021            0.020
Chain 1:   4300       -17876.160             0.021            0.020
Chain 1:   4400       -17832.599             0.018            0.010
Chain 1:   4500       -17735.059             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001241 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48747.354             1.000            1.000
Chain 1:    200       -13467.235             1.810            2.620
Chain 1:    300       -17507.804             1.283            1.000
Chain 1:    400       -21295.413             1.007            1.000
Chain 1:    500       -14921.350             0.891            0.427
Chain 1:    600       -15347.304             0.747            0.427
Chain 1:    700       -12226.163             0.677            0.255
Chain 1:    800       -11222.087             0.604            0.255
Chain 1:    900       -11578.860             0.540            0.231
Chain 1:   1000       -12878.931             0.496            0.231
Chain 1:   1100       -10169.600             0.423            0.231
Chain 1:   1200       -11035.313             0.168            0.178
Chain 1:   1300       -12569.318             0.158            0.122
Chain 1:   1400       -10373.935             0.161            0.122
Chain 1:   1500       -10684.250             0.121            0.101
Chain 1:   1600        -9853.246             0.127            0.101
Chain 1:   1700       -20813.303             0.154            0.101
Chain 1:   1800       -10175.413             0.250            0.122
Chain 1:   1900       -11000.765             0.254            0.122
Chain 1:   2000       -11108.972             0.245            0.122
Chain 1:   2100        -9815.960             0.231            0.122
Chain 1:   2200       -16971.435             0.266            0.132
Chain 1:   2300        -9607.332             0.330            0.212
Chain 1:   2400       -10783.166             0.320            0.132
Chain 1:   2500        -9252.755             0.334            0.165
Chain 1:   2600       -10779.535             0.339            0.165
Chain 1:   2700        -9011.098             0.306            0.165
Chain 1:   2800       -10291.296             0.214            0.142
Chain 1:   2900        -9532.369             0.215            0.142
Chain 1:   3000       -14113.016             0.246            0.165
Chain 1:   3100       -10009.900             0.274            0.196
Chain 1:   3200        -9894.611             0.233            0.165
Chain 1:   3300       -13056.006             0.180            0.165
Chain 1:   3400        -9639.296             0.205            0.196
Chain 1:   3500        -9009.398             0.195            0.196
Chain 1:   3600       -11683.751             0.204            0.229
Chain 1:   3700       -13295.327             0.197            0.229
Chain 1:   3800        -9737.753             0.221            0.242
Chain 1:   3900       -11268.599             0.226            0.242
Chain 1:   4000       -10151.775             0.205            0.229
Chain 1:   4100       -10010.366             0.165            0.136
Chain 1:   4200        -9932.808             0.165            0.136
Chain 1:   4300        -9096.854             0.150            0.121
Chain 1:   4400       -10594.424             0.129            0.121
Chain 1:   4500        -8755.330             0.143            0.136
Chain 1:   4600        -9267.219             0.125            0.121
Chain 1:   4700       -12687.076             0.140            0.136
Chain 1:   4800        -8900.334             0.146            0.136
Chain 1:   4900        -9510.107             0.139            0.110
Chain 1:   5000       -10764.943             0.140            0.117
Chain 1:   5100        -9097.899             0.157            0.141
Chain 1:   5200        -8781.188             0.159            0.141
Chain 1:   5300        -9123.703             0.154            0.141
Chain 1:   5400        -9116.710             0.140            0.117
Chain 1:   5500       -13585.369             0.152            0.117
Chain 1:   5600       -10747.673             0.173            0.183
Chain 1:   5700        -8864.686             0.167            0.183
Chain 1:   5800       -12347.356             0.153            0.183
Chain 1:   5900       -15897.452             0.168            0.212
Chain 1:   6000        -8839.487             0.237            0.223
Chain 1:   6100        -9247.185             0.223            0.223
Chain 1:   6200        -8302.265             0.231            0.223
Chain 1:   6300       -13438.793             0.265            0.264
Chain 1:   6400       -12674.581             0.271            0.264
Chain 1:   6500        -8280.700             0.291            0.264
Chain 1:   6600        -8706.149             0.270            0.223
Chain 1:   6700       -10297.050             0.264            0.223
Chain 1:   6800        -8606.754             0.255            0.196
Chain 1:   6900       -11624.507             0.259            0.196
Chain 1:   7000        -8428.193             0.217            0.196
Chain 1:   7100       -13079.901             0.248            0.260
Chain 1:   7200        -8470.691             0.291            0.356
Chain 1:   7300       -10509.755             0.272            0.260
Chain 1:   7400        -8412.107             0.291            0.260
Chain 1:   7500       -11120.518             0.263            0.249
Chain 1:   7600        -8467.984             0.289            0.260
Chain 1:   7700        -8743.955             0.277            0.260
Chain 1:   7800        -8556.364             0.259            0.260
Chain 1:   7900        -9240.581             0.241            0.249
Chain 1:   8000        -8524.268             0.211            0.244
Chain 1:   8100        -8343.954             0.178            0.194
Chain 1:   8200        -9891.925             0.139            0.156
Chain 1:   8300        -8197.953             0.140            0.156
Chain 1:   8400       -13890.759             0.156            0.156
Chain 1:   8500        -8335.749             0.199            0.156
Chain 1:   8600        -8207.349             0.169            0.084
Chain 1:   8700       -10425.393             0.187            0.156
Chain 1:   8800        -8489.534             0.208            0.207
Chain 1:   8900        -8386.210             0.201            0.207
Chain 1:   9000        -8680.342             0.196            0.207
Chain 1:   9100       -10488.357             0.211            0.207
Chain 1:   9200       -11076.234             0.201            0.207
Chain 1:   9300        -8538.413             0.210            0.213
Chain 1:   9400       -10050.422             0.184            0.172
Chain 1:   9500        -8040.240             0.143            0.172
Chain 1:   9600        -9040.634             0.152            0.172
Chain 1:   9700        -8421.389             0.138            0.150
Chain 1:   9800        -8635.758             0.118            0.111
Chain 1:   9900        -8428.932             0.119            0.111
Chain 1:   10000        -8282.377             0.117            0.111
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57648.974             1.000            1.000
Chain 1:    200       -17550.704             1.642            2.285
Chain 1:    300        -8647.219             1.438            1.030
Chain 1:    400        -8202.077             1.092            1.030
Chain 1:    500        -8083.832             0.877            1.000
Chain 1:    600        -8526.244             0.739            1.000
Chain 1:    700        -7778.171             0.647            0.096
Chain 1:    800        -8079.531             0.571            0.096
Chain 1:    900        -8180.640             0.509            0.054
Chain 1:   1000        -7927.849             0.461            0.054
Chain 1:   1100        -7709.653             0.364            0.052
Chain 1:   1200        -7683.296             0.136            0.037
Chain 1:   1300        -7718.237             0.033            0.032
Chain 1:   1400        -7863.476             0.030            0.028
Chain 1:   1500        -7633.228             0.031            0.030
Chain 1:   1600        -7785.209             0.028            0.028
Chain 1:   1700        -7523.672             0.022            0.028
Chain 1:   1800        -7642.619             0.020            0.020
Chain 1:   1900        -7572.445             0.020            0.020
Chain 1:   2000        -7612.477             0.017            0.018
Chain 1:   2100        -7605.405             0.014            0.016
Chain 1:   2200        -7712.812             0.015            0.016
Chain 1:   2300        -7727.169             0.015            0.016
Chain 1:   2400        -7647.867             0.014            0.014
Chain 1:   2500        -7579.975             0.012            0.010
Chain 1:   2600        -7537.610             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003055 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86945.603             1.000            1.000
Chain 1:    200       -13429.243             3.237            5.474
Chain 1:    300        -9800.381             2.282            1.000
Chain 1:    400       -10673.298             1.732            1.000
Chain 1:    500        -8689.014             1.431            0.370
Chain 1:    600        -8296.050             1.200            0.370
Chain 1:    700        -8245.650             1.030            0.228
Chain 1:    800        -8886.216             0.910            0.228
Chain 1:    900        -8652.846             0.812            0.082
Chain 1:   1000        -8432.826             0.733            0.082
Chain 1:   1100        -8616.003             0.635            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8361.703             0.091            0.047
Chain 1:   1300        -8522.922             0.056            0.030
Chain 1:   1400        -8528.056             0.048            0.027
Chain 1:   1500        -8395.783             0.027            0.026
Chain 1:   1600        -8505.868             0.023            0.021
Chain 1:   1700        -8592.500             0.024            0.021
Chain 1:   1800        -8189.908             0.021            0.021
Chain 1:   1900        -8287.830             0.020            0.019
Chain 1:   2000        -8259.441             0.017            0.016
Chain 1:   2100        -8379.277             0.017            0.014
Chain 1:   2200        -8187.526             0.016            0.014
Chain 1:   2300        -8323.226             0.016            0.014
Chain 1:   2400        -8198.285             0.017            0.015
Chain 1:   2500        -8263.021             0.016            0.014
Chain 1:   2600        -8286.728             0.015            0.014
Chain 1:   2700        -8205.021             0.015            0.014
Chain 1:   2800        -8177.589             0.011            0.012
Chain 1:   2900        -8232.991             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.009248 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 92.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397956.777             1.000            1.000
Chain 1:    200     -1584347.381             2.650            4.301
Chain 1:    300      -890735.317             2.026            1.000
Chain 1:    400      -457561.703             1.756            1.000
Chain 1:    500      -357881.253             1.461            0.947
Chain 1:    600      -232769.761             1.307            0.947
Chain 1:    700      -119055.064             1.257            0.947
Chain 1:    800       -86279.435             1.147            0.947
Chain 1:    900       -66636.420             1.052            0.779
Chain 1:   1000       -51442.010             0.977            0.779
Chain 1:   1100       -38930.873             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38106.519             0.481            0.380
Chain 1:   1300       -26080.142             0.449            0.380
Chain 1:   1400       -25799.519             0.356            0.321
Chain 1:   1500       -22391.193             0.343            0.321
Chain 1:   1600       -21608.662             0.293            0.295
Chain 1:   1700       -20484.830             0.203            0.295
Chain 1:   1800       -20429.363             0.165            0.152
Chain 1:   1900       -20755.295             0.137            0.055
Chain 1:   2000       -19268.028             0.115            0.055
Chain 1:   2100       -19506.393             0.084            0.036
Chain 1:   2200       -19732.462             0.083            0.036
Chain 1:   2300       -19350.015             0.039            0.020
Chain 1:   2400       -19122.209             0.039            0.020
Chain 1:   2500       -18924.112             0.025            0.016
Chain 1:   2600       -18554.701             0.024            0.016
Chain 1:   2700       -18511.757             0.018            0.012
Chain 1:   2800       -18228.707             0.020            0.016
Chain 1:   2900       -18509.790             0.020            0.015
Chain 1:   3000       -18496.030             0.012            0.012
Chain 1:   3100       -18580.991             0.011            0.012
Chain 1:   3200       -18271.857             0.012            0.015
Chain 1:   3300       -18476.419             0.011            0.012
Chain 1:   3400       -17951.660             0.013            0.015
Chain 1:   3500       -18563.043             0.015            0.016
Chain 1:   3600       -17870.322             0.017            0.016
Chain 1:   3700       -18256.691             0.019            0.017
Chain 1:   3800       -17217.320             0.023            0.021
Chain 1:   3900       -17213.456             0.022            0.021
Chain 1:   4000       -17330.780             0.022            0.021
Chain 1:   4100       -17244.602             0.022            0.021
Chain 1:   4200       -17061.022             0.022            0.021
Chain 1:   4300       -17199.312             0.021            0.021
Chain 1:   4400       -17156.312             0.019            0.011
Chain 1:   4500       -17058.835             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001322 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48795.831             1.000            1.000
Chain 1:    200       -19296.536             1.264            1.529
Chain 1:    300       -14024.995             0.968            1.000
Chain 1:    400       -17144.878             0.772            1.000
Chain 1:    500       -17598.043             0.622            0.376
Chain 1:    600       -13087.466             0.576            0.376
Chain 1:    700       -12162.233             0.505            0.345
Chain 1:    800       -13534.038             0.454            0.345
Chain 1:    900       -10879.751             0.431            0.244
Chain 1:   1000       -12497.328             0.401            0.244
Chain 1:   1100       -10299.145             0.322            0.213
Chain 1:   1200       -11284.213             0.178            0.182
Chain 1:   1300       -12670.417             0.151            0.129
Chain 1:   1400       -12261.932             0.136            0.109
Chain 1:   1500       -10200.081             0.154            0.129
Chain 1:   1600       -10221.798             0.120            0.109
Chain 1:   1700       -11291.488             0.122            0.109
Chain 1:   1800       -10098.361             0.123            0.118
Chain 1:   1900       -16848.454             0.139            0.118
Chain 1:   2000       -11184.991             0.177            0.118
Chain 1:   2100       -12703.705             0.167            0.118
Chain 1:   2200       -10435.239             0.180            0.120
Chain 1:   2300       -14572.295             0.198            0.202
Chain 1:   2400        -9161.734             0.254            0.217
Chain 1:   2500        -9326.990             0.235            0.217
Chain 1:   2600        -9237.822             0.236            0.217
Chain 1:   2700        -9949.273             0.234            0.217
Chain 1:   2800       -13276.580             0.247            0.251
Chain 1:   2900       -10990.953             0.228            0.217
Chain 1:   3000        -8792.519             0.202            0.217
Chain 1:   3100        -8600.829             0.192            0.217
Chain 1:   3200        -9311.726             0.178            0.208
Chain 1:   3300       -11925.864             0.172            0.208
Chain 1:   3400       -12855.876             0.120            0.076
Chain 1:   3500       -10416.641             0.141            0.208
Chain 1:   3600        -9872.797             0.146            0.208
Chain 1:   3700        -9026.881             0.148            0.208
Chain 1:   3800       -11366.806             0.144            0.206
Chain 1:   3900       -10597.018             0.130            0.094
Chain 1:   4000        -9703.780             0.114            0.092
Chain 1:   4100        -8780.684             0.123            0.094
Chain 1:   4200        -9167.524             0.119            0.094
Chain 1:   4300       -14620.959             0.135            0.094
Chain 1:   4400        -9020.380             0.189            0.105
Chain 1:   4500        -8923.258             0.167            0.094
Chain 1:   4600       -10864.684             0.180            0.105
Chain 1:   4700       -11678.216             0.177            0.105
Chain 1:   4800        -8447.088             0.195            0.105
Chain 1:   4900        -9381.192             0.197            0.105
Chain 1:   5000        -8629.629             0.197            0.105
Chain 1:   5100        -8575.002             0.187            0.100
Chain 1:   5200       -11762.666             0.210            0.179
Chain 1:   5300       -13381.993             0.185            0.121
Chain 1:   5400       -15390.041             0.136            0.121
Chain 1:   5500       -11070.768             0.174            0.130
Chain 1:   5600       -13171.829             0.172            0.130
Chain 1:   5700        -8454.202             0.221            0.160
Chain 1:   5800        -8952.733             0.188            0.130
Chain 1:   5900        -8210.457             0.187            0.130
Chain 1:   6000        -9365.975             0.191            0.130
Chain 1:   6100        -8671.911             0.198            0.130
Chain 1:   6200        -8101.961             0.178            0.123
Chain 1:   6300       -13203.999             0.204            0.130
Chain 1:   6400       -10924.583             0.212            0.160
Chain 1:   6500        -9426.335             0.189            0.159
Chain 1:   6600        -8170.929             0.189            0.154
Chain 1:   6700       -10907.480             0.158            0.154
Chain 1:   6800        -8284.380             0.184            0.159
Chain 1:   6900        -8059.667             0.178            0.159
Chain 1:   7000        -8209.206             0.167            0.159
Chain 1:   7100        -8906.812             0.167            0.159
Chain 1:   7200        -8149.678             0.169            0.159
Chain 1:   7300        -8940.206             0.139            0.154
Chain 1:   7400        -8223.610             0.127            0.093
Chain 1:   7500       -10135.224             0.130            0.093
Chain 1:   7600       -10946.977             0.122            0.088
Chain 1:   7700        -8523.942             0.126            0.088
Chain 1:   7800        -8268.035             0.097            0.087
Chain 1:   7900       -10183.703             0.113            0.088
Chain 1:   8000       -10765.085             0.117            0.088
Chain 1:   8100        -8232.267             0.140            0.093
Chain 1:   8200        -8718.972             0.136            0.088
Chain 1:   8300       -10906.849             0.147            0.188
Chain 1:   8400       -10147.735             0.146            0.188
Chain 1:   8500       -10512.121             0.131            0.075
Chain 1:   8600        -8108.845             0.153            0.188
Chain 1:   8700        -9381.478             0.138            0.136
Chain 1:   8800        -8380.885             0.147            0.136
Chain 1:   8900       -10278.202             0.146            0.136
Chain 1:   9000       -10173.291             0.142            0.136
Chain 1:   9100        -7960.581             0.139            0.136
Chain 1:   9200       -11045.324             0.161            0.185
Chain 1:   9300        -8146.592             0.177            0.185
Chain 1:   9400       -10838.120             0.194            0.248
Chain 1:   9500        -7897.290             0.228            0.278
Chain 1:   9600        -8356.689             0.204            0.248
Chain 1:   9700        -8251.259             0.192            0.248
Chain 1:   9800        -8354.092             0.181            0.248
Chain 1:   9900       -10865.982             0.186            0.248
Chain 1:   10000        -7897.735             0.222            0.278
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001427 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61289.259             1.000            1.000
Chain 1:    200       -17619.301             1.739            2.479
Chain 1:    300        -8748.144             1.498            1.014
Chain 1:    400        -8282.035             1.137            1.014
Chain 1:    500        -8391.918             0.912            1.000
Chain 1:    600        -8370.246             0.761            1.000
Chain 1:    700        -8183.211             0.655            0.056
Chain 1:    800        -8189.376             0.574            0.056
Chain 1:    900        -7585.904             0.519            0.056
Chain 1:   1000        -7622.936             0.467            0.056
Chain 1:   1100        -7690.342             0.368            0.023
Chain 1:   1200        -7711.019             0.121            0.013
Chain 1:   1300        -7618.373             0.020            0.012
Chain 1:   1400        -7644.697             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002892 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86280.776             1.000            1.000
Chain 1:    200       -13262.338             3.253            5.506
Chain 1:    300        -9670.716             2.292            1.000
Chain 1:    400       -10574.144             1.741            1.000
Chain 1:    500        -8600.162             1.438            0.371
Chain 1:    600        -8161.907             1.208            0.371
Chain 1:    700        -8209.754             1.036            0.230
Chain 1:    800        -8455.101             0.910            0.230
Chain 1:    900        -8521.022             0.810            0.085
Chain 1:   1000        -8194.521             0.733            0.085
Chain 1:   1100        -8480.667             0.636            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8239.088             0.089            0.040
Chain 1:   1300        -8381.257             0.053            0.034
Chain 1:   1400        -8376.176             0.045            0.029
Chain 1:   1500        -8254.071             0.023            0.029
Chain 1:   1600        -8363.685             0.019            0.017
Chain 1:   1700        -8449.018             0.020            0.017
Chain 1:   1800        -8048.656             0.022            0.017
Chain 1:   1900        -8147.891             0.022            0.017
Chain 1:   2000        -8119.114             0.018            0.015
Chain 1:   2100        -8239.026             0.016            0.015
Chain 1:   2200        -8030.052             0.016            0.015
Chain 1:   2300        -8179.911             0.016            0.015
Chain 1:   2400        -8060.288             0.018            0.015
Chain 1:   2500        -8123.392             0.017            0.015
Chain 1:   2600        -8145.063             0.016            0.015
Chain 1:   2700        -8064.036             0.016            0.015
Chain 1:   2800        -8037.813             0.011            0.012
Chain 1:   2900        -8093.203             0.011            0.010
Chain 1:   3000        -7977.260             0.012            0.015
Chain 1:   3100        -8115.239             0.012            0.015
Chain 1:   3200        -7995.079             0.011            0.015
Chain 1:   3300        -8016.697             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003374 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414347.301             1.000            1.000
Chain 1:    200     -1587258.709             2.651            4.301
Chain 1:    300      -891505.203             2.027            1.000
Chain 1:    400      -457888.579             1.757            1.000
Chain 1:    500      -358147.567             1.461            0.947
Chain 1:    600      -232905.977             1.307            0.947
Chain 1:    700      -119006.716             1.257            0.947
Chain 1:    800       -86203.489             1.148            0.947
Chain 1:    900       -66528.150             1.053            0.780
Chain 1:   1000       -51320.502             0.977            0.780
Chain 1:   1100       -38797.730             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37969.095             0.482            0.381
Chain 1:   1300       -25932.137             0.450            0.381
Chain 1:   1400       -25649.595             0.357            0.323
Chain 1:   1500       -22239.393             0.344            0.323
Chain 1:   1600       -21455.973             0.294            0.296
Chain 1:   1700       -20331.051             0.204            0.296
Chain 1:   1800       -20275.178             0.166            0.153
Chain 1:   1900       -20601.029             0.138            0.055
Chain 1:   2000       -19113.373             0.116            0.055
Chain 1:   2100       -19351.617             0.085            0.037
Chain 1:   2200       -19577.844             0.084            0.037
Chain 1:   2300       -19195.309             0.040            0.020
Chain 1:   2400       -18967.529             0.040            0.020
Chain 1:   2500       -18769.507             0.025            0.016
Chain 1:   2600       -18400.033             0.024            0.016
Chain 1:   2700       -18357.053             0.019            0.012
Chain 1:   2800       -18074.088             0.020            0.016
Chain 1:   2900       -18355.140             0.020            0.015
Chain 1:   3000       -18341.364             0.012            0.012
Chain 1:   3100       -18426.328             0.011            0.012
Chain 1:   3200       -18117.187             0.012            0.015
Chain 1:   3300       -18321.732             0.011            0.012
Chain 1:   3400       -17797.013             0.013            0.015
Chain 1:   3500       -18408.377             0.015            0.016
Chain 1:   3600       -17715.694             0.017            0.016
Chain 1:   3700       -18102.032             0.019            0.017
Chain 1:   3800       -17062.757             0.023            0.021
Chain 1:   3900       -17058.913             0.022            0.021
Chain 1:   4000       -17176.217             0.022            0.021
Chain 1:   4100       -17090.074             0.022            0.021
Chain 1:   4200       -16906.498             0.022            0.021
Chain 1:   4300       -17044.760             0.022            0.021
Chain 1:   4400       -17001.759             0.019            0.011
Chain 1:   4500       -16904.330             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48902.724             1.000            1.000
Chain 1:    200       -17510.200             1.396            1.793
Chain 1:    300       -21253.549             0.990            1.000
Chain 1:    400       -11959.030             0.937            1.000
Chain 1:    500       -14312.649             0.782            0.777
Chain 1:    600       -25880.686             0.726            0.777
Chain 1:    700       -15409.133             0.720            0.680
Chain 1:    800       -18390.487             0.650            0.680
Chain 1:    900       -14586.532             0.607            0.447
Chain 1:   1000       -13531.058             0.554            0.447
Chain 1:   1100       -18665.066             0.481            0.275
Chain 1:   1200       -12728.115             0.349            0.275
Chain 1:   1300       -12861.148             0.332            0.275
Chain 1:   1400        -9767.125             0.286            0.275
Chain 1:   1500       -26790.663             0.333            0.317
Chain 1:   1600       -10381.718             0.447            0.317
Chain 1:   1700        -9367.457             0.389            0.275
Chain 1:   1800       -13818.330             0.405            0.317
Chain 1:   1900       -10169.502             0.415            0.322
Chain 1:   2000       -11047.796             0.415            0.322
Chain 1:   2100       -11974.781             0.396            0.322
Chain 1:   2200       -12261.563             0.351            0.317
Chain 1:   2300       -11641.232             0.356            0.317
Chain 1:   2400        -9060.884             0.352            0.285
Chain 1:   2500       -10224.505             0.300            0.114
Chain 1:   2600        -9418.921             0.151            0.108
Chain 1:   2700        -9327.576             0.141            0.086
Chain 1:   2800        -9080.323             0.111            0.079
Chain 1:   2900       -15814.075             0.118            0.079
Chain 1:   3000        -9942.579             0.169            0.086
Chain 1:   3100       -10329.027             0.165            0.086
Chain 1:   3200       -17026.986             0.202            0.114
Chain 1:   3300       -12445.861             0.234            0.285
Chain 1:   3400       -10683.577             0.222            0.165
Chain 1:   3500       -11973.466             0.221            0.165
Chain 1:   3600       -10436.801             0.227            0.165
Chain 1:   3700       -11801.351             0.238            0.165
Chain 1:   3800        -9116.518             0.265            0.295
Chain 1:   3900        -8972.537             0.224            0.165
Chain 1:   4000       -10328.991             0.178            0.147
Chain 1:   4100        -9545.948             0.182            0.147
Chain 1:   4200       -11161.439             0.157            0.145
Chain 1:   4300       -12633.656             0.132            0.131
Chain 1:   4400        -9510.831             0.148            0.131
Chain 1:   4500        -8645.090             0.148            0.131
Chain 1:   4600        -9723.362             0.144            0.117
Chain 1:   4700       -10488.642             0.140            0.117
Chain 1:   4800        -8979.643             0.127            0.117
Chain 1:   4900       -11345.794             0.146            0.131
Chain 1:   5000       -11970.053             0.138            0.117
Chain 1:   5100        -8475.609             0.171            0.145
Chain 1:   5200        -9315.768             0.166            0.117
Chain 1:   5300       -10505.655             0.166            0.113
Chain 1:   5400       -14524.160             0.161            0.113
Chain 1:   5500       -13734.900             0.156            0.113
Chain 1:   5600       -11374.909             0.166            0.168
Chain 1:   5700       -11829.339             0.162            0.168
Chain 1:   5800        -8852.890             0.179            0.207
Chain 1:   5900       -11225.624             0.180            0.207
Chain 1:   6000       -10477.415             0.181            0.207
Chain 1:   6100       -14388.258             0.167            0.207
Chain 1:   6200        -8641.054             0.225            0.211
Chain 1:   6300        -8653.775             0.214            0.211
Chain 1:   6400       -11159.465             0.209            0.211
Chain 1:   6500       -11595.224             0.207            0.211
Chain 1:   6600        -8862.543             0.217            0.225
Chain 1:   6700        -9551.361             0.220            0.225
Chain 1:   6800       -10207.161             0.193            0.211
Chain 1:   6900       -12614.751             0.191            0.191
Chain 1:   7000        -9070.725             0.223            0.225
Chain 1:   7100       -12678.828             0.224            0.225
Chain 1:   7200        -8281.853             0.211            0.225
Chain 1:   7300        -8486.842             0.213            0.225
Chain 1:   7400        -8346.246             0.192            0.191
Chain 1:   7500       -10451.171             0.208            0.201
Chain 1:   7600        -8449.432             0.201            0.201
Chain 1:   7700        -8322.453             0.196            0.201
Chain 1:   7800        -8308.990             0.189            0.201
Chain 1:   7900        -9830.821             0.186            0.201
Chain 1:   8000       -10077.855             0.149            0.155
Chain 1:   8100        -9765.355             0.124            0.032
Chain 1:   8200        -9709.765             0.071            0.025
Chain 1:   8300        -8586.701             0.082            0.032
Chain 1:   8400        -8552.936             0.081            0.032
Chain 1:   8500        -8988.632             0.065            0.032
Chain 1:   8600       -10050.336             0.052            0.032
Chain 1:   8700        -8272.839             0.072            0.048
Chain 1:   8800        -8337.840             0.073            0.048
Chain 1:   8900       -12648.521             0.091            0.048
Chain 1:   9000        -8513.281             0.138            0.106
Chain 1:   9100        -8745.235             0.137            0.106
Chain 1:   9200        -8911.066             0.138            0.106
Chain 1:   9300        -8198.431             0.134            0.087
Chain 1:   9400        -8170.227             0.134            0.087
Chain 1:   9500        -8078.181             0.130            0.087
Chain 1:   9600        -8424.887             0.124            0.041
Chain 1:   9700       -10767.721             0.124            0.041
Chain 1:   9800        -8479.201             0.150            0.087
Chain 1:   9900        -9279.382             0.125            0.086
Chain 1:   10000        -8221.882             0.089            0.086
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61457.720             1.000            1.000
Chain 1:    200       -17758.657             1.730            2.461
Chain 1:    300        -8792.022             1.494            1.020
Chain 1:    400        -9165.988             1.130            1.020
Chain 1:    500        -7960.506             0.935            1.000
Chain 1:    600        -8913.566             0.797            1.000
Chain 1:    700        -7712.249             0.705            0.156
Chain 1:    800        -7998.812             0.621            0.156
Chain 1:    900        -7676.064             0.557            0.151
Chain 1:   1000        -7702.639             0.502            0.151
Chain 1:   1100        -7758.292             0.402            0.107
Chain 1:   1200        -7604.931             0.158            0.042
Chain 1:   1300        -7691.130             0.057            0.041
Chain 1:   1400        -7797.080             0.055            0.036
Chain 1:   1500        -7561.058             0.043            0.031
Chain 1:   1600        -7738.088             0.034            0.023
Chain 1:   1700        -7481.204             0.022            0.023
Chain 1:   1800        -7537.379             0.019            0.020
Chain 1:   1900        -7552.563             0.015            0.014
Chain 1:   2000        -7581.111             0.015            0.014
Chain 1:   2100        -7570.987             0.015            0.014
Chain 1:   2200        -7652.296             0.014            0.011
Chain 1:   2300        -7535.015             0.014            0.014
Chain 1:   2400        -7599.504             0.014            0.011
Chain 1:   2500        -7535.759             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002984 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86694.306             1.000            1.000
Chain 1:    200       -13441.166             3.225            5.450
Chain 1:    300        -9870.975             2.271            1.000
Chain 1:    400       -10661.482             1.721            1.000
Chain 1:    500        -8810.258             1.419            0.362
Chain 1:    600        -8576.770             1.187            0.362
Chain 1:    700        -8493.231             1.019            0.210
Chain 1:    800        -8890.129             0.897            0.210
Chain 1:    900        -8690.663             0.800            0.074
Chain 1:   1000        -8525.837             0.722            0.074
Chain 1:   1100        -8745.207             0.624            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8576.017             0.081            0.027
Chain 1:   1300        -8489.151             0.046            0.025
Chain 1:   1400        -8624.456             0.040            0.023
Chain 1:   1500        -8491.198             0.021            0.020
Chain 1:   1600        -8598.617             0.020            0.019
Chain 1:   1700        -8685.849             0.020            0.019
Chain 1:   1800        -8297.376             0.020            0.019
Chain 1:   1900        -8399.695             0.019            0.016
Chain 1:   2000        -8369.739             0.017            0.016
Chain 1:   2100        -8499.921             0.016            0.015
Chain 1:   2200        -8286.267             0.017            0.015
Chain 1:   2300        -8428.749             0.017            0.016
Chain 1:   2400        -8441.936             0.016            0.015
Chain 1:   2500        -8409.640             0.015            0.012
Chain 1:   2600        -8410.206             0.014            0.012
Chain 1:   2700        -8317.966             0.014            0.012
Chain 1:   2800        -8293.172             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002898 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422158.361             1.000            1.000
Chain 1:    200     -1588322.462             2.651            4.303
Chain 1:    300      -892355.081             2.027            1.000
Chain 1:    400      -458491.873             1.757            1.000
Chain 1:    500      -358463.371             1.462            0.946
Chain 1:    600      -233110.201             1.308            0.946
Chain 1:    700      -119184.088             1.257            0.946
Chain 1:    800       -86373.495             1.148            0.946
Chain 1:    900       -66694.880             1.053            0.780
Chain 1:   1000       -51480.595             0.977            0.780
Chain 1:   1100       -38957.450             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38126.448             0.481            0.380
Chain 1:   1300       -26093.025             0.449            0.380
Chain 1:   1400       -25810.204             0.356            0.321
Chain 1:   1500       -22401.016             0.343            0.321
Chain 1:   1600       -21617.653             0.293            0.296
Chain 1:   1700       -20493.282             0.203            0.295
Chain 1:   1800       -20437.375             0.165            0.152
Chain 1:   1900       -20763.043             0.137            0.055
Chain 1:   2000       -19275.971             0.115            0.055
Chain 1:   2100       -19514.220             0.084            0.036
Chain 1:   2200       -19740.295             0.083            0.036
Chain 1:   2300       -19357.928             0.039            0.020
Chain 1:   2400       -19130.211             0.039            0.020
Chain 1:   2500       -18932.215             0.025            0.016
Chain 1:   2600       -18563.000             0.024            0.016
Chain 1:   2700       -18520.031             0.018            0.012
Chain 1:   2800       -18237.188             0.020            0.016
Chain 1:   2900       -18518.108             0.020            0.015
Chain 1:   3000       -18504.349             0.012            0.012
Chain 1:   3100       -18589.319             0.011            0.012
Chain 1:   3200       -18280.313             0.012            0.015
Chain 1:   3300       -18484.717             0.011            0.012
Chain 1:   3400       -17960.292             0.013            0.015
Chain 1:   3500       -18571.260             0.015            0.016
Chain 1:   3600       -17879.024             0.017            0.016
Chain 1:   3700       -18265.064             0.019            0.017
Chain 1:   3800       -17226.543             0.023            0.021
Chain 1:   3900       -17222.696             0.022            0.021
Chain 1:   4000       -17339.996             0.022            0.021
Chain 1:   4100       -17253.940             0.022            0.021
Chain 1:   4200       -17070.467             0.022            0.021
Chain 1:   4300       -17208.644             0.021            0.021
Chain 1:   4400       -17165.788             0.019            0.011
Chain 1:   4500       -17068.355             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001276 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12672.719             1.000            1.000
Chain 1:    200        -9591.844             0.661            1.000
Chain 1:    300        -8180.107             0.498            0.321
Chain 1:    400        -8402.759             0.380            0.321
Chain 1:    500        -8294.076             0.307            0.173
Chain 1:    600        -8101.755             0.260            0.173
Chain 1:    700        -7971.085             0.225            0.026
Chain 1:    800        -7962.247             0.197            0.026
Chain 1:    900        -7947.876             0.175            0.024
Chain 1:   1000        -8122.927             0.160            0.024
Chain 1:   1100        -8133.090             0.060            0.022
Chain 1:   1200        -8036.129             0.029            0.016
Chain 1:   1300        -7980.570             0.012            0.013
Chain 1:   1400        -7975.661             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49634.081             1.000            1.000
Chain 1:    200       -16477.691             1.506            2.012
Chain 1:    300        -8926.294             1.286            1.000
Chain 1:    400        -8516.762             0.977            1.000
Chain 1:    500        -8189.074             0.789            0.846
Chain 1:    600        -9365.161             0.679            0.846
Chain 1:    700        -7815.070             0.610            0.198
Chain 1:    800        -7709.410             0.535            0.198
Chain 1:    900        -8120.567             0.482            0.126
Chain 1:   1000        -7779.152             0.438            0.126
Chain 1:   1100        -7719.797             0.339            0.051
Chain 1:   1200        -7671.928             0.138            0.048
Chain 1:   1300        -7779.743             0.055            0.044
Chain 1:   1400        -7616.751             0.052            0.040
Chain 1:   1500        -7531.761             0.049            0.021
Chain 1:   1600        -7677.515             0.039            0.019
Chain 1:   1700        -7539.405             0.021            0.018
Chain 1:   1800        -7620.592             0.020            0.018
Chain 1:   1900        -7720.050             0.017            0.014
Chain 1:   2000        -7645.598             0.013            0.013
Chain 1:   2100        -7586.370             0.013            0.013
Chain 1:   2200        -7826.574             0.016            0.014
Chain 1:   2300        -7558.843             0.018            0.018
Chain 1:   2400        -7613.754             0.016            0.013
Chain 1:   2500        -7627.768             0.015            0.013
Chain 1:   2600        -7519.991             0.015            0.013
Chain 1:   2700        -7482.519             0.014            0.011
Chain 1:   2800        -7514.363             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002917 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86520.903             1.000            1.000
Chain 1:    200       -13920.119             3.108            5.216
Chain 1:    300       -10151.959             2.196            1.000
Chain 1:    400       -11920.310             1.684            1.000
Chain 1:    500        -8695.397             1.421            0.371
Chain 1:    600        -8470.919             1.189            0.371
Chain 1:    700        -8541.854             1.020            0.371
Chain 1:    800        -8709.033             0.895            0.371
Chain 1:    900        -8844.064             0.797            0.148
Chain 1:   1000        -9099.525             0.720            0.148
Chain 1:   1100        -8794.631             0.624            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8459.972             0.106            0.035
Chain 1:   1300        -8773.040             0.073            0.035
Chain 1:   1400        -8531.198             0.061            0.028
Chain 1:   1500        -8626.857             0.025            0.028
Chain 1:   1600        -8734.003             0.023            0.028
Chain 1:   1700        -8784.918             0.023            0.028
Chain 1:   1800        -8331.324             0.027            0.028
Chain 1:   1900        -8441.682             0.026            0.028
Chain 1:   2000        -8442.758             0.024            0.028
Chain 1:   2100        -8579.775             0.022            0.016
Chain 1:   2200        -8340.053             0.021            0.016
Chain 1:   2300        -8490.599             0.019            0.016
Chain 1:   2400        -8341.994             0.018            0.016
Chain 1:   2500        -8415.109             0.017            0.016
Chain 1:   2600        -8326.873             0.017            0.016
Chain 1:   2700        -8358.909             0.017            0.016
Chain 1:   2800        -8311.050             0.012            0.013
Chain 1:   2900        -8422.246             0.012            0.013
Chain 1:   3000        -8360.600             0.013            0.013
Chain 1:   3100        -8303.033             0.012            0.011
Chain 1:   3200        -8276.101             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396816.202             1.000            1.000
Chain 1:    200     -1581612.257             2.655            4.309
Chain 1:    300      -891573.845             2.028            1.000
Chain 1:    400      -458427.415             1.757            1.000
Chain 1:    500      -359211.306             1.461            0.945
Chain 1:    600      -234098.136             1.306            0.945
Chain 1:    700      -120020.461             1.256            0.945
Chain 1:    800       -87152.935             1.146            0.945
Chain 1:    900       -67427.558             1.051            0.774
Chain 1:   1000       -52182.038             0.975            0.774
Chain 1:   1100       -39612.676             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38789.344             0.478            0.377
Chain 1:   1300       -26687.344             0.446            0.377
Chain 1:   1400       -26404.031             0.353            0.317
Chain 1:   1500       -22975.871             0.340            0.317
Chain 1:   1600       -22188.682             0.290            0.293
Chain 1:   1700       -21054.801             0.200            0.292
Chain 1:   1800       -20997.613             0.163            0.149
Chain 1:   1900       -21324.439             0.135            0.054
Chain 1:   2000       -19830.361             0.113            0.054
Chain 1:   2100       -20069.023             0.083            0.035
Chain 1:   2200       -20296.604             0.082            0.035
Chain 1:   2300       -19912.671             0.039            0.019
Chain 1:   2400       -19684.427             0.039            0.019
Chain 1:   2500       -19486.646             0.025            0.015
Chain 1:   2600       -19115.897             0.023            0.015
Chain 1:   2700       -19072.594             0.018            0.012
Chain 1:   2800       -18789.209             0.019            0.015
Chain 1:   2900       -19070.901             0.019            0.015
Chain 1:   3000       -19056.969             0.012            0.012
Chain 1:   3100       -19142.060             0.011            0.012
Chain 1:   3200       -18832.217             0.011            0.015
Chain 1:   3300       -19037.363             0.011            0.012
Chain 1:   3400       -18511.410             0.012            0.015
Chain 1:   3500       -19124.652             0.014            0.015
Chain 1:   3600       -18429.605             0.016            0.015
Chain 1:   3700       -18817.732             0.018            0.016
Chain 1:   3800       -17774.748             0.022            0.021
Chain 1:   3900       -17770.850             0.021            0.021
Chain 1:   4000       -17888.137             0.022            0.021
Chain 1:   4100       -17801.763             0.022            0.021
Chain 1:   4200       -17617.417             0.021            0.021
Chain 1:   4300       -17756.202             0.021            0.021
Chain 1:   4400       -17712.547             0.018            0.010
Chain 1:   4500       -17615.019             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001304 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12362.416             1.000            1.000
Chain 1:    200        -9328.688             0.663            1.000
Chain 1:    300        -8099.042             0.492            0.325
Chain 1:    400        -8221.769             0.373            0.325
Chain 1:    500        -8127.290             0.301            0.152
Chain 1:    600        -8029.370             0.253            0.152
Chain 1:    700        -7940.900             0.218            0.015
Chain 1:    800        -7950.472             0.191            0.015
Chain 1:    900        -7842.112             0.171            0.014
Chain 1:   1000        -8051.458             0.157            0.015
Chain 1:   1100        -8087.696             0.057            0.014
Chain 1:   1200        -7979.563             0.026            0.014
Chain 1:   1300        -7916.622             0.012            0.012
Chain 1:   1400        -7933.809             0.010            0.012
Chain 1:   1500        -8022.157             0.010            0.011
Chain 1:   1600        -7985.558             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61938.380             1.000            1.000
Chain 1:    200       -17928.869             1.727            2.455
Chain 1:    300        -8900.405             1.490            1.014
Chain 1:    400        -9515.728             1.133            1.014
Chain 1:    500        -8511.489             0.930            1.000
Chain 1:    600        -8527.564             0.776            1.000
Chain 1:    700        -7902.881             0.676            0.118
Chain 1:    800        -8072.298             0.594            0.118
Chain 1:    900        -8093.390             0.528            0.079
Chain 1:   1000        -7856.892             0.479            0.079
Chain 1:   1100        -7833.096             0.379            0.065
Chain 1:   1200        -7596.916             0.137            0.031
Chain 1:   1300        -7763.518             0.037            0.030
Chain 1:   1400        -7940.939             0.033            0.022
Chain 1:   1500        -7625.665             0.025            0.022
Chain 1:   1600        -7794.994             0.027            0.022
Chain 1:   1700        -7536.966             0.023            0.022
Chain 1:   1800        -7669.783             0.023            0.022
Chain 1:   1900        -7677.050             0.022            0.022
Chain 1:   2000        -7681.673             0.019            0.022
Chain 1:   2100        -7637.223             0.020            0.022
Chain 1:   2200        -7735.552             0.018            0.021
Chain 1:   2300        -7630.145             0.017            0.017
Chain 1:   2400        -7680.188             0.016            0.014
Chain 1:   2500        -7599.220             0.012            0.013
Chain 1:   2600        -7590.211             0.010            0.011
Chain 1:   2700        -7577.316             0.007            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85726.829             1.000            1.000
Chain 1:    200       -13518.172             3.171            5.342
Chain 1:    300        -9908.385             2.235            1.000
Chain 1:    400       -10671.582             1.694            1.000
Chain 1:    500        -8869.970             1.396            0.364
Chain 1:    600        -8380.463             1.173            0.364
Chain 1:    700        -8727.306             1.011            0.203
Chain 1:    800        -8850.116             0.887            0.203
Chain 1:    900        -8765.444             0.789            0.072
Chain 1:   1000        -8436.407             0.714            0.072
Chain 1:   1100        -8788.967             0.618            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8406.801             0.089            0.045
Chain 1:   1300        -8606.833             0.054            0.040
Chain 1:   1400        -8612.421             0.047            0.040
Chain 1:   1500        -8474.858             0.029            0.039
Chain 1:   1600        -8587.470             0.024            0.023
Chain 1:   1700        -8673.171             0.021            0.016
Chain 1:   1800        -8264.936             0.025            0.023
Chain 1:   1900        -8360.793             0.025            0.023
Chain 1:   2000        -8333.353             0.021            0.016
Chain 1:   2100        -8454.549             0.019            0.014
Chain 1:   2200        -8295.968             0.016            0.014
Chain 1:   2300        -8360.419             0.015            0.013
Chain 1:   2400        -8425.020             0.015            0.013
Chain 1:   2500        -8370.959             0.014            0.011
Chain 1:   2600        -8369.574             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003476 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393494.648             1.000            1.000
Chain 1:    200     -1582436.747             2.652            4.304
Chain 1:    300      -890938.561             2.027            1.000
Chain 1:    400      -457902.557             1.756            1.000
Chain 1:    500      -358406.754             1.461            0.946
Chain 1:    600      -233351.432             1.307            0.946
Chain 1:    700      -119430.018             1.256            0.946
Chain 1:    800       -86594.078             1.147            0.946
Chain 1:    900       -66900.611             1.052            0.776
Chain 1:   1000       -51669.876             0.976            0.776
Chain 1:   1100       -39118.223             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38290.385             0.480            0.379
Chain 1:   1300       -26223.090             0.448            0.379
Chain 1:   1400       -25938.617             0.355            0.321
Chain 1:   1500       -22520.618             0.342            0.321
Chain 1:   1600       -21734.922             0.292            0.295
Chain 1:   1700       -20606.358             0.202            0.294
Chain 1:   1800       -20549.924             0.165            0.152
Chain 1:   1900       -20875.896             0.137            0.055
Chain 1:   2000       -19386.095             0.115            0.055
Chain 1:   2100       -19624.391             0.084            0.036
Chain 1:   2200       -19851.034             0.083            0.036
Chain 1:   2300       -19468.189             0.039            0.020
Chain 1:   2400       -19240.343             0.039            0.020
Chain 1:   2500       -19042.413             0.025            0.016
Chain 1:   2600       -18672.629             0.024            0.016
Chain 1:   2700       -18629.592             0.018            0.012
Chain 1:   2800       -18346.532             0.020            0.015
Chain 1:   2900       -18627.803             0.019            0.015
Chain 1:   3000       -18613.948             0.012            0.012
Chain 1:   3100       -18698.917             0.011            0.012
Chain 1:   3200       -18389.629             0.012            0.015
Chain 1:   3300       -18594.334             0.011            0.012
Chain 1:   3400       -18069.364             0.013            0.015
Chain 1:   3500       -18681.038             0.015            0.015
Chain 1:   3600       -17988.060             0.017            0.015
Chain 1:   3700       -18374.659             0.019            0.017
Chain 1:   3800       -17334.785             0.023            0.021
Chain 1:   3900       -17330.973             0.021            0.021
Chain 1:   4000       -17448.278             0.022            0.021
Chain 1:   4100       -17362.047             0.022            0.021
Chain 1:   4200       -17178.405             0.021            0.021
Chain 1:   4300       -17316.704             0.021            0.021
Chain 1:   4400       -17273.610             0.019            0.011
Chain 1:   4500       -17176.203             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12712.929             1.000            1.000
Chain 1:    200        -9504.810             0.669            1.000
Chain 1:    300        -8070.785             0.505            0.338
Chain 1:    400        -8311.550             0.386            0.338
Chain 1:    500        -8144.886             0.313            0.178
Chain 1:    600        -8011.530             0.264            0.178
Chain 1:    700        -8137.723             0.228            0.029
Chain 1:    800        -7915.438             0.203            0.029
Chain 1:    900        -7793.164             0.182            0.028
Chain 1:   1000        -7970.484             0.166            0.028
Chain 1:   1100        -7999.060             0.067            0.022
Chain 1:   1200        -7911.357             0.034            0.020
Chain 1:   1300        -7876.578             0.017            0.017
Chain 1:   1400        -7885.093             0.014            0.016
Chain 1:   1500        -7973.952             0.013            0.016
Chain 1:   1600        -7888.262             0.012            0.011
Chain 1:   1700        -7857.655             0.011            0.011
Chain 1:   1800        -7829.738             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58507.886             1.000            1.000
Chain 1:    200       -18071.856             1.619            2.238
Chain 1:    300        -8884.148             1.424            1.034
Chain 1:    400        -8120.499             1.091            1.034
Chain 1:    500        -9083.548             0.894            1.000
Chain 1:    600        -8515.844             0.756            1.000
Chain 1:    700        -8187.910             0.654            0.106
Chain 1:    800        -8209.969             0.573            0.106
Chain 1:    900        -7792.765             0.515            0.094
Chain 1:   1000        -7847.350             0.464            0.094
Chain 1:   1100        -7781.479             0.365            0.067
Chain 1:   1200        -7732.079             0.142            0.054
Chain 1:   1300        -7699.562             0.039            0.040
Chain 1:   1400        -7896.454             0.032            0.025
Chain 1:   1500        -7577.670             0.026            0.025
Chain 1:   1600        -7811.463             0.022            0.025
Chain 1:   1700        -7614.372             0.021            0.025
Chain 1:   1800        -7686.245             0.021            0.025
Chain 1:   1900        -7763.379             0.017            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003116 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87771.322             1.000            1.000
Chain 1:    200       -13866.639             3.165            5.330
Chain 1:    300       -10076.886             2.235            1.000
Chain 1:    400       -11688.728             1.711            1.000
Chain 1:    500        -8685.988             1.438            0.376
Chain 1:    600        -8525.371             1.201            0.376
Chain 1:    700        -8392.648             1.032            0.346
Chain 1:    800        -8924.254             0.910            0.346
Chain 1:    900        -8804.664             0.811            0.138
Chain 1:   1000        -8951.226             0.731            0.138
Chain 1:   1100        -8850.029             0.632            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8352.808             0.105            0.060
Chain 1:   1300        -8683.394             0.072            0.038
Chain 1:   1400        -8482.908             0.060            0.024
Chain 1:   1500        -8543.215             0.026            0.019
Chain 1:   1600        -8646.663             0.026            0.016
Chain 1:   1700        -8695.514             0.025            0.016
Chain 1:   1800        -8238.962             0.024            0.016
Chain 1:   1900        -8349.570             0.024            0.016
Chain 1:   2000        -8361.715             0.023            0.013
Chain 1:   2100        -8293.210             0.022            0.013
Chain 1:   2200        -8247.954             0.017            0.012
Chain 1:   2300        -8416.646             0.015            0.012
Chain 1:   2400        -8245.759             0.015            0.012
Chain 1:   2500        -8317.077             0.015            0.012
Chain 1:   2600        -8235.020             0.015            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003485 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8462142.464             1.000            1.000
Chain 1:    200     -1591072.540             2.659            4.319
Chain 1:    300      -890199.786             2.035            1.000
Chain 1:    400      -456919.872             1.764            1.000
Chain 1:    500      -356805.705             1.467            0.948
Chain 1:    600      -232017.664             1.312            0.948
Chain 1:    700      -118939.229             1.260            0.948
Chain 1:    800       -86319.231             1.150            0.948
Chain 1:    900       -66804.139             1.055            0.787
Chain 1:   1000       -51717.499             0.978            0.787
Chain 1:   1100       -39297.656             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38494.930             0.480            0.378
Chain 1:   1300       -26538.512             0.447            0.378
Chain 1:   1400       -26270.293             0.353            0.316
Chain 1:   1500       -22879.245             0.340            0.316
Chain 1:   1600       -22103.887             0.289            0.292
Chain 1:   1700       -20986.783             0.200            0.292
Chain 1:   1800       -20933.764             0.162            0.148
Chain 1:   1900       -21260.766             0.134            0.053
Chain 1:   2000       -19775.453             0.113            0.053
Chain 1:   2100       -20013.705             0.082            0.035
Chain 1:   2200       -20239.852             0.081            0.035
Chain 1:   2300       -19857.125             0.038            0.019
Chain 1:   2400       -19629.024             0.038            0.019
Chain 1:   2500       -19430.638             0.025            0.015
Chain 1:   2600       -19060.388             0.023            0.015
Chain 1:   2700       -19017.326             0.018            0.012
Chain 1:   2800       -18733.622             0.019            0.015
Chain 1:   2900       -19015.132             0.019            0.015
Chain 1:   3000       -19001.366             0.012            0.012
Chain 1:   3100       -19086.423             0.011            0.012
Chain 1:   3200       -18776.681             0.011            0.015
Chain 1:   3300       -18981.789             0.011            0.012
Chain 1:   3400       -18455.761             0.012            0.015
Chain 1:   3500       -19068.867             0.014            0.015
Chain 1:   3600       -18373.922             0.016            0.015
Chain 1:   3700       -18761.820             0.018            0.016
Chain 1:   3800       -17718.891             0.023            0.021
Chain 1:   3900       -17714.902             0.021            0.021
Chain 1:   4000       -17832.282             0.022            0.021
Chain 1:   4100       -17745.840             0.022            0.021
Chain 1:   4200       -17561.556             0.021            0.021
Chain 1:   4300       -17700.390             0.021            0.021
Chain 1:   4400       -17656.750             0.018            0.010
Chain 1:   4500       -17559.131             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001462 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12037.893             1.000            1.000
Chain 1:    200        -8952.358             0.672            1.000
Chain 1:    300        -7842.018             0.495            0.345
Chain 1:    400        -7901.951             0.373            0.345
Chain 1:    500        -7831.756             0.301            0.142
Chain 1:    600        -7761.004             0.252            0.142
Chain 1:    700        -7683.505             0.217            0.010
Chain 1:    800        -7907.409             0.194            0.028
Chain 1:    900        -7673.923             0.176            0.028
Chain 1:   1000        -7701.586             0.158            0.028
Chain 1:   1100        -7779.291             0.059            0.010
Chain 1:   1200        -7708.262             0.026            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56384.738             1.000            1.000
Chain 1:    200       -17139.041             1.645            2.290
Chain 1:    300        -8497.354             1.436            1.017
Chain 1:    400        -7878.434             1.096            1.017
Chain 1:    500        -8356.261             0.889            1.000
Chain 1:    600        -8034.683             0.747            1.000
Chain 1:    700        -7734.626             0.646            0.079
Chain 1:    800        -7958.815             0.569            0.079
Chain 1:    900        -7919.032             0.506            0.057
Chain 1:   1000        -7707.866             0.458            0.057
Chain 1:   1100        -7637.141             0.359            0.040
Chain 1:   1200        -7577.788             0.131            0.039
Chain 1:   1300        -7688.405             0.031            0.028
Chain 1:   1400        -7650.728             0.023            0.027
Chain 1:   1500        -7579.443             0.019            0.014
Chain 1:   1600        -7542.378             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86617.797             1.000            1.000
Chain 1:    200       -13150.899             3.293            5.586
Chain 1:    300        -9580.955             2.320            1.000
Chain 1:    400       -10404.651             1.760            1.000
Chain 1:    500        -8502.100             1.452            0.373
Chain 1:    600        -8105.700             1.218            0.373
Chain 1:    700        -8266.710             1.047            0.224
Chain 1:    800        -8847.114             0.924            0.224
Chain 1:    900        -8418.612             0.827            0.079
Chain 1:   1000        -8174.914             0.748            0.079
Chain 1:   1100        -8331.578             0.650            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8106.426             0.094            0.051
Chain 1:   1300        -8309.294             0.059            0.049
Chain 1:   1400        -8293.134             0.051            0.030
Chain 1:   1500        -8193.717             0.030            0.028
Chain 1:   1600        -8294.305             0.026            0.024
Chain 1:   1700        -8381.639             0.025            0.024
Chain 1:   1800        -7989.434             0.024            0.024
Chain 1:   1900        -8091.470             0.020            0.019
Chain 1:   2000        -8061.722             0.017            0.013
Chain 1:   2100        -8187.871             0.017            0.013
Chain 1:   2200        -7973.513             0.017            0.013
Chain 1:   2300        -8120.224             0.016            0.013
Chain 1:   2400        -8135.656             0.016            0.013
Chain 1:   2500        -8102.256             0.015            0.013
Chain 1:   2600        -8104.193             0.014            0.013
Chain 1:   2700        -8011.074             0.014            0.013
Chain 1:   2800        -7984.036             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003479 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8444181.459             1.000            1.000
Chain 1:    200     -1594388.416             2.648            4.296
Chain 1:    300      -892893.587             2.027            1.000
Chain 1:    400      -458057.797             1.758            1.000
Chain 1:    500      -357654.586             1.462            0.949
Chain 1:    600      -232123.074             1.309            0.949
Chain 1:    700      -118534.194             1.259            0.949
Chain 1:    800       -85820.161             1.149            0.949
Chain 1:    900       -66206.238             1.054            0.786
Chain 1:   1000       -51053.747             0.979            0.786
Chain 1:   1100       -38582.962             0.911            0.541   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37760.498             0.483            0.381
Chain 1:   1300       -25777.735             0.451            0.381
Chain 1:   1400       -25500.115             0.357            0.323
Chain 1:   1500       -22104.591             0.345            0.323
Chain 1:   1600       -21325.535             0.294            0.297
Chain 1:   1700       -20207.162             0.204            0.296
Chain 1:   1800       -20152.833             0.166            0.154
Chain 1:   1900       -20478.571             0.138            0.055
Chain 1:   2000       -18994.737             0.116            0.055
Chain 1:   2100       -19232.673             0.085            0.037
Chain 1:   2200       -19458.327             0.084            0.037
Chain 1:   2300       -19076.395             0.040            0.020
Chain 1:   2400       -18848.735             0.040            0.020
Chain 1:   2500       -18650.549             0.026            0.016
Chain 1:   2600       -18281.373             0.024            0.016
Chain 1:   2700       -18238.519             0.019            0.012
Chain 1:   2800       -17955.510             0.020            0.016
Chain 1:   2900       -18236.478             0.020            0.015
Chain 1:   3000       -18222.700             0.012            0.012
Chain 1:   3100       -18307.636             0.011            0.012
Chain 1:   3200       -17998.631             0.012            0.015
Chain 1:   3300       -18203.111             0.011            0.012
Chain 1:   3400       -17678.551             0.013            0.015
Chain 1:   3500       -18289.532             0.015            0.016
Chain 1:   3600       -17597.372             0.017            0.016
Chain 1:   3700       -17983.291             0.019            0.017
Chain 1:   3800       -16944.714             0.023            0.021
Chain 1:   3900       -16940.874             0.022            0.021
Chain 1:   4000       -17058.218             0.023            0.021
Chain 1:   4100       -16972.070             0.023            0.021
Chain 1:   4200       -16788.670             0.022            0.021
Chain 1:   4300       -16926.815             0.022            0.021
Chain 1:   4400       -16883.948             0.019            0.011
Chain 1:   4500       -16786.526             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001405 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12361.158             1.000            1.000
Chain 1:    200        -9242.721             0.669            1.000
Chain 1:    300        -8005.933             0.497            0.337
Chain 1:    400        -8234.040             0.380            0.337
Chain 1:    500        -7955.695             0.311            0.154
Chain 1:    600        -7970.899             0.259            0.154
Chain 1:    700        -7890.448             0.224            0.035
Chain 1:    800        -7928.598             0.196            0.035
Chain 1:    900        -8070.636             0.177            0.028
Chain 1:   1000        -7926.999             0.161            0.028
Chain 1:   1100        -7904.032             0.061            0.018
Chain 1:   1200        -7907.980             0.027            0.018
Chain 1:   1300        -7854.311             0.013            0.010
Chain 1:   1400        -7877.992             0.010            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57118.259             1.000            1.000
Chain 1:    200       -17547.478             1.628            2.255
Chain 1:    300        -8740.011             1.421            1.008
Chain 1:    400        -8243.438             1.081            1.008
Chain 1:    500        -8424.153             0.869            1.000
Chain 1:    600        -8000.963             0.733            1.000
Chain 1:    700        -7855.706             0.631            0.060
Chain 1:    800        -8067.836             0.555            0.060
Chain 1:    900        -7995.684             0.495            0.053
Chain 1:   1000        -7925.127             0.446            0.053
Chain 1:   1100        -7738.094             0.348            0.026
Chain 1:   1200        -7587.116             0.125            0.024
Chain 1:   1300        -7785.511             0.027            0.024
Chain 1:   1400        -7933.294             0.023            0.021
Chain 1:   1500        -7583.860             0.025            0.024
Chain 1:   1600        -7787.147             0.022            0.024
Chain 1:   1700        -7513.953             0.024            0.025
Chain 1:   1800        -7595.910             0.023            0.024
Chain 1:   1900        -7712.715             0.023            0.024
Chain 1:   2000        -7566.073             0.024            0.024
Chain 1:   2100        -7514.494             0.022            0.020
Chain 1:   2200        -7735.029             0.023            0.025
Chain 1:   2300        -7596.342             0.023            0.019
Chain 1:   2400        -7634.532             0.021            0.019
Chain 1:   2500        -7665.291             0.017            0.018
Chain 1:   2600        -7525.790             0.016            0.018
Chain 1:   2700        -7495.391             0.013            0.015
Chain 1:   2800        -7559.934             0.013            0.015
Chain 1:   2900        -7399.607             0.013            0.018
Chain 1:   3000        -7523.959             0.013            0.017
Chain 1:   3100        -7517.802             0.013            0.017
Chain 1:   3200        -7699.397             0.012            0.017
Chain 1:   3300        -7457.889             0.014            0.017
Chain 1:   3400        -7650.662             0.016            0.019
Chain 1:   3500        -7432.223             0.018            0.022
Chain 1:   3600        -7492.814             0.017            0.022
Chain 1:   3700        -7444.306             0.017            0.022
Chain 1:   3800        -7454.472             0.017            0.022
Chain 1:   3900        -7436.421             0.015            0.017
Chain 1:   4000        -7409.736             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003006 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85662.069             1.000            1.000
Chain 1:    200       -13524.312             3.167            5.334
Chain 1:    300        -9881.003             2.234            1.000
Chain 1:    400       -10796.507             1.697            1.000
Chain 1:    500        -8779.116             1.403            0.369
Chain 1:    600        -9342.953             1.180            0.369
Chain 1:    700        -8909.126             1.018            0.230
Chain 1:    800        -8434.773             0.898            0.230
Chain 1:    900        -8330.434             0.799            0.085
Chain 1:   1000        -8424.196             0.721            0.085
Chain 1:   1100        -8733.032             0.624            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8350.941             0.095            0.056
Chain 1:   1300        -8574.998             0.061            0.049
Chain 1:   1400        -8558.970             0.053            0.046
Chain 1:   1500        -8449.010             0.031            0.035
Chain 1:   1600        -8557.247             0.026            0.026
Chain 1:   1700        -8635.430             0.022            0.013
Chain 1:   1800        -8216.568             0.022            0.013
Chain 1:   1900        -8314.857             0.022            0.013
Chain 1:   2000        -8288.896             0.021            0.013
Chain 1:   2100        -8412.934             0.019            0.013
Chain 1:   2200        -8226.032             0.017            0.013
Chain 1:   2300        -8309.554             0.015            0.013
Chain 1:   2400        -8379.053             0.016            0.013
Chain 1:   2500        -8324.994             0.015            0.012
Chain 1:   2600        -8325.368             0.014            0.010
Chain 1:   2700        -8242.514             0.014            0.010
Chain 1:   2800        -8204.020             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003162 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8365558.671             1.000            1.000
Chain 1:    200     -1580513.903             2.646            4.293
Chain 1:    300      -891332.925             2.022            1.000
Chain 1:    400      -458288.650             1.753            1.000
Chain 1:    500      -359126.079             1.457            0.945
Chain 1:    600      -233902.577             1.304            0.945
Chain 1:    700      -119717.610             1.254            0.945
Chain 1:    800       -86824.862             1.144            0.945
Chain 1:    900       -67077.688             1.050            0.773
Chain 1:   1000       -51807.213             0.974            0.773
Chain 1:   1100       -39219.338             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38388.845             0.479            0.379
Chain 1:   1300       -26276.680             0.448            0.379
Chain 1:   1400       -25989.523             0.355            0.321
Chain 1:   1500       -22558.889             0.342            0.321
Chain 1:   1600       -21770.327             0.292            0.295
Chain 1:   1700       -20635.740             0.203            0.294
Chain 1:   1800       -20578.093             0.165            0.152
Chain 1:   1900       -20904.280             0.137            0.055
Chain 1:   2000       -19411.129             0.115            0.055
Chain 1:   2100       -19649.634             0.084            0.036
Chain 1:   2200       -19876.857             0.083            0.036
Chain 1:   2300       -19493.401             0.039            0.020
Chain 1:   2400       -19265.383             0.039            0.020
Chain 1:   2500       -19067.743             0.025            0.016
Chain 1:   2600       -18697.496             0.024            0.016
Chain 1:   2700       -18654.406             0.018            0.012
Chain 1:   2800       -18371.329             0.020            0.015
Chain 1:   2900       -18652.764             0.019            0.015
Chain 1:   3000       -18638.835             0.012            0.012
Chain 1:   3100       -18723.826             0.011            0.012
Chain 1:   3200       -18414.423             0.012            0.015
Chain 1:   3300       -18619.261             0.011            0.012
Chain 1:   3400       -18094.081             0.013            0.015
Chain 1:   3500       -18706.217             0.015            0.015
Chain 1:   3600       -18012.670             0.017            0.015
Chain 1:   3700       -18399.646             0.018            0.017
Chain 1:   3800       -17359.049             0.023            0.021
Chain 1:   3900       -17355.270             0.021            0.021
Chain 1:   4000       -17472.505             0.022            0.021
Chain 1:   4100       -17386.226             0.022            0.021
Chain 1:   4200       -17202.465             0.021            0.021
Chain 1:   4300       -17340.835             0.021            0.021
Chain 1:   4400       -17297.596             0.019            0.011
Chain 1:   4500       -17200.186             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48500.543             1.000            1.000
Chain 1:    200       -16287.959             1.489            1.978
Chain 1:    300       -17385.226             1.014            1.000
Chain 1:    400       -13506.132             0.832            1.000
Chain 1:    500       -11573.471             0.699            0.287
Chain 1:    600       -14798.255             0.619            0.287
Chain 1:    700       -11826.492             0.566            0.251
Chain 1:    800       -14294.380             0.517            0.251
Chain 1:    900       -14079.050             0.461            0.218
Chain 1:   1000       -13372.393             0.420            0.218
Chain 1:   1100       -15911.041             0.336            0.173
Chain 1:   1200       -15930.172             0.139            0.167
Chain 1:   1300       -10768.001             0.180            0.173
Chain 1:   1400       -13102.808             0.170            0.173
Chain 1:   1500       -10544.728             0.177            0.178
Chain 1:   1600       -10512.022             0.156            0.173
Chain 1:   1700        -9469.309             0.141            0.160
Chain 1:   1800       -15248.638             0.162            0.160
Chain 1:   1900       -13937.758             0.170            0.160
Chain 1:   2000        -9751.678             0.208            0.178
Chain 1:   2100        -9701.255             0.192            0.178
Chain 1:   2200       -10021.776             0.195            0.178
Chain 1:   2300       -11182.905             0.158            0.110
Chain 1:   2400        -8680.849             0.169            0.110
Chain 1:   2500       -10352.649             0.161            0.110
Chain 1:   2600        -9696.108             0.167            0.110
Chain 1:   2700        -8975.511             0.164            0.104
Chain 1:   2800       -13814.890             0.161            0.104
Chain 1:   2900        -9058.265             0.204            0.161
Chain 1:   3000        -9241.822             0.163            0.104
Chain 1:   3100        -8454.217             0.172            0.104
Chain 1:   3200        -8574.852             0.170            0.104
Chain 1:   3300        -9661.109             0.171            0.112
Chain 1:   3400        -8961.732             0.150            0.093
Chain 1:   3500        -9092.235             0.136            0.080
Chain 1:   3600       -10820.764             0.145            0.093
Chain 1:   3700        -8846.940             0.159            0.112
Chain 1:   3800       -14287.375             0.162            0.112
Chain 1:   3900       -11231.588             0.137            0.112
Chain 1:   4000        -8441.817             0.168            0.160
Chain 1:   4100       -15001.768             0.202            0.223
Chain 1:   4200        -8391.652             0.280            0.272
Chain 1:   4300       -10083.525             0.285            0.272
Chain 1:   4400       -12112.691             0.294            0.272
Chain 1:   4500        -8702.841             0.332            0.330
Chain 1:   4600        -8840.186             0.317            0.330
Chain 1:   4700        -8495.832             0.299            0.330
Chain 1:   4800        -8163.151             0.265            0.272
Chain 1:   4900        -9321.720             0.250            0.168
Chain 1:   5000        -9296.779             0.218            0.168
Chain 1:   5100        -8886.078             0.178            0.124
Chain 1:   5200       -11833.433             0.125            0.124
Chain 1:   5300       -11812.585             0.108            0.046
Chain 1:   5400       -12809.871             0.099            0.046
Chain 1:   5500        -8167.960             0.117            0.046
Chain 1:   5600        -8222.897             0.116            0.046
Chain 1:   5700       -14460.598             0.155            0.078
Chain 1:   5800        -8786.310             0.215            0.124
Chain 1:   5900        -8606.967             0.205            0.078
Chain 1:   6000       -11481.335             0.230            0.249
Chain 1:   6100       -11848.229             0.228            0.249
Chain 1:   6200        -8089.942             0.250            0.250
Chain 1:   6300        -8595.040             0.256            0.250
Chain 1:   6400       -12705.858             0.280            0.324
Chain 1:   6500        -8889.377             0.266            0.324
Chain 1:   6600       -10785.560             0.283            0.324
Chain 1:   6700       -12483.044             0.254            0.250
Chain 1:   6800        -8554.502             0.235            0.250
Chain 1:   6900        -8549.023             0.233            0.250
Chain 1:   7000       -10409.326             0.226            0.179
Chain 1:   7100        -7962.647             0.253            0.307
Chain 1:   7200       -10914.457             0.234            0.270
Chain 1:   7300       -10666.820             0.230            0.270
Chain 1:   7400       -11670.400             0.207            0.179
Chain 1:   7500        -8733.063             0.197            0.179
Chain 1:   7600        -8169.551             0.187            0.179
Chain 1:   7700        -8851.417             0.181            0.179
Chain 1:   7800        -8202.784             0.143            0.086
Chain 1:   7900        -8659.119             0.148            0.086
Chain 1:   8000        -8043.941             0.138            0.079
Chain 1:   8100       -10170.090             0.128            0.079
Chain 1:   8200       -10478.941             0.104            0.077
Chain 1:   8300       -11385.569             0.109            0.079
Chain 1:   8400        -9336.193             0.123            0.079
Chain 1:   8500        -8948.613             0.094            0.077
Chain 1:   8600        -8499.920             0.092            0.077
Chain 1:   8700        -8180.468             0.088            0.076
Chain 1:   8800        -8304.889             0.082            0.053
Chain 1:   8900        -9178.830             0.086            0.076
Chain 1:   9000       -11557.133             0.099            0.080
Chain 1:   9100        -8676.615             0.111            0.080
Chain 1:   9200        -8399.646             0.112            0.080
Chain 1:   9300        -8172.467             0.106            0.053
Chain 1:   9400       -11642.734             0.114            0.053
Chain 1:   9500        -8416.162             0.148            0.095
Chain 1:   9600        -8193.838             0.146            0.095
Chain 1:   9700        -8717.729             0.148            0.095
Chain 1:   9800        -7977.812             0.156            0.095
Chain 1:   9900       -10586.032             0.171            0.206
Chain 1:   10000        -8116.044             0.180            0.246
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001417 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61389.655             1.000            1.000
Chain 1:    200       -17430.841             1.761            2.522
Chain 1:    300        -8629.416             1.514            1.020
Chain 1:    400        -8837.705             1.141            1.020
Chain 1:    500        -7948.258             0.935            1.000
Chain 1:    600        -8649.917             0.793            1.000
Chain 1:    700        -8206.927             0.687            0.112
Chain 1:    800        -8072.596             0.604            0.112
Chain 1:    900        -7541.495             0.544            0.081
Chain 1:   1000        -7667.334             0.492            0.081
Chain 1:   1100        -7577.761             0.393            0.070
Chain 1:   1200        -7621.041             0.141            0.054
Chain 1:   1300        -7522.168             0.040            0.024
Chain 1:   1400        -7629.270             0.040            0.017
Chain 1:   1500        -7564.611             0.029            0.016
Chain 1:   1600        -7472.315             0.022            0.014
Chain 1:   1700        -7449.626             0.017            0.013
Chain 1:   1800        -7473.025             0.016            0.012
Chain 1:   1900        -7557.854             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003255 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85854.794             1.000            1.000
Chain 1:    200       -13073.083             3.284            5.567
Chain 1:    300        -9580.132             2.311            1.000
Chain 1:    400       -10482.953             1.755            1.000
Chain 1:    500        -8421.751             1.453            0.365
Chain 1:    600        -8197.982             1.215            0.365
Chain 1:    700        -8470.192             1.046            0.245
Chain 1:    800        -8686.624             0.918            0.245
Chain 1:    900        -8477.555             0.819            0.086
Chain 1:   1000        -8232.372             0.740            0.086
Chain 1:   1100        -8477.309             0.643            0.032   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8153.285             0.090            0.032
Chain 1:   1300        -8202.699             0.054            0.030
Chain 1:   1400        -8198.766             0.046            0.029
Chain 1:   1500        -8230.357             0.022            0.027
Chain 1:   1600        -8235.930             0.019            0.025
Chain 1:   1700        -8177.404             0.017            0.025
Chain 1:   1800        -8056.180             0.016            0.015
Chain 1:   1900        -8169.380             0.015            0.014
Chain 1:   2000        -8131.515             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004164 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424870.899             1.000            1.000
Chain 1:    200     -1589506.294             2.650            4.300
Chain 1:    300      -891168.171             2.028            1.000
Chain 1:    400      -456985.353             1.759            1.000
Chain 1:    500      -356797.149             1.463            0.950
Chain 1:    600      -231837.867             1.309            0.950
Chain 1:    700      -118394.020             1.259            0.950
Chain 1:    800       -85688.191             1.149            0.950
Chain 1:    900       -66104.062             1.054            0.784
Chain 1:   1000       -50952.675             0.979            0.784
Chain 1:   1100       -38482.222             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37659.143             0.483            0.382
Chain 1:   1300       -25682.284             0.452            0.382
Chain 1:   1400       -25403.937             0.358            0.324
Chain 1:   1500       -22009.294             0.345            0.324
Chain 1:   1600       -21230.080             0.295            0.297
Chain 1:   1700       -20112.673             0.205            0.296
Chain 1:   1800       -20058.365             0.167            0.154
Chain 1:   1900       -20383.694             0.139            0.056
Chain 1:   2000       -18901.072             0.117            0.056
Chain 1:   2100       -19139.019             0.086            0.037
Chain 1:   2200       -19364.206             0.084            0.037
Chain 1:   2300       -18982.775             0.040            0.020
Chain 1:   2400       -18755.271             0.040            0.020
Chain 1:   2500       -18557.034             0.026            0.016
Chain 1:   2600       -18188.339             0.024            0.016
Chain 1:   2700       -18145.665             0.019            0.012
Chain 1:   2800       -17862.788             0.020            0.016
Chain 1:   2900       -18143.568             0.020            0.015
Chain 1:   3000       -18129.873             0.012            0.012
Chain 1:   3100       -18214.711             0.011            0.012
Chain 1:   3200       -17906.014             0.012            0.015
Chain 1:   3300       -18110.271             0.011            0.012
Chain 1:   3400       -17586.211             0.013            0.015
Chain 1:   3500       -18196.430             0.015            0.016
Chain 1:   3600       -17505.295             0.017            0.016
Chain 1:   3700       -17890.419             0.019            0.017
Chain 1:   3800       -16853.432             0.024            0.022
Chain 1:   3900       -16849.638             0.022            0.022
Chain 1:   4000       -16966.970             0.023            0.022
Chain 1:   4100       -16880.862             0.023            0.022
Chain 1:   4200       -16697.865             0.022            0.022
Chain 1:   4300       -16835.752             0.022            0.022
Chain 1:   4400       -16793.167             0.019            0.011
Chain 1:   4500       -16695.796             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001548 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12512.833             1.000            1.000
Chain 1:    200        -9351.396             0.669            1.000
Chain 1:    300        -8012.993             0.502            0.338
Chain 1:    400        -8103.986             0.379            0.338
Chain 1:    500        -8088.168             0.304            0.167
Chain 1:    600        -7946.932             0.256            0.167
Chain 1:    700        -7840.311             0.221            0.018
Chain 1:    800        -7860.817             0.194            0.018
Chain 1:    900        -7882.151             0.173            0.014
Chain 1:   1000        -8025.292             0.157            0.018
Chain 1:   1100        -7946.300             0.058            0.014
Chain 1:   1200        -7849.586             0.026            0.012
Chain 1:   1300        -7830.834             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00177 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59908.869             1.000            1.000
Chain 1:    200       -17926.301             1.671            2.342
Chain 1:    300        -8721.899             1.466            1.055
Chain 1:    400        -8467.811             1.107            1.055
Chain 1:    500        -8305.546             0.889            1.000
Chain 1:    600        -9113.662             0.756            1.000
Chain 1:    700        -8327.310             0.661            0.094
Chain 1:    800        -8038.962             0.583            0.094
Chain 1:    900        -8085.658             0.519            0.089
Chain 1:   1000        -7631.362             0.473            0.089
Chain 1:   1100        -7823.454             0.376            0.060
Chain 1:   1200        -7935.663             0.143            0.036
Chain 1:   1300        -7724.784             0.040            0.030
Chain 1:   1400        -7925.678             0.040            0.027
Chain 1:   1500        -7565.029             0.042            0.036
Chain 1:   1600        -7649.738             0.035            0.027
Chain 1:   1700        -7498.442             0.027            0.025
Chain 1:   1800        -7589.509             0.025            0.025
Chain 1:   1900        -7551.800             0.025            0.025
Chain 1:   2000        -7610.331             0.019            0.020
Chain 1:   2100        -7557.260             0.018            0.014
Chain 1:   2200        -7675.438             0.018            0.015
Chain 1:   2300        -7584.072             0.016            0.012
Chain 1:   2400        -7629.757             0.014            0.012
Chain 1:   2500        -7577.922             0.010            0.011
Chain 1:   2600        -7499.228             0.010            0.010
Chain 1:   2700        -7515.333             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003948 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86839.198             1.000            1.000
Chain 1:    200       -13528.054             3.210            5.419
Chain 1:    300        -9856.618             2.264            1.000
Chain 1:    400       -10907.096             1.722            1.000
Chain 1:    500        -8829.847             1.425            0.372
Chain 1:    600        -8848.213             1.188            0.372
Chain 1:    700        -8264.984             1.028            0.235
Chain 1:    800        -9218.645             0.912            0.235
Chain 1:    900        -8563.011             0.820            0.103
Chain 1:   1000        -8306.720             0.741            0.103
Chain 1:   1100        -8679.628             0.645            0.096   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8202.241             0.109            0.077
Chain 1:   1300        -8538.529             0.076            0.071
Chain 1:   1400        -8520.069             0.066            0.058
Chain 1:   1500        -8416.404             0.044            0.043
Chain 1:   1600        -8520.119             0.045            0.043
Chain 1:   1700        -8595.774             0.039            0.039
Chain 1:   1800        -8172.568             0.034            0.039
Chain 1:   1900        -8272.976             0.027            0.031
Chain 1:   2000        -8247.466             0.024            0.012
Chain 1:   2100        -8373.032             0.022            0.012
Chain 1:   2200        -8176.097             0.018            0.012
Chain 1:   2300        -8267.877             0.015            0.012
Chain 1:   2400        -8336.648             0.016            0.012
Chain 1:   2500        -8282.866             0.015            0.012
Chain 1:   2600        -8284.232             0.014            0.011
Chain 1:   2700        -8200.976             0.014            0.011
Chain 1:   2800        -8160.826             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003955 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407762.333             1.000            1.000
Chain 1:    200     -1583517.103             2.655            4.310
Chain 1:    300      -891597.187             2.029            1.000
Chain 1:    400      -458399.871             1.758            1.000
Chain 1:    500      -358745.031             1.462            0.945
Chain 1:    600      -233383.625             1.308            0.945
Chain 1:    700      -119422.635             1.257            0.945
Chain 1:    800       -86595.155             1.147            0.945
Chain 1:    900       -66893.000             1.053            0.776
Chain 1:   1000       -51661.508             0.977            0.776
Chain 1:   1100       -39120.280             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38294.607             0.480            0.379
Chain 1:   1300       -26231.930             0.448            0.379
Chain 1:   1400       -25949.376             0.355            0.321
Chain 1:   1500       -22532.058             0.342            0.321
Chain 1:   1600       -21747.606             0.292            0.295
Chain 1:   1700       -20618.942             0.202            0.295
Chain 1:   1800       -20562.719             0.165            0.152
Chain 1:   1900       -20888.917             0.137            0.055
Chain 1:   2000       -19398.823             0.115            0.055
Chain 1:   2100       -19637.248             0.084            0.036
Chain 1:   2200       -19863.959             0.083            0.036
Chain 1:   2300       -19480.904             0.039            0.020
Chain 1:   2400       -19252.917             0.039            0.020
Chain 1:   2500       -19055.042             0.025            0.016
Chain 1:   2600       -18684.977             0.024            0.016
Chain 1:   2700       -18641.912             0.018            0.012
Chain 1:   2800       -18358.741             0.020            0.015
Chain 1:   2900       -18640.102             0.019            0.015
Chain 1:   3000       -18626.173             0.012            0.012
Chain 1:   3100       -18711.221             0.011            0.012
Chain 1:   3200       -18401.794             0.012            0.015
Chain 1:   3300       -18606.625             0.011            0.012
Chain 1:   3400       -18081.410             0.013            0.015
Chain 1:   3500       -18693.495             0.015            0.015
Chain 1:   3600       -17999.880             0.017            0.015
Chain 1:   3700       -18386.926             0.019            0.017
Chain 1:   3800       -17346.192             0.023            0.021
Chain 1:   3900       -17342.334             0.021            0.021
Chain 1:   4000       -17459.622             0.022            0.021
Chain 1:   4100       -17373.346             0.022            0.021
Chain 1:   4200       -17189.505             0.021            0.021
Chain 1:   4300       -17327.950             0.021            0.021
Chain 1:   4400       -17284.709             0.019            0.011
Chain 1:   4500       -17187.209             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13054.691             1.000            1.000
Chain 1:    200        -9729.779             0.671            1.000
Chain 1:    300        -8192.984             0.510            0.342
Chain 1:    400        -8241.082             0.384            0.342
Chain 1:    500        -8201.466             0.308            0.188
Chain 1:    600        -8036.818             0.260            0.188
Chain 1:    700        -7955.216             0.224            0.020
Chain 1:    800        -7960.379             0.196            0.020
Chain 1:    900        -7854.300             0.176            0.014
Chain 1:   1000        -8066.966             0.161            0.020
Chain 1:   1100        -7999.663             0.062            0.014
Chain 1:   1200        -7965.973             0.028            0.010
Chain 1:   1300        -7931.729             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002918 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58103.318             1.000            1.000
Chain 1:    200       -17716.302             1.640            2.280
Chain 1:    300        -8713.229             1.438            1.033
Chain 1:    400        -8224.635             1.093            1.033
Chain 1:    500        -8440.514             0.880            1.000
Chain 1:    600        -8360.243             0.735            1.000
Chain 1:    700        -8458.500             0.631            0.059
Chain 1:    800        -8066.665             0.558            0.059
Chain 1:    900        -7955.274             0.498            0.049
Chain 1:   1000        -7713.699             0.451            0.049
Chain 1:   1100        -7687.706             0.352            0.031
Chain 1:   1200        -7648.176             0.124            0.026
Chain 1:   1300        -7751.957             0.022            0.014
Chain 1:   1400        -7982.175             0.019            0.014
Chain 1:   1500        -7643.525             0.021            0.014
Chain 1:   1600        -7619.943             0.020            0.014
Chain 1:   1700        -7561.779             0.020            0.014
Chain 1:   1800        -7638.890             0.016            0.013
Chain 1:   1900        -7645.700             0.015            0.010
Chain 1:   2000        -7675.391             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003905 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85912.028             1.000            1.000
Chain 1:    200       -13532.400             3.174            5.349
Chain 1:    300        -9899.135             2.239            1.000
Chain 1:    400       -10814.843             1.700            1.000
Chain 1:    500        -8825.401             1.405            0.367
Chain 1:    600        -8457.009             1.178            0.367
Chain 1:    700        -8403.694             1.011            0.225
Chain 1:    800        -9210.789             0.895            0.225
Chain 1:    900        -8700.725             0.802            0.088
Chain 1:   1000        -8475.197             0.725            0.088
Chain 1:   1100        -8710.141             0.628            0.085   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8421.406             0.096            0.059
Chain 1:   1300        -8633.666             0.062            0.044
Chain 1:   1400        -8625.154             0.054            0.034
Chain 1:   1500        -8487.871             0.033            0.027
Chain 1:   1600        -8598.927             0.030            0.027
Chain 1:   1700        -8685.666             0.030            0.027
Chain 1:   1800        -8278.844             0.026            0.027
Chain 1:   1900        -8375.213             0.021            0.025
Chain 1:   2000        -8347.460             0.019            0.016
Chain 1:   2100        -8468.182             0.018            0.014
Chain 1:   2200        -8284.345             0.017            0.014
Chain 1:   2300        -8414.997             0.016            0.014
Chain 1:   2400        -8424.778             0.016            0.014
Chain 1:   2500        -8387.661             0.014            0.013
Chain 1:   2600        -8386.092             0.013            0.012
Chain 1:   2700        -8300.798             0.013            0.012
Chain 1:   2800        -8265.669             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003695 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380392.527             1.000            1.000
Chain 1:    200     -1581442.553             2.650            4.299
Chain 1:    300      -891326.931             2.024            1.000
Chain 1:    400      -458749.338             1.754            1.000
Chain 1:    500      -359338.316             1.459            0.943
Chain 1:    600      -233956.145             1.305            0.943
Chain 1:    700      -119708.697             1.255            0.943
Chain 1:    800       -86797.236             1.145            0.943
Chain 1:    900       -67043.895             1.051            0.774
Chain 1:   1000       -51770.503             0.975            0.774
Chain 1:   1100       -39185.720             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38350.824             0.480            0.379
Chain 1:   1300       -26248.160             0.448            0.379
Chain 1:   1400       -25960.264             0.355            0.321
Chain 1:   1500       -22532.704             0.343            0.321
Chain 1:   1600       -21744.558             0.293            0.295
Chain 1:   1700       -20611.373             0.203            0.295
Chain 1:   1800       -20553.740             0.165            0.152
Chain 1:   1900       -20879.748             0.137            0.055
Chain 1:   2000       -19387.615             0.115            0.055
Chain 1:   2100       -19626.034             0.084            0.036
Chain 1:   2200       -19853.095             0.083            0.036
Chain 1:   2300       -19469.797             0.039            0.020
Chain 1:   2400       -19241.865             0.039            0.020
Chain 1:   2500       -19044.208             0.025            0.016
Chain 1:   2600       -18674.262             0.024            0.016
Chain 1:   2700       -18631.143             0.018            0.012
Chain 1:   2800       -18348.237             0.020            0.015
Chain 1:   2900       -18629.467             0.020            0.015
Chain 1:   3000       -18615.593             0.012            0.012
Chain 1:   3100       -18700.607             0.011            0.012
Chain 1:   3200       -18391.328             0.012            0.015
Chain 1:   3300       -18595.992             0.011            0.012
Chain 1:   3400       -18071.118             0.013            0.015
Chain 1:   3500       -18682.851             0.015            0.015
Chain 1:   3600       -17989.710             0.017            0.015
Chain 1:   3700       -18376.448             0.018            0.017
Chain 1:   3800       -17336.556             0.023            0.021
Chain 1:   3900       -17332.759             0.021            0.021
Chain 1:   4000       -17450.003             0.022            0.021
Chain 1:   4100       -17363.851             0.022            0.021
Chain 1:   4200       -17180.138             0.021            0.021
Chain 1:   4300       -17318.465             0.021            0.021
Chain 1:   4400       -17275.361             0.019            0.011
Chain 1:   4500       -17177.946             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48787.797             1.000            1.000
Chain 1:    200       -14473.094             1.685            2.371
Chain 1:    300       -20621.568             1.223            1.000
Chain 1:    400       -15097.656             1.009            1.000
Chain 1:    500       -18086.603             0.840            0.366
Chain 1:    600       -11733.230             0.790            0.541
Chain 1:    700       -13192.384             0.693            0.366
Chain 1:    800       -14304.544             0.616            0.366
Chain 1:    900       -10977.817             0.581            0.303
Chain 1:   1000       -25166.807             0.580            0.366
Chain 1:   1100       -10441.121             0.621            0.366   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -14366.209             0.411            0.303
Chain 1:   1300       -12481.592             0.396            0.303
Chain 1:   1400       -10632.441             0.377            0.273
Chain 1:   1500       -11843.590             0.371            0.273
Chain 1:   1600       -10738.627             0.327            0.174
Chain 1:   1700       -20047.469             0.362            0.273
Chain 1:   1800       -13981.922             0.398            0.303
Chain 1:   1900       -10657.565             0.399            0.312
Chain 1:   2000       -11869.104             0.353            0.273
Chain 1:   2100        -9481.804             0.237            0.252
Chain 1:   2200       -11195.985             0.225            0.174
Chain 1:   2300        -9138.173             0.232            0.225
Chain 1:   2400        -9333.339             0.217            0.225
Chain 1:   2500        -9690.037             0.210            0.225
Chain 1:   2600        -9518.263             0.202            0.225
Chain 1:   2700        -9147.770             0.159            0.153
Chain 1:   2800       -10446.856             0.128            0.124
Chain 1:   2900        -9997.619             0.102            0.102
Chain 1:   3000       -10858.813             0.099            0.079
Chain 1:   3100       -10704.723             0.076            0.045
Chain 1:   3200        -9037.262             0.079            0.045
Chain 1:   3300       -10633.424             0.071            0.045
Chain 1:   3400        -9090.313             0.086            0.079
Chain 1:   3500        -9475.613             0.087            0.079
Chain 1:   3600       -11607.010             0.103            0.124
Chain 1:   3700       -15123.844             0.122            0.150
Chain 1:   3800        -8657.479             0.185            0.170
Chain 1:   3900        -9764.439             0.192            0.170
Chain 1:   4000        -9312.200             0.188            0.170
Chain 1:   4100        -8833.869             0.192            0.170
Chain 1:   4200        -8649.924             0.176            0.150
Chain 1:   4300       -15557.802             0.205            0.170
Chain 1:   4400        -9181.057             0.258            0.184
Chain 1:   4500        -8871.601             0.257            0.184
Chain 1:   4600       -13844.039             0.275            0.233
Chain 1:   4700       -12298.625             0.264            0.126
Chain 1:   4800       -12690.184             0.193            0.113
Chain 1:   4900        -8583.296             0.229            0.126
Chain 1:   5000       -10477.278             0.242            0.181
Chain 1:   5100        -8494.140             0.260            0.233
Chain 1:   5200       -10689.479             0.279            0.233
Chain 1:   5300       -12472.362             0.249            0.205
Chain 1:   5400       -13246.496             0.185            0.181
Chain 1:   5500        -8574.151             0.236            0.205
Chain 1:   5600        -9907.340             0.214            0.181
Chain 1:   5700        -9065.251             0.210            0.181
Chain 1:   5800        -9389.023             0.211            0.181
Chain 1:   5900        -8853.439             0.169            0.143
Chain 1:   6000        -8721.017             0.152            0.135
Chain 1:   6100        -8457.832             0.132            0.093
Chain 1:   6200        -8481.587             0.112            0.060
Chain 1:   6300       -12904.965             0.132            0.060
Chain 1:   6400        -9910.696             0.156            0.093
Chain 1:   6500       -12136.995             0.120            0.093
Chain 1:   6600        -8397.535             0.151            0.093
Chain 1:   6700        -8385.905             0.142            0.060
Chain 1:   6800        -8947.699             0.145            0.063
Chain 1:   6900        -8814.580             0.140            0.063
Chain 1:   7000       -10899.610             0.158            0.183
Chain 1:   7100        -8275.540             0.186            0.191
Chain 1:   7200        -8738.343             0.191            0.191
Chain 1:   7300       -10306.210             0.172            0.183
Chain 1:   7400        -9106.294             0.155            0.152
Chain 1:   7500       -11541.950             0.158            0.152
Chain 1:   7600       -10335.201             0.125            0.132
Chain 1:   7700        -8183.054             0.151            0.152
Chain 1:   7800        -8244.456             0.146            0.152
Chain 1:   7900        -8168.211             0.145            0.152
Chain 1:   8000        -9675.320             0.142            0.152
Chain 1:   8100        -8615.699             0.122            0.132
Chain 1:   8200        -8645.378             0.117            0.132
Chain 1:   8300       -12271.571             0.132            0.132
Chain 1:   8400        -8930.265             0.156            0.156
Chain 1:   8500       -11638.452             0.158            0.156
Chain 1:   8600        -9245.473             0.172            0.233
Chain 1:   8700        -8115.239             0.160            0.156
Chain 1:   8800        -8641.120             0.165            0.156
Chain 1:   8900       -11457.086             0.189            0.233
Chain 1:   9000       -10505.756             0.182            0.233
Chain 1:   9100        -8339.490             0.196            0.246
Chain 1:   9200        -8295.404             0.196            0.246
Chain 1:   9300       -11754.218             0.196            0.246
Chain 1:   9400        -8243.979             0.201            0.246
Chain 1:   9500        -8200.687             0.179            0.246
Chain 1:   9600        -8341.205             0.154            0.139
Chain 1:   9700        -9955.560             0.157            0.162
Chain 1:   9800        -8311.643             0.170            0.198
Chain 1:   9900        -9284.753             0.156            0.162
Chain 1:   10000        -8082.817             0.162            0.162
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001468 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58347.421             1.000            1.000
Chain 1:    200       -17665.248             1.651            2.303
Chain 1:    300        -8665.457             1.447            1.039
Chain 1:    400        -8242.172             1.098            1.039
Chain 1:    500        -8194.042             0.880            1.000
Chain 1:    600        -8322.476             0.736            1.000
Chain 1:    700        -7809.529             0.640            0.066
Chain 1:    800        -8181.914             0.566            0.066
Chain 1:    900        -7960.680             0.506            0.051
Chain 1:   1000        -7644.678             0.459            0.051
Chain 1:   1100        -7663.845             0.360            0.046
Chain 1:   1200        -7610.208             0.130            0.041
Chain 1:   1300        -7728.413             0.028            0.028
Chain 1:   1400        -7810.353             0.024            0.015
Chain 1:   1500        -7583.341             0.026            0.028
Chain 1:   1600        -7743.646             0.027            0.028
Chain 1:   1700        -7515.422             0.023            0.028
Chain 1:   1800        -7583.599             0.019            0.021
Chain 1:   1900        -7571.859             0.017            0.015
Chain 1:   2000        -7606.319             0.013            0.010
Chain 1:   2100        -7586.861             0.013            0.010
Chain 1:   2200        -7708.887             0.014            0.015
Chain 1:   2300        -7603.133             0.014            0.014
Chain 1:   2400        -7605.672             0.013            0.014
Chain 1:   2500        -7637.387             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003347 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86085.692             1.000            1.000
Chain 1:    200       -13422.061             3.207            5.414
Chain 1:    300        -9822.179             2.260            1.000
Chain 1:    400       -10639.675             1.714            1.000
Chain 1:    500        -8798.163             1.413            0.367
Chain 1:    600        -8300.556             1.188            0.367
Chain 1:    700        -8405.468             1.020            0.209
Chain 1:    800        -8732.776             0.897            0.209
Chain 1:    900        -8643.418             0.799            0.077
Chain 1:   1000        -8452.522             0.721            0.077
Chain 1:   1100        -8682.038             0.624            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8218.712             0.088            0.056
Chain 1:   1300        -8532.297             0.055            0.037
Chain 1:   1400        -8531.539             0.047            0.037
Chain 1:   1500        -8405.466             0.028            0.026
Chain 1:   1600        -8512.962             0.023            0.023
Chain 1:   1700        -8599.228             0.023            0.023
Chain 1:   1800        -8192.585             0.024            0.023
Chain 1:   1900        -8289.463             0.024            0.023
Chain 1:   2000        -8261.522             0.022            0.015
Chain 1:   2100        -8382.067             0.021            0.014
Chain 1:   2200        -8193.175             0.018            0.014
Chain 1:   2300        -8329.236             0.016            0.014
Chain 1:   2400        -8336.559             0.016            0.014
Chain 1:   2500        -8302.650             0.015            0.013
Chain 1:   2600        -8300.615             0.013            0.012
Chain 1:   2700        -8214.746             0.013            0.012
Chain 1:   2800        -8179.907             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003314 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8429437.085             1.000            1.000
Chain 1:    200     -1587763.115             2.655            4.309
Chain 1:    300      -889737.820             2.031            1.000
Chain 1:    400      -457043.605             1.760            1.000
Chain 1:    500      -356876.377             1.464            0.947
Chain 1:    600      -232045.087             1.310            0.947
Chain 1:    700      -118706.704             1.259            0.947
Chain 1:    800       -86028.060             1.149            0.947
Chain 1:    900       -66455.231             1.054            0.785
Chain 1:   1000       -51320.991             0.978            0.785
Chain 1:   1100       -38863.333             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38049.026             0.482            0.380
Chain 1:   1300       -26073.594             0.449            0.380
Chain 1:   1400       -25799.312             0.355            0.321
Chain 1:   1500       -22404.370             0.343            0.321
Chain 1:   1600       -21626.442             0.292            0.295
Chain 1:   1700       -20508.084             0.202            0.295
Chain 1:   1800       -20454.278             0.165            0.152
Chain 1:   1900       -20780.268             0.137            0.055
Chain 1:   2000       -19296.148             0.115            0.055
Chain 1:   2100       -19534.214             0.084            0.036
Chain 1:   2200       -19759.878             0.083            0.036
Chain 1:   2300       -19377.861             0.039            0.020
Chain 1:   2400       -19150.087             0.039            0.020
Chain 1:   2500       -18951.884             0.025            0.016
Chain 1:   2600       -18582.422             0.024            0.016
Chain 1:   2700       -18539.580             0.018            0.012
Chain 1:   2800       -18256.340             0.020            0.016
Chain 1:   2900       -18537.508             0.020            0.015
Chain 1:   3000       -18523.736             0.012            0.012
Chain 1:   3100       -18608.662             0.011            0.012
Chain 1:   3200       -18299.515             0.012            0.015
Chain 1:   3300       -18504.172             0.011            0.012
Chain 1:   3400       -17979.265             0.013            0.015
Chain 1:   3500       -18590.702             0.015            0.016
Chain 1:   3600       -17897.996             0.017            0.016
Chain 1:   3700       -18284.265             0.019            0.017
Chain 1:   3800       -17244.802             0.023            0.021
Chain 1:   3900       -17240.957             0.022            0.021
Chain 1:   4000       -17358.305             0.022            0.021
Chain 1:   4100       -17272.013             0.022            0.021
Chain 1:   4200       -17088.525             0.022            0.021
Chain 1:   4300       -17226.772             0.021            0.021
Chain 1:   4400       -17183.743             0.019            0.011
Chain 1:   4500       -17086.290             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48905.209             1.000            1.000
Chain 1:    200       -15251.819             1.603            2.207
Chain 1:    300       -13695.223             1.107            1.000
Chain 1:    400       -20044.462             0.909            1.000
Chain 1:    500       -21872.997             0.744            0.317
Chain 1:    600       -11758.314             0.763            0.860
Chain 1:    700       -16784.762             0.697            0.317
Chain 1:    800       -13490.542             0.641            0.317
Chain 1:    900       -11113.006             0.593            0.299
Chain 1:   1000       -12160.251             0.542            0.299
Chain 1:   1100       -12507.007             0.445            0.244
Chain 1:   1200       -10793.269             0.240            0.214
Chain 1:   1300        -9903.354             0.238            0.214
Chain 1:   1400       -13001.276             0.230            0.214
Chain 1:   1500       -10390.809             0.247            0.238
Chain 1:   1600       -12259.378             0.176            0.214
Chain 1:   1700       -24917.389             0.197            0.214
Chain 1:   1800       -12884.552             0.266            0.214
Chain 1:   1900       -10770.773             0.264            0.196
Chain 1:   2000       -12763.336             0.271            0.196
Chain 1:   2100        -9643.616             0.301            0.238
Chain 1:   2200       -10295.403             0.291            0.238
Chain 1:   2300       -14933.182             0.313            0.251
Chain 1:   2400       -11130.937             0.324            0.311
Chain 1:   2500       -13443.522             0.316            0.311
Chain 1:   2600        -9833.968             0.337            0.324
Chain 1:   2700        -9167.655             0.294            0.311
Chain 1:   2800        -9410.883             0.203            0.196
Chain 1:   2900        -9118.537             0.186            0.172
Chain 1:   3000       -17260.114             0.218            0.311
Chain 1:   3100       -12513.515             0.224            0.311
Chain 1:   3200       -16027.112             0.239            0.311
Chain 1:   3300        -9222.240             0.282            0.342
Chain 1:   3400       -15645.049             0.289            0.367
Chain 1:   3500        -9083.200             0.344            0.379
Chain 1:   3600        -9022.498             0.308            0.379
Chain 1:   3700        -8797.162             0.303            0.379
Chain 1:   3800        -9238.949             0.305            0.379
Chain 1:   3900        -9327.524             0.303            0.379
Chain 1:   4000       -15811.199             0.297            0.379
Chain 1:   4100        -9703.814             0.322            0.410
Chain 1:   4200        -9072.586             0.307            0.410
Chain 1:   4300       -10062.505             0.243            0.098
Chain 1:   4400        -9424.017             0.209            0.070
Chain 1:   4500        -8453.949             0.148            0.070
Chain 1:   4600       -11846.661             0.176            0.098
Chain 1:   4700       -12831.240             0.181            0.098
Chain 1:   4800       -10637.867             0.197            0.115
Chain 1:   4900        -9627.936             0.206            0.115
Chain 1:   5000       -14702.954             0.200            0.115
Chain 1:   5100        -8639.767             0.207            0.115
Chain 1:   5200        -9185.753             0.206            0.115
Chain 1:   5300       -13298.069             0.227            0.206
Chain 1:   5400        -9941.901             0.254            0.286
Chain 1:   5500       -11883.914             0.259            0.286
Chain 1:   5600       -12839.098             0.238            0.206
Chain 1:   5700       -13016.601             0.232            0.206
Chain 1:   5800        -8762.274             0.260            0.309
Chain 1:   5900       -13052.481             0.282            0.329
Chain 1:   6000       -10727.543             0.269            0.309
Chain 1:   6100        -8606.648             0.224            0.246
Chain 1:   6200       -10451.588             0.235            0.246
Chain 1:   6300        -8718.146             0.224            0.217
Chain 1:   6400        -9061.404             0.194            0.199
Chain 1:   6500       -10504.890             0.192            0.199
Chain 1:   6600        -8425.855             0.209            0.217
Chain 1:   6700        -9331.920             0.217            0.217
Chain 1:   6800       -11804.163             0.190            0.209
Chain 1:   6900       -10599.826             0.168            0.199
Chain 1:   7000        -8566.970             0.170            0.199
Chain 1:   7100        -8339.867             0.148            0.177
Chain 1:   7200       -10943.804             0.154            0.199
Chain 1:   7300        -9361.182             0.151            0.169
Chain 1:   7400        -8335.708             0.160            0.169
Chain 1:   7500        -9456.961             0.158            0.169
Chain 1:   7600       -12041.959             0.155            0.169
Chain 1:   7700        -9720.824             0.169            0.209
Chain 1:   7800       -10281.377             0.153            0.169
Chain 1:   7900        -8526.415             0.163            0.206
Chain 1:   8000       -10108.710             0.155            0.169
Chain 1:   8100        -8455.739             0.171            0.195
Chain 1:   8200        -8954.304             0.153            0.169
Chain 1:   8300        -8307.536             0.144            0.157
Chain 1:   8400        -8206.239             0.133            0.157
Chain 1:   8500        -8545.430             0.125            0.157
Chain 1:   8600        -9925.059             0.118            0.139
Chain 1:   8700        -8532.153             0.110            0.139
Chain 1:   8800        -8748.000             0.107            0.139
Chain 1:   8900        -9149.865             0.091            0.078
Chain 1:   9000        -8674.842             0.081            0.056
Chain 1:   9100        -9658.942             0.071            0.056
Chain 1:   9200       -10027.001             0.069            0.055
Chain 1:   9300       -12728.336             0.083            0.055
Chain 1:   9400        -9651.685             0.113            0.102
Chain 1:   9500        -8142.707             0.128            0.139
Chain 1:   9600        -9488.077             0.128            0.142
Chain 1:   9700        -8375.685             0.125            0.133
Chain 1:   9800        -8618.977             0.126            0.133
Chain 1:   9900        -8648.042             0.122            0.133
Chain 1:   10000        -8593.488             0.117            0.133
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001406 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57894.773             1.000            1.000
Chain 1:    200       -17573.124             1.647            2.295
Chain 1:    300        -8686.376             1.439            1.023
Chain 1:    400        -8210.893             1.094            1.023
Chain 1:    500        -8350.715             0.878            1.000
Chain 1:    600        -8545.791             0.736            1.000
Chain 1:    700        -7800.881             0.644            0.095
Chain 1:    800        -8194.162             0.570            0.095
Chain 1:    900        -8009.499             0.509            0.058
Chain 1:   1000        -7885.516             0.460            0.058
Chain 1:   1100        -7859.807             0.360            0.048
Chain 1:   1200        -7654.060             0.133            0.027
Chain 1:   1300        -7834.897             0.033            0.023
Chain 1:   1400        -7756.780             0.029            0.023
Chain 1:   1500        -7676.144             0.028            0.023
Chain 1:   1600        -7640.613             0.026            0.023
Chain 1:   1700        -7607.548             0.017            0.016
Chain 1:   1800        -7670.056             0.013            0.011
Chain 1:   1900        -7640.965             0.011            0.010
Chain 1:   2000        -7661.744             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003134 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86703.560             1.000            1.000
Chain 1:    200       -13465.346             3.220            5.439
Chain 1:    300        -9912.601             2.266            1.000
Chain 1:    400       -10832.298             1.721            1.000
Chain 1:    500        -8828.039             1.422            0.358
Chain 1:    600        -8510.129             1.191            0.358
Chain 1:    700        -8818.236             1.026            0.227
Chain 1:    800        -9328.134             0.905            0.227
Chain 1:    900        -8778.073             0.811            0.085
Chain 1:   1000        -8527.494             0.733            0.085
Chain 1:   1100        -8801.861             0.636            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8354.463             0.097            0.055
Chain 1:   1300        -8617.367             0.065            0.054
Chain 1:   1400        -8644.291             0.056            0.037
Chain 1:   1500        -8532.752             0.035            0.035
Chain 1:   1600        -8635.323             0.032            0.031
Chain 1:   1700        -8721.908             0.030            0.031
Chain 1:   1800        -8333.970             0.029            0.031
Chain 1:   1900        -8436.367             0.024            0.029
Chain 1:   2000        -8406.651             0.022            0.013
Chain 1:   2100        -8537.573             0.020            0.013
Chain 1:   2200        -8323.659             0.017            0.013
Chain 1:   2300        -8465.778             0.016            0.013
Chain 1:   2400        -8478.544             0.016            0.013
Chain 1:   2500        -8446.461             0.015            0.012
Chain 1:   2600        -8446.655             0.014            0.012
Chain 1:   2700        -8354.629             0.014            0.012
Chain 1:   2800        -8330.249             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417441.879             1.000            1.000
Chain 1:    200     -1587975.050             2.650            4.301
Chain 1:    300      -890448.686             2.028            1.000
Chain 1:    400      -457152.449             1.758            1.000
Chain 1:    500      -357239.705             1.462            0.948
Chain 1:    600      -232205.806             1.308            0.948
Chain 1:    700      -118777.622             1.258            0.948
Chain 1:    800       -86098.730             1.148            0.948
Chain 1:    900       -66507.504             1.053            0.783
Chain 1:   1000       -51356.646             0.977            0.783
Chain 1:   1100       -38890.286             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38068.635             0.482            0.380
Chain 1:   1300       -26089.318             0.449            0.380
Chain 1:   1400       -25812.110             0.355            0.321
Chain 1:   1500       -22416.962             0.343            0.321
Chain 1:   1600       -21638.161             0.292            0.295
Chain 1:   1700       -20520.033             0.202            0.295
Chain 1:   1800       -20465.833             0.165            0.151
Chain 1:   1900       -20791.530             0.137            0.054
Chain 1:   2000       -19307.944             0.115            0.054
Chain 1:   2100       -19545.944             0.084            0.036
Chain 1:   2200       -19771.456             0.083            0.036
Chain 1:   2300       -19389.599             0.039            0.020
Chain 1:   2400       -19161.957             0.039            0.020
Chain 1:   2500       -18963.762             0.025            0.016
Chain 1:   2600       -18594.703             0.024            0.016
Chain 1:   2700       -18551.888             0.018            0.012
Chain 1:   2800       -18268.911             0.020            0.015
Chain 1:   2900       -18549.820             0.020            0.015
Chain 1:   3000       -18536.056             0.012            0.012
Chain 1:   3100       -18620.977             0.011            0.012
Chain 1:   3200       -18312.060             0.012            0.015
Chain 1:   3300       -18516.470             0.011            0.012
Chain 1:   3400       -17992.038             0.013            0.015
Chain 1:   3500       -18602.882             0.015            0.015
Chain 1:   3600       -17910.852             0.017            0.015
Chain 1:   3700       -18296.665             0.019            0.017
Chain 1:   3800       -17258.361             0.023            0.021
Chain 1:   3900       -17254.517             0.022            0.021
Chain 1:   4000       -17371.843             0.022            0.021
Chain 1:   4100       -17285.722             0.022            0.021
Chain 1:   4200       -17102.379             0.022            0.021
Chain 1:   4300       -17240.496             0.021            0.021
Chain 1:   4400       -17197.674             0.019            0.011
Chain 1:   4500       -17100.244             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12418.644             1.000            1.000
Chain 1:    200        -9347.060             0.664            1.000
Chain 1:    300        -7943.122             0.502            0.329
Chain 1:    400        -8198.292             0.384            0.329
Chain 1:    500        -8093.079             0.310            0.177
Chain 1:    600        -7923.321             0.262            0.177
Chain 1:    700        -7823.776             0.226            0.031
Chain 1:    800        -7830.607             0.198            0.031
Chain 1:    900        -7749.992             0.177            0.021
Chain 1:   1000        -7945.160             0.162            0.025
Chain 1:   1100        -7974.289             0.062            0.021
Chain 1:   1200        -7858.451             0.031            0.015
Chain 1:   1300        -7788.183             0.014            0.013
Chain 1:   1400        -7813.896             0.011            0.013
Chain 1:   1500        -7903.315             0.011            0.011
Chain 1:   1600        -7849.144             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57226.864             1.000            1.000
Chain 1:    200       -17583.818             1.627            2.255
Chain 1:    300        -8784.219             1.419            1.002
Chain 1:    400        -8293.067             1.079            1.002
Chain 1:    500        -8303.297             0.863            1.000
Chain 1:    600        -8985.057             0.732            1.000
Chain 1:    700        -7709.843             0.651            0.165
Chain 1:    800        -8346.006             0.579            0.165
Chain 1:    900        -7900.002             0.521            0.076
Chain 1:   1000        -8005.563             0.470            0.076
Chain 1:   1100        -7688.950             0.375            0.076
Chain 1:   1200        -7715.756             0.149            0.059
Chain 1:   1300        -7770.806             0.050            0.056
Chain 1:   1400        -7917.719             0.046            0.041
Chain 1:   1500        -7576.846             0.050            0.045
Chain 1:   1600        -7777.179             0.045            0.041
Chain 1:   1700        -7544.858             0.032            0.031
Chain 1:   1800        -7657.124             0.026            0.026
Chain 1:   1900        -7648.312             0.020            0.019
Chain 1:   2000        -7661.654             0.019            0.019
Chain 1:   2100        -7588.275             0.016            0.015
Chain 1:   2200        -7725.175             0.017            0.018
Chain 1:   2300        -7614.124             0.018            0.018
Chain 1:   2400        -7663.781             0.017            0.015
Chain 1:   2500        -7606.791             0.013            0.015
Chain 1:   2600        -7535.118             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86368.835             1.000            1.000
Chain 1:    200       -13545.349             3.188            5.376
Chain 1:    300        -9851.892             2.250            1.000
Chain 1:    400       -11206.108             1.718            1.000
Chain 1:    500        -8830.625             1.428            0.375
Chain 1:    600        -8789.312             1.191            0.375
Chain 1:    700        -8721.409             1.022            0.269
Chain 1:    800        -8920.034             0.897            0.269
Chain 1:    900        -8619.968             0.801            0.121
Chain 1:   1000        -8662.354             0.722            0.121
Chain 1:   1100        -8545.347             0.623            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8273.931             0.089            0.033
Chain 1:   1300        -8520.809             0.054            0.029
Chain 1:   1400        -8552.799             0.042            0.022
Chain 1:   1500        -8394.399             0.017            0.019
Chain 1:   1600        -8509.584             0.018            0.019
Chain 1:   1700        -8579.896             0.018            0.019
Chain 1:   1800        -8150.301             0.021            0.019
Chain 1:   1900        -8254.081             0.019            0.014
Chain 1:   2000        -8229.169             0.019            0.014
Chain 1:   2100        -8360.601             0.019            0.016
Chain 1:   2200        -8156.870             0.018            0.016
Chain 1:   2300        -8252.004             0.016            0.014
Chain 1:   2400        -8317.428             0.017            0.014
Chain 1:   2500        -8262.742             0.016            0.013
Chain 1:   2600        -8266.592             0.014            0.012
Chain 1:   2700        -8182.084             0.015            0.012
Chain 1:   2800        -8139.212             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003228 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405314.175             1.000            1.000
Chain 1:    200     -1585471.930             2.651            4.301
Chain 1:    300      -892271.123             2.026            1.000
Chain 1:    400      -458753.759             1.756            1.000
Chain 1:    500      -358913.364             1.460            0.945
Chain 1:    600      -233712.986             1.306            0.945
Chain 1:    700      -119590.028             1.256            0.945
Chain 1:    800       -86694.132             1.146            0.945
Chain 1:    900       -66985.336             1.052            0.777
Chain 1:   1000       -51740.076             0.976            0.777
Chain 1:   1100       -39179.073             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38351.289             0.480            0.379
Chain 1:   1300       -26273.674             0.448            0.379
Chain 1:   1400       -25990.103             0.355            0.321
Chain 1:   1500       -22567.923             0.342            0.321
Chain 1:   1600       -21781.477             0.292            0.295
Chain 1:   1700       -20651.408             0.202            0.294
Chain 1:   1800       -20594.624             0.165            0.152
Chain 1:   1900       -20920.921             0.137            0.055
Chain 1:   2000       -19429.313             0.115            0.055
Chain 1:   2100       -19668.040             0.084            0.036
Chain 1:   2200       -19894.905             0.083            0.036
Chain 1:   2300       -19511.652             0.039            0.020
Chain 1:   2400       -19283.608             0.039            0.020
Chain 1:   2500       -19085.641             0.025            0.016
Chain 1:   2600       -18715.712             0.023            0.016
Chain 1:   2700       -18672.485             0.018            0.012
Chain 1:   2800       -18389.303             0.020            0.015
Chain 1:   2900       -18670.662             0.019            0.015
Chain 1:   3000       -18656.873             0.012            0.012
Chain 1:   3100       -18741.922             0.011            0.012
Chain 1:   3200       -18432.439             0.012            0.015
Chain 1:   3300       -18637.222             0.011            0.012
Chain 1:   3400       -18111.914             0.012            0.015
Chain 1:   3500       -18724.202             0.015            0.015
Chain 1:   3600       -18030.286             0.017            0.015
Chain 1:   3700       -18417.600             0.018            0.017
Chain 1:   3800       -17376.430             0.023            0.021
Chain 1:   3900       -17372.510             0.021            0.021
Chain 1:   4000       -17489.837             0.022            0.021
Chain 1:   4100       -17403.609             0.022            0.021
Chain 1:   4200       -17219.564             0.021            0.021
Chain 1:   4300       -17358.162             0.021            0.021
Chain 1:   4400       -17314.846             0.019            0.011
Chain 1:   4500       -17217.312             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12828.148             1.000            1.000
Chain 1:    200        -9659.069             0.664            1.000
Chain 1:    300        -8117.517             0.506            0.328
Chain 1:    400        -8378.461             0.387            0.328
Chain 1:    500        -8192.507             0.314            0.190
Chain 1:    600        -8089.270             0.264            0.190
Chain 1:    700        -7978.941             0.228            0.031
Chain 1:    800        -7978.855             0.200            0.031
Chain 1:    900        -7910.346             0.179            0.023
Chain 1:   1000        -8110.343             0.163            0.025
Chain 1:   1100        -8126.854             0.063            0.023
Chain 1:   1200        -7998.220             0.032            0.016
Chain 1:   1300        -7969.682             0.014            0.014
Chain 1:   1400        -7972.790             0.010            0.013
Chain 1:   1500        -8065.737             0.009            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001558 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47795.882             1.000            1.000
Chain 1:    200       -16155.810             1.479            1.958
Chain 1:    300        -8670.191             1.274            1.000
Chain 1:    400        -8482.408             0.961            1.000
Chain 1:    500        -8527.893             0.770            0.863
Chain 1:    600        -8957.221             0.650            0.863
Chain 1:    700        -8287.872             0.568            0.081
Chain 1:    800        -8107.573             0.500            0.081
Chain 1:    900        -7972.226             0.446            0.048
Chain 1:   1000        -7839.252             0.403            0.048
Chain 1:   1100        -7613.852             0.306            0.030
Chain 1:   1200        -7812.562             0.113            0.025
Chain 1:   1300        -7672.680             0.029            0.022
Chain 1:   1400        -7675.887             0.026            0.022
Chain 1:   1500        -7519.470             0.028            0.022
Chain 1:   1600        -7704.747             0.026            0.022
Chain 1:   1700        -7513.637             0.020            0.022
Chain 1:   1800        -7604.712             0.019            0.021
Chain 1:   1900        -7582.915             0.018            0.021
Chain 1:   2000        -7551.693             0.016            0.021
Chain 1:   2100        -7489.815             0.014            0.018
Chain 1:   2200        -7677.049             0.014            0.018
Chain 1:   2300        -7505.142             0.015            0.021
Chain 1:   2400        -7479.462             0.015            0.021
Chain 1:   2500        -7476.549             0.013            0.012
Chain 1:   2600        -7459.057             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87294.571             1.000            1.000
Chain 1:    200       -13814.017             3.160            5.319
Chain 1:    300       -10086.570             2.230            1.000
Chain 1:    400       -11379.356             1.701            1.000
Chain 1:    500        -9034.503             1.412            0.370
Chain 1:    600        -8986.447             1.178            0.370
Chain 1:    700        -9153.955             1.012            0.260
Chain 1:    800        -8379.642             0.897            0.260
Chain 1:    900        -8422.186             0.798            0.114
Chain 1:   1000        -8672.639             0.721            0.114
Chain 1:   1100        -8680.533             0.621            0.092   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8484.562             0.092            0.029
Chain 1:   1300        -8752.246             0.058            0.029
Chain 1:   1400        -8724.741             0.047            0.023
Chain 1:   1500        -8590.801             0.022            0.018
Chain 1:   1600        -8701.737             0.023            0.018
Chain 1:   1700        -8762.963             0.022            0.016
Chain 1:   1800        -8324.721             0.018            0.016
Chain 1:   1900        -8428.810             0.019            0.016
Chain 1:   2000        -8407.450             0.016            0.013
Chain 1:   2100        -8391.940             0.016            0.013
Chain 1:   2200        -8346.826             0.014            0.012
Chain 1:   2300        -8481.916             0.013            0.012
Chain 1:   2400        -8327.147             0.014            0.013
Chain 1:   2500        -8398.251             0.014            0.012
Chain 1:   2600        -8310.864             0.014            0.011
Chain 1:   2700        -8348.369             0.013            0.011
Chain 1:   2800        -8306.315             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397846.750             1.000            1.000
Chain 1:    200     -1583709.544             2.651            4.303
Chain 1:    300      -890240.884             2.027            1.000
Chain 1:    400      -456884.884             1.758            1.000
Chain 1:    500      -357433.125             1.462            0.949
Chain 1:    600      -232666.921             1.307            0.949
Chain 1:    700      -119297.712             1.256            0.949
Chain 1:    800       -86559.856             1.147            0.949
Chain 1:    900       -66975.775             1.052            0.779
Chain 1:   1000       -51825.197             0.976            0.779
Chain 1:   1100       -39336.810             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38525.497             0.479            0.378
Chain 1:   1300       -26509.974             0.447            0.378
Chain 1:   1400       -26234.238             0.353            0.317
Chain 1:   1500       -22827.185             0.340            0.317
Chain 1:   1600       -22045.994             0.290            0.292
Chain 1:   1700       -20922.656             0.200            0.292
Chain 1:   1800       -20867.865             0.163            0.149
Chain 1:   1900       -21194.569             0.135            0.054
Chain 1:   2000       -19706.071             0.113            0.054
Chain 1:   2100       -19944.647             0.083            0.035
Chain 1:   2200       -20171.034             0.082            0.035
Chain 1:   2300       -19788.152             0.038            0.019
Chain 1:   2400       -19560.095             0.039            0.019
Chain 1:   2500       -19361.797             0.025            0.015
Chain 1:   2600       -18991.822             0.023            0.015
Chain 1:   2700       -18948.771             0.018            0.012
Chain 1:   2800       -18665.241             0.019            0.015
Chain 1:   2900       -18946.717             0.019            0.015
Chain 1:   3000       -18932.969             0.012            0.012
Chain 1:   3100       -19017.965             0.011            0.012
Chain 1:   3200       -18708.420             0.011            0.015
Chain 1:   3300       -18913.354             0.011            0.012
Chain 1:   3400       -18387.684             0.012            0.015
Chain 1:   3500       -19000.338             0.015            0.015
Chain 1:   3600       -18306.058             0.016            0.015
Chain 1:   3700       -18693.531             0.018            0.017
Chain 1:   3800       -17651.612             0.023            0.021
Chain 1:   3900       -17647.674             0.021            0.021
Chain 1:   4000       -17765.038             0.022            0.021
Chain 1:   4100       -17678.653             0.022            0.021
Chain 1:   4200       -17494.581             0.021            0.021
Chain 1:   4300       -17633.261             0.021            0.021
Chain 1:   4400       -17589.819             0.018            0.011
Chain 1:   4500       -17492.246             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12194.225             1.000            1.000
Chain 1:    200        -9118.634             0.669            1.000
Chain 1:    300        -8118.910             0.487            0.337
Chain 1:    400        -8232.782             0.369            0.337
Chain 1:    500        -8045.094             0.300            0.123
Chain 1:    600        -7938.203             0.252            0.123
Chain 1:    700        -7892.897             0.217            0.023
Chain 1:    800        -7867.970             0.190            0.023
Chain 1:    900        -7965.606             0.170            0.014
Chain 1:   1000        -7947.597             0.153            0.014
Chain 1:   1100        -8002.704             0.054            0.013
Chain 1:   1200        -7906.030             0.022            0.012
Chain 1:   1300        -7929.898             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56688.795             1.000            1.000
Chain 1:    200       -17085.273             1.659            2.318
Chain 1:    300        -8614.036             1.434            1.000
Chain 1:    400        -7894.703             1.098            1.000
Chain 1:    500        -8396.422             0.890            0.983
Chain 1:    600        -8989.224             0.753            0.983
Chain 1:    700        -7781.379             0.668            0.155
Chain 1:    800        -8051.458             0.588            0.155
Chain 1:    900        -7949.574             0.524            0.091
Chain 1:   1000        -7879.502             0.473            0.091
Chain 1:   1100        -7694.943             0.375            0.066
Chain 1:   1200        -7593.929             0.145            0.060
Chain 1:   1300        -7588.999             0.047            0.034
Chain 1:   1400        -7896.988             0.041            0.034
Chain 1:   1500        -7598.338             0.039            0.034
Chain 1:   1600        -7565.224             0.033            0.024
Chain 1:   1700        -7499.653             0.018            0.013
Chain 1:   1800        -7572.927             0.016            0.013
Chain 1:   1900        -7608.864             0.015            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002575 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86209.556             1.000            1.000
Chain 1:    200       -13245.703             3.254            5.508
Chain 1:    300        -9726.833             2.290            1.000
Chain 1:    400       -10614.093             1.738            1.000
Chain 1:    500        -8597.093             1.438            0.362
Chain 1:    600        -8333.839             1.203            0.362
Chain 1:    700        -8386.922             1.032            0.235
Chain 1:    800        -8800.773             0.909            0.235
Chain 1:    900        -8606.098             0.811            0.084
Chain 1:   1000        -8379.563             0.732            0.084
Chain 1:   1100        -8623.677             0.635            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8286.266             0.088            0.041
Chain 1:   1300        -8339.295             0.053            0.032
Chain 1:   1400        -8335.132             0.045            0.028
Chain 1:   1500        -8366.439             0.021            0.027
Chain 1:   1600        -8371.427             0.018            0.023
Chain 1:   1700        -8310.240             0.018            0.023
Chain 1:   1800        -8189.956             0.015            0.015
Chain 1:   1900        -8304.625             0.014            0.014
Chain 1:   2000        -8265.482             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003193 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410058.862             1.000            1.000
Chain 1:    200     -1585922.608             2.651            4.303
Chain 1:    300      -891127.728             2.028            1.000
Chain 1:    400      -457688.106             1.757            1.000
Chain 1:    500      -357731.525             1.462            0.947
Chain 1:    600      -232502.713             1.308            0.947
Chain 1:    700      -118808.994             1.258            0.947
Chain 1:    800       -86052.768             1.148            0.947
Chain 1:    900       -66408.390             1.053            0.780
Chain 1:   1000       -51218.297             0.978            0.780
Chain 1:   1100       -38716.780             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37889.063             0.482            0.381
Chain 1:   1300       -25878.339             0.450            0.381
Chain 1:   1400       -25596.748             0.357            0.323
Chain 1:   1500       -22193.614             0.344            0.323
Chain 1:   1600       -21412.250             0.294            0.297
Chain 1:   1700       -20290.574             0.204            0.296
Chain 1:   1800       -20235.445             0.166            0.153
Chain 1:   1900       -20560.827             0.138            0.055
Chain 1:   2000       -19076.010             0.116            0.055
Chain 1:   2100       -19313.952             0.085            0.036
Chain 1:   2200       -19539.632             0.084            0.036
Chain 1:   2300       -19157.733             0.040            0.020
Chain 1:   2400       -18930.170             0.040            0.020
Chain 1:   2500       -18732.137             0.025            0.016
Chain 1:   2600       -18363.108             0.024            0.016
Chain 1:   2700       -18320.347             0.019            0.012
Chain 1:   2800       -18037.546             0.020            0.016
Chain 1:   2900       -18318.384             0.020            0.015
Chain 1:   3000       -18304.597             0.012            0.012
Chain 1:   3100       -18389.492             0.011            0.012
Chain 1:   3200       -18080.683             0.012            0.015
Chain 1:   3300       -18285.022             0.011            0.012
Chain 1:   3400       -17760.861             0.013            0.015
Chain 1:   3500       -18371.339             0.015            0.016
Chain 1:   3600       -17679.842             0.017            0.016
Chain 1:   3700       -18065.264             0.019            0.017
Chain 1:   3800       -17027.818             0.023            0.021
Chain 1:   3900       -17024.052             0.022            0.021
Chain 1:   4000       -17141.333             0.022            0.021
Chain 1:   4100       -17055.241             0.022            0.021
Chain 1:   4200       -16872.138             0.022            0.021
Chain 1:   4300       -17010.074             0.022            0.021
Chain 1:   4400       -16967.400             0.019            0.011
Chain 1:   4500       -16870.048             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001357 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49704.473             1.000            1.000
Chain 1:    200       -14742.122             1.686            2.372
Chain 1:    300       -15526.521             1.141            1.000
Chain 1:    400       -14130.779             0.880            1.000
Chain 1:    500       -18825.427             0.754            0.249
Chain 1:    600       -17282.726             0.643            0.249
Chain 1:    700       -14807.669             0.575            0.167
Chain 1:    800       -15102.556             0.506            0.167
Chain 1:    900       -12793.688             0.470            0.167
Chain 1:   1000       -13294.777             0.426            0.167
Chain 1:   1100       -12005.367             0.337            0.107
Chain 1:   1200       -13890.583             0.114            0.107
Chain 1:   1300       -11478.030             0.130            0.136
Chain 1:   1400       -27963.984             0.179            0.167
Chain 1:   1500       -12643.716             0.275            0.167
Chain 1:   1600       -12766.647             0.267            0.167
Chain 1:   1700       -12101.050             0.256            0.136
Chain 1:   1800       -17730.430             0.285            0.180
Chain 1:   1900       -11861.725             0.317            0.210
Chain 1:   2000       -24080.342             0.364            0.317
Chain 1:   2100       -20058.368             0.373            0.317
Chain 1:   2200       -11639.232             0.432            0.495
Chain 1:   2300       -10418.862             0.423            0.495
Chain 1:   2400       -11437.016             0.373            0.317
Chain 1:   2500       -13756.833             0.268            0.201
Chain 1:   2600        -9718.469             0.309            0.317
Chain 1:   2700        -9687.110             0.304            0.317
Chain 1:   2800       -12117.331             0.292            0.201
Chain 1:   2900        -9762.643             0.267            0.201
Chain 1:   3000       -13318.640             0.243            0.201
Chain 1:   3100       -10589.728             0.248            0.241
Chain 1:   3200       -15707.548             0.209            0.241
Chain 1:   3300       -14510.728             0.205            0.241
Chain 1:   3400       -17293.265             0.212            0.241
Chain 1:   3500        -9950.284             0.269            0.258
Chain 1:   3600       -10854.042             0.236            0.241
Chain 1:   3700       -12551.547             0.249            0.241
Chain 1:   3800       -11252.391             0.241            0.241
Chain 1:   3900        -9429.971             0.236            0.193
Chain 1:   4000        -9175.981             0.212            0.161
Chain 1:   4100       -10178.882             0.196            0.135
Chain 1:   4200       -13927.627             0.190            0.135
Chain 1:   4300       -10393.139             0.216            0.161
Chain 1:   4400       -13547.826             0.223            0.193
Chain 1:   4500        -9201.214             0.197            0.193
Chain 1:   4600       -15164.964             0.228            0.233
Chain 1:   4700        -9323.300             0.277            0.269
Chain 1:   4800        -9562.950             0.268            0.269
Chain 1:   4900       -10380.650             0.256            0.269
Chain 1:   5000       -16405.063             0.290            0.340
Chain 1:   5100        -9214.487             0.359            0.367
Chain 1:   5200       -10048.130             0.340            0.367
Chain 1:   5300       -10223.622             0.308            0.367
Chain 1:   5400        -9804.038             0.289            0.367
Chain 1:   5500        -9924.073             0.243            0.083
Chain 1:   5600       -14330.387             0.234            0.083
Chain 1:   5700       -11550.788             0.195            0.083
Chain 1:   5800        -9180.190             0.219            0.241
Chain 1:   5900       -10121.496             0.220            0.241
Chain 1:   6000       -13352.376             0.208            0.241
Chain 1:   6100        -9493.014             0.170            0.241
Chain 1:   6200       -10040.090             0.167            0.241
Chain 1:   6300       -11718.720             0.180            0.241
Chain 1:   6400        -9344.581             0.201            0.242
Chain 1:   6500        -9449.438             0.201            0.242
Chain 1:   6600        -9364.256             0.171            0.241
Chain 1:   6700       -10079.730             0.154            0.143
Chain 1:   6800        -9582.796             0.134            0.093
Chain 1:   6900       -12642.319             0.149            0.143
Chain 1:   7000        -9100.926             0.163            0.143
Chain 1:   7100        -9664.299             0.128            0.071
Chain 1:   7200       -12303.318             0.144            0.143
Chain 1:   7300       -11673.026             0.136            0.071
Chain 1:   7400        -8689.426             0.144            0.071
Chain 1:   7500       -10734.502             0.162            0.191
Chain 1:   7600        -9318.533             0.177            0.191
Chain 1:   7700        -9580.067             0.172            0.191
Chain 1:   7800        -8713.305             0.177            0.191
Chain 1:   7900       -10964.553             0.173            0.191
Chain 1:   8000        -9328.875             0.152            0.175
Chain 1:   8100       -10935.562             0.161            0.175
Chain 1:   8200        -9970.558             0.149            0.152
Chain 1:   8300        -8807.925             0.157            0.152
Chain 1:   8400        -8578.484             0.125            0.147
Chain 1:   8500        -8844.040             0.109            0.132
Chain 1:   8600        -8928.839             0.095            0.099
Chain 1:   8700        -9164.717             0.095            0.099
Chain 1:   8800        -9478.421             0.088            0.097
Chain 1:   8900       -10884.214             0.081            0.097
Chain 1:   9000       -10451.648             0.067            0.041
Chain 1:   9100        -8818.889             0.071            0.041
Chain 1:   9200        -8664.150             0.063            0.033
Chain 1:   9300        -8616.959             0.050            0.030
Chain 1:   9400        -8634.484             0.048            0.030
Chain 1:   9500        -8708.918             0.046            0.026
Chain 1:   9600        -8789.136             0.046            0.026
Chain 1:   9700        -8576.538             0.046            0.025
Chain 1:   9800       -10163.067             0.058            0.025
Chain 1:   9900       -10088.226             0.046            0.018
Chain 1:   10000        -8758.457             0.057            0.018
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58850.017             1.000            1.000
Chain 1:    200       -18442.432             1.596            2.191
Chain 1:    300        -9045.401             1.410            1.039
Chain 1:    400        -8187.494             1.084            1.039
Chain 1:    500        -8481.405             0.874            1.000
Chain 1:    600        -8517.133             0.729            1.000
Chain 1:    700        -8514.012             0.625            0.105
Chain 1:    800        -8496.654             0.547            0.105
Chain 1:    900        -7816.699             0.496            0.087
Chain 1:   1000        -8207.247             0.451            0.087
Chain 1:   1100        -7728.540             0.357            0.062
Chain 1:   1200        -7753.988             0.138            0.048
Chain 1:   1300        -7773.790             0.035            0.035
Chain 1:   1400        -7998.697             0.027            0.028
Chain 1:   1500        -7724.825             0.027            0.028
Chain 1:   1600        -7942.976             0.030            0.028
Chain 1:   1700        -7742.813             0.032            0.028
Chain 1:   1800        -7728.320             0.032            0.028
Chain 1:   1900        -7704.300             0.024            0.027
Chain 1:   2000        -7775.921             0.020            0.026
Chain 1:   2100        -7662.274             0.015            0.015
Chain 1:   2200        -7874.421             0.018            0.026
Chain 1:   2300        -7645.690             0.020            0.027
Chain 1:   2400        -7801.707             0.019            0.026
Chain 1:   2500        -7714.803             0.017            0.020
Chain 1:   2600        -7621.249             0.016            0.015
Chain 1:   2700        -7602.365             0.013            0.012
Chain 1:   2800        -7626.312             0.013            0.012
Chain 1:   2900        -7471.076             0.015            0.015
Chain 1:   3000        -7631.789             0.016            0.020
Chain 1:   3100        -7622.892             0.015            0.020
Chain 1:   3200        -7845.406             0.015            0.020
Chain 1:   3300        -7544.331             0.016            0.020
Chain 1:   3400        -7803.321             0.017            0.021
Chain 1:   3500        -7540.750             0.020            0.021
Chain 1:   3600        -7599.173             0.019            0.021
Chain 1:   3700        -7554.485             0.020            0.021
Chain 1:   3800        -7555.903             0.019            0.021
Chain 1:   3900        -7520.919             0.018            0.021
Chain 1:   4000        -7499.976             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87607.577             1.000            1.000
Chain 1:    200       -14148.127             3.096            5.192
Chain 1:    300       -10434.603             2.183            1.000
Chain 1:    400       -11744.826             1.665            1.000
Chain 1:    500        -9410.786             1.382            0.356
Chain 1:    600        -8825.417             1.162            0.356
Chain 1:    700        -8980.300             0.999            0.248
Chain 1:    800        -9571.662             0.882            0.248
Chain 1:    900        -9264.477             0.787            0.112
Chain 1:   1000        -9367.393             0.710            0.112
Chain 1:   1100        -9024.509             0.614            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8809.790             0.097            0.062
Chain 1:   1300        -9113.989             0.064            0.038
Chain 1:   1400        -9105.089             0.053            0.033
Chain 1:   1500        -8940.764             0.030            0.033
Chain 1:   1600        -9052.308             0.025            0.024
Chain 1:   1700        -9122.082             0.024            0.024
Chain 1:   1800        -8685.733             0.023            0.024
Chain 1:   1900        -8790.501             0.021            0.018
Chain 1:   2000        -8766.690             0.020            0.018
Chain 1:   2100        -8909.211             0.018            0.016
Chain 1:   2200        -8697.438             0.018            0.016
Chain 1:   2300        -8855.242             0.016            0.016
Chain 1:   2400        -8692.875             0.018            0.018
Chain 1:   2500        -8764.296             0.017            0.016
Chain 1:   2600        -8676.624             0.017            0.016
Chain 1:   2700        -8710.759             0.016            0.016
Chain 1:   2800        -8670.757             0.012            0.012
Chain 1:   2900        -8764.132             0.012            0.011
Chain 1:   3000        -8596.903             0.013            0.016
Chain 1:   3100        -8753.251             0.014            0.018
Chain 1:   3200        -8625.378             0.013            0.015
Chain 1:   3300        -8633.011             0.011            0.011
Chain 1:   3400        -8792.724             0.011            0.011
Chain 1:   3500        -8800.484             0.010            0.011
Chain 1:   3600        -8581.913             0.012            0.015
Chain 1:   3700        -8727.766             0.013            0.017
Chain 1:   3800        -8588.441             0.014            0.017
Chain 1:   3900        -8523.002             0.014            0.017
Chain 1:   4000        -8598.620             0.013            0.016
Chain 1:   4100        -8587.657             0.011            0.015
Chain 1:   4200        -8578.775             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8435163.616             1.000            1.000
Chain 1:    200     -1592761.025             2.648            4.296
Chain 1:    300      -893306.451             2.026            1.000
Chain 1:    400      -459101.852             1.756            1.000
Chain 1:    500      -358773.730             1.461            0.946
Chain 1:    600      -233446.722             1.307            0.946
Chain 1:    700      -119769.177             1.256            0.946
Chain 1:    800       -86970.440             1.146            0.946
Chain 1:    900       -67345.542             1.051            0.783
Chain 1:   1000       -52174.048             0.975            0.783
Chain 1:   1100       -39675.780             0.906            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38859.765             0.479            0.377
Chain 1:   1300       -26842.615             0.445            0.377
Chain 1:   1400       -26564.970             0.352            0.315
Chain 1:   1500       -23158.642             0.339            0.315
Chain 1:   1600       -22377.188             0.288            0.291
Chain 1:   1700       -21254.296             0.199            0.291
Chain 1:   1800       -21199.382             0.161            0.147
Chain 1:   1900       -21525.909             0.134            0.053
Chain 1:   2000       -20038.060             0.112            0.053
Chain 1:   2100       -20276.396             0.082            0.035
Chain 1:   2200       -20502.735             0.081            0.035
Chain 1:   2300       -20119.999             0.038            0.019
Chain 1:   2400       -19892.028             0.038            0.019
Chain 1:   2500       -19693.744             0.024            0.015
Chain 1:   2600       -19323.746             0.023            0.015
Chain 1:   2700       -19280.739             0.018            0.012
Chain 1:   2800       -18997.233             0.019            0.015
Chain 1:   2900       -19278.720             0.019            0.015
Chain 1:   3000       -19264.920             0.011            0.012
Chain 1:   3100       -19349.895             0.011            0.011
Chain 1:   3200       -19040.393             0.011            0.015
Chain 1:   3300       -19245.311             0.010            0.011
Chain 1:   3400       -18719.698             0.012            0.015
Chain 1:   3500       -19332.189             0.014            0.015
Chain 1:   3600       -18638.209             0.016            0.015
Chain 1:   3700       -19025.431             0.018            0.016
Chain 1:   3800       -17983.876             0.022            0.020
Chain 1:   3900       -17979.980             0.021            0.020
Chain 1:   4000       -18097.346             0.021            0.020
Chain 1:   4100       -18010.940             0.021            0.020
Chain 1:   4200       -17826.984             0.021            0.020
Chain 1:   4300       -17965.556             0.020            0.020
Chain 1:   4400       -17922.182             0.018            0.010
Chain 1:   4500       -17824.668             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48484.287             1.000            1.000
Chain 1:    200       -22357.807             1.084            1.169
Chain 1:    300       -11697.929             1.027            1.000
Chain 1:    400       -31566.176             0.927            1.000
Chain 1:    500       -13374.711             1.014            1.000
Chain 1:    600       -13281.695             0.846            1.000
Chain 1:    700       -13754.270             0.730            0.911
Chain 1:    800       -13720.234             0.639            0.911
Chain 1:    900       -14667.430             0.575            0.629
Chain 1:   1000       -16425.569             0.528            0.629
Chain 1:   1100       -12117.620             0.464            0.356
Chain 1:   1200       -13303.118             0.356            0.107
Chain 1:   1300       -25330.693             0.312            0.107
Chain 1:   1400        -9318.008             0.421            0.107
Chain 1:   1500       -10053.623             0.293            0.089
Chain 1:   1600        -9622.076             0.296            0.089
Chain 1:   1700       -24800.748             0.354            0.107
Chain 1:   1800       -10053.942             0.501            0.356   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -11050.392             0.503            0.356   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -11741.091             0.498            0.356
Chain 1:   2100        -9965.613             0.481            0.178
Chain 1:   2200        -9067.603             0.482            0.178
Chain 1:   2300        -9120.462             0.435            0.099
Chain 1:   2400       -10569.438             0.277            0.099
Chain 1:   2500        -9040.447             0.286            0.137
Chain 1:   2600        -8912.512             0.283            0.137
Chain 1:   2700       -10281.557             0.235            0.133
Chain 1:   2800        -9181.471             0.101            0.120
Chain 1:   2900        -9236.722             0.092            0.120
Chain 1:   3000        -8634.627             0.093            0.120
Chain 1:   3100        -9556.258             0.085            0.099
Chain 1:   3200       -10630.325             0.085            0.101
Chain 1:   3300        -8815.630             0.105            0.120
Chain 1:   3400        -9452.251             0.098            0.101
Chain 1:   3500        -8921.560             0.087            0.096
Chain 1:   3600       -12818.405             0.116            0.101
Chain 1:   3700        -9137.346             0.143            0.101
Chain 1:   3800       -13013.253             0.161            0.101
Chain 1:   3900        -8489.509             0.214            0.206
Chain 1:   4000        -8605.430             0.208            0.206
Chain 1:   4100        -8879.798             0.202            0.206
Chain 1:   4200       -12869.270             0.222            0.298
Chain 1:   4300        -9669.332             0.235            0.304
Chain 1:   4400        -9691.802             0.228            0.304
Chain 1:   4500        -9584.886             0.224            0.304
Chain 1:   4600        -8647.939             0.204            0.298
Chain 1:   4700       -13009.756             0.197            0.298
Chain 1:   4800        -8687.696             0.217            0.310
Chain 1:   4900        -9344.215             0.171            0.108
Chain 1:   5000       -10302.599             0.179            0.108
Chain 1:   5100        -9296.887             0.187            0.108
Chain 1:   5200        -8540.458             0.165            0.108
Chain 1:   5300        -8661.529             0.133            0.093
Chain 1:   5400        -9358.804             0.140            0.093
Chain 1:   5500        -8355.900             0.151            0.108
Chain 1:   5600       -12390.367             0.173            0.108
Chain 1:   5700       -13947.590             0.150            0.108
Chain 1:   5800        -8508.669             0.165            0.108
Chain 1:   5900       -14081.354             0.197            0.112
Chain 1:   6000        -9804.481             0.231            0.120
Chain 1:   6100       -10693.829             0.229            0.120
Chain 1:   6200        -8607.503             0.244            0.242
Chain 1:   6300       -12650.653             0.275            0.320
Chain 1:   6400       -11910.999             0.274            0.320
Chain 1:   6500        -8520.086             0.301            0.326
Chain 1:   6600        -8877.072             0.273            0.320
Chain 1:   6700       -10216.399             0.275            0.320
Chain 1:   6800        -9247.165             0.221            0.242
Chain 1:   6900       -11425.962             0.201            0.191
Chain 1:   7000        -8572.588             0.190            0.191
Chain 1:   7100        -7950.199             0.190            0.191
Chain 1:   7200        -8768.912             0.175            0.131
Chain 1:   7300        -9600.082             0.152            0.105
Chain 1:   7400        -8139.159             0.164            0.131
Chain 1:   7500        -8600.970             0.129            0.105
Chain 1:   7600        -9865.639             0.138            0.128
Chain 1:   7700        -9347.082             0.130            0.105
Chain 1:   7800        -8564.115             0.129            0.093
Chain 1:   7900        -8517.105             0.110            0.091
Chain 1:   8000        -8841.001             0.081            0.087
Chain 1:   8100        -7985.462             0.084            0.091
Chain 1:   8200       -11127.128             0.103            0.091
Chain 1:   8300        -8089.657             0.132            0.107
Chain 1:   8400        -8462.334             0.118            0.091
Chain 1:   8500       -10029.689             0.128            0.107
Chain 1:   8600        -8726.692             0.130            0.107
Chain 1:   8700        -8381.903             0.129            0.107
Chain 1:   8800        -8425.799             0.120            0.107
Chain 1:   8900        -8616.978             0.122            0.107
Chain 1:   9000       -10834.538             0.139            0.149
Chain 1:   9100        -8616.285             0.154            0.156
Chain 1:   9200        -8471.543             0.127            0.149
Chain 1:   9300       -10488.386             0.109            0.149
Chain 1:   9400        -8696.457             0.125            0.156
Chain 1:   9500        -8233.987             0.115            0.149
Chain 1:   9600        -9076.264             0.110            0.093
Chain 1:   9700        -8291.295             0.115            0.095
Chain 1:   9800        -9052.295             0.123            0.095
Chain 1:   9900        -8574.943             0.126            0.095
Chain 1:   10000        -8002.747             0.113            0.093
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57681.788             1.000            1.000
Chain 1:    200       -17376.127             1.660            2.320
Chain 1:    300        -8570.778             1.449            1.027
Chain 1:    400        -8174.746             1.099            1.027
Chain 1:    500        -8158.522             0.879            1.000
Chain 1:    600        -8441.244             0.738            1.000
Chain 1:    700        -7853.624             0.644            0.075
Chain 1:    800        -8019.214             0.566            0.075
Chain 1:    900        -7921.981             0.504            0.048
Chain 1:   1000        -7749.018             0.456            0.048
Chain 1:   1100        -7778.619             0.356            0.033
Chain 1:   1200        -7833.546             0.125            0.022
Chain 1:   1300        -7615.187             0.025            0.022
Chain 1:   1400        -7851.294             0.024            0.022
Chain 1:   1500        -7621.637             0.026            0.029
Chain 1:   1600        -7535.304             0.024            0.022
Chain 1:   1700        -7524.020             0.017            0.021
Chain 1:   1800        -7560.517             0.015            0.012
Chain 1:   1900        -7591.390             0.014            0.011
Chain 1:   2000        -7597.397             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003169 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86769.588             1.000            1.000
Chain 1:    200       -13198.980             3.287            5.574
Chain 1:    300        -9626.260             2.315            1.000
Chain 1:    400       -10382.873             1.754            1.000
Chain 1:    500        -8561.332             1.446            0.371
Chain 1:    600        -8484.438             1.207            0.371
Chain 1:    700        -8481.021             1.034            0.213
Chain 1:    800        -9020.849             0.913            0.213
Chain 1:    900        -8387.259             0.820            0.076
Chain 1:   1000        -8293.988             0.739            0.076
Chain 1:   1100        -8494.736             0.641            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8225.407             0.087            0.060
Chain 1:   1300        -8371.998             0.052            0.033
Chain 1:   1400        -8368.809             0.044            0.024
Chain 1:   1500        -8241.725             0.025            0.018
Chain 1:   1600        -8347.875             0.025            0.018
Chain 1:   1700        -8433.780             0.026            0.018
Chain 1:   1800        -8043.377             0.025            0.018
Chain 1:   1900        -8145.215             0.018            0.015
Chain 1:   2000        -8115.487             0.018            0.015
Chain 1:   2100        -8243.037             0.017            0.015
Chain 1:   2200        -8029.352             0.016            0.015
Chain 1:   2300        -8174.140             0.016            0.015
Chain 1:   2400        -8189.161             0.016            0.015
Chain 1:   2500        -8155.742             0.015            0.013
Chain 1:   2600        -8157.362             0.014            0.013
Chain 1:   2700        -8064.481             0.014            0.013
Chain 1:   2800        -8038.176             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426209.599             1.000            1.000
Chain 1:    200     -1588055.106             2.653            4.306
Chain 1:    300      -891360.895             2.029            1.000
Chain 1:    400      -457630.331             1.759            1.000
Chain 1:    500      -357572.139             1.463            0.948
Chain 1:    600      -232370.043             1.309            0.948
Chain 1:    700      -118743.665             1.259            0.948
Chain 1:    800       -85965.726             1.149            0.948
Chain 1:    900       -66341.460             1.054            0.782
Chain 1:   1000       -51160.340             0.978            0.782
Chain 1:   1100       -38664.399             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37839.027             0.482            0.381
Chain 1:   1300       -25836.489             0.451            0.381
Chain 1:   1400       -25556.285             0.357            0.323
Chain 1:   1500       -22154.469             0.344            0.323
Chain 1:   1600       -21373.175             0.294            0.297
Chain 1:   1700       -20252.853             0.204            0.296
Chain 1:   1800       -20197.848             0.166            0.154
Chain 1:   1900       -20523.591             0.138            0.055
Chain 1:   2000       -19038.395             0.116            0.055
Chain 1:   2100       -19276.611             0.085            0.037
Chain 1:   2200       -19502.256             0.084            0.037
Chain 1:   2300       -19120.272             0.040            0.020
Chain 1:   2400       -18892.602             0.040            0.020
Chain 1:   2500       -18694.319             0.026            0.016
Chain 1:   2600       -18325.290             0.024            0.016
Chain 1:   2700       -18282.435             0.019            0.012
Chain 1:   2800       -17999.424             0.020            0.016
Chain 1:   2900       -18280.370             0.020            0.015
Chain 1:   3000       -18266.668             0.012            0.012
Chain 1:   3100       -18351.586             0.011            0.012
Chain 1:   3200       -18042.623             0.012            0.015
Chain 1:   3300       -18247.036             0.011            0.012
Chain 1:   3400       -17722.522             0.013            0.015
Chain 1:   3500       -18333.461             0.015            0.016
Chain 1:   3600       -17641.337             0.017            0.016
Chain 1:   3700       -18027.257             0.019            0.017
Chain 1:   3800       -16988.723             0.023            0.021
Chain 1:   3900       -16984.861             0.022            0.021
Chain 1:   4000       -17102.214             0.022            0.021
Chain 1:   4100       -17016.076             0.023            0.021
Chain 1:   4200       -16832.668             0.022            0.021
Chain 1:   4300       -16970.849             0.022            0.021
Chain 1:   4400       -16928.014             0.019            0.011
Chain 1:   4500       -16830.553             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001273 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49406.315             1.000            1.000
Chain 1:    200       -21137.399             1.169            1.337
Chain 1:    300       -16863.758             0.864            1.000
Chain 1:    400       -21865.857             0.705            1.000
Chain 1:    500       -12402.315             0.717            0.763
Chain 1:    600       -14769.840             0.624            0.763
Chain 1:    700       -16795.956             0.552            0.253
Chain 1:    800       -11053.405             0.548            0.520
Chain 1:    900       -14627.634             0.514            0.253
Chain 1:   1000       -15488.244             0.468            0.253
Chain 1:   1100       -11216.840             0.406            0.253
Chain 1:   1200       -10333.363             0.281            0.244
Chain 1:   1300       -12162.000             0.271            0.229
Chain 1:   1400       -11033.539             0.258            0.160
Chain 1:   1500       -12422.380             0.193            0.150
Chain 1:   1600       -13402.626             0.184            0.121
Chain 1:   1700       -10256.877             0.203            0.150
Chain 1:   1800       -10095.510             0.153            0.112
Chain 1:   1900       -14197.563             0.157            0.112
Chain 1:   2000       -13558.589             0.156            0.112
Chain 1:   2100        -9772.634             0.157            0.112
Chain 1:   2200       -18088.936             0.194            0.150
Chain 1:   2300        -9730.411             0.265            0.289
Chain 1:   2400       -13672.054             0.284            0.289
Chain 1:   2500       -12666.158             0.281            0.289
Chain 1:   2600        -9911.900             0.301            0.289
Chain 1:   2700        -9387.674             0.276            0.288
Chain 1:   2800       -10544.348             0.285            0.288
Chain 1:   2900        -9439.353             0.268            0.278
Chain 1:   3000       -13668.093             0.294            0.288
Chain 1:   3100       -10098.322             0.291            0.288
Chain 1:   3200       -15016.720             0.278            0.288
Chain 1:   3300       -13300.728             0.205            0.278
Chain 1:   3400       -10490.822             0.203            0.268
Chain 1:   3500       -13461.222             0.217            0.268
Chain 1:   3600        -9593.570             0.229            0.268
Chain 1:   3700       -11377.691             0.239            0.268
Chain 1:   3800        -8933.642             0.256            0.274
Chain 1:   3900        -9034.560             0.245            0.274
Chain 1:   4000        -9915.126             0.223            0.268
Chain 1:   4100       -11094.898             0.198            0.221
Chain 1:   4200       -10739.130             0.169            0.157
Chain 1:   4300       -10233.770             0.161            0.157
Chain 1:   4400       -10277.481             0.135            0.106
Chain 1:   4500       -11044.070             0.120            0.089
Chain 1:   4600        -8915.109             0.103            0.089
Chain 1:   4700       -12046.057             0.113            0.089
Chain 1:   4800        -9170.262             0.117            0.089
Chain 1:   4900       -11873.127             0.139            0.106
Chain 1:   5000       -10424.372             0.144            0.139
Chain 1:   5100        -9429.965             0.144            0.139
Chain 1:   5200        -8885.374             0.147            0.139
Chain 1:   5300       -10576.495             0.158            0.160
Chain 1:   5400       -10921.436             0.161            0.160
Chain 1:   5500        -8681.775             0.180            0.228
Chain 1:   5600       -10345.152             0.172            0.161
Chain 1:   5700       -10376.650             0.146            0.160
Chain 1:   5800        -8789.622             0.133            0.160
Chain 1:   5900       -12533.368             0.140            0.160
Chain 1:   6000        -8935.708             0.166            0.161
Chain 1:   6100        -8432.836             0.162            0.161
Chain 1:   6200        -8742.516             0.159            0.161
Chain 1:   6300       -10576.877             0.160            0.173
Chain 1:   6400        -8701.292             0.179            0.181
Chain 1:   6500        -9393.027             0.160            0.173
Chain 1:   6600        -9200.586             0.146            0.173
Chain 1:   6700        -8622.938             0.153            0.173
Chain 1:   6800        -9045.910             0.139            0.074
Chain 1:   6900       -12619.895             0.138            0.074
Chain 1:   7000        -8612.253             0.144            0.074
Chain 1:   7100       -10605.014             0.157            0.173
Chain 1:   7200       -14034.771             0.178            0.188
Chain 1:   7300       -12533.592             0.172            0.188
Chain 1:   7400        -8864.953             0.192            0.188
Chain 1:   7500        -9250.598             0.189            0.188
Chain 1:   7600        -9631.203             0.191            0.188
Chain 1:   7700        -8479.889             0.198            0.188
Chain 1:   7800        -8528.249             0.194            0.188
Chain 1:   7900        -9095.102             0.172            0.136
Chain 1:   8000       -10268.929             0.137            0.120
Chain 1:   8100       -10060.604             0.120            0.114
Chain 1:   8200        -9517.336             0.101            0.062
Chain 1:   8300        -9900.825             0.093            0.057
Chain 1:   8400        -9658.791             0.054            0.042
Chain 1:   8500        -9667.201             0.050            0.040
Chain 1:   8600        -8612.266             0.058            0.057
Chain 1:   8700        -8633.287             0.045            0.039
Chain 1:   8800        -8461.024             0.046            0.039
Chain 1:   8900       -10300.193             0.058            0.039
Chain 1:   9000        -9950.635             0.050            0.035
Chain 1:   9100        -9028.321             0.058            0.039
Chain 1:   9200        -9250.651             0.055            0.035
Chain 1:   9300        -8749.671             0.057            0.035
Chain 1:   9400        -9221.520             0.059            0.051
Chain 1:   9500        -9420.880             0.061            0.051
Chain 1:   9600       -10177.205             0.057            0.051
Chain 1:   9700        -8256.885             0.080            0.057
Chain 1:   9800       -11802.638             0.108            0.074
Chain 1:   9900        -9762.571             0.111            0.074
Chain 1:   10000        -8642.546             0.120            0.102
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001649 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56702.456             1.000            1.000
Chain 1:    200       -17631.181             1.608            2.216
Chain 1:    300        -8804.143             1.406            1.003
Chain 1:    400        -8159.076             1.074            1.003
Chain 1:    500        -8753.763             0.873            1.000
Chain 1:    600        -8074.360             0.742            1.000
Chain 1:    700        -7914.704             0.639            0.084
Chain 1:    800        -8116.222             0.562            0.084
Chain 1:    900        -7922.047             0.502            0.079
Chain 1:   1000        -7977.788             0.453            0.079
Chain 1:   1100        -7835.928             0.354            0.068
Chain 1:   1200        -7911.427             0.134            0.025
Chain 1:   1300        -7693.124             0.036            0.025
Chain 1:   1400        -8055.640             0.033            0.025
Chain 1:   1500        -7680.931             0.031            0.025
Chain 1:   1600        -7878.275             0.025            0.025
Chain 1:   1700        -7629.009             0.026            0.025
Chain 1:   1800        -7685.718             0.025            0.025
Chain 1:   1900        -7705.299             0.022            0.025
Chain 1:   2000        -7732.471             0.022            0.025
Chain 1:   2100        -7733.534             0.020            0.025
Chain 1:   2200        -7831.414             0.021            0.025
Chain 1:   2300        -7711.327             0.019            0.016
Chain 1:   2400        -7770.636             0.016            0.012
Chain 1:   2500        -7690.615             0.012            0.010
Chain 1:   2600        -7639.795             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002476 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86502.948             1.000            1.000
Chain 1:    200       -13713.308             3.154            5.308
Chain 1:    300       -10041.415             2.225            1.000
Chain 1:    400       -11343.204             1.697            1.000
Chain 1:    500        -8712.214             1.418            0.366
Chain 1:    600        -8578.283             1.184            0.366
Chain 1:    700        -8377.223             1.019            0.302
Chain 1:    800        -9546.404             0.907            0.302
Chain 1:    900        -8799.467             0.815            0.122
Chain 1:   1000        -8672.274             0.735            0.122
Chain 1:   1100        -8802.688             0.637            0.115   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8331.223             0.112            0.085
Chain 1:   1300        -8557.056             0.078            0.057
Chain 1:   1400        -8746.984             0.068            0.026
Chain 1:   1500        -8567.945             0.040            0.024
Chain 1:   1600        -8681.474             0.040            0.024
Chain 1:   1700        -8748.307             0.038            0.022
Chain 1:   1800        -8317.630             0.031            0.022
Chain 1:   1900        -8421.568             0.024            0.021
Chain 1:   2000        -8396.885             0.023            0.021
Chain 1:   2100        -8533.765             0.023            0.021
Chain 1:   2200        -8327.039             0.020            0.021
Chain 1:   2300        -8415.401             0.018            0.016
Chain 1:   2400        -8488.360             0.017            0.013
Chain 1:   2500        -8430.460             0.015            0.012
Chain 1:   2600        -8436.091             0.014            0.010
Chain 1:   2700        -8350.507             0.014            0.010
Chain 1:   2800        -8305.848             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003172 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8443180.638             1.000            1.000
Chain 1:    200     -1588931.544             2.657            4.314
Chain 1:    300      -890388.678             2.033            1.000
Chain 1:    400      -457647.435             1.761            1.000
Chain 1:    500      -357769.972             1.465            0.946
Chain 1:    600      -232679.648             1.310            0.946
Chain 1:    700      -119130.454             1.259            0.946
Chain 1:    800       -86454.227             1.149            0.946
Chain 1:    900       -66844.979             1.054            0.785
Chain 1:   1000       -51695.812             0.978            0.785
Chain 1:   1100       -39226.969             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38409.565             0.480            0.378
Chain 1:   1300       -26409.012             0.447            0.378
Chain 1:   1400       -26133.274             0.354            0.318
Chain 1:   1500       -22732.919             0.341            0.318
Chain 1:   1600       -21954.118             0.291            0.293
Chain 1:   1700       -20832.186             0.201            0.293
Chain 1:   1800       -20777.655             0.163            0.150
Chain 1:   1900       -21104.154             0.135            0.054
Chain 1:   2000       -19617.598             0.114            0.054
Chain 1:   2100       -19855.542             0.083            0.035
Chain 1:   2200       -20082.036             0.082            0.035
Chain 1:   2300       -19699.176             0.039            0.019
Chain 1:   2400       -19471.257             0.039            0.019
Chain 1:   2500       -19273.287             0.025            0.015
Chain 1:   2600       -18903.128             0.023            0.015
Chain 1:   2700       -18860.075             0.018            0.012
Chain 1:   2800       -18576.859             0.019            0.015
Chain 1:   2900       -18858.143             0.019            0.015
Chain 1:   3000       -18844.251             0.012            0.012
Chain 1:   3100       -18929.303             0.011            0.012
Chain 1:   3200       -18619.817             0.012            0.015
Chain 1:   3300       -18824.708             0.011            0.012
Chain 1:   3400       -18299.320             0.012            0.015
Chain 1:   3500       -18911.621             0.015            0.015
Chain 1:   3600       -18217.728             0.016            0.015
Chain 1:   3700       -18604.926             0.018            0.017
Chain 1:   3800       -17563.758             0.023            0.021
Chain 1:   3900       -17559.902             0.021            0.021
Chain 1:   4000       -17677.186             0.022            0.021
Chain 1:   4100       -17590.926             0.022            0.021
Chain 1:   4200       -17406.993             0.021            0.021
Chain 1:   4300       -17545.496             0.021            0.021
Chain 1:   4400       -17502.146             0.018            0.011
Chain 1:   4500       -17404.680             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00125 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49088.664             1.000            1.000
Chain 1:    200       -19520.048             1.257            1.515
Chain 1:    300       -19922.139             0.845            1.000
Chain 1:    400       -42818.117             0.767            1.000
Chain 1:    500       -11679.153             1.147            1.000
Chain 1:    600       -12201.615             0.963            1.000
Chain 1:    700       -13391.867             0.838            0.535
Chain 1:    800       -11819.772             0.750            0.535
Chain 1:    900       -14995.207             0.690            0.212
Chain 1:   1000       -16537.213             0.631            0.212
Chain 1:   1100       -11535.780             0.574            0.212   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12865.529             0.433            0.133
Chain 1:   1300       -18587.135             0.462            0.212
Chain 1:   1400       -14632.698             0.435            0.212
Chain 1:   1500       -10437.458             0.209            0.212
Chain 1:   1600       -11826.720             0.216            0.212
Chain 1:   1700       -12641.932             0.214            0.212
Chain 1:   1800       -10678.633             0.219            0.212
Chain 1:   1900       -10563.655             0.199            0.184
Chain 1:   2000        -9614.235             0.199            0.184
Chain 1:   2100       -10486.040             0.164            0.117
Chain 1:   2200       -11018.062             0.159            0.117
Chain 1:   2300        -9516.278             0.144            0.117
Chain 1:   2400       -11339.079             0.133            0.117
Chain 1:   2500       -13027.028             0.106            0.117
Chain 1:   2600        -9310.660             0.134            0.130
Chain 1:   2700       -10165.635             0.136            0.130
Chain 1:   2800        -9972.970             0.119            0.099
Chain 1:   2900        -9956.117             0.118            0.099
Chain 1:   3000        -9467.513             0.114            0.084
Chain 1:   3100        -8908.082             0.112            0.084
Chain 1:   3200       -15798.065             0.150            0.130
Chain 1:   3300        -9714.101             0.197            0.130
Chain 1:   3400        -9274.441             0.186            0.084
Chain 1:   3500        -9411.669             0.174            0.063
Chain 1:   3600        -9995.079             0.140            0.058
Chain 1:   3700       -10072.093             0.133            0.052
Chain 1:   3800       -11885.339             0.146            0.058
Chain 1:   3900       -11846.602             0.146            0.058
Chain 1:   4000       -10782.021             0.151            0.063
Chain 1:   4100        -8767.018             0.167            0.099
Chain 1:   4200        -8826.010             0.125            0.058
Chain 1:   4300        -9685.694             0.071            0.058
Chain 1:   4400       -12389.092             0.088            0.089
Chain 1:   4500        -8835.033             0.127            0.099
Chain 1:   4600        -8919.756             0.122            0.099
Chain 1:   4700       -13458.118             0.155            0.153
Chain 1:   4800        -9050.730             0.188            0.218
Chain 1:   4900        -8986.825             0.189            0.218
Chain 1:   5000        -9577.227             0.185            0.218
Chain 1:   5100        -8812.867             0.171            0.089
Chain 1:   5200       -15928.013             0.215            0.218
Chain 1:   5300       -14341.046             0.217            0.218
Chain 1:   5400       -14646.628             0.197            0.111
Chain 1:   5500       -10741.054             0.193            0.111
Chain 1:   5600        -8494.307             0.219            0.265
Chain 1:   5700       -14988.148             0.228            0.265
Chain 1:   5800        -9022.368             0.246            0.265
Chain 1:   5900       -15360.620             0.286            0.364
Chain 1:   6000        -9185.655             0.347            0.413
Chain 1:   6100       -14131.026             0.374            0.413
Chain 1:   6200        -9211.201             0.382            0.413
Chain 1:   6300        -9007.558             0.374            0.413
Chain 1:   6400        -9539.077             0.377            0.413
Chain 1:   6500        -9351.944             0.343            0.413
Chain 1:   6600        -8862.092             0.322            0.413
Chain 1:   6700        -8835.036             0.279            0.350
Chain 1:   6800        -8517.902             0.216            0.056
Chain 1:   6900        -8789.221             0.178            0.055
Chain 1:   7000       -16268.581             0.157            0.055
Chain 1:   7100        -8309.748             0.218            0.055
Chain 1:   7200        -8928.883             0.171            0.055
Chain 1:   7300       -11186.355             0.189            0.056
Chain 1:   7400        -9918.824             0.196            0.069
Chain 1:   7500       -10882.612             0.203            0.089
Chain 1:   7600        -8546.645             0.225            0.128
Chain 1:   7700        -9815.509             0.238            0.129
Chain 1:   7800       -11537.635             0.249            0.149
Chain 1:   7900        -8487.850             0.282            0.202
Chain 1:   8000        -9793.582             0.249            0.149
Chain 1:   8100        -8962.839             0.162            0.133
Chain 1:   8200        -9915.138             0.165            0.133
Chain 1:   8300        -8313.018             0.164            0.133
Chain 1:   8400        -8254.695             0.152            0.133
Chain 1:   8500        -8788.755             0.149            0.133
Chain 1:   8600        -8794.437             0.122            0.129
Chain 1:   8700        -8087.667             0.118            0.096
Chain 1:   8800       -11440.707             0.132            0.096
Chain 1:   8900        -8448.768             0.132            0.096
Chain 1:   9000       -11103.512             0.142            0.096
Chain 1:   9100        -8221.961             0.168            0.193
Chain 1:   9200       -10856.100             0.183            0.239
Chain 1:   9300        -8174.474             0.196            0.243
Chain 1:   9400        -8633.169             0.201            0.243
Chain 1:   9500        -8093.100             0.202            0.243
Chain 1:   9600       -10640.125             0.225            0.243
Chain 1:   9700       -10170.777             0.221            0.243
Chain 1:   9800        -8462.413             0.212            0.239
Chain 1:   9900        -8289.375             0.179            0.239
Chain 1:   10000        -8346.097             0.156            0.202
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00142 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58101.853             1.000            1.000
Chain 1:    200       -17848.399             1.628            2.255
Chain 1:    300        -8765.317             1.431            1.036
Chain 1:    400        -8119.198             1.093            1.036
Chain 1:    500        -8972.202             0.893            1.000
Chain 1:    600        -8672.723             0.750            1.000
Chain 1:    700        -8118.394             0.653            0.095
Chain 1:    800        -8156.318             0.572            0.095
Chain 1:    900        -7931.251             0.511            0.080
Chain 1:   1000        -7898.422             0.461            0.080
Chain 1:   1100        -7762.383             0.362            0.068
Chain 1:   1200        -7791.173             0.137            0.035
Chain 1:   1300        -7688.658             0.035            0.028
Chain 1:   1400        -7831.388             0.029            0.018
Chain 1:   1500        -7620.394             0.022            0.018
Chain 1:   1600        -7798.955             0.021            0.018
Chain 1:   1700        -7565.900             0.017            0.018
Chain 1:   1800        -7672.018             0.018            0.018
Chain 1:   1900        -7624.383             0.016            0.018
Chain 1:   2000        -7685.380             0.016            0.018
Chain 1:   2100        -7614.603             0.015            0.014
Chain 1:   2200        -7742.606             0.017            0.017
Chain 1:   2300        -7632.594             0.017            0.017
Chain 1:   2400        -7673.097             0.015            0.014
Chain 1:   2500        -7582.401             0.014            0.014
Chain 1:   2600        -7539.256             0.012            0.012
Chain 1:   2700        -7532.759             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003252 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86621.081             1.000            1.000
Chain 1:    200       -13694.186             3.163            5.325
Chain 1:    300        -9968.964             2.233            1.000
Chain 1:    400       -11112.374             1.700            1.000
Chain 1:    500        -8991.231             1.408            0.374
Chain 1:    600        -8372.725             1.185            0.374
Chain 1:    700        -8862.790             1.024            0.236
Chain 1:    800        -8703.453             0.898            0.236
Chain 1:    900        -8805.454             0.800            0.103
Chain 1:   1000        -8776.275             0.720            0.103
Chain 1:   1100        -8544.316             0.623            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8334.021             0.093            0.055
Chain 1:   1300        -8631.880             0.059            0.035
Chain 1:   1400        -8621.011             0.049            0.027
Chain 1:   1500        -8476.730             0.027            0.025
Chain 1:   1600        -8592.673             0.021            0.018
Chain 1:   1700        -8655.052             0.016            0.017
Chain 1:   1800        -8217.651             0.019            0.017
Chain 1:   1900        -8322.135             0.019            0.017
Chain 1:   2000        -8299.676             0.019            0.017
Chain 1:   2100        -8275.389             0.017            0.013
Chain 1:   2200        -8243.487             0.015            0.013
Chain 1:   2300        -8377.793             0.013            0.013
Chain 1:   2400        -8219.365             0.015            0.013
Chain 1:   2500        -8291.814             0.014            0.013
Chain 1:   2600        -8205.116             0.014            0.011
Chain 1:   2700        -8241.702             0.013            0.011
Chain 1:   2800        -8200.187             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003464 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380325.708             1.000            1.000
Chain 1:    200     -1584491.550             2.644            4.289
Chain 1:    300      -890812.201             2.023            1.000
Chain 1:    400      -457173.969             1.754            1.000
Chain 1:    500      -357639.764             1.459            0.949
Chain 1:    600      -232772.955             1.305            0.949
Chain 1:    700      -119274.092             1.255            0.949
Chain 1:    800       -86502.168             1.145            0.949
Chain 1:    900       -66904.703             1.050            0.779
Chain 1:   1000       -51739.998             0.975            0.779
Chain 1:   1100       -39238.857             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38425.569             0.480            0.379
Chain 1:   1300       -26400.437             0.447            0.379
Chain 1:   1400       -26123.885             0.354            0.319
Chain 1:   1500       -22713.842             0.341            0.319
Chain 1:   1600       -21931.485             0.291            0.293
Chain 1:   1700       -20807.314             0.201            0.293
Chain 1:   1800       -20752.219             0.163            0.150
Chain 1:   1900       -21078.853             0.136            0.054
Chain 1:   2000       -19589.672             0.114            0.054
Chain 1:   2100       -19828.484             0.083            0.036
Chain 1:   2200       -20054.840             0.082            0.036
Chain 1:   2300       -19671.935             0.039            0.019
Chain 1:   2400       -19443.858             0.039            0.019
Chain 1:   2500       -19245.556             0.025            0.015
Chain 1:   2600       -18875.736             0.023            0.015
Chain 1:   2700       -18832.636             0.018            0.012
Chain 1:   2800       -18549.137             0.019            0.015
Chain 1:   2900       -18830.566             0.019            0.015
Chain 1:   3000       -18816.882             0.012            0.012
Chain 1:   3100       -18901.888             0.011            0.012
Chain 1:   3200       -18592.374             0.012            0.015
Chain 1:   3300       -18797.230             0.011            0.012
Chain 1:   3400       -18271.651             0.012            0.015
Chain 1:   3500       -18884.223             0.015            0.015
Chain 1:   3600       -18189.985             0.016            0.015
Chain 1:   3700       -18577.452             0.018            0.017
Chain 1:   3800       -17535.651             0.023            0.021
Chain 1:   3900       -17531.682             0.021            0.021
Chain 1:   4000       -17649.061             0.022            0.021
Chain 1:   4100       -17562.717             0.022            0.021
Chain 1:   4200       -17378.618             0.021            0.021
Chain 1:   4300       -17517.317             0.021            0.021
Chain 1:   4400       -17473.897             0.018            0.011
Chain 1:   4500       -17376.301             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001472 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48403.705             1.000            1.000
Chain 1:    200       -14405.190             1.680            2.360
Chain 1:    300       -14347.304             1.121            1.000
Chain 1:    400       -12379.250             0.881            1.000
Chain 1:    500       -11911.071             0.712            0.159
Chain 1:    600       -10887.649             0.609            0.159
Chain 1:    700       -20899.236             0.591            0.159
Chain 1:    800       -18391.269             0.534            0.159
Chain 1:    900       -11552.161             0.540            0.159
Chain 1:   1000       -18247.700             0.523            0.367
Chain 1:   1100        -9878.820             0.508            0.367   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12003.250             0.289            0.177
Chain 1:   1300       -11328.624             0.295            0.177
Chain 1:   1400       -23383.285             0.331            0.367
Chain 1:   1500       -54989.578             0.384            0.479
Chain 1:   1600        -9166.916             0.875            0.516   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700       -21084.631             0.883            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800       -10441.406             0.972            0.575   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -22872.200             0.967            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -12190.953             1.018            0.575   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100        -9119.525             0.967            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200        -9179.299             0.950            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300       -10926.000             0.960            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400        -8886.474             0.931            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500       -14718.818             0.913            0.543   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600        -9663.530             0.466            0.523   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2700       -11629.398             0.426            0.396
Chain 1:   2800        -8905.046             0.355            0.337
Chain 1:   2900        -9670.402             0.308            0.306
Chain 1:   3000        -8574.124             0.233            0.230
Chain 1:   3100        -8515.115             0.200            0.169
Chain 1:   3200        -8697.993             0.202            0.169
Chain 1:   3300       -10389.606             0.202            0.169
Chain 1:   3400        -9814.565             0.185            0.163
Chain 1:   3500        -8867.405             0.156            0.128
Chain 1:   3600       -13645.848             0.139            0.128
Chain 1:   3700        -8412.625             0.184            0.128
Chain 1:   3800        -9148.129             0.162            0.107
Chain 1:   3900        -9128.744             0.154            0.107
Chain 1:   4000        -9096.703             0.141            0.080
Chain 1:   4100        -8631.970             0.146            0.080
Chain 1:   4200       -12738.569             0.176            0.107
Chain 1:   4300        -9013.239             0.201            0.107
Chain 1:   4400       -11885.792             0.220            0.242
Chain 1:   4500       -13583.389             0.221            0.242
Chain 1:   4600        -8950.045             0.238            0.242
Chain 1:   4700        -9990.172             0.186            0.125
Chain 1:   4800        -8420.672             0.197            0.186
Chain 1:   4900        -8608.939             0.199            0.186
Chain 1:   5000       -11649.032             0.225            0.242
Chain 1:   5100        -8431.345             0.258            0.261
Chain 1:   5200        -8527.793             0.226            0.242
Chain 1:   5300        -9472.557             0.195            0.186
Chain 1:   5400        -8494.395             0.182            0.125
Chain 1:   5500       -11905.676             0.199            0.186
Chain 1:   5600        -9404.900             0.173            0.186
Chain 1:   5700       -10681.354             0.175            0.186
Chain 1:   5800       -10501.927             0.158            0.120
Chain 1:   5900        -9604.902             0.165            0.120
Chain 1:   6000        -8168.407             0.157            0.120
Chain 1:   6100        -8699.143             0.125            0.115
Chain 1:   6200        -8083.352             0.131            0.115
Chain 1:   6300       -12015.490             0.154            0.120
Chain 1:   6400       -13225.002             0.151            0.120
Chain 1:   6500        -9010.279             0.170            0.120
Chain 1:   6600        -8659.500             0.147            0.093
Chain 1:   6700        -9959.772             0.148            0.093
Chain 1:   6800       -12774.760             0.168            0.131
Chain 1:   6900        -9728.619             0.190            0.176
Chain 1:   7000        -8429.735             0.188            0.154
Chain 1:   7100       -12165.979             0.213            0.220
Chain 1:   7200       -10679.614             0.219            0.220
Chain 1:   7300        -8420.140             0.213            0.220
Chain 1:   7400        -8955.721             0.210            0.220
Chain 1:   7500        -9721.251             0.171            0.154
Chain 1:   7600        -8745.744             0.178            0.154
Chain 1:   7700        -8079.925             0.173            0.154
Chain 1:   7800       -11775.667             0.183            0.154
Chain 1:   7900       -10225.095             0.167            0.152
Chain 1:   8000        -9388.477             0.160            0.139
Chain 1:   8100        -8116.128             0.145            0.139
Chain 1:   8200       -10378.946             0.153            0.152
Chain 1:   8300        -8265.294             0.152            0.152
Chain 1:   8400        -8017.698             0.149            0.152
Chain 1:   8500        -8385.889             0.145            0.152
Chain 1:   8600       -10571.830             0.155            0.157
Chain 1:   8700        -9787.617             0.155            0.157
Chain 1:   8800       -10187.448             0.127            0.152
Chain 1:   8900       -10414.644             0.114            0.089
Chain 1:   9000       -11538.250             0.115            0.097
Chain 1:   9100        -8563.470             0.134            0.097
Chain 1:   9200        -8003.251             0.119            0.080
Chain 1:   9300        -9012.676             0.105            0.080
Chain 1:   9400        -9077.441             0.103            0.080
Chain 1:   9500        -8719.949             0.102            0.080
Chain 1:   9600        -8073.657             0.090            0.080
Chain 1:   9700       -11322.765             0.110            0.080
Chain 1:   9800        -9515.451             0.125            0.097
Chain 1:   9900        -9745.120             0.126            0.097
Chain 1:   10000        -8057.149             0.137            0.112
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56545.748             1.000            1.000
Chain 1:    200       -17030.491             1.660            2.320
Chain 1:    300        -8563.823             1.436            1.000
Chain 1:    400        -8908.649             1.087            1.000
Chain 1:    500        -8500.142             0.879            0.989
Chain 1:    600        -9091.076             0.743            0.989
Chain 1:    700        -7765.868             0.662            0.171
Chain 1:    800        -8083.371             0.584            0.171
Chain 1:    900        -7899.933             0.522            0.065
Chain 1:   1000        -7735.623             0.472            0.065
Chain 1:   1100        -7663.197             0.372            0.048
Chain 1:   1200        -7576.592             0.142            0.039
Chain 1:   1300        -7687.956             0.044            0.039
Chain 1:   1400        -7874.840             0.043            0.024
Chain 1:   1500        -7619.851             0.041            0.024
Chain 1:   1600        -7521.936             0.036            0.023
Chain 1:   1700        -7528.582             0.019            0.021
Chain 1:   1800        -7537.275             0.015            0.014
Chain 1:   1900        -7502.992             0.013            0.013
Chain 1:   2000        -7602.445             0.013            0.013
Chain 1:   2100        -7669.404             0.012            0.013
Chain 1:   2200        -7679.588             0.011            0.013
Chain 1:   2300        -7576.864             0.011            0.013
Chain 1:   2400        -7614.770             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002551 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86766.212             1.000            1.000
Chain 1:    200       -13109.245             3.309            5.619
Chain 1:    300        -9571.763             2.329            1.000
Chain 1:    400       -10422.068             1.767            1.000
Chain 1:    500        -8497.687             1.459            0.370
Chain 1:    600        -8372.586             1.219            0.370
Chain 1:    700        -8387.579             1.045            0.226
Chain 1:    800        -8861.660             0.921            0.226
Chain 1:    900        -8401.875             0.825            0.082
Chain 1:   1000        -8216.707             0.744            0.082
Chain 1:   1100        -8491.943             0.648            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8229.104             0.089            0.053
Chain 1:   1300        -8165.860             0.053            0.032
Chain 1:   1400        -8165.770             0.045            0.032
Chain 1:   1500        -8198.630             0.022            0.023
Chain 1:   1600        -8204.085             0.021            0.023
Chain 1:   1700        -8139.463             0.022            0.023
Chain 1:   1800        -8021.257             0.018            0.015
Chain 1:   1900        -8136.727             0.014            0.014
Chain 1:   2000        -8097.145             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003117 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412724.227             1.000            1.000
Chain 1:    200     -1588140.314             2.649            4.297
Chain 1:    300      -890863.088             2.027            1.000
Chain 1:    400      -456574.611             1.758            1.000
Chain 1:    500      -356541.338             1.462            0.951
Chain 1:    600      -231649.184             1.308            0.951
Chain 1:    700      -118355.931             1.258            0.951
Chain 1:    800       -85654.785             1.149            0.951
Chain 1:    900       -66097.800             1.054            0.783
Chain 1:   1000       -50958.541             0.978            0.783
Chain 1:   1100       -38496.084             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37678.127             0.483            0.382
Chain 1:   1300       -25707.602             0.451            0.382
Chain 1:   1400       -25431.052             0.357            0.324
Chain 1:   1500       -22036.788             0.345            0.324
Chain 1:   1600       -21257.824             0.294            0.297
Chain 1:   1700       -20141.052             0.204            0.296
Chain 1:   1800       -20087.029             0.166            0.154
Chain 1:   1900       -20412.569             0.138            0.055
Chain 1:   2000       -18929.653             0.117            0.055
Chain 1:   2100       -19167.867             0.085            0.037
Chain 1:   2200       -19392.951             0.084            0.037
Chain 1:   2300       -19011.505             0.040            0.020
Chain 1:   2400       -18783.918             0.040            0.020
Chain 1:   2500       -18585.541             0.026            0.016
Chain 1:   2600       -18216.844             0.024            0.016
Chain 1:   2700       -18174.176             0.019            0.012
Chain 1:   2800       -17891.121             0.020            0.016
Chain 1:   2900       -18172.000             0.020            0.015
Chain 1:   3000       -18158.353             0.012            0.012
Chain 1:   3100       -18243.184             0.011            0.012
Chain 1:   3200       -17934.437             0.012            0.015
Chain 1:   3300       -18138.743             0.011            0.012
Chain 1:   3400       -17614.487             0.013            0.015
Chain 1:   3500       -18224.977             0.015            0.016
Chain 1:   3600       -17533.497             0.017            0.016
Chain 1:   3700       -17918.853             0.019            0.017
Chain 1:   3800       -16881.291             0.024            0.022
Chain 1:   3900       -16877.449             0.022            0.022
Chain 1:   4000       -16994.815             0.023            0.022
Chain 1:   4100       -16908.637             0.023            0.022
Chain 1:   4200       -16725.523             0.022            0.022
Chain 1:   4300       -16863.527             0.022            0.022
Chain 1:   4400       -16820.858             0.019            0.011
Chain 1:   4500       -16723.417             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13774.740             1.000            1.000
Chain 1:    200       -10465.930             0.658            1.000
Chain 1:    300        -8447.654             0.518            0.316
Chain 1:    400        -8651.284             0.395            0.316
Chain 1:    500        -8634.424             0.316            0.239
Chain 1:    600        -8415.595             0.268            0.239
Chain 1:    700        -8397.783             0.230            0.026
Chain 1:    800        -8364.469             0.202            0.026
Chain 1:    900        -8402.036             0.180            0.024
Chain 1:   1000        -8365.810             0.162            0.024
Chain 1:   1100        -8426.557             0.063            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58869.814             1.000            1.000
Chain 1:    200       -18516.469             1.590            2.179
Chain 1:    300        -9128.855             1.403            1.028
Chain 1:    400        -8235.446             1.079            1.028
Chain 1:    500        -8625.132             0.872            1.000
Chain 1:    600        -8530.333             0.729            1.000
Chain 1:    700        -7945.224             0.635            0.108
Chain 1:    800        -8605.215             0.565            0.108
Chain 1:    900        -8294.506             0.507            0.077
Chain 1:   1000        -7868.874             0.461            0.077
Chain 1:   1100        -7882.854             0.362            0.074
Chain 1:   1200        -8188.589             0.147            0.054
Chain 1:   1300        -8254.595             0.045            0.045
Chain 1:   1400        -7840.883             0.040            0.045
Chain 1:   1500        -7677.137             0.037            0.037
Chain 1:   1600        -7949.453             0.040            0.037
Chain 1:   1700        -7798.346             0.034            0.037
Chain 1:   1800        -7719.087             0.028            0.034
Chain 1:   1900        -7853.564             0.026            0.021
Chain 1:   2000        -7834.742             0.020            0.019
Chain 1:   2100        -7726.505             0.022            0.019
Chain 1:   2200        -7975.275             0.021            0.019
Chain 1:   2300        -7760.884             0.023            0.021
Chain 1:   2400        -7767.058             0.018            0.019
Chain 1:   2500        -7682.541             0.017            0.017
Chain 1:   2600        -7683.446             0.013            0.014
Chain 1:   2700        -7664.276             0.012            0.011
Chain 1:   2800        -7676.469             0.011            0.011
Chain 1:   2900        -7581.864             0.010            0.011
Chain 1:   3000        -7703.296             0.012            0.012
Chain 1:   3100        -7683.702             0.011            0.011
Chain 1:   3200        -7891.485             0.010            0.011
Chain 1:   3300        -7581.234             0.011            0.011
Chain 1:   3400        -7801.428             0.014            0.012
Chain 1:   3500        -7591.695             0.016            0.016
Chain 1:   3600        -7653.690             0.017            0.016
Chain 1:   3700        -7605.778             0.017            0.016
Chain 1:   3800        -7578.838             0.017            0.016
Chain 1:   3900        -7553.294             0.016            0.016
Chain 1:   4000        -7548.527             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003418 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87197.932             1.000            1.000
Chain 1:    200       -14276.496             3.054            5.108
Chain 1:    300       -10503.823             2.156            1.000
Chain 1:    400       -11978.332             1.648            1.000
Chain 1:    500        -9410.612             1.373            0.359
Chain 1:    600        -8948.453             1.152            0.359
Chain 1:    700        -9192.497             0.992            0.273
Chain 1:    800        -9688.544             0.874            0.273
Chain 1:    900        -9123.770             0.784            0.123
Chain 1:   1000        -9219.139             0.706            0.123
Chain 1:   1100        -9318.583             0.608            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8782.595             0.103            0.061
Chain 1:   1300        -9125.730             0.071            0.052
Chain 1:   1400        -8872.475             0.061            0.051
Chain 1:   1500        -8971.527             0.035            0.038
Chain 1:   1600        -9080.533             0.031            0.029
Chain 1:   1700        -9129.229             0.029            0.029
Chain 1:   1800        -8673.435             0.029            0.029
Chain 1:   1900        -8784.127             0.024            0.013
Chain 1:   2000        -8793.286             0.023            0.013
Chain 1:   2100        -8732.709             0.023            0.013
Chain 1:   2200        -8714.284             0.017            0.012
Chain 1:   2300        -8893.713             0.015            0.012
Chain 1:   2400        -8682.079             0.015            0.012
Chain 1:   2500        -8753.322             0.015            0.012
Chain 1:   2600        -8668.590             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003519 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413991.602             1.000            1.000
Chain 1:    200     -1588555.891             2.648            4.297
Chain 1:    300      -890958.125             2.027            1.000
Chain 1:    400      -457909.989             1.756            1.000
Chain 1:    500      -358197.731             1.461            0.946
Chain 1:    600      -233099.306             1.307            0.946
Chain 1:    700      -119693.551             1.255            0.946
Chain 1:    800       -86983.662             1.145            0.946
Chain 1:    900       -67402.325             1.050            0.783
Chain 1:   1000       -52271.759             0.974            0.783
Chain 1:   1100       -39803.642             0.906            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38994.311             0.478            0.376
Chain 1:   1300       -26993.504             0.444            0.376
Chain 1:   1400       -26719.982             0.351            0.313
Chain 1:   1500       -23317.515             0.337            0.313
Chain 1:   1600       -22538.193             0.287            0.291
Chain 1:   1700       -21416.296             0.198            0.289
Chain 1:   1800       -21362.015             0.160            0.146
Chain 1:   1900       -21688.991             0.133            0.052
Chain 1:   2000       -20200.843             0.111            0.052
Chain 1:   2100       -20439.337             0.081            0.035
Chain 1:   2200       -20665.904             0.080            0.035
Chain 1:   2300       -20282.763             0.038            0.019
Chain 1:   2400       -20054.610             0.038            0.019
Chain 1:   2500       -19856.358             0.024            0.015
Chain 1:   2600       -19486.101             0.023            0.015
Chain 1:   2700       -19442.912             0.018            0.012
Chain 1:   2800       -19159.299             0.019            0.015
Chain 1:   2900       -19440.851             0.019            0.014
Chain 1:   3000       -19427.039             0.011            0.012
Chain 1:   3100       -19512.142             0.011            0.011
Chain 1:   3200       -19202.403             0.011            0.014
Chain 1:   3300       -19407.453             0.010            0.011
Chain 1:   3400       -18881.480             0.012            0.014
Chain 1:   3500       -19494.647             0.014            0.015
Chain 1:   3600       -18799.597             0.016            0.015
Chain 1:   3700       -19187.648             0.018            0.016
Chain 1:   3800       -18144.644             0.022            0.020
Chain 1:   3900       -18140.665             0.021            0.020
Chain 1:   4000       -18258.020             0.021            0.020
Chain 1:   4100       -18171.644             0.021            0.020
Chain 1:   4200       -17987.273             0.021            0.020
Chain 1:   4300       -18126.146             0.020            0.020
Chain 1:   4400       -18082.489             0.018            0.010
Chain 1:   4500       -17984.874             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001312 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48900.071             1.000            1.000
Chain 1:    200       -20845.615             1.173            1.346
Chain 1:    300       -19412.835             0.807            1.000
Chain 1:    400       -12949.649             0.730            1.000
Chain 1:    500       -15195.677             0.613            0.499
Chain 1:    600       -13152.602             0.537            0.499
Chain 1:    700       -12323.560             0.470            0.155
Chain 1:    800       -14084.099             0.427            0.155
Chain 1:    900       -10701.725             0.414            0.155
Chain 1:   1000       -13428.652             0.393            0.203
Chain 1:   1100       -23297.801             0.336            0.203
Chain 1:   1200       -11624.311             0.302            0.203
Chain 1:   1300       -10268.717             0.307            0.203
Chain 1:   1400       -20684.283             0.308            0.203
Chain 1:   1500       -10270.135             0.394            0.316
Chain 1:   1600        -9895.385             0.383            0.316
Chain 1:   1700       -10387.802             0.381            0.316
Chain 1:   1800       -23983.037             0.425            0.424
Chain 1:   1900       -10721.721             0.517            0.504   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000        -9952.586             0.504            0.504   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100        -9667.191             0.465            0.504   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200       -11816.955             0.383            0.182
Chain 1:   2300        -9918.970             0.389            0.191
Chain 1:   2400        -8927.597             0.349            0.182
Chain 1:   2500       -15732.722             0.291            0.182
Chain 1:   2600       -16503.661             0.292            0.182
Chain 1:   2700        -9453.214             0.362            0.191
Chain 1:   2800       -16507.558             0.348            0.191
Chain 1:   2900        -9321.724             0.301            0.191
Chain 1:   3000       -11400.872             0.312            0.191
Chain 1:   3100       -15666.913             0.336            0.272
Chain 1:   3200        -9157.292             0.389            0.427
Chain 1:   3300        -9255.456             0.371            0.427
Chain 1:   3400       -13626.271             0.392            0.427
Chain 1:   3500        -9187.117             0.397            0.427
Chain 1:   3600        -8990.268             0.395            0.427
Chain 1:   3700        -9145.478             0.322            0.321
Chain 1:   3800        -9677.725             0.284            0.272
Chain 1:   3900       -15032.656             0.243            0.272
Chain 1:   4000        -9497.894             0.283            0.321
Chain 1:   4100        -8866.940             0.263            0.321
Chain 1:   4200       -12902.399             0.223            0.313
Chain 1:   4300       -11842.922             0.231            0.313
Chain 1:   4400       -14446.590             0.217            0.180
Chain 1:   4500        -8940.152             0.230            0.180
Chain 1:   4600       -13224.126             0.260            0.313
Chain 1:   4700       -14381.224             0.267            0.313
Chain 1:   4800        -8595.480             0.329            0.324
Chain 1:   4900       -10590.105             0.312            0.313
Chain 1:   5000        -9825.769             0.261            0.188
Chain 1:   5100       -11900.012             0.272            0.188
Chain 1:   5200       -10610.110             0.253            0.180
Chain 1:   5300       -12276.357             0.257            0.180
Chain 1:   5400        -9975.448             0.262            0.188
Chain 1:   5500        -8800.925             0.214            0.174
Chain 1:   5600        -8422.997             0.186            0.136
Chain 1:   5700        -8887.889             0.183            0.136
Chain 1:   5800        -8610.093             0.119            0.133
Chain 1:   5900       -10519.795             0.118            0.133
Chain 1:   6000        -9279.406             0.124            0.134
Chain 1:   6100       -10494.639             0.118            0.133
Chain 1:   6200       -11413.799             0.114            0.133
Chain 1:   6300       -11524.867             0.101            0.116
Chain 1:   6400       -10735.910             0.086            0.081
Chain 1:   6500       -10371.052             0.076            0.073
Chain 1:   6600        -9591.721             0.080            0.081
Chain 1:   6700        -8963.983             0.081            0.081
Chain 1:   6800        -8542.903             0.083            0.081
Chain 1:   6900        -8696.641             0.067            0.073
Chain 1:   7000        -8516.391             0.055            0.070
Chain 1:   7100        -8504.228             0.044            0.049
Chain 1:   7200        -8241.397             0.039            0.035
Chain 1:   7300        -8272.604             0.039            0.035
Chain 1:   7400        -8400.547             0.033            0.032
Chain 1:   7500        -9448.765             0.040            0.032
Chain 1:   7600        -9612.991             0.034            0.021
Chain 1:   7700        -8476.201             0.040            0.021
Chain 1:   7800       -11826.120             0.064            0.021
Chain 1:   7900        -8391.891             0.103            0.032
Chain 1:   8000       -12862.509             0.135            0.111
Chain 1:   8100       -11862.161             0.144            0.111
Chain 1:   8200        -9351.463             0.167            0.134
Chain 1:   8300        -9176.966             0.169            0.134
Chain 1:   8400        -9230.700             0.168            0.134
Chain 1:   8500        -8870.815             0.161            0.134
Chain 1:   8600        -8792.554             0.160            0.134
Chain 1:   8700       -10189.548             0.160            0.137
Chain 1:   8800        -8747.631             0.149            0.137
Chain 1:   8900        -9303.126             0.114            0.084
Chain 1:   9000        -8246.658             0.092            0.084
Chain 1:   9100        -8592.436             0.087            0.060
Chain 1:   9200        -9722.293             0.072            0.060
Chain 1:   9300        -8155.604             0.089            0.116
Chain 1:   9400        -8792.959             0.096            0.116
Chain 1:   9500        -8696.235             0.093            0.116
Chain 1:   9600        -8359.235             0.096            0.116
Chain 1:   9700        -8422.773             0.083            0.072
Chain 1:   9800       -11297.341             0.092            0.072
Chain 1:   9900       -11337.813             0.087            0.072
Chain 1:   10000        -8317.428             0.110            0.072
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58001.095             1.000            1.000
Chain 1:    200       -17736.949             1.635            2.270
Chain 1:    300        -8694.672             1.437            1.040
Chain 1:    400        -8215.994             1.092            1.040
Chain 1:    500        -8098.782             0.877            1.000
Chain 1:    600        -8659.451             0.741            1.000
Chain 1:    700        -8245.900             0.643            0.065
Chain 1:    800        -8268.727             0.563            0.065
Chain 1:    900        -8001.988             0.504            0.058
Chain 1:   1000        -7810.167             0.456            0.058
Chain 1:   1100        -7712.109             0.357            0.050
Chain 1:   1200        -7549.386             0.132            0.033
Chain 1:   1300        -7746.359             0.031            0.025
Chain 1:   1400        -7896.547             0.027            0.025
Chain 1:   1500        -7590.766             0.029            0.025
Chain 1:   1600        -7777.264             0.025            0.025
Chain 1:   1700        -7565.643             0.023            0.025
Chain 1:   1800        -7576.398             0.023            0.025
Chain 1:   1900        -7598.964             0.020            0.024
Chain 1:   2000        -7611.364             0.018            0.022
Chain 1:   2100        -7594.914             0.017            0.022
Chain 1:   2200        -7701.672             0.016            0.019
Chain 1:   2300        -7577.334             0.015            0.016
Chain 1:   2400        -7591.118             0.013            0.014
Chain 1:   2500        -7657.957             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86998.860             1.000            1.000
Chain 1:    200       -13555.335             3.209            5.418
Chain 1:    300        -9940.338             2.261            1.000
Chain 1:    400       -10902.250             1.717            1.000
Chain 1:    500        -8803.177             1.422            0.364
Chain 1:    600        -8440.047             1.192            0.364
Chain 1:    700        -8492.905             1.023            0.238
Chain 1:    800        -8900.110             0.900            0.238
Chain 1:    900        -8712.532             0.803            0.088
Chain 1:   1000        -8454.430             0.726            0.088
Chain 1:   1100        -8825.348             0.630            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8421.369             0.093            0.046
Chain 1:   1300        -8638.896             0.059            0.043
Chain 1:   1400        -8678.302             0.051            0.042
Chain 1:   1500        -8511.753             0.029            0.031
Chain 1:   1600        -8632.598             0.026            0.025
Chain 1:   1700        -8718.194             0.026            0.025
Chain 1:   1800        -8311.310             0.026            0.025
Chain 1:   1900        -8407.597             0.025            0.025
Chain 1:   2000        -8379.960             0.023            0.020
Chain 1:   2100        -8500.636             0.020            0.014
Chain 1:   2200        -8315.920             0.017            0.014
Chain 1:   2300        -8447.566             0.016            0.014
Chain 1:   2400        -8457.075             0.016            0.014
Chain 1:   2500        -8420.394             0.014            0.014
Chain 1:   2600        -8418.697             0.013            0.011
Chain 1:   2700        -8333.258             0.013            0.011
Chain 1:   2800        -8298.168             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8428255.347             1.000            1.000
Chain 1:    200     -1589974.427             2.650            4.301
Chain 1:    300      -891336.624             2.028            1.000
Chain 1:    400      -457630.004             1.758            1.000
Chain 1:    500      -357545.376             1.462            0.948
Chain 1:    600      -232436.880             1.308            0.948
Chain 1:    700      -118975.818             1.258            0.948
Chain 1:    800       -86229.801             1.148            0.948
Chain 1:    900       -66639.784             1.053            0.784
Chain 1:   1000       -51484.797             0.977            0.784
Chain 1:   1100       -39009.806             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38192.708             0.481            0.380
Chain 1:   1300       -26204.985             0.449            0.380
Chain 1:   1400       -25927.966             0.355            0.320
Chain 1:   1500       -22529.736             0.342            0.320
Chain 1:   1600       -21750.259             0.292            0.294
Chain 1:   1700       -20631.119             0.202            0.294
Chain 1:   1800       -20576.821             0.164            0.151
Chain 1:   1900       -20902.878             0.136            0.054
Chain 1:   2000       -19418.069             0.114            0.054
Chain 1:   2100       -19656.205             0.084            0.036
Chain 1:   2200       -19881.889             0.083            0.036
Chain 1:   2300       -19499.825             0.039            0.020
Chain 1:   2400       -19272.073             0.039            0.020
Chain 1:   2500       -19073.741             0.025            0.016
Chain 1:   2600       -18704.332             0.023            0.016
Chain 1:   2700       -18661.527             0.018            0.012
Chain 1:   2800       -18378.253             0.019            0.015
Chain 1:   2900       -18659.437             0.019            0.015
Chain 1:   3000       -18645.673             0.012            0.012
Chain 1:   3100       -18730.571             0.011            0.012
Chain 1:   3200       -18421.451             0.012            0.015
Chain 1:   3300       -18626.091             0.011            0.012
Chain 1:   3400       -18101.135             0.012            0.015
Chain 1:   3500       -18712.625             0.015            0.015
Chain 1:   3600       -18019.916             0.017            0.015
Chain 1:   3700       -18406.179             0.018            0.017
Chain 1:   3800       -17366.622             0.023            0.021
Chain 1:   3900       -17362.775             0.021            0.021
Chain 1:   4000       -17480.129             0.022            0.021
Chain 1:   4100       -17393.822             0.022            0.021
Chain 1:   4200       -17210.317             0.021            0.021
Chain 1:   4300       -17348.584             0.021            0.021
Chain 1:   4400       -17305.567             0.018            0.011
Chain 1:   4500       -17208.097             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001594 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13059.163             1.000            1.000
Chain 1:    200        -9962.684             0.655            1.000
Chain 1:    300        -8484.623             0.495            0.311
Chain 1:    400        -8727.713             0.378            0.311
Chain 1:    500        -8626.956             0.305            0.174
Chain 1:    600        -8458.246             0.257            0.174
Chain 1:    700        -8359.823             0.222            0.028
Chain 1:    800        -8329.902             0.195            0.028
Chain 1:    900        -8366.632             0.174            0.020
Chain 1:   1000        -8443.875             0.157            0.020
Chain 1:   1100        -8468.915             0.058            0.012
Chain 1:   1200        -8380.624             0.028            0.012
Chain 1:   1300        -8322.290             0.011            0.011
Chain 1:   1400        -8340.728             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001405 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63008.164             1.000            1.000
Chain 1:    200       -18513.091             1.702            2.403
Chain 1:    300        -9239.733             1.469            1.004
Chain 1:    400        -8414.207             1.126            1.004
Chain 1:    500        -8481.639             0.903            1.000
Chain 1:    600        -9016.163             0.762            1.000
Chain 1:    700        -8783.915             0.657            0.098
Chain 1:    800        -8285.517             0.582            0.098
Chain 1:    900        -8168.079             0.519            0.060
Chain 1:   1000        -7884.471             0.471            0.060
Chain 1:   1100        -7655.910             0.374            0.059
Chain 1:   1200        -7701.818             0.134            0.036
Chain 1:   1300        -8134.797             0.039            0.036
Chain 1:   1400        -7810.898             0.033            0.036
Chain 1:   1500        -7643.022             0.035            0.036
Chain 1:   1600        -7937.420             0.033            0.036
Chain 1:   1700        -7556.220             0.035            0.037
Chain 1:   1800        -7734.236             0.031            0.036
Chain 1:   1900        -7826.725             0.031            0.036
Chain 1:   2000        -7720.517             0.029            0.030
Chain 1:   2100        -7612.531             0.027            0.023
Chain 1:   2200        -7959.492             0.031            0.037
Chain 1:   2300        -7656.388             0.030            0.037
Chain 1:   2400        -7819.028             0.028            0.023
Chain 1:   2500        -7616.126             0.028            0.027
Chain 1:   2600        -7600.596             0.025            0.023
Chain 1:   2700        -7568.566             0.020            0.021
Chain 1:   2800        -7739.748             0.020            0.021
Chain 1:   2900        -7444.911             0.023            0.022
Chain 1:   3000        -7598.161             0.023            0.022
Chain 1:   3100        -7597.323             0.022            0.022
Chain 1:   3200        -7798.127             0.020            0.022
Chain 1:   3300        -7536.253             0.020            0.022
Chain 1:   3400        -7759.149             0.020            0.026
Chain 1:   3500        -7508.717             0.021            0.026
Chain 1:   3600        -7574.270             0.022            0.026
Chain 1:   3700        -7533.957             0.022            0.026
Chain 1:   3800        -7537.608             0.020            0.026
Chain 1:   3900        -7481.424             0.016            0.020
Chain 1:   4000        -7469.168             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003124 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87055.893             1.000            1.000
Chain 1:    200       -14212.344             3.063            5.125
Chain 1:    300       -10487.757             2.160            1.000
Chain 1:    400       -11806.813             1.648            1.000
Chain 1:    500        -9432.911             1.369            0.355
Chain 1:    600        -9607.811             1.144            0.355
Chain 1:    700        -9135.149             0.988            0.252
Chain 1:    800        -8793.462             0.869            0.252
Chain 1:    900        -8835.475             0.773            0.112
Chain 1:   1000        -9116.441             0.699            0.112
Chain 1:   1100        -9222.951             0.600            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8803.296             0.092            0.048
Chain 1:   1300        -9160.562             0.061            0.039
Chain 1:   1400        -9120.199             0.050            0.039
Chain 1:   1500        -8986.736             0.026            0.031
Chain 1:   1600        -9086.913             0.025            0.031
Chain 1:   1700        -9146.412             0.021            0.015
Chain 1:   1800        -8706.304             0.022            0.015
Chain 1:   1900        -8811.007             0.023            0.015
Chain 1:   2000        -8794.277             0.020            0.012
Chain 1:   2100        -8920.607             0.020            0.014
Chain 1:   2200        -8709.655             0.018            0.014
Chain 1:   2300        -8804.183             0.015            0.012
Chain 1:   2400        -8871.453             0.015            0.012
Chain 1:   2500        -8819.551             0.014            0.011
Chain 1:   2600        -8831.936             0.013            0.011
Chain 1:   2700        -8740.226             0.014            0.011
Chain 1:   2800        -8688.559             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003237 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396608.703             1.000            1.000
Chain 1:    200     -1579481.513             2.658            4.316
Chain 1:    300      -889046.647             2.031            1.000
Chain 1:    400      -457037.388             1.759            1.000
Chain 1:    500      -357920.855             1.463            0.945
Chain 1:    600      -233219.854             1.308            0.945
Chain 1:    700      -119761.476             1.257            0.945
Chain 1:    800       -87051.703             1.147            0.945
Chain 1:    900       -67442.609             1.051            0.777
Chain 1:   1000       -52274.442             0.975            0.777
Chain 1:   1100       -39776.579             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38962.395             0.477            0.376
Chain 1:   1300       -26927.358             0.444            0.376
Chain 1:   1400       -26650.499             0.351            0.314
Chain 1:   1500       -23239.620             0.338            0.314
Chain 1:   1600       -22457.955             0.288            0.291
Chain 1:   1700       -21331.779             0.198            0.290
Chain 1:   1800       -21276.537             0.161            0.147
Chain 1:   1900       -21603.290             0.133            0.053
Chain 1:   2000       -20113.642             0.112            0.053
Chain 1:   2100       -20352.028             0.082            0.035
Chain 1:   2200       -20578.877             0.081            0.035
Chain 1:   2300       -20195.593             0.038            0.019
Chain 1:   2400       -19967.514             0.038            0.019
Chain 1:   2500       -19769.514             0.024            0.015
Chain 1:   2600       -19399.151             0.023            0.015
Chain 1:   2700       -19356.016             0.018            0.012
Chain 1:   2800       -19072.653             0.019            0.015
Chain 1:   2900       -19354.119             0.019            0.015
Chain 1:   3000       -19340.254             0.011            0.012
Chain 1:   3100       -19425.317             0.011            0.011
Chain 1:   3200       -19115.654             0.011            0.015
Chain 1:   3300       -19320.679             0.010            0.011
Chain 1:   3400       -18794.972             0.012            0.015
Chain 1:   3500       -19407.783             0.014            0.015
Chain 1:   3600       -18713.239             0.016            0.015
Chain 1:   3700       -19100.955             0.018            0.016
Chain 1:   3800       -18058.751             0.022            0.020
Chain 1:   3900       -18054.856             0.021            0.020
Chain 1:   4000       -18172.148             0.021            0.020
Chain 1:   4100       -18085.819             0.021            0.020
Chain 1:   4200       -17901.676             0.021            0.020
Chain 1:   4300       -18040.353             0.020            0.020
Chain 1:   4400       -17996.836             0.018            0.010
Chain 1:   4500       -17899.315             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001285 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12027.529             1.000            1.000
Chain 1:    200        -8940.955             0.673            1.000
Chain 1:    300        -7912.795             0.492            0.345
Chain 1:    400        -8013.470             0.372            0.345
Chain 1:    500        -7867.158             0.301            0.130
Chain 1:    600        -7735.730             0.254            0.130
Chain 1:    700        -7673.512             0.219            0.019
Chain 1:    800        -7683.665             0.192            0.019
Chain 1:    900        -7694.916             0.170            0.017
Chain 1:   1000        -7730.496             0.154            0.017
Chain 1:   1100        -7789.237             0.055            0.013
Chain 1:   1200        -7683.653             0.021            0.013
Chain 1:   1300        -7704.956             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45599.680             1.000            1.000
Chain 1:    200       -15027.498             1.517            2.034
Chain 1:    300        -8492.657             1.268            1.000
Chain 1:    400        -8337.255             0.956            1.000
Chain 1:    500        -8158.333             0.769            0.769
Chain 1:    600        -7769.983             0.649            0.769
Chain 1:    700        -7775.677             0.556            0.050
Chain 1:    800        -8039.987             0.491            0.050
Chain 1:    900        -7859.347             0.439            0.033
Chain 1:   1000        -7720.547             0.397            0.033
Chain 1:   1100        -7720.034             0.297            0.023
Chain 1:   1200        -7583.415             0.095            0.022
Chain 1:   1300        -7607.559             0.019            0.019
Chain 1:   1400        -7890.931             0.020            0.022
Chain 1:   1500        -7609.493             0.022            0.023
Chain 1:   1600        -7512.985             0.018            0.018
Chain 1:   1700        -7515.771             0.018            0.018
Chain 1:   1800        -7532.399             0.015            0.018
Chain 1:   1900        -7524.947             0.013            0.013
Chain 1:   2000        -7572.810             0.012            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003082 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86490.455             1.000            1.000
Chain 1:    200       -13057.161             3.312            5.624
Chain 1:    300        -9526.083             2.332            1.000
Chain 1:    400       -10331.663             1.768            1.000
Chain 1:    500        -8425.621             1.460            0.371
Chain 1:    600        -8218.376             1.221            0.371
Chain 1:    700        -8331.903             1.048            0.226
Chain 1:    800        -8872.822             0.925            0.226
Chain 1:    900        -8385.120             0.829            0.078
Chain 1:   1000        -8156.304             0.748            0.078
Chain 1:   1100        -8440.840             0.652            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8181.630             0.093            0.058
Chain 1:   1300        -8286.064             0.057            0.034
Chain 1:   1400        -8274.305             0.049            0.032
Chain 1:   1500        -8171.977             0.028            0.028
Chain 1:   1600        -8268.103             0.026            0.028
Chain 1:   1700        -8367.887             0.026            0.028
Chain 1:   1800        -7977.986             0.025            0.028
Chain 1:   1900        -8079.039             0.020            0.013
Chain 1:   2000        -8049.066             0.018            0.013
Chain 1:   2100        -8188.060             0.016            0.013
Chain 1:   2200        -7969.408             0.016            0.013
Chain 1:   2300        -8111.664             0.016            0.013
Chain 1:   2400        -7996.639             0.018            0.014
Chain 1:   2500        -8055.591             0.017            0.014
Chain 1:   2600        -8069.925             0.016            0.014
Chain 1:   2700        -7992.223             0.016            0.014
Chain 1:   2800        -7973.640             0.011            0.013
Chain 1:   2900        -7985.012             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003156 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417319.228             1.000            1.000
Chain 1:    200     -1589092.083             2.648            4.297
Chain 1:    300      -890954.762             2.027            1.000
Chain 1:    400      -456982.591             1.758            1.000
Chain 1:    500      -356837.617             1.462            0.950
Chain 1:    600      -231716.976             1.308            0.950
Chain 1:    700      -118317.603             1.258            0.950
Chain 1:    800       -85632.132             1.149            0.950
Chain 1:    900       -66052.153             1.054            0.784
Chain 1:   1000       -50910.966             0.978            0.784
Chain 1:   1100       -38451.816             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37630.311             0.483            0.382
Chain 1:   1300       -25663.026             0.452            0.382
Chain 1:   1400       -25386.271             0.358            0.324
Chain 1:   1500       -21993.992             0.345            0.324
Chain 1:   1600       -21215.518             0.295            0.297
Chain 1:   1700       -20099.163             0.205            0.296
Chain 1:   1800       -20045.206             0.167            0.154
Chain 1:   1900       -20370.696             0.139            0.056
Chain 1:   2000       -18888.144             0.117            0.056
Chain 1:   2100       -19126.210             0.086            0.037
Chain 1:   2200       -19351.414             0.084            0.037
Chain 1:   2300       -18969.869             0.040            0.020
Chain 1:   2400       -18742.280             0.040            0.020
Chain 1:   2500       -18543.981             0.026            0.016
Chain 1:   2600       -18175.224             0.024            0.016
Chain 1:   2700       -18132.486             0.019            0.012
Chain 1:   2800       -17849.532             0.020            0.016
Chain 1:   2900       -18130.322             0.020            0.015
Chain 1:   3000       -18116.648             0.012            0.012
Chain 1:   3100       -18201.532             0.011            0.012
Chain 1:   3200       -17892.739             0.012            0.015
Chain 1:   3300       -18097.029             0.011            0.012
Chain 1:   3400       -17572.808             0.013            0.015
Chain 1:   3500       -18183.313             0.015            0.016
Chain 1:   3600       -17491.717             0.017            0.016
Chain 1:   3700       -17877.189             0.019            0.017
Chain 1:   3800       -16839.535             0.024            0.022
Chain 1:   3900       -16835.683             0.022            0.022
Chain 1:   4000       -16953.034             0.023            0.022
Chain 1:   4100       -16866.929             0.023            0.022
Chain 1:   4200       -16683.729             0.022            0.022
Chain 1:   4300       -16821.764             0.022            0.022
Chain 1:   4400       -16779.056             0.019            0.011
Chain 1:   4500       -16681.623             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12356.012             1.000            1.000
Chain 1:    200        -9156.784             0.675            1.000
Chain 1:    300        -8083.809             0.494            0.349
Chain 1:    400        -8205.995             0.374            0.349
Chain 1:    500        -8138.946             0.301            0.133
Chain 1:    600        -7989.249             0.254            0.133
Chain 1:    700        -7907.826             0.219            0.019
Chain 1:    800        -7915.663             0.192            0.019
Chain 1:    900        -7839.565             0.172            0.015
Chain 1:   1000        -7961.422             0.156            0.015
Chain 1:   1100        -7937.103             0.056            0.015
Chain 1:   1200        -7927.214             0.022            0.010
Chain 1:   1300        -7866.305             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57316.926             1.000            1.000
Chain 1:    200       -17521.260             1.636            2.271
Chain 1:    300        -8763.110             1.424            1.000
Chain 1:    400        -8289.180             1.082            1.000
Chain 1:    500        -8403.303             0.868            0.999
Chain 1:    600        -8569.947             0.727            0.999
Chain 1:    700        -8168.742             0.630            0.057
Chain 1:    800        -8191.558             0.552            0.057
Chain 1:    900        -7818.794             0.496            0.049
Chain 1:   1000        -7710.794             0.447            0.049
Chain 1:   1100        -7659.559             0.348            0.048
Chain 1:   1200        -7613.523             0.122            0.019
Chain 1:   1300        -7787.379             0.024            0.019
Chain 1:   1400        -7832.775             0.019            0.014
Chain 1:   1500        -7622.186             0.020            0.019
Chain 1:   1600        -7580.826             0.019            0.014
Chain 1:   1700        -7546.616             0.014            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003991 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85983.759             1.000            1.000
Chain 1:    200       -13539.950             3.175            5.350
Chain 1:    300        -9911.566             2.239            1.000
Chain 1:    400       -10874.523             1.701            1.000
Chain 1:    500        -8737.606             1.410            0.366
Chain 1:    600        -8559.178             1.178            0.366
Chain 1:    700        -8601.895             1.011            0.245
Chain 1:    800        -8822.361             0.888            0.245
Chain 1:    900        -8858.811             0.789            0.089
Chain 1:   1000        -8603.593             0.713            0.089
Chain 1:   1100        -8767.838             0.615            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8369.643             0.085            0.030
Chain 1:   1300        -8594.370             0.051            0.026
Chain 1:   1400        -8608.384             0.042            0.025
Chain 1:   1500        -8462.545             0.020            0.021
Chain 1:   1600        -8575.599             0.019            0.019
Chain 1:   1700        -8655.999             0.019            0.019
Chain 1:   1800        -8238.619             0.022            0.019
Chain 1:   1900        -8336.649             0.023            0.019
Chain 1:   2000        -8310.462             0.020            0.017
Chain 1:   2100        -8434.224             0.020            0.015
Chain 1:   2200        -8249.661             0.017            0.015
Chain 1:   2300        -8331.232             0.015            0.013
Chain 1:   2400        -8400.825             0.016            0.013
Chain 1:   2500        -8346.685             0.015            0.012
Chain 1:   2600        -8346.867             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401234.600             1.000            1.000
Chain 1:    200     -1582680.536             2.654            4.308
Chain 1:    300      -890565.830             2.028            1.000
Chain 1:    400      -458008.040             1.757            1.000
Chain 1:    500      -358659.188             1.461            0.944
Chain 1:    600      -233405.303             1.307            0.944
Chain 1:    700      -119422.895             1.257            0.944
Chain 1:    800       -86650.558             1.147            0.944
Chain 1:    900       -66940.173             1.052            0.777
Chain 1:   1000       -51709.251             0.977            0.777
Chain 1:   1100       -39168.113             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38337.685             0.480            0.378
Chain 1:   1300       -26265.682             0.448            0.378
Chain 1:   1400       -25982.543             0.355            0.320
Chain 1:   1500       -22563.885             0.342            0.320
Chain 1:   1600       -21779.302             0.292            0.295
Chain 1:   1700       -20648.947             0.202            0.294
Chain 1:   1800       -20592.304             0.165            0.152
Chain 1:   1900       -20918.546             0.137            0.055
Chain 1:   2000       -19427.808             0.115            0.055
Chain 1:   2100       -19666.042             0.084            0.036
Chain 1:   2200       -19893.170             0.083            0.036
Chain 1:   2300       -19509.766             0.039            0.020
Chain 1:   2400       -19281.763             0.039            0.020
Chain 1:   2500       -19084.194             0.025            0.016
Chain 1:   2600       -18713.996             0.023            0.016
Chain 1:   2700       -18670.794             0.018            0.012
Chain 1:   2800       -18387.869             0.020            0.015
Chain 1:   2900       -18669.124             0.019            0.015
Chain 1:   3000       -18655.150             0.012            0.012
Chain 1:   3100       -18740.239             0.011            0.012
Chain 1:   3200       -18430.818             0.012            0.015
Chain 1:   3300       -18635.594             0.011            0.012
Chain 1:   3400       -18110.523             0.012            0.015
Chain 1:   3500       -18722.602             0.015            0.015
Chain 1:   3600       -18028.907             0.017            0.015
Chain 1:   3700       -18416.052             0.018            0.017
Chain 1:   3800       -17375.420             0.023            0.021
Chain 1:   3900       -17371.590             0.021            0.021
Chain 1:   4000       -17488.813             0.022            0.021
Chain 1:   4100       -17402.687             0.022            0.021
Chain 1:   4200       -17218.755             0.021            0.021
Chain 1:   4300       -17357.206             0.021            0.021
Chain 1:   4400       -17313.940             0.019            0.011
Chain 1:   4500       -17216.493             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49018.879             1.000            1.000
Chain 1:    200       -16517.376             1.484            1.968
Chain 1:    300       -19423.573             1.039            1.000
Chain 1:    400       -12198.915             0.927            1.000
Chain 1:    500       -38614.989             0.879            0.684
Chain 1:    600       -13802.724             1.032            1.000
Chain 1:    700       -16165.181             0.905            0.684
Chain 1:    800       -13128.368             0.821            0.684
Chain 1:    900       -12995.996             0.731            0.592
Chain 1:   1000       -14661.473             0.669            0.592
Chain 1:   1100       -14380.753             0.571            0.231   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10032.332             0.418            0.231
Chain 1:   1300        -9745.591             0.406            0.231
Chain 1:   1400       -12566.445             0.369            0.224
Chain 1:   1500       -10858.661             0.316            0.157
Chain 1:   1600       -11956.766             0.146            0.146
Chain 1:   1700       -10360.101             0.147            0.154
Chain 1:   1800        -9936.491             0.128            0.114
Chain 1:   1900       -12834.353             0.149            0.154
Chain 1:   2000       -10142.427             0.164            0.157
Chain 1:   2100       -11791.461             0.176            0.157
Chain 1:   2200       -10063.642             0.150            0.157
Chain 1:   2300        -9146.570             0.157            0.157
Chain 1:   2400        -9939.322             0.143            0.154
Chain 1:   2500        -8978.687             0.138            0.140
Chain 1:   2600        -9699.409             0.136            0.140
Chain 1:   2700       -10443.170             0.128            0.107
Chain 1:   2800       -10100.758             0.127            0.107
Chain 1:   2900       -15281.994             0.138            0.107
Chain 1:   3000        -8903.098             0.183            0.107
Chain 1:   3100       -10145.996             0.182            0.107
Chain 1:   3200       -14221.000             0.193            0.107
Chain 1:   3300        -9973.961             0.226            0.123
Chain 1:   3400       -13519.820             0.244            0.262
Chain 1:   3500       -12855.096             0.238            0.262
Chain 1:   3600        -9984.018             0.260            0.287
Chain 1:   3700       -11923.399             0.269            0.287
Chain 1:   3800        -8587.002             0.304            0.288
Chain 1:   3900        -9013.905             0.275            0.287
Chain 1:   4000       -12671.462             0.232            0.287
Chain 1:   4100        -9134.577             0.259            0.288
Chain 1:   4200        -9329.554             0.232            0.288
Chain 1:   4300        -9702.826             0.194            0.262
Chain 1:   4400        -8925.373             0.176            0.163
Chain 1:   4500        -8805.526             0.172            0.163
Chain 1:   4600       -12292.324             0.172            0.163
Chain 1:   4700       -12302.881             0.156            0.087
Chain 1:   4800        -8775.252             0.157            0.087
Chain 1:   4900        -9118.226             0.156            0.087
Chain 1:   5000       -12663.895             0.155            0.087
Chain 1:   5100        -8617.706             0.163            0.087
Chain 1:   5200        -8603.546             0.161            0.087
Chain 1:   5300        -9201.160             0.164            0.087
Chain 1:   5400        -9056.007             0.157            0.065
Chain 1:   5500        -8645.876             0.160            0.065
Chain 1:   5600        -8860.276             0.134            0.047
Chain 1:   5700        -8912.716             0.135            0.047
Chain 1:   5800        -8677.236             0.097            0.038
Chain 1:   5900       -10785.370             0.113            0.047
Chain 1:   6000        -8550.934             0.111            0.047
Chain 1:   6100        -8744.970             0.067            0.027
Chain 1:   6200        -8739.465             0.067            0.027
Chain 1:   6300       -12721.926             0.091            0.027
Chain 1:   6400       -14898.481             0.104            0.047
Chain 1:   6500        -9715.127             0.153            0.146
Chain 1:   6600        -9443.221             0.153            0.146
Chain 1:   6700        -9376.460             0.154            0.146
Chain 1:   6800        -8399.460             0.162            0.146
Chain 1:   6900        -8411.272             0.143            0.116
Chain 1:   7000       -16966.763             0.167            0.116
Chain 1:   7100       -11252.886             0.216            0.146
Chain 1:   7200       -11474.646             0.218            0.146
Chain 1:   7300        -9958.486             0.202            0.146
Chain 1:   7400       -11047.338             0.197            0.116
Chain 1:   7500       -10907.573             0.145            0.099
Chain 1:   7600        -8582.997             0.169            0.116
Chain 1:   7700        -9991.214             0.182            0.141
Chain 1:   7800        -9484.870             0.176            0.141
Chain 1:   7900        -8345.182             0.190            0.141
Chain 1:   8000        -8268.509             0.140            0.137
Chain 1:   8100        -8643.555             0.094            0.099
Chain 1:   8200        -8985.250             0.096            0.099
Chain 1:   8300        -9442.222             0.085            0.053
Chain 1:   8400        -9660.766             0.078            0.048
Chain 1:   8500        -8326.496             0.092            0.053
Chain 1:   8600        -9444.151             0.077            0.053
Chain 1:   8700       -11397.983             0.080            0.053
Chain 1:   8800        -8270.614             0.113            0.118
Chain 1:   8900        -8674.713             0.104            0.048
Chain 1:   9000        -8563.112             0.104            0.048
Chain 1:   9100        -8559.633             0.100            0.048
Chain 1:   9200        -8407.805             0.098            0.048
Chain 1:   9300        -8255.781             0.095            0.047
Chain 1:   9400        -8144.244             0.094            0.047
Chain 1:   9500        -8271.787             0.079            0.018
Chain 1:   9600        -8426.895             0.069            0.018
Chain 1:   9700        -8123.438             0.056            0.018
Chain 1:   9800        -8577.483             0.023            0.018
Chain 1:   9900        -9183.022             0.025            0.018
Chain 1:   10000        -9357.117             0.026            0.018
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57078.889             1.000            1.000
Chain 1:    200       -17541.099             1.627            2.254
Chain 1:    300        -8819.981             1.414            1.000
Chain 1:    400        -8452.502             1.072            1.000
Chain 1:    500        -8312.299             0.861            0.989
Chain 1:    600        -8739.319             0.725            0.989
Chain 1:    700        -8459.726             0.626            0.049
Chain 1:    800        -8217.355             0.552            0.049
Chain 1:    900        -7993.446             0.494            0.043
Chain 1:   1000        -7859.923             0.446            0.043
Chain 1:   1100        -7867.789             0.346            0.033
Chain 1:   1200        -7768.228             0.122            0.029
Chain 1:   1300        -7812.476             0.024            0.028
Chain 1:   1400        -7741.487             0.020            0.017
Chain 1:   1500        -7653.281             0.020            0.017
Chain 1:   1600        -7887.400             0.018            0.017
Chain 1:   1700        -7587.195             0.018            0.017
Chain 1:   1800        -7664.116             0.016            0.013
Chain 1:   1900        -7660.046             0.014            0.012
Chain 1:   2000        -7708.942             0.013            0.010
Chain 1:   2100        -7671.321             0.013            0.010
Chain 1:   2200        -7779.807             0.013            0.010
Chain 1:   2300        -7654.787             0.014            0.012
Chain 1:   2400        -7720.491             0.014            0.012
Chain 1:   2500        -7558.313             0.015            0.014
Chain 1:   2600        -7583.350             0.012            0.010
Chain 1:   2700        -7643.523             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86456.242             1.000            1.000
Chain 1:    200       -13573.859             3.185            5.369
Chain 1:    300        -9906.914             2.246            1.000
Chain 1:    400       -10902.413             1.708            1.000
Chain 1:    500        -8892.644             1.411            0.370
Chain 1:    600        -8357.617             1.187            0.370
Chain 1:    700        -8324.016             1.018            0.226
Chain 1:    800        -9037.513             0.900            0.226
Chain 1:    900        -8673.837             0.805            0.091
Chain 1:   1000        -8668.326             0.725            0.091
Chain 1:   1100        -8573.458             0.626            0.079   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8340.148             0.092            0.064
Chain 1:   1300        -8586.905             0.057            0.042
Chain 1:   1400        -8558.467             0.049            0.029
Chain 1:   1500        -8456.655             0.027            0.028
Chain 1:   1600        -8562.694             0.022            0.012
Chain 1:   1700        -8644.622             0.023            0.012
Chain 1:   1800        -8219.217             0.020            0.012
Chain 1:   1900        -8321.317             0.017            0.012
Chain 1:   2000        -8295.874             0.017            0.012
Chain 1:   2100        -8422.029             0.018            0.012
Chain 1:   2200        -8222.959             0.017            0.012
Chain 1:   2300        -8316.207             0.015            0.012
Chain 1:   2400        -8384.667             0.016            0.012
Chain 1:   2500        -8330.911             0.015            0.012
Chain 1:   2600        -8332.723             0.014            0.011
Chain 1:   2700        -8249.252             0.014            0.011
Chain 1:   2800        -8208.523             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400219.680             1.000            1.000
Chain 1:    200     -1582825.722             2.654            4.307
Chain 1:    300      -891044.830             2.028            1.000
Chain 1:    400      -457595.007             1.758            1.000
Chain 1:    500      -358100.671             1.462            0.947
Chain 1:    600      -233245.270             1.307            0.947
Chain 1:    700      -119464.151             1.257            0.947
Chain 1:    800       -86616.339             1.147            0.947
Chain 1:    900       -66946.223             1.052            0.776
Chain 1:   1000       -51723.189             0.976            0.776
Chain 1:   1100       -39178.062             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38354.633             0.480            0.379
Chain 1:   1300       -26291.477             0.448            0.379
Chain 1:   1400       -26008.824             0.354            0.320
Chain 1:   1500       -22589.935             0.342            0.320
Chain 1:   1600       -21804.686             0.292            0.294
Chain 1:   1700       -20676.426             0.202            0.294
Chain 1:   1800       -20620.123             0.164            0.151
Chain 1:   1900       -20946.410             0.137            0.055
Chain 1:   2000       -19455.993             0.115            0.055
Chain 1:   2100       -19694.617             0.084            0.036
Chain 1:   2200       -19921.157             0.083            0.036
Chain 1:   2300       -19538.235             0.039            0.020
Chain 1:   2400       -19310.277             0.039            0.020
Chain 1:   2500       -19112.180             0.025            0.016
Chain 1:   2600       -18742.329             0.023            0.016
Chain 1:   2700       -18699.295             0.018            0.012
Chain 1:   2800       -18416.025             0.019            0.015
Chain 1:   2900       -18697.403             0.019            0.015
Chain 1:   3000       -18683.606             0.012            0.012
Chain 1:   3100       -18768.586             0.011            0.012
Chain 1:   3200       -18459.194             0.012            0.015
Chain 1:   3300       -18663.996             0.011            0.012
Chain 1:   3400       -18138.719             0.012            0.015
Chain 1:   3500       -18750.825             0.015            0.015
Chain 1:   3600       -18057.269             0.017            0.015
Chain 1:   3700       -18444.271             0.018            0.017
Chain 1:   3800       -17403.494             0.023            0.021
Chain 1:   3900       -17399.625             0.021            0.021
Chain 1:   4000       -17516.950             0.022            0.021
Chain 1:   4100       -17430.628             0.022            0.021
Chain 1:   4200       -17246.817             0.021            0.021
Chain 1:   4300       -17385.287             0.021            0.021
Chain 1:   4400       -17342.065             0.018            0.011
Chain 1:   4500       -17244.550             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48126.838             1.000            1.000
Chain 1:    200       -22211.679             1.083            1.167
Chain 1:    300       -18500.009             0.789            1.000
Chain 1:    400       -30051.199             0.688            1.000
Chain 1:    500       -18380.874             0.677            0.635
Chain 1:    600       -16201.147             0.587            0.635
Chain 1:    700       -11008.125             0.570            0.472
Chain 1:    800        -9875.536             0.513            0.472
Chain 1:    900       -14315.141             0.491            0.384
Chain 1:   1000       -11498.535             0.466            0.384
Chain 1:   1100        -9430.157             0.388            0.310
Chain 1:   1200       -20765.152             0.326            0.310
Chain 1:   1300       -11588.230             0.385            0.384
Chain 1:   1400       -11841.117             0.349            0.310
Chain 1:   1500        -9602.413             0.309            0.245
Chain 1:   1600       -11185.691             0.309            0.245
Chain 1:   1700        -9461.439             0.281            0.233
Chain 1:   1800       -11764.479             0.289            0.233
Chain 1:   1900        -9573.418             0.280            0.229
Chain 1:   2000       -11169.978             0.270            0.219
Chain 1:   2100       -16023.515             0.279            0.229
Chain 1:   2200        -9397.205             0.295            0.229
Chain 1:   2300        -8541.016             0.225            0.196
Chain 1:   2400       -10016.692             0.238            0.196
Chain 1:   2500       -10652.927             0.221            0.182
Chain 1:   2600        -8487.209             0.232            0.196
Chain 1:   2700       -15629.077             0.260            0.229
Chain 1:   2800        -9708.254             0.301            0.255
Chain 1:   2900        -8898.566             0.287            0.255
Chain 1:   3000        -8248.711             0.281            0.255
Chain 1:   3100        -9243.280             0.261            0.147
Chain 1:   3200       -12384.430             0.216            0.147
Chain 1:   3300       -14790.270             0.222            0.163
Chain 1:   3400        -9719.051             0.260            0.254
Chain 1:   3500        -9772.455             0.254            0.254
Chain 1:   3600        -9461.673             0.232            0.163
Chain 1:   3700        -8772.610             0.194            0.108
Chain 1:   3800        -9439.866             0.140            0.091
Chain 1:   3900       -13193.161             0.160            0.108
Chain 1:   4000        -8967.950             0.199            0.163
Chain 1:   4100        -8418.335             0.195            0.163
Chain 1:   4200       -10765.787             0.191            0.163
Chain 1:   4300        -9395.597             0.189            0.146
Chain 1:   4400        -8404.188             0.149            0.118
Chain 1:   4500        -8861.483             0.154            0.118
Chain 1:   4600       -13511.618             0.185            0.146
Chain 1:   4700       -11101.147             0.199            0.217
Chain 1:   4800        -8139.055             0.228            0.218
Chain 1:   4900       -10852.178             0.225            0.218
Chain 1:   5000        -9161.718             0.196            0.217
Chain 1:   5100       -16397.128             0.233            0.218
Chain 1:   5200       -14119.089             0.228            0.217
Chain 1:   5300       -11084.805             0.241            0.250
Chain 1:   5400        -7956.304             0.268            0.274
Chain 1:   5500        -8105.871             0.265            0.274
Chain 1:   5600       -10263.680             0.251            0.250
Chain 1:   5700        -9148.309             0.242            0.250
Chain 1:   5800        -8139.469             0.218            0.210
Chain 1:   5900       -13715.175             0.234            0.210
Chain 1:   6000        -8917.106             0.269            0.274
Chain 1:   6100        -8000.056             0.236            0.210
Chain 1:   6200        -7885.872             0.222            0.210
Chain 1:   6300        -8666.863             0.203            0.124
Chain 1:   6400       -10576.434             0.182            0.124
Chain 1:   6500        -8774.969             0.201            0.181
Chain 1:   6600        -8187.299             0.187            0.124
Chain 1:   6700       -11794.687             0.205            0.181
Chain 1:   6800        -7828.469             0.243            0.205
Chain 1:   6900        -8002.470             0.205            0.181
Chain 1:   7000        -8403.055             0.156            0.115
Chain 1:   7100        -7814.887             0.152            0.090
Chain 1:   7200       -10254.111             0.174            0.181
Chain 1:   7300        -8503.874             0.186            0.205
Chain 1:   7400        -7922.111             0.175            0.205
Chain 1:   7500        -7900.298             0.155            0.075
Chain 1:   7600        -8547.284             0.155            0.076
Chain 1:   7700        -8059.768             0.131            0.075
Chain 1:   7800       -11302.199             0.109            0.075
Chain 1:   7900        -7817.988             0.151            0.076
Chain 1:   8000       -11195.876             0.177            0.206
Chain 1:   8100       -10761.264             0.173            0.206
Chain 1:   8200        -7745.325             0.188            0.206
Chain 1:   8300        -7663.132             0.169            0.076
Chain 1:   8400       -10524.287             0.189            0.272
Chain 1:   8500        -7733.848             0.224            0.287
Chain 1:   8600        -7885.245             0.219            0.287
Chain 1:   8700        -9133.753             0.226            0.287
Chain 1:   8800        -9170.861             0.198            0.272
Chain 1:   8900       -10939.879             0.170            0.162
Chain 1:   9000        -8116.075             0.174            0.162
Chain 1:   9100        -9536.565             0.185            0.162
Chain 1:   9200        -8292.639             0.161            0.150
Chain 1:   9300        -7940.331             0.165            0.150
Chain 1:   9400        -8101.982             0.139            0.149
Chain 1:   9500        -8009.390             0.104            0.137
Chain 1:   9600        -7875.819             0.104            0.137
Chain 1:   9700       -10066.069             0.112            0.149
Chain 1:   9800        -7871.383             0.140            0.150
Chain 1:   9900       -10384.564             0.148            0.150
Chain 1:   10000        -9250.826             0.125            0.149
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56474.636             1.000            1.000
Chain 1:    200       -16724.023             1.688            2.377
Chain 1:    300        -8370.130             1.458            1.000
Chain 1:    400        -8471.397             1.097            1.000
Chain 1:    500        -7876.143             0.892            0.998
Chain 1:    600        -8181.493             0.750            0.998
Chain 1:    700        -7567.069             0.654            0.081
Chain 1:    800        -7869.407             0.577            0.081
Chain 1:    900        -7738.569             0.515            0.076
Chain 1:   1000        -7693.270             0.464            0.076
Chain 1:   1100        -7549.817             0.366            0.038
Chain 1:   1200        -7449.074             0.130            0.037
Chain 1:   1300        -7525.725             0.031            0.019
Chain 1:   1400        -7724.683             0.032            0.026
Chain 1:   1500        -7481.050             0.028            0.026
Chain 1:   1600        -7395.417             0.026            0.019
Chain 1:   1700        -7371.945             0.018            0.017
Chain 1:   1800        -7396.703             0.014            0.014
Chain 1:   1900        -7460.183             0.013            0.012
Chain 1:   2000        -7469.316             0.013            0.012
Chain 1:   2100        -7398.452             0.012            0.010
Chain 1:   2200        -7512.826             0.012            0.010
Chain 1:   2300        -7450.482             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004035 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85908.830             1.000            1.000
Chain 1:    200       -12801.299             3.355            5.711
Chain 1:    300        -9306.035             2.362            1.000
Chain 1:    400       -10233.899             1.794            1.000
Chain 1:    500        -8139.204             1.487            0.376
Chain 1:    600        -7921.547             1.244            0.376
Chain 1:    700        -7935.432             1.066            0.257
Chain 1:    800        -8167.415             0.937            0.257
Chain 1:    900        -8183.459             0.833            0.091
Chain 1:   1000        -7925.593             0.753            0.091
Chain 1:   1100        -8185.922             0.656            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7921.151             0.088            0.033
Chain 1:   1300        -8066.330             0.052            0.032
Chain 1:   1400        -8061.765             0.043            0.028
Chain 1:   1500        -7978.427             0.019            0.027
Chain 1:   1600        -8058.744             0.017            0.018
Chain 1:   1700        -8164.043             0.018            0.018
Chain 1:   1800        -7782.443             0.020            0.018
Chain 1:   1900        -7880.679             0.021            0.018
Chain 1:   2000        -7850.901             0.018            0.013
Chain 1:   2100        -7996.188             0.017            0.013
Chain 1:   2200        -7773.829             0.016            0.013
Chain 1:   2300        -7907.694             0.016            0.013
Chain 1:   2400        -7798.324             0.018            0.014
Chain 1:   2500        -7857.795             0.017            0.014
Chain 1:   2600        -7870.744             0.017            0.014
Chain 1:   2700        -7791.704             0.016            0.014
Chain 1:   2800        -7776.890             0.012            0.012
Chain 1:   2900        -7765.249             0.010            0.010
Chain 1:   3000        -7781.667             0.010            0.010
Chain 1:   3100        -7844.715             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003166 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407678.947             1.000            1.000
Chain 1:    200     -1582912.611             2.656            4.312
Chain 1:    300      -889948.746             2.030            1.000
Chain 1:    400      -457297.045             1.759            1.000
Chain 1:    500      -357547.327             1.463            0.946
Chain 1:    600      -232360.458             1.309            0.946
Chain 1:    700      -118511.084             1.259            0.946
Chain 1:    800       -85721.706             1.150            0.946
Chain 1:    900       -66047.836             1.055            0.779
Chain 1:   1000       -50825.479             0.979            0.779
Chain 1:   1100       -38302.336             0.912            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37470.855             0.483            0.383
Chain 1:   1300       -25441.832             0.453            0.383
Chain 1:   1400       -25158.185             0.359            0.327
Chain 1:   1500       -21750.767             0.347            0.327
Chain 1:   1600       -20968.012             0.297            0.300
Chain 1:   1700       -19844.207             0.206            0.298
Chain 1:   1800       -19788.558             0.168            0.157
Chain 1:   1900       -20113.853             0.140            0.057
Chain 1:   2000       -18628.216             0.118            0.057
Chain 1:   2100       -18866.167             0.087            0.037
Chain 1:   2200       -19091.953             0.086            0.037
Chain 1:   2300       -18709.994             0.041            0.020
Chain 1:   2400       -18482.432             0.041            0.020
Chain 1:   2500       -18284.504             0.026            0.016
Chain 1:   2600       -17915.425             0.024            0.016
Chain 1:   2700       -17872.639             0.019            0.013
Chain 1:   2800       -17589.899             0.020            0.016
Chain 1:   2900       -17870.790             0.020            0.016
Chain 1:   3000       -17856.936             0.012            0.013
Chain 1:   3100       -17941.818             0.012            0.012
Chain 1:   3200       -17633.031             0.012            0.016
Chain 1:   3300       -17837.375             0.011            0.012
Chain 1:   3400       -17313.282             0.013            0.016
Chain 1:   3500       -17923.640             0.015            0.016
Chain 1:   3600       -17232.311             0.017            0.016
Chain 1:   3700       -17617.638             0.019            0.018
Chain 1:   3800       -16580.419             0.024            0.022
Chain 1:   3900       -16576.671             0.022            0.022
Chain 1:   4000       -16693.952             0.023            0.022
Chain 1:   4100       -16607.853             0.023            0.022
Chain 1:   4200       -16424.797             0.022            0.022
Chain 1:   4300       -16562.685             0.022            0.022
Chain 1:   4400       -16520.055             0.019            0.011
Chain 1:   4500       -16422.716             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003763 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11875.857             1.000            1.000
Chain 1:    200        -8900.827             0.667            1.000
Chain 1:    300        -7875.438             0.488            0.334
Chain 1:    400        -7913.982             0.367            0.334
Chain 1:    500        -7773.106             0.297            0.130
Chain 1:    600        -7718.945             0.249            0.130
Chain 1:    700        -7660.829             0.215            0.018
Chain 1:    800        -7718.198             0.189            0.018
Chain 1:    900        -7888.417             0.170            0.018
Chain 1:   1000        -7697.690             0.156            0.022
Chain 1:   1100        -7752.233             0.056            0.018
Chain 1:   1200        -7663.405             0.024            0.012
Chain 1:   1300        -7693.946             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00162 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57450.639             1.000            1.000
Chain 1:    200       -17081.318             1.682            2.363
Chain 1:    300        -8443.565             1.462            1.023
Chain 1:    400        -8504.925             1.098            1.023
Chain 1:    500        -8422.742             0.881            1.000
Chain 1:    600        -8228.011             0.738            1.000
Chain 1:    700        -7922.799             0.638            0.039
Chain 1:    800        -8028.385             0.560            0.039
Chain 1:    900        -7809.656             0.501            0.028
Chain 1:   1000        -7730.874             0.452            0.028
Chain 1:   1100        -7725.627             0.352            0.024
Chain 1:   1200        -7580.086             0.117            0.019
Chain 1:   1300        -7674.711             0.016            0.013
Chain 1:   1400        -7695.938             0.016            0.013
Chain 1:   1500        -7595.524             0.016            0.013
Chain 1:   1600        -7510.140             0.015            0.013
Chain 1:   1700        -7495.928             0.011            0.012
Chain 1:   1800        -7516.519             0.010            0.011
Chain 1:   1900        -7583.107             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003255 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85764.062             1.000            1.000
Chain 1:    200       -12935.684             3.315            5.630
Chain 1:    300        -9454.518             2.333            1.000
Chain 1:    400       -10220.204             1.768            1.000
Chain 1:    500        -8305.723             1.461            0.368
Chain 1:    600        -8182.399             1.220            0.368
Chain 1:    700        -8363.949             1.049            0.231
Chain 1:    800        -8780.482             0.923            0.231
Chain 1:    900        -8341.554             0.827            0.075
Chain 1:   1000        -8115.451             0.747            0.075
Chain 1:   1100        -8391.853             0.650            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8158.838             0.090            0.047
Chain 1:   1300        -8260.341             0.054            0.033
Chain 1:   1400        -8241.915             0.047            0.029
Chain 1:   1500        -8152.927             0.025            0.028
Chain 1:   1600        -8230.701             0.025            0.028
Chain 1:   1700        -8330.308             0.024            0.028
Chain 1:   1800        -7961.839             0.024            0.028
Chain 1:   1900        -8057.656             0.019            0.012
Chain 1:   2000        -8029.300             0.017            0.012
Chain 1:   2100        -8177.844             0.016            0.012
Chain 1:   2200        -7953.328             0.015            0.012
Chain 1:   2300        -8034.987             0.015            0.012
Chain 1:   2400        -8102.383             0.016            0.012
Chain 1:   2500        -8063.933             0.015            0.012
Chain 1:   2600        -8057.615             0.014            0.012
Chain 1:   2700        -7969.974             0.014            0.011
Chain 1:   2800        -7956.675             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003519 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417704.700             1.000            1.000
Chain 1:    200     -1588525.070             2.650            4.299
Chain 1:    300      -891628.651             2.027            1.000
Chain 1:    400      -457421.320             1.757            1.000
Chain 1:    500      -357438.895             1.462            0.949
Chain 1:    600      -232164.766             1.308            0.949
Chain 1:    700      -118492.401             1.258            0.949
Chain 1:    800       -85712.033             1.149            0.949
Chain 1:    900       -66076.459             1.054            0.782
Chain 1:   1000       -50884.516             0.979            0.782
Chain 1:   1100       -38382.851             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37552.441             0.484            0.382
Chain 1:   1300       -25550.775             0.452            0.382
Chain 1:   1400       -25268.013             0.359            0.326
Chain 1:   1500       -21867.421             0.346            0.326
Chain 1:   1600       -21085.952             0.296            0.299
Chain 1:   1700       -19966.165             0.206            0.297
Chain 1:   1800       -19910.997             0.168            0.156
Chain 1:   1900       -20236.251             0.139            0.056
Chain 1:   2000       -18752.458             0.118            0.056
Chain 1:   2100       -18990.397             0.086            0.037
Chain 1:   2200       -19215.778             0.085            0.037
Chain 1:   2300       -18834.194             0.040            0.020
Chain 1:   2400       -18606.740             0.040            0.020
Chain 1:   2500       -18408.564             0.026            0.016
Chain 1:   2600       -18039.935             0.024            0.016
Chain 1:   2700       -17997.215             0.019            0.013
Chain 1:   2800       -17714.482             0.020            0.016
Chain 1:   2900       -17995.204             0.020            0.016
Chain 1:   3000       -17981.475             0.012            0.013
Chain 1:   3100       -18066.326             0.011            0.012
Chain 1:   3200       -17757.685             0.012            0.016
Chain 1:   3300       -17961.866             0.011            0.012
Chain 1:   3400       -17437.951             0.013            0.016
Chain 1:   3500       -18048.003             0.015            0.016
Chain 1:   3600       -17357.073             0.017            0.016
Chain 1:   3700       -17742.113             0.019            0.017
Chain 1:   3800       -16705.449             0.024            0.022
Chain 1:   3900       -16701.678             0.022            0.022
Chain 1:   4000       -16818.993             0.023            0.022
Chain 1:   4100       -16732.957             0.023            0.022
Chain 1:   4200       -16549.983             0.022            0.022
Chain 1:   4300       -16687.831             0.022            0.022
Chain 1:   4400       -16645.324             0.019            0.011
Chain 1:   4500       -16547.976             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001352 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12811.830             1.000            1.000
Chain 1:    200        -9722.141             0.659            1.000
Chain 1:    300        -8216.213             0.500            0.318
Chain 1:    400        -8471.773             0.383            0.318
Chain 1:    500        -8351.704             0.309            0.183
Chain 1:    600        -8202.899             0.261            0.183
Chain 1:    700        -8296.892             0.225            0.030
Chain 1:    800        -8143.043             0.199            0.030
Chain 1:    900        -8194.872             0.178            0.019
Chain 1:   1000        -8142.088             0.161            0.019
Chain 1:   1100        -8237.048             0.062            0.018
Chain 1:   1200        -8127.860             0.031            0.014
Chain 1:   1300        -8059.533             0.014            0.013
Chain 1:   1400        -8081.453             0.011            0.012
Chain 1:   1500        -8177.478             0.011            0.012
Chain 1:   1600        -8088.732             0.010            0.011
Chain 1:   1700        -8059.052             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62146.359             1.000            1.000
Chain 1:    200       -18294.566             1.698            2.397
Chain 1:    300        -9082.632             1.470            1.014
Chain 1:    400        -7885.647             1.141            1.014
Chain 1:    500        -7836.171             0.914            1.000
Chain 1:    600        -9395.863             0.789            1.000
Chain 1:    700        -7921.248             0.703            0.186
Chain 1:    800        -8273.299             0.621            0.186
Chain 1:    900        -8032.765             0.555            0.166
Chain 1:   1000        -7661.769             0.504            0.166
Chain 1:   1100        -7725.575             0.405            0.152
Chain 1:   1200        -8259.383             0.172            0.065
Chain 1:   1300        -7811.891             0.076            0.057
Chain 1:   1400        -7629.858             0.063            0.048
Chain 1:   1500        -7515.201             0.064            0.048
Chain 1:   1600        -7823.921             0.052            0.043
Chain 1:   1700        -7446.155             0.038            0.043
Chain 1:   1800        -7551.132             0.035            0.039
Chain 1:   1900        -7578.483             0.033            0.039
Chain 1:   2000        -7675.676             0.029            0.024
Chain 1:   2100        -7527.373             0.030            0.024
Chain 1:   2200        -7734.579             0.026            0.024
Chain 1:   2300        -7543.635             0.023            0.024
Chain 1:   2400        -7646.565             0.022            0.020
Chain 1:   2500        -7537.576             0.022            0.020
Chain 1:   2600        -7495.711             0.019            0.014
Chain 1:   2700        -7432.975             0.014            0.014
Chain 1:   2800        -7616.118             0.015            0.014
Chain 1:   2900        -7385.723             0.018            0.020
Chain 1:   3000        -7504.835             0.018            0.020
Chain 1:   3100        -7505.148             0.017            0.016
Chain 1:   3200        -7711.298             0.017            0.016
Chain 1:   3300        -7414.611             0.018            0.016
Chain 1:   3400        -7666.656             0.020            0.024
Chain 1:   3500        -7411.198             0.022            0.027
Chain 1:   3600        -7468.514             0.022            0.027
Chain 1:   3700        -7425.458             0.022            0.027
Chain 1:   3800        -7432.399             0.020            0.027
Chain 1:   3900        -7397.284             0.017            0.016
Chain 1:   4000        -7369.232             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003873 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86280.215             1.000            1.000
Chain 1:    200       -13982.452             3.085            5.171
Chain 1:    300       -10228.175             2.179            1.000
Chain 1:    400       -11800.825             1.668            1.000
Chain 1:    500        -8982.643             1.397            0.367
Chain 1:    600        -9737.359             1.177            0.367
Chain 1:    700        -8585.361             1.028            0.314
Chain 1:    800        -9277.035             0.909            0.314
Chain 1:    900        -9046.691             0.811            0.134
Chain 1:   1000        -8587.957             0.735            0.134
Chain 1:   1100        -9042.618             0.640            0.133   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8566.292             0.129            0.078
Chain 1:   1300        -8853.448             0.095            0.075
Chain 1:   1400        -8783.735             0.083            0.056
Chain 1:   1500        -8711.039             0.052            0.053
Chain 1:   1600        -8822.661             0.045            0.050
Chain 1:   1700        -8875.082             0.033            0.032
Chain 1:   1800        -8429.755             0.030            0.032
Chain 1:   1900        -8534.995             0.029            0.032
Chain 1:   2000        -8516.841             0.024            0.013
Chain 1:   2100        -8658.379             0.021            0.013
Chain 1:   2200        -8428.691             0.018            0.013
Chain 1:   2300        -8585.176             0.016            0.013
Chain 1:   2400        -8427.889             0.017            0.016
Chain 1:   2500        -8505.806             0.018            0.016
Chain 1:   2600        -8538.743             0.017            0.016
Chain 1:   2700        -8458.589             0.017            0.016
Chain 1:   2800        -8410.201             0.012            0.012
Chain 1:   2900        -8520.531             0.012            0.013
Chain 1:   3000        -8407.240             0.014            0.013
Chain 1:   3100        -8395.041             0.012            0.013
Chain 1:   3200        -8370.466             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003589 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391132.666             1.000            1.000
Chain 1:    200     -1578620.745             2.658            4.315
Chain 1:    300      -890312.462             2.030            1.000
Chain 1:    400      -457909.195             1.758            1.000
Chain 1:    500      -358804.531             1.462            0.944
Chain 1:    600      -233995.638             1.307            0.944
Chain 1:    700      -120036.906             1.256            0.944
Chain 1:    800       -87185.973             1.146            0.944
Chain 1:    900       -67480.525             1.051            0.773
Chain 1:   1000       -52235.604             0.975            0.773
Chain 1:   1100       -39669.632             0.907            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38845.611             0.477            0.377
Chain 1:   1300       -26742.656             0.445            0.377
Chain 1:   1400       -26459.192             0.352            0.317
Chain 1:   1500       -23030.182             0.339            0.317
Chain 1:   1600       -22243.116             0.290            0.292
Chain 1:   1700       -21108.945             0.200            0.292
Chain 1:   1800       -21051.664             0.163            0.149
Chain 1:   1900       -21378.411             0.135            0.054
Chain 1:   2000       -19884.292             0.113            0.054
Chain 1:   2100       -20123.010             0.083            0.035
Chain 1:   2200       -20350.545             0.082            0.035
Chain 1:   2300       -19966.611             0.038            0.019
Chain 1:   2400       -19738.401             0.038            0.019
Chain 1:   2500       -19540.662             0.025            0.015
Chain 1:   2600       -19170.014             0.023            0.015
Chain 1:   2700       -19126.723             0.018            0.012
Chain 1:   2800       -18843.462             0.019            0.015
Chain 1:   2900       -19125.009             0.019            0.015
Chain 1:   3000       -19111.120             0.012            0.012
Chain 1:   3100       -19196.233             0.011            0.012
Chain 1:   3200       -18886.440             0.011            0.015
Chain 1:   3300       -19091.522             0.011            0.012
Chain 1:   3400       -18565.726             0.012            0.015
Chain 1:   3500       -19178.801             0.014            0.015
Chain 1:   3600       -18483.890             0.016            0.015
Chain 1:   3700       -18871.942             0.018            0.016
Chain 1:   3800       -17829.283             0.022            0.021
Chain 1:   3900       -17825.397             0.021            0.021
Chain 1:   4000       -17942.653             0.022            0.021
Chain 1:   4100       -17856.329             0.022            0.021
Chain 1:   4200       -17672.058             0.021            0.021
Chain 1:   4300       -17810.807             0.021            0.021
Chain 1:   4400       -17767.214             0.018            0.010
Chain 1:   4500       -17669.677             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00197 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48520.518             1.000            1.000
Chain 1:    200       -16490.556             1.471            1.942
Chain 1:    300       -18421.860             1.016            1.000
Chain 1:    400       -14219.451             0.836            1.000
Chain 1:    500       -11398.235             0.718            0.296
Chain 1:    600       -26016.219             0.692            0.562
Chain 1:    700       -15897.738             0.684            0.562
Chain 1:    800       -12335.862             0.635            0.562
Chain 1:    900       -10680.241             0.581            0.296
Chain 1:   1000       -11093.433             0.527            0.296
Chain 1:   1100       -12371.646             0.437            0.289
Chain 1:   1200       -15705.235             0.264            0.248
Chain 1:   1300       -10256.641             0.307            0.289
Chain 1:   1400       -16547.824             0.315            0.289
Chain 1:   1500       -12105.308             0.327            0.367
Chain 1:   1600       -30159.961             0.331            0.367
Chain 1:   1700       -10933.139             0.443            0.367
Chain 1:   1800       -13976.489             0.436            0.367
Chain 1:   1900        -9864.506             0.462            0.380
Chain 1:   2000       -12001.963             0.476            0.380
Chain 1:   2100        -9145.479             0.497            0.380
Chain 1:   2200       -11310.590             0.495            0.380
Chain 1:   2300        -9545.099             0.461            0.367
Chain 1:   2400        -8939.148             0.429            0.312
Chain 1:   2500       -10028.681             0.404            0.218
Chain 1:   2600       -10794.029             0.351            0.191
Chain 1:   2700        -8866.864             0.197            0.191
Chain 1:   2800        -8964.328             0.176            0.185
Chain 1:   2900        -9710.546             0.142            0.178
Chain 1:   3000        -9082.016             0.131            0.109
Chain 1:   3100        -9404.013             0.103            0.077
Chain 1:   3200        -9844.260             0.089            0.071
Chain 1:   3300        -9440.758             0.074            0.069
Chain 1:   3400       -10445.781             0.077            0.071
Chain 1:   3500       -11996.538             0.079            0.071
Chain 1:   3600       -10157.914             0.090            0.077
Chain 1:   3700        -9171.899             0.079            0.077
Chain 1:   3800        -8446.632             0.087            0.086
Chain 1:   3900       -10397.973             0.098            0.096
Chain 1:   4000       -11829.327             0.103            0.108
Chain 1:   4100        -8578.035             0.138            0.121
Chain 1:   4200        -9206.180             0.140            0.121
Chain 1:   4300       -12524.693             0.162            0.129
Chain 1:   4400       -12111.124             0.156            0.129
Chain 1:   4500        -8589.560             0.184            0.181
Chain 1:   4600        -9728.740             0.178            0.121
Chain 1:   4700       -10271.214             0.172            0.121
Chain 1:   4800       -10664.039             0.167            0.121
Chain 1:   4900        -8609.824             0.172            0.121
Chain 1:   5000       -13509.034             0.196            0.239
Chain 1:   5100        -8326.157             0.221            0.239
Chain 1:   5200        -9216.238             0.224            0.239
Chain 1:   5300       -11622.560             0.218            0.207
Chain 1:   5400        -9071.560             0.243            0.239
Chain 1:   5500       -11606.384             0.223            0.218
Chain 1:   5600        -8327.312             0.251            0.239
Chain 1:   5700       -14798.749             0.289            0.281
Chain 1:   5800        -8594.104             0.358            0.363
Chain 1:   5900       -14611.763             0.375            0.394
Chain 1:   6000        -8177.210             0.418            0.412
Chain 1:   6100        -9626.437             0.371            0.394
Chain 1:   6200        -8192.550             0.378            0.394
Chain 1:   6300       -12374.354             0.391            0.394
Chain 1:   6400       -10756.265             0.378            0.394
Chain 1:   6500        -8603.956             0.382            0.394
Chain 1:   6600       -11330.441             0.366            0.338
Chain 1:   6700        -8943.127             0.349            0.267
Chain 1:   6800       -11591.340             0.300            0.250
Chain 1:   6900       -11315.851             0.261            0.241
Chain 1:   7000       -13748.445             0.200            0.228
Chain 1:   7100       -10475.440             0.216            0.241
Chain 1:   7200        -8210.002             0.226            0.250
Chain 1:   7300        -8551.201             0.197            0.241
Chain 1:   7400        -8630.727             0.182            0.241
Chain 1:   7500       -11199.118             0.180            0.229
Chain 1:   7600        -9962.688             0.169            0.228
Chain 1:   7700        -8399.811             0.161            0.186
Chain 1:   7800       -11603.889             0.165            0.186
Chain 1:   7900        -8424.327             0.201            0.229
Chain 1:   8000       -10333.475             0.202            0.229
Chain 1:   8100        -9272.273             0.182            0.186
Chain 1:   8200        -8739.312             0.160            0.185
Chain 1:   8300        -9210.100             0.161            0.185
Chain 1:   8400        -8125.517             0.174            0.185
Chain 1:   8500        -8216.932             0.152            0.133
Chain 1:   8600        -8811.486             0.146            0.133
Chain 1:   8700        -9185.509             0.132            0.114
Chain 1:   8800        -8053.020             0.118            0.114
Chain 1:   8900        -8232.788             0.083            0.067
Chain 1:   9000        -8640.966             0.069            0.061
Chain 1:   9100        -8509.413             0.059            0.051
Chain 1:   9200        -8635.522             0.054            0.047
Chain 1:   9300       -10062.831             0.063            0.047
Chain 1:   9400        -9839.154             0.052            0.041
Chain 1:   9500       -10686.902             0.059            0.047
Chain 1:   9600        -8904.563             0.072            0.047
Chain 1:   9700       -11148.288             0.089            0.079
Chain 1:   9800        -8089.215             0.112            0.079
Chain 1:   9900        -9255.286             0.123            0.126
Chain 1:   10000        -8327.494             0.129            0.126
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57014.472             1.000            1.000
Chain 1:    200       -17231.531             1.654            2.309
Chain 1:    300        -8608.603             1.437            1.002
Chain 1:    400        -8207.254             1.090            1.002
Chain 1:    500        -8074.591             0.875            1.000
Chain 1:    600        -8766.513             0.742            1.000
Chain 1:    700        -7863.140             0.653            0.115
Chain 1:    800        -8049.337             0.574            0.115
Chain 1:    900        -7874.114             0.513            0.079
Chain 1:   1000        -7838.229             0.462            0.079
Chain 1:   1100        -7582.109             0.365            0.049
Chain 1:   1200        -7536.879             0.135            0.034
Chain 1:   1300        -7703.090             0.037            0.023
Chain 1:   1400        -7618.528             0.033            0.022
Chain 1:   1500        -7570.830             0.032            0.022
Chain 1:   1600        -7531.828             0.025            0.022
Chain 1:   1700        -7479.032             0.014            0.011
Chain 1:   1800        -7545.607             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005709 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 57.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85888.248             1.000            1.000
Chain 1:    200       -13264.965             3.237            5.475
Chain 1:    300        -9681.367             2.282            1.000
Chain 1:    400       -10616.797             1.733            1.000
Chain 1:    500        -8618.165             1.433            0.370
Chain 1:    600        -8227.411             1.202            0.370
Chain 1:    700        -8403.661             1.033            0.232
Chain 1:    800        -8659.521             0.908            0.232
Chain 1:    900        -8495.261             0.809            0.088
Chain 1:   1000        -8265.479             0.731            0.088
Chain 1:   1100        -8520.956             0.634            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8291.066             0.089            0.030
Chain 1:   1300        -8424.686             0.054            0.030
Chain 1:   1400        -8428.524             0.045            0.028
Chain 1:   1500        -8292.516             0.024            0.028
Chain 1:   1600        -8400.666             0.020            0.021
Chain 1:   1700        -8486.155             0.019            0.019
Chain 1:   1800        -8092.324             0.021            0.019
Chain 1:   1900        -8193.071             0.020            0.016
Chain 1:   2000        -8163.744             0.018            0.016
Chain 1:   2100        -8286.235             0.016            0.015
Chain 1:   2200        -8067.501             0.016            0.015
Chain 1:   2300        -8221.890             0.017            0.015
Chain 1:   2400        -8235.762             0.017            0.015
Chain 1:   2500        -8205.176             0.015            0.013
Chain 1:   2600        -8207.799             0.014            0.012
Chain 1:   2700        -8114.046             0.014            0.012
Chain 1:   2800        -8085.213             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.007477 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 74.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8394711.439             1.000            1.000
Chain 1:    200     -1583805.993             2.650            4.300
Chain 1:    300      -890285.034             2.026            1.000
Chain 1:    400      -457192.025             1.757            1.000
Chain 1:    500      -357473.242             1.461            0.947
Chain 1:    600      -232601.115             1.307            0.947
Chain 1:    700      -118910.285             1.257            0.947
Chain 1:    800       -86149.956             1.147            0.947
Chain 1:    900       -66507.881             1.053            0.779
Chain 1:   1000       -51310.153             0.977            0.779
Chain 1:   1100       -38793.420             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37968.541             0.481            0.380
Chain 1:   1300       -25934.577             0.450            0.380
Chain 1:   1400       -25652.869             0.356            0.323
Chain 1:   1500       -22243.217             0.344            0.323
Chain 1:   1600       -21460.360             0.294            0.296
Chain 1:   1700       -20335.414             0.204            0.295
Chain 1:   1800       -20279.787             0.166            0.153
Chain 1:   1900       -20605.555             0.138            0.055
Chain 1:   2000       -19118.245             0.116            0.055
Chain 1:   2100       -19356.422             0.085            0.036
Chain 1:   2200       -19582.604             0.084            0.036
Chain 1:   2300       -19200.190             0.040            0.020
Chain 1:   2400       -18972.435             0.040            0.020
Chain 1:   2500       -18774.504             0.025            0.016
Chain 1:   2600       -18405.023             0.024            0.016
Chain 1:   2700       -18362.114             0.019            0.012
Chain 1:   2800       -18079.143             0.020            0.016
Chain 1:   2900       -18360.250             0.020            0.015
Chain 1:   3000       -18346.412             0.012            0.012
Chain 1:   3100       -18431.349             0.011            0.012
Chain 1:   3200       -18122.282             0.012            0.015
Chain 1:   3300       -18326.841             0.011            0.012
Chain 1:   3400       -17802.215             0.013            0.015
Chain 1:   3500       -18413.399             0.015            0.016
Chain 1:   3600       -17721.025             0.017            0.016
Chain 1:   3700       -18107.112             0.019            0.017
Chain 1:   3800       -17068.279             0.023            0.021
Chain 1:   3900       -17064.489             0.022            0.021
Chain 1:   4000       -17181.777             0.022            0.021
Chain 1:   4100       -17095.586             0.022            0.021
Chain 1:   4200       -16912.187             0.022            0.021
Chain 1:   4300       -17050.333             0.021            0.021
Chain 1:   4400       -17007.414             0.019            0.011
Chain 1:   4500       -16910.011             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48961.536             1.000            1.000
Chain 1:    200       -15138.424             1.617            2.234
Chain 1:    300       -21035.746             1.172            1.000
Chain 1:    400       -13564.869             1.016            1.000
Chain 1:    500       -14764.397             0.829            0.551
Chain 1:    600       -12466.779             0.722            0.551
Chain 1:    700       -23636.476             0.686            0.473
Chain 1:    800       -13133.188             0.700            0.551
Chain 1:    900       -12076.285             0.632            0.473
Chain 1:   1000       -10526.453             0.584            0.473
Chain 1:   1100       -16768.631             0.521            0.372   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12177.308             0.335            0.372
Chain 1:   1300       -10196.212             0.327            0.372
Chain 1:   1400       -16955.149             0.311            0.372
Chain 1:   1500       -11832.446             0.347            0.377
Chain 1:   1600       -12072.189             0.330            0.377
Chain 1:   1700       -10405.925             0.299            0.372
Chain 1:   1800       -10721.599             0.222            0.194
Chain 1:   1900       -15532.902             0.244            0.310
Chain 1:   2000       -10158.495             0.282            0.372
Chain 1:   2100       -10007.485             0.247            0.310
Chain 1:   2200        -9668.737             0.212            0.194
Chain 1:   2300       -16307.664             0.234            0.310
Chain 1:   2400       -13889.000             0.211            0.174
Chain 1:   2500       -10695.231             0.198            0.174
Chain 1:   2600        -9279.411             0.211            0.174
Chain 1:   2700       -16963.333             0.240            0.299
Chain 1:   2800        -9355.508             0.319            0.310
Chain 1:   2900       -16811.165             0.332            0.407
Chain 1:   3000        -8945.372             0.367            0.407
Chain 1:   3100       -12028.991             0.391            0.407
Chain 1:   3200       -10734.841             0.400            0.407
Chain 1:   3300       -16958.128             0.396            0.367
Chain 1:   3400        -9315.933             0.460            0.443
Chain 1:   3500        -9523.149             0.433            0.443
Chain 1:   3600        -9901.116             0.421            0.443
Chain 1:   3700       -10098.066             0.378            0.367
Chain 1:   3800       -10100.808             0.297            0.256
Chain 1:   3900        -9577.719             0.258            0.121
Chain 1:   4000        -9345.464             0.172            0.055
Chain 1:   4100        -9120.648             0.149            0.038
Chain 1:   4200       -13146.655             0.168            0.038
Chain 1:   4300        -9639.353             0.167            0.038
Chain 1:   4400        -8970.749             0.093            0.038
Chain 1:   4500        -8579.831             0.095            0.046
Chain 1:   4600       -14077.873             0.130            0.055
Chain 1:   4700       -10656.940             0.161            0.075
Chain 1:   4800        -8987.912             0.179            0.186
Chain 1:   4900        -9196.397             0.176            0.186
Chain 1:   5000        -8563.123             0.181            0.186
Chain 1:   5100       -16486.923             0.226            0.306
Chain 1:   5200       -10196.231             0.258            0.321
Chain 1:   5300        -9448.860             0.229            0.186
Chain 1:   5400       -16652.135             0.265            0.321
Chain 1:   5500       -12594.365             0.293            0.322
Chain 1:   5600        -9047.736             0.293            0.322
Chain 1:   5700       -10663.073             0.276            0.322
Chain 1:   5800       -11561.335             0.265            0.322
Chain 1:   5900       -10908.582             0.269            0.322
Chain 1:   6000        -9226.157             0.279            0.322
Chain 1:   6100        -8762.868             0.237            0.182
Chain 1:   6200        -8459.307             0.179            0.151
Chain 1:   6300       -10889.362             0.193            0.182
Chain 1:   6400       -13005.674             0.166            0.163
Chain 1:   6500        -9197.321             0.175            0.163
Chain 1:   6600        -8863.061             0.140            0.151
Chain 1:   6700        -8535.704             0.128            0.078
Chain 1:   6800       -10335.568             0.138            0.163
Chain 1:   6900        -8866.125             0.149            0.166
Chain 1:   7000        -8928.545             0.131            0.163
Chain 1:   7100       -11766.831             0.150            0.166
Chain 1:   7200        -8779.488             0.180            0.174
Chain 1:   7300        -8530.475             0.161            0.166
Chain 1:   7400        -8642.915             0.146            0.166
Chain 1:   7500       -10728.287             0.124            0.166
Chain 1:   7600        -9127.483             0.138            0.174
Chain 1:   7700        -9012.352             0.135            0.174
Chain 1:   7800       -12778.628             0.147            0.175
Chain 1:   7900        -8271.217             0.185            0.194
Chain 1:   8000        -8600.971             0.188            0.194
Chain 1:   8100        -9022.491             0.169            0.175
Chain 1:   8200       -10709.698             0.151            0.158
Chain 1:   8300       -13239.708             0.167            0.175
Chain 1:   8400        -9133.634             0.211            0.191
Chain 1:   8500        -9398.983             0.194            0.175
Chain 1:   8600        -8345.003             0.189            0.158
Chain 1:   8700        -9641.835             0.201            0.158
Chain 1:   8800        -8731.314             0.182            0.135
Chain 1:   8900       -10086.593             0.141            0.134
Chain 1:   9000        -9241.052             0.146            0.134
Chain 1:   9100       -10620.358             0.155            0.134
Chain 1:   9200        -9277.702             0.153            0.134
Chain 1:   9300        -9217.176             0.135            0.130
Chain 1:   9400        -9498.670             0.093            0.126
Chain 1:   9500        -8344.560             0.104            0.130
Chain 1:   9600        -8490.303             0.093            0.130
Chain 1:   9700        -8865.042             0.084            0.104
Chain 1:   9800       -11227.113             0.094            0.130
Chain 1:   9900        -8902.202             0.107            0.130
Chain 1:   10000        -9308.289             0.102            0.130
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62135.255             1.000            1.000
Chain 1:    200       -18184.182             1.708            2.417
Chain 1:    300        -8979.773             1.481            1.025
Chain 1:    400        -8439.983             1.126            1.025
Chain 1:    500        -8646.905             0.906            1.000
Chain 1:    600        -8324.588             0.761            1.000
Chain 1:    700        -7857.932             0.661            0.064
Chain 1:    800        -8197.938             0.584            0.064
Chain 1:    900        -7804.857             0.524            0.059
Chain 1:   1000        -7894.561             0.473            0.059
Chain 1:   1100        -7709.320             0.376            0.050
Chain 1:   1200        -7542.499             0.136            0.041
Chain 1:   1300        -7801.198             0.037            0.039
Chain 1:   1400        -7820.120             0.031            0.033
Chain 1:   1500        -7580.964             0.031            0.033
Chain 1:   1600        -7634.724             0.028            0.032
Chain 1:   1700        -7558.211             0.023            0.024
Chain 1:   1800        -7573.495             0.019            0.022
Chain 1:   1900        -7580.484             0.014            0.011
Chain 1:   2000        -7645.168             0.014            0.010
Chain 1:   2100        -7571.647             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00342 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86623.014             1.000            1.000
Chain 1:    200       -13762.660             3.147            5.294
Chain 1:    300       -10077.133             2.220            1.000
Chain 1:    400       -11265.527             1.691            1.000
Chain 1:    500        -9025.392             1.403            0.366
Chain 1:    600        -8608.651             1.177            0.366
Chain 1:    700        -8829.842             1.012            0.248
Chain 1:    800        -9618.923             0.896            0.248
Chain 1:    900        -8731.098             0.808            0.105
Chain 1:   1000        -8795.966             0.728            0.105
Chain 1:   1100        -8715.322             0.629            0.102   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8471.878             0.102            0.082
Chain 1:   1300        -8756.925             0.069            0.048
Chain 1:   1400        -8718.613             0.059            0.033
Chain 1:   1500        -8607.020             0.035            0.029
Chain 1:   1600        -8715.382             0.032            0.025
Chain 1:   1700        -8790.114             0.030            0.013
Chain 1:   1800        -8359.665             0.027            0.013
Chain 1:   1900        -8463.568             0.018            0.012
Chain 1:   2000        -8438.758             0.018            0.012
Chain 1:   2100        -8573.690             0.018            0.013
Chain 1:   2200        -8367.853             0.018            0.013
Chain 1:   2300        -8463.587             0.016            0.012
Chain 1:   2400        -8527.465             0.016            0.012
Chain 1:   2500        -8472.158             0.015            0.012
Chain 1:   2600        -8476.259             0.014            0.011
Chain 1:   2700        -8391.559             0.014            0.011
Chain 1:   2800        -8348.316             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00489 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 48.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399258.934             1.000            1.000
Chain 1:    200     -1582046.828             2.655            4.309
Chain 1:    300      -891237.591             2.028            1.000
Chain 1:    400      -458167.892             1.757            1.000
Chain 1:    500      -358810.350             1.461            0.945
Chain 1:    600      -233806.156             1.307            0.945
Chain 1:    700      -119815.262             1.256            0.945
Chain 1:    800       -86957.235             1.146            0.945
Chain 1:    900       -67243.255             1.051            0.775
Chain 1:   1000       -51992.335             0.976            0.775
Chain 1:   1100       -39422.213             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38595.397             0.479            0.378
Chain 1:   1300       -26500.998             0.447            0.378
Chain 1:   1400       -26216.033             0.353            0.319
Chain 1:   1500       -22790.230             0.341            0.319
Chain 1:   1600       -22003.276             0.291            0.293
Chain 1:   1700       -20870.819             0.201            0.293
Chain 1:   1800       -20813.688             0.164            0.150
Chain 1:   1900       -21140.085             0.136            0.054
Chain 1:   2000       -19647.514             0.114            0.054
Chain 1:   2100       -19886.104             0.084            0.036
Chain 1:   2200       -20113.265             0.083            0.036
Chain 1:   2300       -19729.799             0.039            0.019
Chain 1:   2400       -19501.736             0.039            0.019
Chain 1:   2500       -19303.948             0.025            0.015
Chain 1:   2600       -18933.674             0.023            0.015
Chain 1:   2700       -18890.473             0.018            0.012
Chain 1:   2800       -18607.301             0.019            0.015
Chain 1:   2900       -18888.789             0.019            0.015
Chain 1:   3000       -18874.836             0.012            0.012
Chain 1:   3100       -18959.906             0.011            0.012
Chain 1:   3200       -18650.337             0.012            0.015
Chain 1:   3300       -18855.269             0.011            0.012
Chain 1:   3400       -18329.840             0.012            0.015
Chain 1:   3500       -18942.274             0.015            0.015
Chain 1:   3600       -18248.259             0.016            0.015
Chain 1:   3700       -18635.662             0.018            0.017
Chain 1:   3800       -17594.276             0.023            0.021
Chain 1:   3900       -17590.426             0.021            0.021
Chain 1:   4000       -17707.704             0.022            0.021
Chain 1:   4100       -17621.406             0.022            0.021
Chain 1:   4200       -17437.417             0.021            0.021
Chain 1:   4300       -17575.959             0.021            0.021
Chain 1:   4400       -17532.610             0.018            0.011
Chain 1:   4500       -17435.109             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12300.399             1.000            1.000
Chain 1:    200        -9163.155             0.671            1.000
Chain 1:    300        -8045.447             0.494            0.342
Chain 1:    400        -8215.824             0.376            0.342
Chain 1:    500        -8076.457             0.304            0.139
Chain 1:    600        -7998.079             0.255            0.139
Chain 1:    700        -7917.445             0.220            0.021
Chain 1:    800        -7948.840             0.193            0.021
Chain 1:    900        -8114.424             0.174            0.020
Chain 1:   1000        -7957.690             0.158            0.020
Chain 1:   1100        -7998.352             0.059            0.020
Chain 1:   1200        -7951.974             0.025            0.017
Chain 1:   1300        -7892.271             0.012            0.010
Chain 1:   1400        -7901.125             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61507.360             1.000            1.000
Chain 1:    200       -17652.562             1.742            2.484
Chain 1:    300        -8793.806             1.497            1.007
Chain 1:    400        -8286.539             1.138            1.007
Chain 1:    500        -8293.530             0.911            1.000
Chain 1:    600        -8345.951             0.760            1.000
Chain 1:    700        -8042.836             0.657            0.061
Chain 1:    800        -7921.098             0.577            0.061
Chain 1:    900        -7860.170             0.513            0.038
Chain 1:   1000        -8068.232             0.465            0.038
Chain 1:   1100        -7661.736             0.370            0.038
Chain 1:   1200        -7759.534             0.123            0.026
Chain 1:   1300        -7592.352             0.024            0.022
Chain 1:   1400        -7774.378             0.020            0.022
Chain 1:   1500        -7563.488             0.023            0.023
Chain 1:   1600        -7583.580             0.023            0.023
Chain 1:   1700        -7499.441             0.020            0.022
Chain 1:   1800        -7521.486             0.019            0.022
Chain 1:   1900        -7536.424             0.018            0.022
Chain 1:   2000        -7574.222             0.016            0.013
Chain 1:   2100        -7554.435             0.011            0.011
Chain 1:   2200        -7671.797             0.012            0.011
Chain 1:   2300        -7578.880             0.011            0.011
Chain 1:   2400        -7606.326             0.009            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002975 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86339.323             1.000            1.000
Chain 1:    200       -13389.592             3.224            5.448
Chain 1:    300        -9822.840             2.270            1.000
Chain 1:    400       -10653.889             1.722            1.000
Chain 1:    500        -8742.936             1.422            0.363
Chain 1:    600        -8347.675             1.193            0.363
Chain 1:    700        -8429.719             1.024            0.219
Chain 1:    800        -8779.210             0.901            0.219
Chain 1:    900        -8610.909             0.803            0.078
Chain 1:   1000        -8347.996             0.726            0.078
Chain 1:   1100        -8717.040             0.630            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8342.456             0.089            0.045
Chain 1:   1300        -8503.963             0.055            0.042
Chain 1:   1400        -8564.940             0.048            0.040
Chain 1:   1500        -8425.612             0.028            0.031
Chain 1:   1600        -8528.867             0.024            0.020
Chain 1:   1700        -8623.062             0.024            0.020
Chain 1:   1800        -8226.652             0.025            0.020
Chain 1:   1900        -8329.458             0.024            0.019
Chain 1:   2000        -8299.545             0.022            0.017
Chain 1:   2100        -8424.335             0.019            0.015
Chain 1:   2200        -8208.545             0.017            0.015
Chain 1:   2300        -8357.895             0.017            0.015
Chain 1:   2400        -8372.843             0.016            0.015
Chain 1:   2500        -8340.558             0.015            0.012
Chain 1:   2600        -8342.763             0.014            0.012
Chain 1:   2700        -8249.322             0.014            0.012
Chain 1:   2800        -8221.546             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003038 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8439175.657             1.000            1.000
Chain 1:    200     -1588910.820             2.656            4.311
Chain 1:    300      -890785.046             2.032            1.000
Chain 1:    400      -457333.410             1.761            1.000
Chain 1:    500      -357248.561             1.465            0.948
Chain 1:    600      -232150.924             1.310            0.948
Chain 1:    700      -118684.947             1.260            0.948
Chain 1:    800       -86019.726             1.150            0.948
Chain 1:    900       -66429.801             1.055            0.784
Chain 1:   1000       -51283.386             0.979            0.784
Chain 1:   1100       -38823.631             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38001.635             0.482            0.380
Chain 1:   1300       -26022.086             0.450            0.380
Chain 1:   1400       -25745.774             0.356            0.321
Chain 1:   1500       -22351.082             0.343            0.321
Chain 1:   1600       -21572.875             0.293            0.295
Chain 1:   1700       -20454.371             0.203            0.295
Chain 1:   1800       -20400.136             0.165            0.152
Chain 1:   1900       -20725.955             0.137            0.055
Chain 1:   2000       -19242.162             0.115            0.055
Chain 1:   2100       -19480.017             0.084            0.036
Chain 1:   2200       -19705.771             0.083            0.036
Chain 1:   2300       -19323.716             0.039            0.020
Chain 1:   2400       -19096.033             0.039            0.020
Chain 1:   2500       -18897.964             0.025            0.016
Chain 1:   2600       -18528.638             0.024            0.016
Chain 1:   2700       -18485.771             0.018            0.012
Chain 1:   2800       -18202.832             0.020            0.016
Chain 1:   2900       -18483.739             0.020            0.015
Chain 1:   3000       -18469.975             0.012            0.012
Chain 1:   3100       -18554.920             0.011            0.012
Chain 1:   3200       -18245.890             0.012            0.015
Chain 1:   3300       -18450.385             0.011            0.012
Chain 1:   3400       -17925.814             0.013            0.015
Chain 1:   3500       -18536.878             0.015            0.016
Chain 1:   3600       -17844.549             0.017            0.016
Chain 1:   3700       -18230.582             0.019            0.017
Chain 1:   3800       -17191.852             0.023            0.021
Chain 1:   3900       -17188.021             0.022            0.021
Chain 1:   4000       -17305.329             0.022            0.021
Chain 1:   4100       -17219.207             0.022            0.021
Chain 1:   4200       -17035.772             0.022            0.021
Chain 1:   4300       -17173.933             0.021            0.021
Chain 1:   4400       -17131.016             0.019            0.011
Chain 1:   4500       -17033.602             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001135 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12719.268             1.000            1.000
Chain 1:    200        -9498.366             0.670            1.000
Chain 1:    300        -8225.049             0.498            0.339
Chain 1:    400        -8385.721             0.378            0.339
Chain 1:    500        -8267.504             0.305            0.155
Chain 1:    600        -8184.258             0.256            0.155
Chain 1:    700        -8065.487             0.222            0.019
Chain 1:    800        -8061.623             0.194            0.019
Chain 1:    900        -8219.664             0.175            0.019
Chain 1:   1000        -8133.942             0.158            0.019
Chain 1:   1100        -8106.180             0.059            0.015
Chain 1:   1200        -8097.706             0.025            0.014
Chain 1:   1300        -8043.281             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62065.856             1.000            1.000
Chain 1:    200       -18279.799             1.698            2.395
Chain 1:    300        -9113.199             1.467            1.006
Chain 1:    400        -8428.244             1.121            1.006
Chain 1:    500        -8827.110             0.906            1.000
Chain 1:    600        -8127.359             0.769            1.000
Chain 1:    700        -8351.745             0.663            0.086
Chain 1:    800        -8218.185             0.582            0.086
Chain 1:    900        -8098.878             0.519            0.081
Chain 1:   1000        -7800.724             0.471            0.081
Chain 1:   1100        -7642.240             0.373            0.045
Chain 1:   1200        -7826.228             0.136            0.038
Chain 1:   1300        -7856.807             0.036            0.027
Chain 1:   1400        -8074.733             0.030            0.027
Chain 1:   1500        -7583.737             0.032            0.027
Chain 1:   1600        -7821.388             0.027            0.027
Chain 1:   1700        -7591.752             0.027            0.027
Chain 1:   1800        -7710.874             0.027            0.027
Chain 1:   1900        -7725.118             0.026            0.027
Chain 1:   2000        -7682.551             0.022            0.024
Chain 1:   2100        -7525.121             0.022            0.024
Chain 1:   2200        -7816.132             0.024            0.027
Chain 1:   2300        -7648.315             0.026            0.027
Chain 1:   2400        -7709.615             0.024            0.022
Chain 1:   2500        -7649.454             0.018            0.021
Chain 1:   2600        -7588.860             0.016            0.015
Chain 1:   2700        -7581.346             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004961 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86689.232             1.000            1.000
Chain 1:    200       -13870.522             3.125            5.250
Chain 1:    300       -10153.961             2.205            1.000
Chain 1:    400       -11591.601             1.685            1.000
Chain 1:    500        -9021.114             1.405            0.366
Chain 1:    600        -9216.393             1.174            0.366
Chain 1:    700        -8839.095             1.013            0.285
Chain 1:    800        -9506.498             0.895            0.285
Chain 1:    900        -8989.554             0.802            0.124
Chain 1:   1000        -8823.889             0.724            0.124
Chain 1:   1100        -8908.099             0.624            0.070   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8421.325             0.105            0.058
Chain 1:   1300        -9014.125             0.075            0.058
Chain 1:   1400        -8747.079             0.066            0.058
Chain 1:   1500        -8675.752             0.038            0.043
Chain 1:   1600        -8781.763             0.037            0.043
Chain 1:   1700        -8844.280             0.034            0.031
Chain 1:   1800        -8406.944             0.032            0.031
Chain 1:   1900        -8510.894             0.027            0.019
Chain 1:   2000        -8486.542             0.026            0.012
Chain 1:   2100        -8628.874             0.027            0.016
Chain 1:   2200        -8416.986             0.023            0.016
Chain 1:   2300        -8577.142             0.019            0.016
Chain 1:   2400        -8412.803             0.017            0.016
Chain 1:   2500        -8484.316             0.017            0.016
Chain 1:   2600        -8396.353             0.017            0.016
Chain 1:   2700        -8430.560             0.017            0.016
Chain 1:   2800        -8390.335             0.012            0.012
Chain 1:   2900        -8483.974             0.012            0.011
Chain 1:   3000        -8317.894             0.014            0.016
Chain 1:   3100        -8473.049             0.014            0.018
Chain 1:   3200        -8344.858             0.013            0.015
Chain 1:   3300        -8352.781             0.011            0.011
Chain 1:   3400        -8514.180             0.011            0.011
Chain 1:   3500        -8524.590             0.011            0.011
Chain 1:   3600        -8301.280             0.012            0.015
Chain 1:   3700        -8447.750             0.013            0.017
Chain 1:   3800        -8307.701             0.015            0.017
Chain 1:   3900        -8242.095             0.014            0.017
Chain 1:   4000        -8318.528             0.013            0.017
Chain 1:   4100        -8313.429             0.012            0.015
Chain 1:   4200        -8297.440             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003077 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422390.370             1.000            1.000
Chain 1:    200     -1585265.649             2.656            4.313
Chain 1:    300      -891325.891             2.030            1.000
Chain 1:    400      -458192.208             1.759            1.000
Chain 1:    500      -358268.745             1.463            0.945
Chain 1:    600      -233250.644             1.309            0.945
Chain 1:    700      -119535.060             1.258            0.945
Chain 1:    800       -86766.330             1.148            0.945
Chain 1:    900       -67124.709             1.053            0.779
Chain 1:   1000       -51940.776             0.977            0.779
Chain 1:   1100       -39434.464             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38616.443             0.479            0.378
Chain 1:   1300       -26580.160             0.447            0.378
Chain 1:   1400       -26302.901             0.353            0.317
Chain 1:   1500       -22891.379             0.340            0.317
Chain 1:   1600       -22109.148             0.290            0.293
Chain 1:   1700       -20983.035             0.200            0.292
Chain 1:   1800       -20927.727             0.163            0.149
Chain 1:   1900       -21254.210             0.135            0.054
Chain 1:   2000       -19764.866             0.113            0.054
Chain 1:   2100       -20003.285             0.083            0.035
Chain 1:   2200       -20229.965             0.082            0.035
Chain 1:   2300       -19846.893             0.038            0.019
Chain 1:   2400       -19618.826             0.039            0.019
Chain 1:   2500       -19420.823             0.025            0.015
Chain 1:   2600       -19050.608             0.023            0.015
Chain 1:   2700       -19007.512             0.018            0.012
Chain 1:   2800       -18724.148             0.019            0.015
Chain 1:   2900       -19005.567             0.019            0.015
Chain 1:   3000       -18991.779             0.012            0.012
Chain 1:   3100       -19076.803             0.011            0.012
Chain 1:   3200       -18767.231             0.011            0.015
Chain 1:   3300       -18972.163             0.011            0.012
Chain 1:   3400       -18446.604             0.012            0.015
Chain 1:   3500       -19059.186             0.014            0.015
Chain 1:   3600       -18364.949             0.016            0.015
Chain 1:   3700       -18752.377             0.018            0.016
Chain 1:   3800       -17710.679             0.023            0.021
Chain 1:   3900       -17706.774             0.021            0.021
Chain 1:   4000       -17824.089             0.022            0.021
Chain 1:   4100       -17737.752             0.022            0.021
Chain 1:   4200       -17553.715             0.021            0.021
Chain 1:   4300       -17692.322             0.021            0.021
Chain 1:   4400       -17648.879             0.018            0.010
Chain 1:   4500       -17551.366             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001444 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12808.543             1.000            1.000
Chain 1:    200        -9791.885             0.654            1.000
Chain 1:    300        -8398.373             0.491            0.308
Chain 1:    400        -8588.297             0.374            0.308
Chain 1:    500        -8585.030             0.299            0.166
Chain 1:    600        -8357.157             0.254            0.166
Chain 1:    700        -8263.749             0.219            0.027
Chain 1:    800        -8287.868             0.192            0.027
Chain 1:    900        -8393.045             0.172            0.022
Chain 1:   1000        -8296.975             0.156            0.022
Chain 1:   1100        -8339.061             0.057            0.013
Chain 1:   1200        -8294.358             0.026            0.012
Chain 1:   1300        -8216.466             0.011            0.011
Chain 1:   1400        -8246.460             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001477 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -60687.297             1.000            1.000
Chain 1:    200       -18562.526             1.635            2.269
Chain 1:    300        -9017.048             1.443            1.059
Chain 1:    400        -8307.035             1.103            1.059
Chain 1:    500        -8567.798             0.889            1.000
Chain 1:    600        -8209.978             0.748            1.000
Chain 1:    700        -8054.757             0.644            0.085
Chain 1:    800        -8495.146             0.570            0.085
Chain 1:    900        -8105.197             0.512            0.052
Chain 1:   1000        -7795.393             0.465            0.052
Chain 1:   1100        -7789.026             0.365            0.048
Chain 1:   1200        -7633.818             0.140            0.044
Chain 1:   1300        -7901.787             0.037            0.040
Chain 1:   1400        -7886.441             0.029            0.034
Chain 1:   1500        -7657.338             0.029            0.034
Chain 1:   1600        -7886.593             0.027            0.030
Chain 1:   1700        -7665.850             0.028            0.030
Chain 1:   1800        -7770.563             0.025            0.029
Chain 1:   1900        -7603.202             0.022            0.029
Chain 1:   2000        -7740.090             0.020            0.022
Chain 1:   2100        -7653.272             0.021            0.022
Chain 1:   2200        -7802.476             0.021            0.022
Chain 1:   2300        -7630.287             0.020            0.022
Chain 1:   2400        -7714.130             0.020            0.022
Chain 1:   2500        -7737.926             0.018            0.019
Chain 1:   2600        -7600.423             0.017            0.018
Chain 1:   2700        -7600.063             0.014            0.018
Chain 1:   2800        -7583.950             0.013            0.018
Chain 1:   2900        -7444.386             0.012            0.018
Chain 1:   3000        -7593.699             0.013            0.018
Chain 1:   3100        -7594.106             0.011            0.018
Chain 1:   3200        -7811.319             0.012            0.018
Chain 1:   3300        -7535.654             0.014            0.018
Chain 1:   3400        -7770.084             0.016            0.019
Chain 1:   3500        -7504.826             0.019            0.020
Chain 1:   3600        -7572.384             0.018            0.020
Chain 1:   3700        -7522.178             0.019            0.020
Chain 1:   3800        -7524.006             0.018            0.020
Chain 1:   3900        -7481.115             0.017            0.020
Chain 1:   4000        -7473.481             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003009 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87053.757             1.000            1.000
Chain 1:    200       -14011.462             3.107            5.213
Chain 1:    300       -10310.830             2.191            1.000
Chain 1:    400       -11502.515             1.669            1.000
Chain 1:    500        -9303.622             1.382            0.359
Chain 1:    600        -8774.630             1.162            0.359
Chain 1:    700        -8679.045             0.998            0.236
Chain 1:    800        -9845.317             0.888            0.236
Chain 1:    900        -9081.794             0.798            0.118
Chain 1:   1000        -8964.690             0.720            0.118
Chain 1:   1100        -9102.506             0.621            0.104   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8697.680             0.105            0.084
Chain 1:   1300        -8944.627             0.072            0.060
Chain 1:   1400        -8956.207             0.061            0.047
Chain 1:   1500        -8844.919             0.039            0.028
Chain 1:   1600        -8953.181             0.034            0.015
Chain 1:   1700        -9023.849             0.034            0.015
Chain 1:   1800        -8591.605             0.027            0.015
Chain 1:   1900        -8695.910             0.020            0.013
Chain 1:   2000        -8671.296             0.019            0.013
Chain 1:   2100        -8620.262             0.018            0.012
Chain 1:   2200        -8614.814             0.013            0.012
Chain 1:   2300        -8745.169             0.012            0.012
Chain 1:   2400        -8599.204             0.014            0.012
Chain 1:   2500        -8666.452             0.013            0.012
Chain 1:   2600        -8587.287             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003745 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8435429.779             1.000            1.000
Chain 1:    200     -1589296.579             2.654            4.308
Chain 1:    300      -892559.628             2.029            1.000
Chain 1:    400      -458799.661             1.758            1.000
Chain 1:    500      -358885.516             1.462            0.945
Chain 1:    600      -233539.754             1.308            0.945
Chain 1:    700      -119743.611             1.257            0.945
Chain 1:    800       -86957.766             1.147            0.945
Chain 1:    900       -67291.192             1.052            0.781
Chain 1:   1000       -52093.730             0.976            0.781
Chain 1:   1100       -39578.566             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38757.928             0.479            0.377
Chain 1:   1300       -26718.109             0.446            0.377
Chain 1:   1400       -26438.590             0.353            0.316
Chain 1:   1500       -23027.352             0.339            0.316
Chain 1:   1600       -22245.108             0.289            0.292
Chain 1:   1700       -21119.001             0.200            0.292
Chain 1:   1800       -21063.442             0.162            0.148
Chain 1:   1900       -21389.892             0.134            0.053
Chain 1:   2000       -19900.803             0.113            0.053
Chain 1:   2100       -20139.119             0.082            0.035
Chain 1:   2200       -20365.804             0.081            0.035
Chain 1:   2300       -19982.759             0.038            0.019
Chain 1:   2400       -19754.748             0.038            0.019
Chain 1:   2500       -19556.798             0.025            0.015
Chain 1:   2600       -19186.627             0.023            0.015
Chain 1:   2700       -19143.512             0.018            0.012
Chain 1:   2800       -18860.218             0.019            0.015
Chain 1:   2900       -19141.656             0.019            0.015
Chain 1:   3000       -19127.724             0.012            0.012
Chain 1:   3100       -19212.795             0.011            0.012
Chain 1:   3200       -18903.252             0.011            0.015
Chain 1:   3300       -19108.186             0.011            0.012
Chain 1:   3400       -18582.714             0.012            0.015
Chain 1:   3500       -19195.115             0.014            0.015
Chain 1:   3600       -18501.111             0.016            0.015
Chain 1:   3700       -18888.442             0.018            0.016
Chain 1:   3800       -17847.051             0.022            0.021
Chain 1:   3900       -17843.174             0.021            0.021
Chain 1:   4000       -17960.483             0.021            0.021
Chain 1:   4100       -17874.167             0.022            0.021
Chain 1:   4200       -17690.189             0.021            0.021
Chain 1:   4300       -17828.731             0.021            0.021
Chain 1:   4400       -17785.372             0.018            0.010
Chain 1:   4500       -17687.863             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001247 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12111.376             1.000            1.000
Chain 1:    200        -9015.550             0.672            1.000
Chain 1:    300        -7828.349             0.498            0.343
Chain 1:    400        -7995.063             0.379            0.343
Chain 1:    500        -7870.935             0.306            0.152
Chain 1:    600        -7745.760             0.258            0.152
Chain 1:    700        -7754.876             0.221            0.021
Chain 1:    800        -7665.827             0.195            0.021
Chain 1:    900        -7667.181             0.173            0.016
Chain 1:   1000        -7726.193             0.157            0.016
Chain 1:   1100        -7811.319             0.058            0.016
Chain 1:   1200        -7686.512             0.025            0.016
Chain 1:   1300        -7699.639             0.010            0.012
Chain 1:   1400        -7672.525             0.008            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46073.042             1.000            1.000
Chain 1:    200       -15235.984             1.512            2.024
Chain 1:    300        -8490.696             1.273            1.000
Chain 1:    400        -8352.285             0.959            1.000
Chain 1:    500        -8138.214             0.772            0.794
Chain 1:    600        -8797.160             0.656            0.794
Chain 1:    700        -8069.480             0.575            0.090
Chain 1:    800        -7869.372             0.506            0.090
Chain 1:    900        -7601.878             0.454            0.075
Chain 1:   1000        -7651.342             0.409            0.075
Chain 1:   1100        -7552.518             0.311            0.035
Chain 1:   1200        -7504.461             0.109            0.026
Chain 1:   1300        -7471.415             0.030            0.025
Chain 1:   1400        -7783.022             0.032            0.026
Chain 1:   1500        -7506.770             0.033            0.035
Chain 1:   1600        -7393.717             0.027            0.025
Chain 1:   1700        -7405.376             0.018            0.015
Chain 1:   1800        -7451.451             0.017            0.013
Chain 1:   1900        -7475.184             0.013            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003301 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86165.013             1.000            1.000
Chain 1:    200       -13174.264             3.270            5.540
Chain 1:    300        -9590.834             2.305            1.000
Chain 1:    400       -10382.243             1.748            1.000
Chain 1:    500        -8544.985             1.441            0.374
Chain 1:    600        -8088.868             1.210            0.374
Chain 1:    700        -8282.651             1.041            0.215
Chain 1:    800        -8572.988             0.915            0.215
Chain 1:    900        -8401.792             0.815            0.076
Chain 1:   1000        -8150.430             0.737            0.076
Chain 1:   1100        -8462.328             0.641            0.056   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8018.681             0.092            0.055
Chain 1:   1300        -8301.136             0.058            0.037
Chain 1:   1400        -8295.336             0.051            0.034
Chain 1:   1500        -8203.603             0.030            0.034
Chain 1:   1600        -8305.812             0.026            0.031
Chain 1:   1700        -8389.467             0.025            0.031
Chain 1:   1800        -7994.969             0.026            0.031
Chain 1:   1900        -8096.213             0.025            0.031
Chain 1:   2000        -8066.948             0.023            0.013
Chain 1:   2100        -8189.031             0.020            0.013
Chain 1:   2200        -7969.475             0.018            0.013
Chain 1:   2300        -8125.071             0.016            0.013
Chain 1:   2400        -8138.685             0.016            0.013
Chain 1:   2500        -8108.471             0.015            0.013
Chain 1:   2600        -8111.171             0.014            0.013
Chain 1:   2700        -8017.385             0.014            0.013
Chain 1:   2800        -7988.292             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004603 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 46.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407861.146             1.000            1.000
Chain 1:    200     -1587599.042             2.648            4.296
Chain 1:    300      -891543.100             2.026            1.000
Chain 1:    400      -457764.490             1.756            1.000
Chain 1:    500      -357806.818             1.461            0.948
Chain 1:    600      -232714.652             1.307            0.948
Chain 1:    700      -118895.243             1.257            0.948
Chain 1:    800       -86089.461             1.147            0.948
Chain 1:    900       -66429.796             1.053            0.781
Chain 1:   1000       -51223.831             0.977            0.781
Chain 1:   1100       -38700.878             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37875.013             0.482            0.381
Chain 1:   1300       -25840.629             0.451            0.381
Chain 1:   1400       -25559.372             0.357            0.324
Chain 1:   1500       -22149.126             0.344            0.324
Chain 1:   1600       -21365.582             0.294            0.297
Chain 1:   1700       -20240.924             0.204            0.296
Chain 1:   1800       -20185.246             0.166            0.154
Chain 1:   1900       -20511.030             0.138            0.056
Chain 1:   2000       -19023.501             0.117            0.056
Chain 1:   2100       -19261.893             0.085            0.037
Chain 1:   2200       -19487.933             0.084            0.037
Chain 1:   2300       -19105.575             0.040            0.020
Chain 1:   2400       -18877.779             0.040            0.020
Chain 1:   2500       -18679.733             0.026            0.016
Chain 1:   2600       -18310.419             0.024            0.016
Chain 1:   2700       -18267.467             0.019            0.012
Chain 1:   2800       -17984.450             0.020            0.016
Chain 1:   2900       -18265.543             0.020            0.015
Chain 1:   3000       -18251.797             0.012            0.012
Chain 1:   3100       -18336.721             0.011            0.012
Chain 1:   3200       -18027.657             0.012            0.015
Chain 1:   3300       -18232.158             0.011            0.012
Chain 1:   3400       -17707.525             0.013            0.015
Chain 1:   3500       -18318.715             0.015            0.016
Chain 1:   3600       -17626.278             0.017            0.016
Chain 1:   3700       -18012.432             0.019            0.017
Chain 1:   3800       -16973.479             0.023            0.021
Chain 1:   3900       -16969.623             0.022            0.021
Chain 1:   4000       -17086.961             0.023            0.021
Chain 1:   4100       -17000.783             0.023            0.021
Chain 1:   4200       -16817.288             0.022            0.021
Chain 1:   4300       -16955.505             0.022            0.021
Chain 1:   4400       -16912.575             0.019            0.011
Chain 1:   4500       -16815.127             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11727.893             1.000            1.000
Chain 1:    200        -8666.699             0.677            1.000
Chain 1:    300        -7770.041             0.490            0.353
Chain 1:    400        -7830.280             0.369            0.353
Chain 1:    500        -7692.609             0.299            0.115
Chain 1:    600        -7629.124             0.250            0.115
Chain 1:    700        -7578.338             0.216            0.018
Chain 1:    800        -7531.778             0.189            0.018
Chain 1:    900        -7497.359             0.169            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001693 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56336.528             1.000            1.000
Chain 1:    200       -16686.348             1.688            2.376
Chain 1:    300        -8415.418             1.453            1.000
Chain 1:    400        -8038.101             1.101            1.000
Chain 1:    500        -8275.319             0.887            0.983
Chain 1:    600        -7950.152             0.746            0.983
Chain 1:    700        -7738.390             0.643            0.047
Chain 1:    800        -7970.715             0.567            0.047
Chain 1:    900        -7814.018             0.506            0.041
Chain 1:   1000        -7690.176             0.457            0.041
Chain 1:   1100        -7673.095             0.357            0.029
Chain 1:   1200        -7541.686             0.121            0.029
Chain 1:   1300        -7580.516             0.023            0.027
Chain 1:   1400        -7817.679             0.022            0.027
Chain 1:   1500        -7574.467             0.022            0.027
Chain 1:   1600        -7498.821             0.019            0.020
Chain 1:   1700        -7461.731             0.017            0.017
Chain 1:   1800        -7503.925             0.014            0.016
Chain 1:   1900        -7463.762             0.013            0.010
Chain 1:   2000        -7553.390             0.013            0.010
Chain 1:   2100        -7479.913             0.013            0.010
Chain 1:   2200        -7604.039             0.013            0.010
Chain 1:   2300        -7730.900             0.014            0.012
Chain 1:   2400        -7559.800             0.014            0.012
Chain 1:   2500        -7454.303             0.012            0.012
Chain 1:   2600        -7473.383             0.011            0.012
Chain 1:   2700        -7516.143             0.011            0.012
Chain 1:   2800        -7462.563             0.011            0.012
Chain 1:   2900        -7396.735             0.012            0.012
Chain 1:   3000        -7519.276             0.012            0.014
Chain 1:   3100        -7481.191             0.012            0.014
Chain 1:   3200        -7622.314             0.012            0.014
Chain 1:   3300        -7442.979             0.013            0.014
Chain 1:   3400        -7557.063             0.012            0.014
Chain 1:   3500        -7424.675             0.012            0.015
Chain 1:   3600        -7460.107             0.012            0.015
Chain 1:   3700        -7424.184             0.012            0.015
Chain 1:   3800        -7448.631             0.012            0.015
Chain 1:   3900        -7432.926             0.011            0.015
Chain 1:   4000        -7405.344             0.010            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003099 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85366.519             1.000            1.000
Chain 1:    200       -12781.686             3.339            5.679
Chain 1:    300        -9339.589             2.349            1.000
Chain 1:    400        -9789.504             1.773            1.000
Chain 1:    500        -8241.983             1.456            0.369
Chain 1:    600        -8365.737             1.216            0.369
Chain 1:    700        -8087.528             1.047            0.188
Chain 1:    800        -8319.856             0.920            0.188
Chain 1:    900        -8254.137             0.818            0.046
Chain 1:   1000        -8015.765             0.740            0.046
Chain 1:   1100        -8283.100             0.643            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8068.093             0.078            0.032
Chain 1:   1300        -8176.647             0.042            0.030
Chain 1:   1400        -8065.649             0.039            0.028
Chain 1:   1500        -8042.577             0.020            0.027
Chain 1:   1600        -8159.741             0.020            0.027
Chain 1:   1700        -8224.667             0.018            0.014
Chain 1:   1800        -7881.587             0.019            0.014
Chain 1:   1900        -7975.588             0.020            0.014
Chain 1:   2000        -7947.376             0.017            0.014
Chain 1:   2100        -8101.400             0.016            0.014
Chain 1:   2200        -7873.623             0.016            0.014
Chain 1:   2300        -7952.381             0.016            0.014
Chain 1:   2400        -8012.421             0.015            0.012
Chain 1:   2500        -7979.447             0.015            0.012
Chain 1:   2600        -7970.914             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003958 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398173.222             1.000            1.000
Chain 1:    200     -1584109.578             2.651            4.302
Chain 1:    300      -890359.421             2.027            1.000
Chain 1:    400      -457420.862             1.757            1.000
Chain 1:    500      -357653.548             1.461            0.946
Chain 1:    600      -232503.527             1.307            0.946
Chain 1:    700      -118554.371             1.258            0.946
Chain 1:    800       -85743.318             1.149            0.946
Chain 1:    900       -66051.064             1.054            0.779
Chain 1:   1000       -50815.568             0.979            0.779
Chain 1:   1100       -38280.979             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37443.452             0.483            0.383
Chain 1:   1300       -25408.852             0.453            0.383
Chain 1:   1400       -25123.074             0.359            0.327
Chain 1:   1500       -21714.300             0.347            0.327
Chain 1:   1600       -20930.414             0.297            0.300
Chain 1:   1700       -19806.474             0.207            0.298
Chain 1:   1800       -19750.454             0.169            0.157
Chain 1:   1900       -20075.371             0.140            0.057
Chain 1:   2000       -18590.204             0.118            0.057
Chain 1:   2100       -18828.169             0.087            0.037
Chain 1:   2200       -19053.704             0.086            0.037
Chain 1:   2300       -18672.037             0.041            0.020
Chain 1:   2400       -18444.583             0.041            0.020
Chain 1:   2500       -18246.704             0.026            0.016
Chain 1:   2600       -17878.001             0.024            0.016
Chain 1:   2700       -17835.314             0.019            0.013
Chain 1:   2800       -17552.754             0.020            0.016
Chain 1:   2900       -17833.439             0.020            0.016
Chain 1:   3000       -17819.668             0.012            0.013
Chain 1:   3100       -17904.486             0.012            0.012
Chain 1:   3200       -17595.944             0.012            0.016
Chain 1:   3300       -17800.067             0.011            0.012
Chain 1:   3400       -17276.431             0.013            0.016
Chain 1:   3500       -17886.149             0.015            0.016
Chain 1:   3600       -17195.639             0.017            0.016
Chain 1:   3700       -17580.344             0.019            0.018
Chain 1:   3800       -16544.451             0.024            0.022
Chain 1:   3900       -16540.732             0.022            0.022
Chain 1:   4000       -16657.994             0.023            0.022
Chain 1:   4100       -16571.978             0.023            0.022
Chain 1:   4200       -16389.201             0.022            0.022
Chain 1:   4300       -16526.891             0.022            0.022
Chain 1:   4400       -16484.486             0.019            0.011
Chain 1:   4500       -16387.191             0.017            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001757 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12619.725             1.000            1.000
Chain 1:    200        -9610.217             0.657            1.000
Chain 1:    300        -8318.406             0.489            0.313
Chain 1:    400        -8356.865             0.368            0.313
Chain 1:    500        -8276.860             0.297            0.155
Chain 1:    600        -8225.187             0.248            0.155
Chain 1:    700        -8121.667             0.215            0.013
Chain 1:    800        -8128.753             0.188            0.013
Chain 1:    900        -8100.484             0.167            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57504.644             1.000            1.000
Chain 1:    200       -17799.139             1.615            2.231
Chain 1:    300        -8880.327             1.412            1.004
Chain 1:    400        -8330.462             1.075            1.004
Chain 1:    500        -8630.119             0.867            1.000
Chain 1:    600        -9244.282             0.734            1.000
Chain 1:    700        -8049.973             0.650            0.148
Chain 1:    800        -8342.378             0.573            0.148
Chain 1:    900        -8013.850             0.514            0.066
Chain 1:   1000        -7791.267             0.466            0.066
Chain 1:   1100        -7884.962             0.367            0.066
Chain 1:   1200        -7653.188             0.147            0.041
Chain 1:   1300        -7715.591             0.047            0.035
Chain 1:   1400        -7797.038             0.041            0.035
Chain 1:   1500        -7613.870             0.040            0.030
Chain 1:   1600        -7604.416             0.034            0.029
Chain 1:   1700        -7589.529             0.019            0.024
Chain 1:   1800        -7573.445             0.016            0.012
Chain 1:   1900        -7609.563             0.012            0.010
Chain 1:   2000        -7663.802             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003671 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86636.348             1.000            1.000
Chain 1:    200       -13791.266             3.141            5.282
Chain 1:    300       -10125.294             2.215            1.000
Chain 1:    400       -11136.570             1.684            1.000
Chain 1:    500        -9108.199             1.392            0.362
Chain 1:    600        -8762.385             1.166            0.362
Chain 1:    700        -8624.883             1.002            0.223
Chain 1:    800        -9260.241             0.885            0.223
Chain 1:    900        -8816.392             0.792            0.091
Chain 1:   1000        -8913.033             0.714            0.091
Chain 1:   1100        -8799.196             0.616            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8607.720             0.090            0.050
Chain 1:   1300        -8810.501             0.056            0.039
Chain 1:   1400        -8808.142             0.047            0.023
Chain 1:   1500        -8680.477             0.026            0.022
Chain 1:   1600        -8789.632             0.023            0.016
Chain 1:   1700        -8868.862             0.022            0.015
Chain 1:   1800        -8445.981             0.021            0.015
Chain 1:   1900        -8546.789             0.017            0.013
Chain 1:   2000        -8521.289             0.016            0.013
Chain 1:   2100        -8646.717             0.016            0.015
Chain 1:   2200        -8450.128             0.016            0.015
Chain 1:   2300        -8541.618             0.015            0.012
Chain 1:   2400        -8610.455             0.016            0.012
Chain 1:   2500        -8556.703             0.015            0.012
Chain 1:   2600        -8558.014             0.014            0.011
Chain 1:   2700        -8474.754             0.014            0.011
Chain 1:   2800        -8434.715             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005451 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 54.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391479.163             1.000            1.000
Chain 1:    200     -1582025.420             2.652            4.304
Chain 1:    300      -892454.223             2.026            1.000
Chain 1:    400      -458724.228             1.756            1.000
Chain 1:    500      -359394.280             1.460            0.946
Chain 1:    600      -234254.200             1.306            0.946
Chain 1:    700      -120057.677             1.255            0.946
Chain 1:    800       -87124.337             1.145            0.946
Chain 1:    900       -67372.145             1.051            0.773
Chain 1:   1000       -52087.001             0.975            0.773
Chain 1:   1100       -39489.903             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38657.701             0.479            0.378
Chain 1:   1300       -26540.211             0.447            0.378
Chain 1:   1400       -26252.526             0.353            0.319
Chain 1:   1500       -22819.838             0.341            0.319
Chain 1:   1600       -22030.600             0.291            0.293
Chain 1:   1700       -20895.595             0.201            0.293
Chain 1:   1800       -20837.678             0.164            0.150
Chain 1:   1900       -21163.914             0.136            0.054
Chain 1:   2000       -19670.128             0.114            0.054
Chain 1:   2100       -19908.891             0.084            0.036
Chain 1:   2200       -20136.066             0.083            0.036
Chain 1:   2300       -19752.587             0.039            0.019
Chain 1:   2400       -19524.534             0.039            0.019
Chain 1:   2500       -19326.808             0.025            0.015
Chain 1:   2600       -18956.697             0.023            0.015
Chain 1:   2700       -18913.545             0.018            0.012
Chain 1:   2800       -18630.451             0.019            0.015
Chain 1:   2900       -18911.875             0.019            0.015
Chain 1:   3000       -18897.976             0.012            0.012
Chain 1:   3100       -18983.000             0.011            0.012
Chain 1:   3200       -18673.577             0.011            0.015
Chain 1:   3300       -18878.386             0.011            0.012
Chain 1:   3400       -18353.189             0.012            0.015
Chain 1:   3500       -18965.351             0.015            0.015
Chain 1:   3600       -18271.695             0.016            0.015
Chain 1:   3700       -18658.819             0.018            0.017
Chain 1:   3800       -17618.057             0.023            0.021
Chain 1:   3900       -17614.221             0.021            0.021
Chain 1:   4000       -17731.485             0.022            0.021
Chain 1:   4100       -17645.228             0.022            0.021
Chain 1:   4200       -17461.359             0.021            0.021
Chain 1:   4300       -17599.815             0.021            0.021
Chain 1:   4400       -17556.586             0.018            0.011
Chain 1:   4500       -17459.094             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00179 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48395.223             1.000            1.000
Chain 1:    200       -39867.815             0.607            1.000
Chain 1:    300       -20942.904             0.706            0.904
Chain 1:    400       -12215.901             0.708            0.904
Chain 1:    500       -11328.112             0.582            0.714
Chain 1:    600       -12761.772             0.504            0.714
Chain 1:    700       -11662.939             0.445            0.214
Chain 1:    800       -14508.066             0.414            0.214
Chain 1:    900       -13837.758             0.373            0.196
Chain 1:   1000       -13580.738             0.338            0.196
Chain 1:   1100       -10291.264             0.270            0.196
Chain 1:   1200       -10411.887             0.250            0.112
Chain 1:   1300       -19947.796             0.207            0.112
Chain 1:   1400       -11496.335             0.209            0.112
Chain 1:   1500       -12377.501             0.209            0.112
Chain 1:   1600       -13842.203             0.208            0.106
Chain 1:   1700        -9244.725             0.248            0.196
Chain 1:   1800        -9897.789             0.235            0.106
Chain 1:   1900       -13571.290             0.257            0.271
Chain 1:   2000       -16584.374             0.274            0.271
Chain 1:   2100        -9181.041             0.322            0.271
Chain 1:   2200        -9485.329             0.324            0.271
Chain 1:   2300        -8888.408             0.283            0.182
Chain 1:   2400        -8764.165             0.211            0.106
Chain 1:   2500        -8860.346             0.205            0.106
Chain 1:   2600        -8983.156             0.196            0.067
Chain 1:   2700       -16081.062             0.190            0.067
Chain 1:   2800       -10119.594             0.243            0.182
Chain 1:   2900       -13829.642             0.242            0.182
Chain 1:   3000       -10171.386             0.260            0.268
Chain 1:   3100        -9445.566             0.187            0.077
Chain 1:   3200        -8379.662             0.197            0.127
Chain 1:   3300       -10720.216             0.212            0.218
Chain 1:   3400       -12370.485             0.224            0.218
Chain 1:   3500       -10628.328             0.239            0.218
Chain 1:   3600       -10990.385             0.241            0.218
Chain 1:   3700       -16110.592             0.229            0.218
Chain 1:   3800        -8702.919             0.255            0.218
Chain 1:   3900       -15321.623             0.271            0.218
Chain 1:   4000        -9144.714             0.303            0.218
Chain 1:   4100       -11734.992             0.317            0.221
Chain 1:   4200       -12717.889             0.312            0.221
Chain 1:   4300        -9154.386             0.329            0.318
Chain 1:   4400        -9174.376             0.316            0.318
Chain 1:   4500        -9330.071             0.302            0.318
Chain 1:   4600       -13026.599             0.327            0.318
Chain 1:   4700        -8464.584             0.349            0.389
Chain 1:   4800        -8601.058             0.265            0.284
Chain 1:   4900        -8562.091             0.222            0.221
Chain 1:   5000        -8987.054             0.160            0.077
Chain 1:   5100        -8693.431             0.141            0.047
Chain 1:   5200        -8569.835             0.135            0.034
Chain 1:   5300        -8988.346             0.100            0.034
Chain 1:   5400       -10482.503             0.114            0.047
Chain 1:   5500        -8672.647             0.134            0.047
Chain 1:   5600        -8434.819             0.108            0.047
Chain 1:   5700        -8329.833             0.055            0.034
Chain 1:   5800        -8261.210             0.055            0.034
Chain 1:   5900        -8694.238             0.059            0.047
Chain 1:   6000       -11520.317             0.079            0.047
Chain 1:   6100        -9379.069             0.098            0.050
Chain 1:   6200        -8080.194             0.113            0.143
Chain 1:   6300        -8357.621             0.112            0.143
Chain 1:   6400       -11039.545             0.122            0.161
Chain 1:   6500        -8779.724             0.127            0.161
Chain 1:   6600        -8312.639             0.129            0.161
Chain 1:   6700        -8864.451             0.134            0.161
Chain 1:   6800        -8074.431             0.143            0.161
Chain 1:   6900        -8172.172             0.140            0.161
Chain 1:   7000        -9452.749             0.129            0.135
Chain 1:   7100       -11208.350             0.121            0.135
Chain 1:   7200        -8950.125             0.131            0.135
Chain 1:   7300        -8708.786             0.130            0.135
Chain 1:   7400        -9939.565             0.118            0.124
Chain 1:   7500        -9140.286             0.101            0.098
Chain 1:   7600        -8444.423             0.104            0.098
Chain 1:   7700        -8252.363             0.100            0.098
Chain 1:   7800        -8136.864             0.092            0.087
Chain 1:   7900        -8084.019             0.091            0.087
Chain 1:   8000        -9486.298             0.092            0.087
Chain 1:   8100        -8155.642             0.093            0.087
Chain 1:   8200        -8348.489             0.070            0.082
Chain 1:   8300       -11339.932             0.094            0.087
Chain 1:   8400        -8266.375             0.118            0.087
Chain 1:   8500        -7977.683             0.113            0.082
Chain 1:   8600        -8496.180             0.111            0.061
Chain 1:   8700        -8970.012             0.114            0.061
Chain 1:   8800       -10121.628             0.124            0.114
Chain 1:   8900        -9991.777             0.125            0.114
Chain 1:   9000       -11028.052             0.119            0.094
Chain 1:   9100        -8652.137             0.130            0.094
Chain 1:   9200        -8193.812             0.134            0.094
Chain 1:   9300        -8995.266             0.116            0.089
Chain 1:   9400        -8116.655             0.090            0.089
Chain 1:   9500        -7900.481             0.089            0.089
Chain 1:   9600        -7937.525             0.083            0.089
Chain 1:   9700        -7867.934             0.079            0.089
Chain 1:   9800        -8279.102             0.073            0.056
Chain 1:   9900        -9199.611             0.081            0.089
Chain 1:   10000        -8027.027             0.086            0.089
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57748.165             1.000            1.000
Chain 1:    200       -17282.602             1.671            2.341
Chain 1:    300        -8484.775             1.459            1.037
Chain 1:    400        -8068.009             1.107            1.037
Chain 1:    500        -8454.656             0.895            1.000
Chain 1:    600        -8563.928             0.748            1.000
Chain 1:    700        -7824.458             0.655            0.095
Chain 1:    800        -8029.943             0.576            0.095
Chain 1:    900        -7859.319             0.514            0.052
Chain 1:   1000        -7654.947             0.466            0.052
Chain 1:   1100        -7609.057             0.366            0.046
Chain 1:   1200        -7722.855             0.134            0.027
Chain 1:   1300        -7643.860             0.031            0.026
Chain 1:   1400        -7795.389             0.028            0.022
Chain 1:   1500        -7579.399             0.026            0.022
Chain 1:   1600        -7474.523             0.026            0.022
Chain 1:   1700        -7461.525             0.017            0.019
Chain 1:   1800        -7501.716             0.015            0.015
Chain 1:   1900        -7536.097             0.013            0.014
Chain 1:   2000        -7529.543             0.011            0.010
Chain 1:   2100        -7543.320             0.010            0.010
Chain 1:   2200        -7622.554             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003127 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86025.826             1.000            1.000
Chain 1:    200       -13118.681             3.279            5.558
Chain 1:    300        -9596.579             2.308            1.000
Chain 1:    400       -10295.893             1.748            1.000
Chain 1:    500        -8535.253             1.440            0.367
Chain 1:    600        -8120.727             1.208            0.367
Chain 1:    700        -8308.471             1.039            0.206
Chain 1:    800        -8452.160             0.911            0.206
Chain 1:    900        -8473.522             0.810            0.068
Chain 1:   1000        -8253.187             0.732            0.068
Chain 1:   1100        -8496.812             0.635            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8270.187             0.082            0.029
Chain 1:   1300        -8360.279             0.046            0.027
Chain 1:   1400        -8344.640             0.039            0.027
Chain 1:   1500        -8247.993             0.020            0.023
Chain 1:   1600        -8339.396             0.016            0.017
Chain 1:   1700        -8443.360             0.015            0.012
Chain 1:   1800        -8055.779             0.018            0.012
Chain 1:   1900        -8155.458             0.019            0.012
Chain 1:   2000        -8125.663             0.017            0.012
Chain 1:   2100        -8267.621             0.016            0.012
Chain 1:   2200        -8047.297             0.016            0.012
Chain 1:   2300        -8189.599             0.016            0.012
Chain 1:   2400        -8074.865             0.018            0.014
Chain 1:   2500        -8132.505             0.017            0.014
Chain 1:   2600        -8146.236             0.016            0.014
Chain 1:   2700        -8067.798             0.016            0.014
Chain 1:   2800        -8050.882             0.011            0.012
Chain 1:   2900        -8058.790             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003408 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397996.523             1.000            1.000
Chain 1:    200     -1582019.638             2.654            4.308
Chain 1:    300      -889926.926             2.029            1.000
Chain 1:    400      -457302.147             1.758            1.000
Chain 1:    500      -357893.740             1.462            0.946
Chain 1:    600      -232820.658             1.308            0.946
Chain 1:    700      -118934.687             1.258            0.946
Chain 1:    800       -86109.932             1.148            0.946
Chain 1:    900       -66424.893             1.054            0.778
Chain 1:   1000       -51188.891             0.978            0.778
Chain 1:   1100       -38646.931             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37812.107             0.482            0.381
Chain 1:   1300       -25764.748             0.451            0.381
Chain 1:   1400       -25478.792             0.357            0.325
Chain 1:   1500       -22066.491             0.345            0.325
Chain 1:   1600       -21281.843             0.295            0.298
Chain 1:   1700       -20156.178             0.205            0.296
Chain 1:   1800       -20099.855             0.167            0.155
Chain 1:   1900       -20425.291             0.139            0.056
Chain 1:   2000       -18938.063             0.117            0.056
Chain 1:   2100       -19176.201             0.086            0.037
Chain 1:   2200       -19402.271             0.085            0.037
Chain 1:   2300       -19019.996             0.040            0.020
Chain 1:   2400       -18792.373             0.040            0.020
Chain 1:   2500       -18594.444             0.026            0.016
Chain 1:   2600       -18225.380             0.024            0.016
Chain 1:   2700       -18182.473             0.019            0.012
Chain 1:   2800       -17899.774             0.020            0.016
Chain 1:   2900       -18180.642             0.020            0.015
Chain 1:   3000       -18166.807             0.012            0.012
Chain 1:   3100       -18251.744             0.011            0.012
Chain 1:   3200       -17942.904             0.012            0.015
Chain 1:   3300       -18147.207             0.011            0.012
Chain 1:   3400       -17623.062             0.013            0.015
Chain 1:   3500       -18233.622             0.015            0.016
Chain 1:   3600       -17541.961             0.017            0.016
Chain 1:   3700       -17927.608             0.019            0.017
Chain 1:   3800       -16889.944             0.023            0.022
Chain 1:   3900       -16886.162             0.022            0.022
Chain 1:   4000       -17003.433             0.023            0.022
Chain 1:   4100       -16917.409             0.023            0.022
Chain 1:   4200       -16734.155             0.022            0.022
Chain 1:   4300       -16872.174             0.022            0.022
Chain 1:   4400       -16829.478             0.019            0.011
Chain 1:   4500       -16732.100             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12621.568             1.000            1.000
Chain 1:    200        -9459.141             0.667            1.000
Chain 1:    300        -8103.618             0.501            0.334
Chain 1:    400        -8339.113             0.382            0.334
Chain 1:    500        -8110.644             0.312            0.167
Chain 1:    600        -8062.792             0.261            0.167
Chain 1:    700        -8131.475             0.225            0.028
Chain 1:    800        -8065.004             0.198            0.028
Chain 1:    900        -7886.018             0.178            0.028
Chain 1:   1000        -8019.503             0.162            0.028
Chain 1:   1100        -8053.460             0.062            0.023
Chain 1:   1200        -8001.698             0.030            0.017
Chain 1:   1300        -7948.951             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46516.789             1.000            1.000
Chain 1:    200       -15784.000             1.474            1.947
Chain 1:    300        -8825.318             1.245            1.000
Chain 1:    400        -8308.924             0.949            1.000
Chain 1:    500        -8281.845             0.760            0.788
Chain 1:    600        -8594.671             0.640            0.788
Chain 1:    700        -7647.502             0.566            0.124
Chain 1:    800        -8296.953             0.505            0.124
Chain 1:    900        -8020.994             0.453            0.078
Chain 1:   1000        -7835.502             0.410            0.078
Chain 1:   1100        -7759.543             0.311            0.062
Chain 1:   1200        -7830.194             0.117            0.036
Chain 1:   1300        -7732.136             0.039            0.034
Chain 1:   1400        -7658.236             0.034            0.024
Chain 1:   1500        -7605.822             0.034            0.024
Chain 1:   1600        -7777.644             0.033            0.022
Chain 1:   1700        -7550.556             0.024            0.022
Chain 1:   1800        -7659.267             0.017            0.014
Chain 1:   1900        -7667.175             0.014            0.013
Chain 1:   2000        -7686.030             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003034 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86685.105             1.000            1.000
Chain 1:    200       -13749.110             3.152            5.305
Chain 1:    300       -10044.674             2.225            1.000
Chain 1:    400       -11214.858             1.694            1.000
Chain 1:    500        -9040.337             1.404            0.369
Chain 1:    600        -8461.521             1.181            0.369
Chain 1:    700        -8644.111             1.015            0.241
Chain 1:    800        -8846.640             0.891            0.241
Chain 1:    900        -8836.555             0.792            0.104
Chain 1:   1000        -8698.338             0.715            0.104
Chain 1:   1100        -8783.738             0.616            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8416.102             0.090            0.044
Chain 1:   1300        -8712.591             0.056            0.034
Chain 1:   1400        -8709.171             0.046            0.023
Chain 1:   1500        -8568.691             0.023            0.021
Chain 1:   1600        -8683.943             0.018            0.016
Chain 1:   1700        -8749.348             0.016            0.016
Chain 1:   1800        -8315.821             0.019            0.016
Chain 1:   1900        -8419.867             0.021            0.016
Chain 1:   2000        -8395.288             0.019            0.013
Chain 1:   2100        -8532.363             0.020            0.016
Chain 1:   2200        -8326.015             0.018            0.016
Chain 1:   2300        -8472.543             0.016            0.016
Chain 1:   2400        -8324.188             0.018            0.016
Chain 1:   2500        -8393.803             0.017            0.016
Chain 1:   2600        -8306.973             0.017            0.016
Chain 1:   2700        -8340.042             0.017            0.016
Chain 1:   2800        -8301.150             0.012            0.012
Chain 1:   2900        -8393.272             0.012            0.011
Chain 1:   3000        -8219.953             0.014            0.016
Chain 1:   3100        -8383.253             0.014            0.017
Chain 1:   3200        -8256.057             0.013            0.015
Chain 1:   3300        -8265.022             0.011            0.011
Chain 1:   3400        -8417.057             0.011            0.011
Chain 1:   3500        -8409.810             0.011            0.011
Chain 1:   3600        -8213.245             0.012            0.015
Chain 1:   3700        -8356.583             0.013            0.017
Chain 1:   3800        -8220.077             0.014            0.017
Chain 1:   3900        -8155.250             0.014            0.017
Chain 1:   4000        -8229.488             0.013            0.017
Chain 1:   4100        -8220.642             0.011            0.015
Chain 1:   4200        -8209.229             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390533.192             1.000            1.000
Chain 1:    200     -1588722.076             2.641            4.281
Chain 1:    300      -892958.029             2.020            1.000
Chain 1:    400      -458510.116             1.752            1.000
Chain 1:    500      -358630.427             1.457            0.948
Chain 1:    600      -233312.115             1.304            0.948
Chain 1:    700      -119518.442             1.254            0.948
Chain 1:    800       -86668.899             1.144            0.948
Chain 1:    900       -67026.583             1.050            0.779
Chain 1:   1000       -51839.865             0.974            0.779
Chain 1:   1100       -39317.547             0.906            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38501.592             0.480            0.379
Chain 1:   1300       -26460.470             0.448            0.379
Chain 1:   1400       -26181.404             0.354            0.318
Chain 1:   1500       -22767.640             0.341            0.318
Chain 1:   1600       -21983.809             0.291            0.293
Chain 1:   1700       -20858.241             0.201            0.293
Chain 1:   1800       -20802.752             0.163            0.150
Chain 1:   1900       -21129.200             0.136            0.054
Chain 1:   2000       -19639.595             0.114            0.054
Chain 1:   2100       -19878.319             0.083            0.036
Chain 1:   2200       -20104.717             0.082            0.036
Chain 1:   2300       -19721.863             0.039            0.019
Chain 1:   2400       -19493.835             0.039            0.019
Chain 1:   2500       -19295.583             0.025            0.015
Chain 1:   2600       -18925.727             0.023            0.015
Chain 1:   2700       -18882.688             0.018            0.012
Chain 1:   2800       -18599.196             0.019            0.015
Chain 1:   2900       -18880.691             0.019            0.015
Chain 1:   3000       -18866.937             0.012            0.012
Chain 1:   3100       -18951.912             0.011            0.012
Chain 1:   3200       -18642.455             0.012            0.015
Chain 1:   3300       -18847.313             0.011            0.012
Chain 1:   3400       -18321.786             0.012            0.015
Chain 1:   3500       -18934.233             0.015            0.015
Chain 1:   3600       -18240.282             0.016            0.015
Chain 1:   3700       -18627.494             0.018            0.017
Chain 1:   3800       -17586.071             0.023            0.021
Chain 1:   3900       -17582.158             0.021            0.021
Chain 1:   4000       -17699.520             0.022            0.021
Chain 1:   4100       -17613.130             0.022            0.021
Chain 1:   4200       -17429.184             0.021            0.021
Chain 1:   4300       -17567.762             0.021            0.021
Chain 1:   4400       -17524.409             0.018            0.011
Chain 1:   4500       -17426.875             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00127 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12523.700             1.000            1.000
Chain 1:    200        -9386.459             0.667            1.000
Chain 1:    300        -7920.942             0.506            0.334
Chain 1:    400        -8073.253             0.385            0.334
Chain 1:    500        -7939.536             0.311            0.185
Chain 1:    600        -7906.076             0.260            0.185
Chain 1:    700        -7800.180             0.225            0.019
Chain 1:    800        -7808.993             0.197            0.019
Chain 1:    900        -7743.986             0.176            0.017
Chain 1:   1000        -7922.279             0.160            0.019
Chain 1:   1100        -7939.817             0.061            0.017
Chain 1:   1200        -7821.643             0.029            0.015
Chain 1:   1300        -7774.026             0.011            0.014
Chain 1:   1400        -7796.665             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004467 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 44.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56914.692             1.000            1.000
Chain 1:    200       -17432.478             1.632            2.265
Chain 1:    300        -8650.647             1.427            1.015
Chain 1:    400        -8286.497             1.081            1.015
Chain 1:    500        -8713.799             0.875            1.000
Chain 1:    600        -8876.144             0.732            1.000
Chain 1:    700        -7793.253             0.647            0.139
Chain 1:    800        -8204.191             0.573            0.139
Chain 1:    900        -7502.450             0.519            0.094
Chain 1:   1000        -7589.666             0.469            0.094
Chain 1:   1100        -7712.999             0.370            0.050
Chain 1:   1200        -7517.873             0.146            0.049
Chain 1:   1300        -7493.650             0.045            0.044
Chain 1:   1400        -7505.032             0.041            0.026
Chain 1:   1500        -7495.510             0.036            0.018
Chain 1:   1600        -7673.458             0.037            0.023
Chain 1:   1700        -7337.961             0.027            0.023
Chain 1:   1800        -7535.833             0.025            0.023
Chain 1:   1900        -7533.795             0.015            0.016
Chain 1:   2000        -7519.267             0.015            0.016
Chain 1:   2100        -7477.648             0.013            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002976 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85390.927             1.000            1.000
Chain 1:    200       -13450.607             3.174            5.348
Chain 1:    300        -9804.735             2.240            1.000
Chain 1:    400       -10624.918             1.699            1.000
Chain 1:    500        -8771.423             1.402            0.372
Chain 1:    600        -8316.085             1.177            0.372
Chain 1:    700        -8300.742             1.009            0.211
Chain 1:    800        -8521.685             0.886            0.211
Chain 1:    900        -8561.947             0.788            0.077
Chain 1:   1000        -8483.298             0.711            0.077
Chain 1:   1100        -8678.116             0.613            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8267.786             0.083            0.050
Chain 1:   1300        -8499.421             0.048            0.027
Chain 1:   1400        -8510.496             0.041            0.026
Chain 1:   1500        -8358.369             0.022            0.022
Chain 1:   1600        -8472.470             0.017            0.018
Chain 1:   1700        -8550.803             0.018            0.018
Chain 1:   1800        -8129.520             0.021            0.018
Chain 1:   1900        -8229.642             0.021            0.018
Chain 1:   2000        -8203.804             0.021            0.018
Chain 1:   2100        -8328.670             0.020            0.015
Chain 1:   2200        -8135.564             0.018            0.015
Chain 1:   2300        -8224.288             0.016            0.013
Chain 1:   2400        -8293.420             0.017            0.013
Chain 1:   2500        -8239.533             0.015            0.012
Chain 1:   2600        -8240.412             0.014            0.011
Chain 1:   2700        -8157.381             0.014            0.011
Chain 1:   2800        -8117.978             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8352459.830             1.000            1.000
Chain 1:    200     -1576512.972             2.649            4.298
Chain 1:    300      -890584.403             2.023            1.000
Chain 1:    400      -457826.339             1.753            1.000
Chain 1:    500      -358867.174             1.458            0.945
Chain 1:    600      -233912.268             1.304            0.945
Chain 1:    700      -119729.830             1.254            0.945
Chain 1:    800       -86815.609             1.145            0.945
Chain 1:    900       -67062.507             1.050            0.770
Chain 1:   1000       -51778.301             0.975            0.770
Chain 1:   1100       -39175.881             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38344.743             0.479            0.379
Chain 1:   1300       -26217.150             0.448            0.379
Chain 1:   1400       -25929.220             0.355            0.322
Chain 1:   1500       -22493.949             0.343            0.322
Chain 1:   1600       -21703.981             0.293            0.295
Chain 1:   1700       -20567.435             0.203            0.295
Chain 1:   1800       -20509.334             0.165            0.153
Chain 1:   1900       -20835.563             0.138            0.055
Chain 1:   2000       -19341.080             0.116            0.055
Chain 1:   2100       -19579.819             0.085            0.036
Chain 1:   2200       -19807.190             0.084            0.036
Chain 1:   2300       -19423.553             0.039            0.020
Chain 1:   2400       -19195.471             0.040            0.020
Chain 1:   2500       -18997.868             0.025            0.016
Chain 1:   2600       -18627.649             0.024            0.016
Chain 1:   2700       -18584.491             0.018            0.012
Chain 1:   2800       -18301.437             0.020            0.015
Chain 1:   2900       -18582.882             0.020            0.015
Chain 1:   3000       -18568.974             0.012            0.012
Chain 1:   3100       -18653.982             0.011            0.012
Chain 1:   3200       -18344.565             0.012            0.015
Chain 1:   3300       -18549.376             0.011            0.012
Chain 1:   3400       -18024.209             0.013            0.015
Chain 1:   3500       -18636.393             0.015            0.015
Chain 1:   3600       -17942.727             0.017            0.015
Chain 1:   3700       -18329.844             0.019            0.017
Chain 1:   3800       -17289.110             0.023            0.021
Chain 1:   3900       -17285.300             0.022            0.021
Chain 1:   4000       -17402.545             0.022            0.021
Chain 1:   4100       -17316.295             0.022            0.021
Chain 1:   4200       -17132.444             0.022            0.021
Chain 1:   4300       -17270.879             0.021            0.021
Chain 1:   4400       -17227.628             0.019            0.011
Chain 1:   4500       -17130.177             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001273 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12956.770             1.000            1.000
Chain 1:    200        -9829.256             0.659            1.000
Chain 1:    300        -8237.313             0.504            0.318
Chain 1:    400        -8487.126             0.385            0.318
Chain 1:    500        -8190.222             0.315            0.193
Chain 1:    600        -8190.290             0.263            0.193
Chain 1:    700        -8044.740             0.228            0.036
Chain 1:    800        -8018.501             0.200            0.036
Chain 1:    900        -7989.892             0.178            0.029
Chain 1:   1000        -8184.367             0.163            0.029
Chain 1:   1100        -8496.924             0.066            0.029
Chain 1:   1200        -8068.148             0.040            0.029
Chain 1:   1300        -8027.574             0.021            0.024
Chain 1:   1400        -8040.133             0.018            0.018
Chain 1:   1500        -8149.204             0.016            0.013
Chain 1:   1600        -8036.656             0.017            0.014
Chain 1:   1700        -8014.059             0.016            0.013
Chain 1:   1800        -7986.333             0.016            0.013
Chain 1:   1900        -8015.053             0.016            0.013
Chain 1:   2000        -7947.026             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001618 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59075.076             1.000            1.000
Chain 1:    200       -18320.867             1.612            2.224
Chain 1:    300        -8956.577             1.423            1.046
Chain 1:    400        -8068.179             1.095            1.046
Chain 1:    500        -8106.685             0.877            1.000
Chain 1:    600        -8156.500             0.732            1.000
Chain 1:    700        -8786.180             0.638            0.110
Chain 1:    800        -8412.584             0.563            0.110
Chain 1:    900        -7832.018             0.509            0.074
Chain 1:   1000        -7775.670             0.459            0.074
Chain 1:   1100        -7701.659             0.360            0.072
Chain 1:   1200        -7569.629             0.139            0.044
Chain 1:   1300        -7550.849             0.035            0.017
Chain 1:   1400        -7958.829             0.029            0.017
Chain 1:   1500        -7567.473             0.034            0.044
Chain 1:   1600        -7779.007             0.036            0.044
Chain 1:   1700        -7604.612             0.031            0.027
Chain 1:   1800        -7699.905             0.028            0.023
Chain 1:   1900        -7595.100             0.022            0.017
Chain 1:   2000        -7693.251             0.022            0.017
Chain 1:   2100        -7540.016             0.023            0.020
Chain 1:   2200        -7824.099             0.025            0.023
Chain 1:   2300        -7615.086             0.028            0.027
Chain 1:   2400        -7516.444             0.024            0.023
Chain 1:   2500        -7575.423             0.019            0.020
Chain 1:   2600        -7522.455             0.017            0.014
Chain 1:   2700        -7502.685             0.015            0.013
Chain 1:   2800        -7531.233             0.015            0.013
Chain 1:   2900        -7381.263             0.015            0.013
Chain 1:   3000        -7539.569             0.016            0.020
Chain 1:   3100        -7524.888             0.014            0.013
Chain 1:   3200        -7745.774             0.013            0.013
Chain 1:   3300        -7437.158             0.015            0.013
Chain 1:   3400        -7695.279             0.017            0.020
Chain 1:   3500        -7442.415             0.019            0.021
Chain 1:   3600        -7495.892             0.019            0.021
Chain 1:   3700        -7453.087             0.020            0.021
Chain 1:   3800        -7420.432             0.020            0.021
Chain 1:   3900        -7401.588             0.018            0.021
Chain 1:   4000        -7397.555             0.016            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003952 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87259.631             1.000            1.000
Chain 1:    200       -14050.897             3.105            5.210
Chain 1:    300       -10250.028             2.194            1.000
Chain 1:    400       -12066.655             1.683            1.000
Chain 1:    500        -8673.412             1.425            0.391
Chain 1:    600        -8656.579             1.187            0.391
Chain 1:    700        -9187.040             1.026            0.371
Chain 1:    800        -8873.986             0.902            0.371
Chain 1:    900        -8984.375             0.803            0.151
Chain 1:   1000        -8565.400             0.728            0.151
Chain 1:   1100        -8721.014             0.630            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8599.646             0.110            0.049
Chain 1:   1300        -8817.831             0.075            0.035
Chain 1:   1400        -8753.620             0.061            0.025
Chain 1:   1500        -8730.940             0.022            0.018
Chain 1:   1600        -8764.424             0.022            0.018
Chain 1:   1700        -8847.908             0.018            0.014
Chain 1:   1800        -8407.086             0.019            0.014
Chain 1:   1900        -8504.713             0.019            0.014
Chain 1:   2000        -8524.342             0.015            0.011
Chain 1:   2100        -8621.086             0.014            0.011
Chain 1:   2200        -8394.572             0.015            0.011
Chain 1:   2300        -8613.857             0.015            0.011
Chain 1:   2400        -8399.859             0.017            0.011
Chain 1:   2500        -8476.574             0.018            0.011
Chain 1:   2600        -8386.071             0.018            0.011
Chain 1:   2700        -8419.675             0.018            0.011
Chain 1:   2800        -8370.431             0.013            0.011
Chain 1:   2900        -8485.487             0.013            0.011
Chain 1:   3000        -8397.431             0.014            0.011
Chain 1:   3100        -8362.691             0.014            0.011
Chain 1:   3200        -8334.324             0.011            0.010
Chain 1:   3300        -8595.018             0.012            0.010
Chain 1:   3400        -8637.962             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8437955.408             1.000            1.000
Chain 1:    200     -1589464.959             2.654            4.309
Chain 1:    300      -891035.255             2.031            1.000
Chain 1:    400      -458397.541             1.759            1.000
Chain 1:    500      -358442.281             1.463            0.944
Chain 1:    600      -233350.745             1.309            0.944
Chain 1:    700      -119658.671             1.257            0.944
Chain 1:    800       -86910.648             1.147            0.944
Chain 1:    900       -67280.371             1.052            0.784
Chain 1:   1000       -52113.880             0.976            0.784
Chain 1:   1100       -39620.009             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38806.000             0.479            0.377
Chain 1:   1300       -26778.895             0.445            0.377
Chain 1:   1400       -26502.778             0.352            0.315
Chain 1:   1500       -23094.403             0.339            0.315
Chain 1:   1600       -22313.447             0.289            0.292
Chain 1:   1700       -21188.196             0.199            0.291
Chain 1:   1800       -21133.185             0.162            0.148
Chain 1:   1900       -21460.183             0.134            0.053
Chain 1:   2000       -19970.430             0.112            0.053
Chain 1:   2100       -20208.839             0.082            0.035
Chain 1:   2200       -20435.841             0.081            0.035
Chain 1:   2300       -20052.337             0.038            0.019
Chain 1:   2400       -19824.158             0.038            0.019
Chain 1:   2500       -19626.095             0.024            0.015
Chain 1:   2600       -19255.495             0.023            0.015
Chain 1:   2700       -19212.188             0.018            0.012
Chain 1:   2800       -18928.659             0.019            0.015
Chain 1:   2900       -19210.276             0.019            0.015
Chain 1:   3000       -19196.379             0.012            0.012
Chain 1:   3100       -19281.542             0.011            0.012
Chain 1:   3200       -18971.653             0.011            0.015
Chain 1:   3300       -19176.811             0.010            0.012
Chain 1:   3400       -18650.722             0.012            0.015
Chain 1:   3500       -19264.097             0.014            0.015
Chain 1:   3600       -18568.759             0.016            0.015
Chain 1:   3700       -18957.070             0.018            0.016
Chain 1:   3800       -17913.660             0.022            0.020
Chain 1:   3900       -17909.703             0.021            0.020
Chain 1:   4000       -18027.023             0.021            0.020
Chain 1:   4100       -17940.661             0.021            0.020
Chain 1:   4200       -17756.187             0.021            0.020
Chain 1:   4300       -17895.096             0.021            0.020
Chain 1:   4400       -17851.363             0.018            0.010
Chain 1:   4500       -17753.779             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12461.077             1.000            1.000
Chain 1:    200        -9374.102             0.665            1.000
Chain 1:    300        -8061.622             0.497            0.329
Chain 1:    400        -8288.141             0.380            0.329
Chain 1:    500        -7916.646             0.313            0.163
Chain 1:    600        -8011.260             0.263            0.163
Chain 1:    700        -8130.812             0.228            0.047
Chain 1:    800        -7987.465             0.201            0.047
Chain 1:    900        -7868.910             0.181            0.027
Chain 1:   1000        -8052.276             0.165            0.027
Chain 1:   1100        -8041.753             0.065            0.023
Chain 1:   1200        -7963.934             0.033            0.018
Chain 1:   1300        -7913.176             0.017            0.015
Chain 1:   1400        -7926.969             0.015            0.015
Chain 1:   1500        -8016.738             0.011            0.012
Chain 1:   1600        -7969.876             0.011            0.011
Chain 1:   1700        -7909.602             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56969.645             1.000            1.000
Chain 1:    200       -17615.572             1.617            2.234
Chain 1:    300        -8773.263             1.414            1.008
Chain 1:    400        -8372.694             1.072            1.008
Chain 1:    500        -8557.000             0.862            1.000
Chain 1:    600        -8709.909             0.721            1.000
Chain 1:    700        -8079.134             0.630            0.078
Chain 1:    800        -7949.413             0.553            0.078
Chain 1:    900        -7898.978             0.492            0.048
Chain 1:   1000        -7990.030             0.444            0.048
Chain 1:   1100        -7780.393             0.347            0.027
Chain 1:   1200        -7622.219             0.125            0.022
Chain 1:   1300        -7780.686             0.027            0.021
Chain 1:   1400        -7899.110             0.023            0.020
Chain 1:   1500        -7608.264             0.025            0.020
Chain 1:   1600        -7772.570             0.025            0.021
Chain 1:   1700        -7576.401             0.020            0.021
Chain 1:   1800        -7682.200             0.020            0.021
Chain 1:   1900        -7494.270             0.022            0.021
Chain 1:   2000        -7642.237             0.023            0.021
Chain 1:   2100        -7627.744             0.020            0.021
Chain 1:   2200        -7745.816             0.020            0.020
Chain 1:   2300        -7613.976             0.019            0.019
Chain 1:   2400        -7664.777             0.018            0.019
Chain 1:   2500        -7609.585             0.015            0.017
Chain 1:   2600        -7544.464             0.014            0.015
Chain 1:   2700        -7582.448             0.012            0.014
Chain 1:   2800        -7522.715             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003314 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86269.216             1.000            1.000
Chain 1:    200       -13605.630             3.170            5.341
Chain 1:    300        -9950.729             2.236            1.000
Chain 1:    400       -10881.786             1.698            1.000
Chain 1:    500        -8891.836             1.403            0.367
Chain 1:    600        -8750.385             1.172            0.367
Chain 1:    700        -8493.394             1.009            0.224
Chain 1:    800        -8933.955             0.889            0.224
Chain 1:    900        -8705.489             0.793            0.086
Chain 1:   1000        -8518.812             0.716            0.086
Chain 1:   1100        -8626.916             0.617            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8393.332             0.086            0.030
Chain 1:   1300        -8635.954             0.052            0.028
Chain 1:   1400        -8567.369             0.044            0.028
Chain 1:   1500        -8498.565             0.023            0.026
Chain 1:   1600        -8604.669             0.022            0.026
Chain 1:   1700        -8687.428             0.020            0.022
Chain 1:   1800        -8263.927             0.021            0.022
Chain 1:   1900        -8365.129             0.019            0.013
Chain 1:   2000        -8339.408             0.017            0.012
Chain 1:   2100        -8464.590             0.018            0.012
Chain 1:   2200        -8269.471             0.017            0.012
Chain 1:   2300        -8359.706             0.015            0.012
Chain 1:   2400        -8428.710             0.015            0.012
Chain 1:   2500        -8374.896             0.015            0.012
Chain 1:   2600        -8375.986             0.014            0.011
Chain 1:   2700        -8292.846             0.014            0.011
Chain 1:   2800        -8253.121             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003407 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8379314.680             1.000            1.000
Chain 1:    200     -1580778.031             2.650            4.301
Chain 1:    300      -890916.846             2.025            1.000
Chain 1:    400      -457964.775             1.755            1.000
Chain 1:    500      -358799.346             1.459            0.945
Chain 1:    600      -233796.608             1.305            0.945
Chain 1:    700      -119704.230             1.255            0.945
Chain 1:    800       -86838.477             1.145            0.945
Chain 1:    900       -67113.617             1.051            0.774
Chain 1:   1000       -51854.807             0.975            0.774
Chain 1:   1100       -39276.522             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38446.904             0.479            0.378
Chain 1:   1300       -26342.979             0.448            0.378
Chain 1:   1400       -26057.381             0.354            0.320
Chain 1:   1500       -22628.719             0.342            0.320
Chain 1:   1600       -21840.754             0.292            0.294
Chain 1:   1700       -20706.979             0.202            0.294
Chain 1:   1800       -20649.452             0.165            0.152
Chain 1:   1900       -20975.723             0.137            0.055
Chain 1:   2000       -19482.676             0.115            0.055
Chain 1:   2100       -19721.351             0.084            0.036
Chain 1:   2200       -19948.532             0.083            0.036
Chain 1:   2300       -19565.027             0.039            0.020
Chain 1:   2400       -19336.972             0.039            0.020
Chain 1:   2500       -19139.295             0.025            0.016
Chain 1:   2600       -18769.158             0.023            0.016
Chain 1:   2700       -18725.981             0.018            0.012
Chain 1:   2800       -18442.925             0.019            0.015
Chain 1:   2900       -18724.298             0.019            0.015
Chain 1:   3000       -18710.413             0.012            0.012
Chain 1:   3100       -18795.447             0.011            0.012
Chain 1:   3200       -18486.017             0.012            0.015
Chain 1:   3300       -18690.814             0.011            0.012
Chain 1:   3400       -18165.644             0.012            0.015
Chain 1:   3500       -18777.832             0.015            0.015
Chain 1:   3600       -18084.097             0.017            0.015
Chain 1:   3700       -18471.262             0.018            0.017
Chain 1:   3800       -17430.460             0.023            0.021
Chain 1:   3900       -17426.615             0.021            0.021
Chain 1:   4000       -17543.866             0.022            0.021
Chain 1:   4100       -17457.655             0.022            0.021
Chain 1:   4200       -17273.739             0.021            0.021
Chain 1:   4300       -17412.215             0.021            0.021
Chain 1:   4400       -17368.947             0.018            0.011
Chain 1:   4500       -17271.472             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001241 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49422.913             1.000            1.000
Chain 1:    200       -24277.066             1.018            1.036
Chain 1:    300       -14176.825             0.916            1.000
Chain 1:    400       -12838.633             0.713            1.000
Chain 1:    500       -13034.591             0.573            0.712
Chain 1:    600       -16672.820             0.514            0.712
Chain 1:    700       -15171.293             0.455            0.218
Chain 1:    800       -15454.421             0.400            0.218
Chain 1:    900       -19785.619             0.380            0.218
Chain 1:   1000       -12881.033             0.396            0.219
Chain 1:   1100       -20743.547             0.334            0.219
Chain 1:   1200       -14992.673             0.268            0.219
Chain 1:   1300       -13915.524             0.205            0.218
Chain 1:   1400       -13348.878             0.199            0.218
Chain 1:   1500       -11386.091             0.215            0.218
Chain 1:   1600       -11954.177             0.197            0.172
Chain 1:   1700       -10002.994             0.207            0.195
Chain 1:   1800       -10237.683             0.208            0.195
Chain 1:   1900       -10532.418             0.188            0.172
Chain 1:   2000       -24010.635             0.191            0.172
Chain 1:   2100       -10102.855             0.291            0.172
Chain 1:   2200       -10028.320             0.253            0.077
Chain 1:   2300       -11924.749             0.261            0.159
Chain 1:   2400       -10255.129             0.273            0.163
Chain 1:   2500       -16978.870             0.296            0.163
Chain 1:   2600        -9721.769             0.366            0.195
Chain 1:   2700        -9554.166             0.348            0.163
Chain 1:   2800       -20024.872             0.398            0.396
Chain 1:   2900       -15524.802             0.424            0.396
Chain 1:   3000        -9378.747             0.433            0.396
Chain 1:   3100       -10215.110             0.304            0.290
Chain 1:   3200        -9634.362             0.309            0.290
Chain 1:   3300       -17083.820             0.337            0.396
Chain 1:   3400       -10498.625             0.383            0.436
Chain 1:   3500        -9576.900             0.353            0.436
Chain 1:   3600       -19718.380             0.330            0.436
Chain 1:   3700        -9015.956             0.447            0.514   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   3800       -10910.791             0.412            0.436
Chain 1:   3900       -15560.842             0.413            0.436
Chain 1:   4000       -10401.133             0.397            0.436
Chain 1:   4100        -9617.690             0.397            0.436
Chain 1:   4200        -9022.431             0.398            0.436
Chain 1:   4300       -15470.320             0.396            0.417
Chain 1:   4400        -9504.221             0.396            0.417
Chain 1:   4500        -9343.737             0.388            0.417
Chain 1:   4600        -8901.752             0.341            0.299
Chain 1:   4700        -9535.456             0.229            0.174
Chain 1:   4800        -9156.090             0.216            0.081
Chain 1:   4900        -9340.995             0.188            0.066
Chain 1:   5000       -10542.743             0.150            0.066
Chain 1:   5100        -9322.792             0.155            0.066
Chain 1:   5200       -17005.411             0.194            0.114
Chain 1:   5300       -12534.896             0.188            0.114
Chain 1:   5400       -16533.360             0.149            0.114
Chain 1:   5500       -13084.912             0.174            0.131
Chain 1:   5600       -13295.572             0.170            0.131
Chain 1:   5700       -12633.183             0.169            0.131
Chain 1:   5800        -9279.544             0.201            0.242
Chain 1:   5900        -8783.258             0.204            0.242
Chain 1:   6000        -9313.198             0.199            0.242
Chain 1:   6100        -9097.366             0.188            0.242
Chain 1:   6200        -8933.154             0.145            0.057
Chain 1:   6300        -9109.987             0.111            0.057
Chain 1:   6400        -8975.462             0.088            0.052
Chain 1:   6500        -8787.894             0.064            0.024
Chain 1:   6600        -8966.383             0.064            0.024
Chain 1:   6700       -10975.785             0.078            0.024
Chain 1:   6800        -9579.697             0.056            0.024
Chain 1:   6900        -9211.982             0.054            0.024
Chain 1:   7000       -12949.314             0.078            0.024
Chain 1:   7100        -8502.119             0.127            0.040
Chain 1:   7200       -10278.220             0.143            0.146
Chain 1:   7300       -10861.778             0.146            0.146
Chain 1:   7400        -8424.503             0.174            0.173
Chain 1:   7500       -10393.289             0.191            0.183
Chain 1:   7600        -9646.188             0.196            0.183
Chain 1:   7700        -9333.574             0.181            0.173
Chain 1:   7800       -11969.212             0.189            0.189
Chain 1:   7900        -8893.601             0.219            0.220
Chain 1:   8000       -13101.394             0.223            0.220
Chain 1:   8100        -8981.894             0.216            0.220
Chain 1:   8200        -8973.393             0.199            0.220
Chain 1:   8300        -8536.342             0.199            0.220
Chain 1:   8400        -8332.239             0.172            0.189
Chain 1:   8500       -11684.661             0.182            0.220
Chain 1:   8600        -8438.205             0.213            0.287
Chain 1:   8700        -8339.775             0.211            0.287
Chain 1:   8800        -8411.822             0.189            0.287
Chain 1:   8900       -10222.943             0.173            0.177
Chain 1:   9000        -9351.277             0.150            0.093
Chain 1:   9100       -10206.908             0.112            0.084
Chain 1:   9200       -10113.725             0.113            0.084
Chain 1:   9300        -9358.657             0.116            0.084
Chain 1:   9400        -8458.431             0.124            0.093
Chain 1:   9500        -9211.048             0.104            0.084
Chain 1:   9600        -8952.773             0.068            0.082
Chain 1:   9700        -8388.316             0.074            0.082
Chain 1:   9800        -8574.047             0.075            0.082
Chain 1:   9900       -10970.921             0.079            0.082
Chain 1:   10000        -9697.220             0.083            0.082
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58385.774             1.000            1.000
Chain 1:    200       -18040.147             1.618            2.236
Chain 1:    300        -8846.126             1.425            1.039
Chain 1:    400        -8030.060             1.094            1.039
Chain 1:    500        -8164.704             0.879            1.000
Chain 1:    600        -8740.080             0.743            1.000
Chain 1:    700        -8551.408             0.640            0.102
Chain 1:    800        -8324.917             0.564            0.102
Chain 1:    900        -7798.458             0.508            0.068
Chain 1:   1000        -7914.514             0.459            0.068
Chain 1:   1100        -7942.568             0.359            0.066
Chain 1:   1200        -7590.850             0.140            0.046
Chain 1:   1300        -7854.579             0.040            0.034
Chain 1:   1400        -7792.296             0.031            0.027
Chain 1:   1500        -7580.676             0.032            0.028
Chain 1:   1600        -7660.182             0.026            0.027
Chain 1:   1700        -7586.271             0.025            0.027
Chain 1:   1800        -7674.436             0.023            0.015
Chain 1:   1900        -7604.415             0.017            0.011
Chain 1:   2000        -7657.963             0.017            0.010
Chain 1:   2100        -7588.053             0.017            0.010
Chain 1:   2200        -7928.770             0.017            0.010
Chain 1:   2300        -7530.476             0.019            0.010
Chain 1:   2400        -7700.850             0.020            0.011
Chain 1:   2500        -7568.952             0.019            0.011
Chain 1:   2600        -7537.298             0.019            0.011
Chain 1:   2700        -7507.129             0.018            0.011
Chain 1:   2800        -7506.879             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003148 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86689.640             1.000            1.000
Chain 1:    200       -13877.452             3.123            5.247
Chain 1:    300       -10147.231             2.205            1.000
Chain 1:    400       -11483.973             1.683            1.000
Chain 1:    500        -9163.636             1.397            0.368
Chain 1:    600        -8496.189             1.177            0.368
Chain 1:    700        -8490.106             1.009            0.253
Chain 1:    800        -9560.775             0.897            0.253
Chain 1:    900        -8951.161             0.805            0.116
Chain 1:   1000        -8979.170             0.725            0.116
Chain 1:   1100        -8911.964             0.625            0.112   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8372.845             0.107            0.079
Chain 1:   1300        -8784.210             0.075            0.068
Chain 1:   1400        -8798.232             0.064            0.064
Chain 1:   1500        -8644.147             0.040            0.047
Chain 1:   1600        -8758.105             0.034            0.018
Chain 1:   1700        -8813.224             0.034            0.018
Chain 1:   1800        -8373.077             0.028            0.018
Chain 1:   1900        -8477.159             0.023            0.013
Chain 1:   2000        -8460.909             0.022            0.013
Chain 1:   2100        -8582.258             0.023            0.014
Chain 1:   2200        -8374.654             0.019            0.014
Chain 1:   2300        -8469.466             0.016            0.013
Chain 1:   2400        -8536.765             0.016            0.013
Chain 1:   2500        -8484.953             0.015            0.012
Chain 1:   2600        -8498.227             0.014            0.011
Chain 1:   2700        -8406.066             0.014            0.011
Chain 1:   2800        -8353.985             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003021 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401813.652             1.000            1.000
Chain 1:    200     -1586954.056             2.647            4.294
Chain 1:    300      -891831.409             2.025            1.000
Chain 1:    400      -458520.299             1.755            1.000
Chain 1:    500      -358511.532             1.460            0.945
Chain 1:    600      -233385.671             1.306            0.945
Chain 1:    700      -119600.655             1.255            0.945
Chain 1:    800       -86811.511             1.145            0.945
Chain 1:    900       -67160.670             1.051            0.779
Chain 1:   1000       -51972.605             0.975            0.779
Chain 1:   1100       -39459.637             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38643.642             0.479            0.378
Chain 1:   1300       -26602.545             0.446            0.378
Chain 1:   1400       -26325.205             0.353            0.317
Chain 1:   1500       -22911.843             0.340            0.317
Chain 1:   1600       -22129.130             0.290            0.293
Chain 1:   1700       -21002.454             0.200            0.292
Chain 1:   1800       -20947.054             0.163            0.149
Chain 1:   1900       -21273.682             0.135            0.054
Chain 1:   2000       -19783.605             0.113            0.054
Chain 1:   2100       -20022.271             0.083            0.035
Chain 1:   2200       -20248.960             0.082            0.035
Chain 1:   2300       -19865.769             0.038            0.019
Chain 1:   2400       -19637.628             0.039            0.019
Chain 1:   2500       -19439.590             0.025            0.015
Chain 1:   2600       -19069.343             0.023            0.015
Chain 1:   2700       -19026.200             0.018            0.012
Chain 1:   2800       -18742.725             0.019            0.015
Chain 1:   2900       -19024.246             0.019            0.015
Chain 1:   3000       -19010.455             0.012            0.012
Chain 1:   3100       -19095.502             0.011            0.012
Chain 1:   3200       -18785.875             0.011            0.015
Chain 1:   3300       -18990.851             0.011            0.012
Chain 1:   3400       -18465.152             0.012            0.015
Chain 1:   3500       -19077.957             0.014            0.015
Chain 1:   3600       -18383.417             0.016            0.015
Chain 1:   3700       -18771.065             0.018            0.016
Chain 1:   3800       -17728.906             0.023            0.021
Chain 1:   3900       -17724.974             0.021            0.021
Chain 1:   4000       -17842.303             0.022            0.021
Chain 1:   4100       -17755.928             0.022            0.021
Chain 1:   4200       -17571.784             0.021            0.021
Chain 1:   4300       -17710.479             0.021            0.021
Chain 1:   4400       -17666.962             0.018            0.010
Chain 1:   4500       -17569.408             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12467.344             1.000            1.000
Chain 1:    200        -9342.389             0.667            1.000
Chain 1:    300        -8297.026             0.487            0.334
Chain 1:    400        -8401.840             0.368            0.334
Chain 1:    500        -8263.551             0.298            0.126
Chain 1:    600        -8122.161             0.251            0.126
Chain 1:    700        -8036.817             0.217            0.017
Chain 1:    800        -8046.310             0.190            0.017
Chain 1:    900        -7966.265             0.170            0.017
Chain 1:   1000        -8145.500             0.155            0.017
Chain 1:   1100        -8177.067             0.055            0.017
Chain 1:   1200        -8074.282             0.023            0.013
Chain 1:   1300        -8019.064             0.011            0.012
Chain 1:   1400        -8035.107             0.010            0.011
Chain 1:   1500        -8120.893             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001438 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57142.865             1.000            1.000
Chain 1:    200       -17531.853             1.630            2.259
Chain 1:    300        -8821.807             1.416            1.000
Chain 1:    400        -8311.732             1.077            1.000
Chain 1:    500        -8464.583             0.865            0.987
Chain 1:    600        -8493.766             0.722            0.987
Chain 1:    700        -8068.983             0.626            0.061
Chain 1:    800        -8352.850             0.552            0.061
Chain 1:    900        -8006.927             0.495            0.053
Chain 1:   1000        -8065.976             0.447            0.053
Chain 1:   1100        -7723.806             0.351            0.044
Chain 1:   1200        -7836.170             0.127            0.043
Chain 1:   1300        -7828.019             0.028            0.034
Chain 1:   1400        -7995.139             0.024            0.021
Chain 1:   1500        -7676.889             0.026            0.034
Chain 1:   1600        -7745.963             0.027            0.034
Chain 1:   1700        -7608.042             0.023            0.021
Chain 1:   1800        -7695.124             0.021            0.018
Chain 1:   1900        -7724.574             0.017            0.014
Chain 1:   2000        -7677.914             0.017            0.014
Chain 1:   2100        -7668.043             0.013            0.011
Chain 1:   2200        -7780.414             0.013            0.011
Chain 1:   2300        -7678.803             0.014            0.013
Chain 1:   2400        -7713.164             0.012            0.011
Chain 1:   2500        -7707.374             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86696.952             1.000            1.000
Chain 1:    200       -13603.514             3.187            5.373
Chain 1:    300        -9987.140             2.245            1.000
Chain 1:    400       -10979.706             1.706            1.000
Chain 1:    500        -8934.674             1.411            0.362
Chain 1:    600        -8519.171             1.184            0.362
Chain 1:    700        -8541.363             1.015            0.229
Chain 1:    800        -9039.989             0.895            0.229
Chain 1:    900        -8842.093             0.798            0.090
Chain 1:   1000        -8498.513             0.722            0.090
Chain 1:   1100        -8868.243             0.627            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8473.354             0.094            0.049
Chain 1:   1300        -8746.389             0.061            0.047
Chain 1:   1400        -8495.269             0.055            0.042
Chain 1:   1500        -8541.090             0.032            0.040
Chain 1:   1600        -8536.219             0.028            0.031
Chain 1:   1700        -8446.840             0.028            0.031
Chain 1:   1800        -8341.276             0.024            0.030
Chain 1:   1900        -8463.139             0.023            0.030
Chain 1:   2000        -8424.722             0.020            0.014
Chain 1:   2100        -8550.678             0.017            0.014
Chain 1:   2200        -8353.704             0.015            0.014
Chain 1:   2300        -8493.591             0.013            0.014
Chain 1:   2400        -8366.483             0.012            0.014
Chain 1:   2500        -8431.552             0.012            0.014
Chain 1:   2600        -8456.079             0.012            0.014
Chain 1:   2700        -8373.635             0.012            0.014
Chain 1:   2800        -8345.217             0.011            0.014
Chain 1:   2900        -8400.936             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003344 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410965.622             1.000            1.000
Chain 1:    200     -1587421.383             2.649            4.299
Chain 1:    300      -892285.217             2.026            1.000
Chain 1:    400      -459032.420             1.755            1.000
Chain 1:    500      -359057.220             1.460            0.944
Chain 1:    600      -233592.520             1.306            0.944
Chain 1:    700      -119484.268             1.256            0.944
Chain 1:    800       -86659.111             1.146            0.944
Chain 1:    900       -66949.850             1.052            0.779
Chain 1:   1000       -51719.877             0.976            0.779
Chain 1:   1100       -39179.223             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38349.266             0.480            0.379
Chain 1:   1300       -26290.803             0.448            0.379
Chain 1:   1400       -26006.956             0.355            0.320
Chain 1:   1500       -22592.045             0.342            0.320
Chain 1:   1600       -21807.614             0.292            0.294
Chain 1:   1700       -20679.759             0.202            0.294
Chain 1:   1800       -20623.397             0.164            0.151
Chain 1:   1900       -20949.338             0.137            0.055
Chain 1:   2000       -19460.321             0.115            0.055
Chain 1:   2100       -19698.466             0.084            0.036
Chain 1:   2200       -19925.095             0.083            0.036
Chain 1:   2300       -19542.214             0.039            0.020
Chain 1:   2400       -19314.386             0.039            0.020
Chain 1:   2500       -19116.580             0.025            0.016
Chain 1:   2600       -18746.757             0.023            0.016
Chain 1:   2700       -18703.700             0.018            0.012
Chain 1:   2800       -18420.775             0.019            0.015
Chain 1:   2900       -18701.943             0.019            0.015
Chain 1:   3000       -18688.040             0.012            0.012
Chain 1:   3100       -18773.052             0.011            0.012
Chain 1:   3200       -18463.792             0.012            0.015
Chain 1:   3300       -18668.460             0.011            0.012
Chain 1:   3400       -18143.618             0.012            0.015
Chain 1:   3500       -18755.189             0.015            0.015
Chain 1:   3600       -18062.250             0.017            0.015
Chain 1:   3700       -18448.810             0.018            0.017
Chain 1:   3800       -17409.154             0.023            0.021
Chain 1:   3900       -17405.343             0.021            0.021
Chain 1:   4000       -17522.613             0.022            0.021
Chain 1:   4100       -17436.460             0.022            0.021
Chain 1:   4200       -17252.810             0.021            0.021
Chain 1:   4300       -17391.083             0.021            0.021
Chain 1:   4400       -17348.011             0.018            0.011
Chain 1:   4500       -17250.611             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48736.918             1.000            1.000
Chain 1:    200       -21371.346             1.140            1.280
Chain 1:    300       -17824.293             0.826            1.000
Chain 1:    400       -21581.803             0.663            1.000
Chain 1:    500       -18223.381             0.568            0.199
Chain 1:    600       -16714.266             0.488            0.199
Chain 1:    700       -19493.676             0.439            0.184
Chain 1:    800       -11822.221             0.465            0.199
Chain 1:    900       -14235.147             0.432            0.184
Chain 1:   1000       -10834.621             0.420            0.199
Chain 1:   1100       -22866.760             0.373            0.199
Chain 1:   1200       -12198.041             0.332            0.199
Chain 1:   1300       -11989.768             0.314            0.184
Chain 1:   1400       -12385.568             0.300            0.184
Chain 1:   1500       -10026.577             0.305            0.235
Chain 1:   1600       -13165.263             0.320            0.238
Chain 1:   1700       -10247.809             0.334            0.285
Chain 1:   1800       -11567.377             0.281            0.238
Chain 1:   1900       -10101.070             0.278            0.238
Chain 1:   2000       -10788.942             0.253            0.235
Chain 1:   2100        -9345.208             0.216            0.154
Chain 1:   2200       -12346.780             0.153            0.154
Chain 1:   2300        -9474.597             0.181            0.235
Chain 1:   2400        -9139.543             0.182            0.235
Chain 1:   2500        -9273.047             0.160            0.154
Chain 1:   2600       -15386.105             0.176            0.154
Chain 1:   2700        -9126.004             0.216            0.154
Chain 1:   2800       -18855.784             0.256            0.243
Chain 1:   2900       -10526.923             0.321            0.303
Chain 1:   3000       -11168.489             0.320            0.303
Chain 1:   3100        -8803.715             0.331            0.303
Chain 1:   3200        -9370.227             0.313            0.303
Chain 1:   3300       -12461.178             0.308            0.269
Chain 1:   3400       -13381.496             0.311            0.269
Chain 1:   3500       -11056.091             0.330            0.269
Chain 1:   3600       -10019.114             0.301            0.248
Chain 1:   3700       -16824.146             0.273            0.248
Chain 1:   3800        -8927.050             0.310            0.248
Chain 1:   3900        -9651.387             0.238            0.210
Chain 1:   4000       -14130.364             0.264            0.248
Chain 1:   4100       -10101.787             0.277            0.248
Chain 1:   4200        -9669.155             0.276            0.248
Chain 1:   4300        -8709.131             0.262            0.210
Chain 1:   4400       -10945.646             0.275            0.210
Chain 1:   4500        -8786.928             0.279            0.246
Chain 1:   4600        -9944.211             0.280            0.246
Chain 1:   4700       -11284.809             0.252            0.204
Chain 1:   4800        -9603.639             0.181            0.175
Chain 1:   4900       -11313.800             0.188            0.175
Chain 1:   5000        -9549.766             0.175            0.175
Chain 1:   5100       -14318.755             0.168            0.175
Chain 1:   5200        -8889.910             0.225            0.185
Chain 1:   5300       -12392.901             0.242            0.204
Chain 1:   5400        -8955.574             0.260            0.246
Chain 1:   5500       -13746.408             0.270            0.283
Chain 1:   5600        -9053.828             0.311            0.333
Chain 1:   5700        -8504.796             0.305            0.333
Chain 1:   5800       -10796.679             0.309            0.333
Chain 1:   5900       -10119.621             0.301            0.333
Chain 1:   6000        -8664.021             0.299            0.333
Chain 1:   6100        -8797.987             0.267            0.283
Chain 1:   6200        -8448.294             0.210            0.212
Chain 1:   6300        -8412.974             0.182            0.168
Chain 1:   6400       -11408.029             0.170            0.168
Chain 1:   6500        -9324.716             0.158            0.168
Chain 1:   6600       -12464.185             0.131            0.168
Chain 1:   6700        -8325.265             0.174            0.212
Chain 1:   6800       -12707.201             0.188            0.223
Chain 1:   6900        -8595.203             0.229            0.252
Chain 1:   7000        -8205.244             0.217            0.252
Chain 1:   7100        -8330.853             0.217            0.252
Chain 1:   7200        -8134.010             0.215            0.252
Chain 1:   7300        -9268.174             0.227            0.252
Chain 1:   7400       -13705.146             0.233            0.252
Chain 1:   7500       -11166.775             0.233            0.252
Chain 1:   7600        -8940.111             0.233            0.249
Chain 1:   7700        -8243.149             0.192            0.227
Chain 1:   7800       -10983.113             0.182            0.227
Chain 1:   7900        -8128.511             0.169            0.227
Chain 1:   8000        -8109.409             0.165            0.227
Chain 1:   8100        -8793.189             0.171            0.227
Chain 1:   8200        -8265.896             0.175            0.227
Chain 1:   8300        -9043.650             0.172            0.227
Chain 1:   8400        -8470.191             0.146            0.086
Chain 1:   8500        -8171.642             0.127            0.085
Chain 1:   8600        -9190.307             0.113            0.085
Chain 1:   8700        -7999.941             0.119            0.086
Chain 1:   8800        -8344.210             0.099            0.078
Chain 1:   8900        -8400.535             0.064            0.068
Chain 1:   9000        -8358.860             0.064            0.068
Chain 1:   9100       -11816.230             0.086            0.068
Chain 1:   9200        -8982.184             0.111            0.086
Chain 1:   9300        -9495.783             0.108            0.068
Chain 1:   9400       -13457.240             0.131            0.111
Chain 1:   9500        -7974.774             0.196            0.149
Chain 1:   9600        -8752.592             0.193            0.149
Chain 1:   9700        -8164.531             0.186            0.089
Chain 1:   9800       -10075.471             0.201            0.190
Chain 1:   9900        -8664.990             0.216            0.190
Chain 1:   10000       -10788.928             0.235            0.197
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001426 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58183.132             1.000            1.000
Chain 1:    200       -17464.472             1.666            2.332
Chain 1:    300        -8621.204             1.452            1.026
Chain 1:    400        -8040.019             1.107            1.026
Chain 1:    500        -8073.572             0.887            1.000
Chain 1:    600        -8902.579             0.754            1.000
Chain 1:    700        -7933.895             0.664            0.122
Chain 1:    800        -8091.939             0.584            0.122
Chain 1:    900        -7945.906             0.521            0.093
Chain 1:   1000        -7770.763             0.471            0.093
Chain 1:   1100        -7731.772             0.371            0.072
Chain 1:   1200        -7624.566             0.140            0.023
Chain 1:   1300        -7739.073             0.039            0.020
Chain 1:   1400        -7979.935             0.034            0.020
Chain 1:   1500        -7628.550             0.039            0.023
Chain 1:   1600        -7611.211             0.029            0.020
Chain 1:   1700        -7525.468             0.018            0.018
Chain 1:   1800        -7603.384             0.017            0.015
Chain 1:   1900        -7572.205             0.016            0.014
Chain 1:   2000        -7655.077             0.015            0.011
Chain 1:   2100        -7605.603             0.015            0.011
Chain 1:   2200        -7704.842             0.015            0.011
Chain 1:   2300        -7616.093             0.015            0.011
Chain 1:   2400        -7655.032             0.012            0.011
Chain 1:   2500        -7591.041             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002919 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86033.954             1.000            1.000
Chain 1:    200       -13367.048             3.218            5.436
Chain 1:    300        -9761.086             2.269            1.000
Chain 1:    400       -10611.932             1.721            1.000
Chain 1:    500        -8665.494             1.422            0.369
Chain 1:    600        -8362.918             1.191            0.369
Chain 1:    700        -8373.191             1.021            0.225
Chain 1:    800        -8634.074             0.897            0.225
Chain 1:    900        -8540.466             0.799            0.080
Chain 1:   1000        -8364.324             0.721            0.080
Chain 1:   1100        -8582.676             0.624            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8288.042             0.083            0.036
Chain 1:   1300        -8475.831             0.049            0.030
Chain 1:   1400        -8477.493             0.041            0.025
Chain 1:   1500        -8336.910             0.020            0.022
Chain 1:   1600        -8448.680             0.018            0.021
Chain 1:   1700        -8535.512             0.019            0.021
Chain 1:   1800        -8129.687             0.021            0.021
Chain 1:   1900        -8226.110             0.021            0.021
Chain 1:   2000        -8198.254             0.019            0.017
Chain 1:   2100        -8318.762             0.018            0.014
Chain 1:   2200        -8129.375             0.017            0.014
Chain 1:   2300        -8265.883             0.016            0.014
Chain 1:   2400        -8273.032             0.016            0.014
Chain 1:   2500        -8239.422             0.015            0.013
Chain 1:   2600        -8237.394             0.013            0.012
Chain 1:   2700        -8151.458             0.014            0.012
Chain 1:   2800        -8116.653             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003333 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386555.561             1.000            1.000
Chain 1:    200     -1581368.727             2.652            4.303
Chain 1:    300      -889727.915             2.027            1.000
Chain 1:    400      -456734.884             1.757            1.000
Chain 1:    500      -357387.436             1.461            0.948
Chain 1:    600      -232440.729             1.307            0.948
Chain 1:    700      -118941.516             1.257            0.948
Chain 1:    800       -86187.026             1.147            0.948
Chain 1:    900       -66567.232             1.053            0.777
Chain 1:   1000       -51387.137             0.977            0.777
Chain 1:   1100       -38884.026             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38063.645             0.481            0.380
Chain 1:   1300       -26041.077             0.449            0.380
Chain 1:   1400       -25761.153             0.356            0.322
Chain 1:   1500       -22353.576             0.343            0.322
Chain 1:   1600       -21571.527             0.293            0.295
Chain 1:   1700       -20447.992             0.203            0.295
Chain 1:   1800       -20392.935             0.165            0.152
Chain 1:   1900       -20718.903             0.137            0.055
Chain 1:   2000       -19231.802             0.116            0.055
Chain 1:   2100       -19470.039             0.085            0.036
Chain 1:   2200       -19696.121             0.084            0.036
Chain 1:   2300       -19313.707             0.039            0.020
Chain 1:   2400       -19085.920             0.039            0.020
Chain 1:   2500       -18887.771             0.025            0.016
Chain 1:   2600       -18518.248             0.024            0.016
Chain 1:   2700       -18475.374             0.018            0.012
Chain 1:   2800       -18192.235             0.020            0.016
Chain 1:   2900       -18473.373             0.020            0.015
Chain 1:   3000       -18459.594             0.012            0.012
Chain 1:   3100       -18544.545             0.011            0.012
Chain 1:   3200       -18235.381             0.012            0.015
Chain 1:   3300       -18440.011             0.011            0.012
Chain 1:   3400       -17915.135             0.013            0.015
Chain 1:   3500       -18526.667             0.015            0.016
Chain 1:   3600       -17833.828             0.017            0.016
Chain 1:   3700       -18220.233             0.019            0.017
Chain 1:   3800       -17180.651             0.023            0.021
Chain 1:   3900       -17176.831             0.022            0.021
Chain 1:   4000       -17294.127             0.022            0.021
Chain 1:   4100       -17207.887             0.022            0.021
Chain 1:   4200       -17024.370             0.022            0.021
Chain 1:   4300       -17162.635             0.021            0.021
Chain 1:   4400       -17119.587             0.019            0.011
Chain 1:   4500       -17022.162             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49187.350             1.000            1.000
Chain 1:    200       -20897.009             1.177            1.354
Chain 1:    300       -15583.302             0.898            1.000
Chain 1:    400       -14573.276             0.691            1.000
Chain 1:    500       -12545.329             0.585            0.341
Chain 1:    600       -22086.121             0.560            0.432
Chain 1:    700       -15785.203             0.537            0.399
Chain 1:    800       -14328.320             0.482            0.399
Chain 1:    900       -14398.924             0.429            0.341
Chain 1:   1000       -13119.951             0.396            0.341
Chain 1:   1100       -16182.836             0.315            0.189
Chain 1:   1200       -11259.839             0.223            0.189
Chain 1:   1300       -12487.074             0.199            0.162
Chain 1:   1400       -10114.958             0.216            0.189
Chain 1:   1500       -10892.570             0.207            0.189
Chain 1:   1600       -12704.313             0.178            0.143
Chain 1:   1700       -12220.928             0.142            0.102
Chain 1:   1800       -30074.551             0.191            0.143
Chain 1:   1900       -10851.588             0.368            0.189
Chain 1:   2000       -18780.031             0.400            0.235
Chain 1:   2100        -9494.008             0.479            0.422
Chain 1:   2200        -9665.711             0.437            0.235
Chain 1:   2300        -9311.275             0.431            0.235
Chain 1:   2400       -10212.365             0.416            0.143
Chain 1:   2500        -9445.335             0.417            0.143
Chain 1:   2600        -9572.121             0.404            0.088
Chain 1:   2700        -9309.280             0.403            0.088
Chain 1:   2800       -11236.707             0.361            0.088
Chain 1:   2900        -9772.643             0.199            0.088
Chain 1:   3000        -8915.062             0.166            0.088
Chain 1:   3100        -9984.142             0.079            0.088
Chain 1:   3200       -10298.582             0.080            0.088
Chain 1:   3300        -9049.271             0.090            0.096
Chain 1:   3400        -9054.671             0.082            0.096
Chain 1:   3500        -9615.144             0.079            0.096
Chain 1:   3600       -10485.778             0.086            0.096
Chain 1:   3700        -8698.877             0.104            0.107
Chain 1:   3800       -10294.605             0.102            0.107
Chain 1:   3900       -10200.196             0.088            0.096
Chain 1:   4000       -10843.089             0.085            0.083
Chain 1:   4100       -11669.458             0.081            0.071
Chain 1:   4200       -16151.035             0.106            0.083
Chain 1:   4300        -8838.874             0.175            0.083
Chain 1:   4400        -9455.179             0.181            0.083
Chain 1:   4500        -9984.744             0.181            0.083
Chain 1:   4600        -9728.217             0.175            0.071
Chain 1:   4700        -9794.349             0.155            0.065
Chain 1:   4800        -8529.599             0.154            0.065
Chain 1:   4900       -13724.004             0.191            0.071
Chain 1:   5000        -9394.531             0.231            0.148
Chain 1:   5100        -8602.174             0.234            0.148
Chain 1:   5200        -9002.801             0.210            0.092
Chain 1:   5300       -13516.178             0.161            0.092
Chain 1:   5400        -8732.511             0.209            0.148
Chain 1:   5500       -12008.974             0.231            0.273
Chain 1:   5600        -8617.556             0.268            0.334
Chain 1:   5700       -13046.381             0.301            0.339
Chain 1:   5800        -8740.125             0.336            0.378
Chain 1:   5900        -8362.372             0.302            0.339
Chain 1:   6000        -8694.257             0.260            0.334
Chain 1:   6100       -13161.800             0.285            0.339
Chain 1:   6200        -8663.565             0.332            0.339
Chain 1:   6300        -9120.440             0.304            0.339
Chain 1:   6400        -9847.192             0.256            0.339
Chain 1:   6500        -8828.366             0.241            0.339
Chain 1:   6600        -8572.532             0.204            0.115
Chain 1:   6700       -10674.939             0.190            0.115
Chain 1:   6800        -9395.614             0.154            0.115
Chain 1:   6900       -10959.339             0.164            0.136
Chain 1:   7000        -9353.252             0.178            0.143
Chain 1:   7100       -11176.362             0.160            0.143
Chain 1:   7200        -9065.679             0.131            0.143
Chain 1:   7300        -8322.671             0.135            0.143
Chain 1:   7400        -8562.669             0.131            0.143
Chain 1:   7500       -11609.585             0.145            0.163
Chain 1:   7600        -8707.088             0.176            0.172
Chain 1:   7700        -8328.141             0.161            0.163
Chain 1:   7800       -12592.129             0.181            0.172
Chain 1:   7900        -8403.217             0.216            0.233
Chain 1:   8000        -9642.646             0.212            0.233
Chain 1:   8100       -11750.684             0.214            0.233
Chain 1:   8200        -8137.551             0.235            0.262
Chain 1:   8300        -8111.268             0.226            0.262
Chain 1:   8400        -9574.912             0.239            0.262
Chain 1:   8500        -8179.573             0.229            0.179
Chain 1:   8600        -8065.399             0.198            0.171
Chain 1:   8700        -8184.701             0.194            0.171
Chain 1:   8800       -10014.513             0.179            0.171
Chain 1:   8900       -12369.708             0.148            0.171
Chain 1:   9000        -9382.445             0.167            0.179
Chain 1:   9100        -8216.765             0.163            0.171
Chain 1:   9200        -8291.016             0.120            0.153
Chain 1:   9300        -8888.636             0.126            0.153
Chain 1:   9400        -8207.031             0.119            0.142
Chain 1:   9500        -8514.101             0.106            0.083
Chain 1:   9600        -8426.582             0.105            0.083
Chain 1:   9700        -8189.905             0.107            0.083
Chain 1:   9800       -10351.820             0.109            0.083
Chain 1:   9900        -8305.669             0.115            0.083
Chain 1:   10000        -8482.340             0.085            0.067
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57509.286             1.000            1.000
Chain 1:    200       -17563.127             1.637            2.274
Chain 1:    300        -8783.343             1.425            1.000
Chain 1:    400        -8121.362             1.089            1.000
Chain 1:    500        -8237.075             0.874            1.000
Chain 1:    600        -8102.126             0.731            1.000
Chain 1:    700        -7750.359             0.633            0.082
Chain 1:    800        -8284.648             0.562            0.082
Chain 1:    900        -7985.269             0.504            0.064
Chain 1:   1000        -7836.132             0.455            0.064
Chain 1:   1100        -7660.433             0.358            0.045
Chain 1:   1200        -7675.161             0.130            0.037
Chain 1:   1300        -7802.967             0.032            0.023
Chain 1:   1400        -7797.079             0.024            0.019
Chain 1:   1500        -7491.512             0.027            0.023
Chain 1:   1600        -7691.432             0.028            0.026
Chain 1:   1700        -7479.997             0.026            0.026
Chain 1:   1800        -7578.280             0.021            0.023
Chain 1:   1900        -7640.724             0.018            0.019
Chain 1:   2000        -7509.018             0.018            0.018
Chain 1:   2100        -7533.257             0.016            0.016
Chain 1:   2200        -7666.656             0.017            0.017
Chain 1:   2300        -7531.304             0.017            0.018
Chain 1:   2400        -7579.520             0.018            0.018
Chain 1:   2500        -7519.489             0.015            0.017
Chain 1:   2600        -7478.056             0.013            0.013
Chain 1:   2700        -7476.949             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 64.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86104.011             1.000            1.000
Chain 1:    200       -13657.719             3.152            5.304
Chain 1:    300        -9946.046             2.226            1.000
Chain 1:    400       -11216.138             1.698            1.000
Chain 1:    500        -8955.944             1.409            0.373
Chain 1:    600        -8328.463             1.186            0.373
Chain 1:    700        -8509.844             1.020            0.252
Chain 1:    800        -8632.196             0.894            0.252
Chain 1:    900        -8677.519             0.795            0.113
Chain 1:   1000        -8774.627             0.717            0.113
Chain 1:   1100        -8550.660             0.620            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8320.043             0.092            0.028
Chain 1:   1300        -8630.952             0.058            0.028
Chain 1:   1400        -8613.064             0.047            0.026
Chain 1:   1500        -8466.423             0.024            0.021
Chain 1:   1600        -8578.146             0.017            0.017
Chain 1:   1700        -8649.873             0.016            0.014
Chain 1:   1800        -8214.137             0.020            0.017
Chain 1:   1900        -8319.000             0.021            0.017
Chain 1:   2000        -8294.750             0.020            0.017
Chain 1:   2100        -8250.968             0.018            0.013
Chain 1:   2200        -8237.286             0.015            0.013
Chain 1:   2300        -8375.092             0.013            0.013
Chain 1:   2400        -8219.625             0.015            0.013
Chain 1:   2500        -8289.242             0.014            0.013
Chain 1:   2600        -8207.054             0.014            0.010
Chain 1:   2700        -8238.946             0.013            0.010
Chain 1:   2800        -8198.873             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003351 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389381.000             1.000            1.000
Chain 1:    200     -1585824.869             2.645            4.290
Chain 1:    300      -891577.271             2.023            1.000
Chain 1:    400      -457578.337             1.754            1.000
Chain 1:    500      -357832.920             1.459            0.948
Chain 1:    600      -232784.405             1.306            0.948
Chain 1:    700      -119218.438             1.255            0.948
Chain 1:    800       -86445.470             1.146            0.948
Chain 1:    900       -66845.227             1.051            0.779
Chain 1:   1000       -51690.827             0.975            0.779
Chain 1:   1100       -39195.234             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38382.650             0.480            0.379
Chain 1:   1300       -26361.109             0.448            0.379
Chain 1:   1400       -26084.486             0.354            0.319
Chain 1:   1500       -22675.962             0.341            0.319
Chain 1:   1600       -21894.097             0.291            0.293
Chain 1:   1700       -20770.306             0.201            0.293
Chain 1:   1800       -20715.324             0.164            0.150
Chain 1:   1900       -21041.875             0.136            0.054
Chain 1:   2000       -19553.154             0.114            0.054
Chain 1:   2100       -19791.768             0.083            0.036
Chain 1:   2200       -20018.172             0.082            0.036
Chain 1:   2300       -19635.286             0.039            0.019
Chain 1:   2400       -19407.278             0.039            0.019
Chain 1:   2500       -19209.025             0.025            0.016
Chain 1:   2600       -18839.173             0.023            0.016
Chain 1:   2700       -18796.078             0.018            0.012
Chain 1:   2800       -18512.649             0.019            0.015
Chain 1:   2900       -18794.029             0.019            0.015
Chain 1:   3000       -18780.325             0.012            0.012
Chain 1:   3100       -18865.350             0.011            0.012
Chain 1:   3200       -18555.833             0.012            0.015
Chain 1:   3300       -18760.691             0.011            0.012
Chain 1:   3400       -18235.168             0.012            0.015
Chain 1:   3500       -18847.655             0.015            0.015
Chain 1:   3600       -18153.543             0.016            0.015
Chain 1:   3700       -18540.921             0.018            0.017
Chain 1:   3800       -17499.330             0.023            0.021
Chain 1:   3900       -17495.400             0.021            0.021
Chain 1:   4000       -17612.756             0.022            0.021
Chain 1:   4100       -17526.434             0.022            0.021
Chain 1:   4200       -17342.406             0.021            0.021
Chain 1:   4300       -17481.042             0.021            0.021
Chain 1:   4400       -17437.649             0.018            0.011
Chain 1:   4500       -17340.108             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001308 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12383.898             1.000            1.000
Chain 1:    200        -9334.449             0.663            1.000
Chain 1:    300        -8158.500             0.490            0.327
Chain 1:    400        -8255.381             0.371            0.327
Chain 1:    500        -8224.377             0.297            0.144
Chain 1:    600        -8041.764             0.252            0.144
Chain 1:    700        -7995.713             0.216            0.023
Chain 1:    800        -7995.642             0.189            0.023
Chain 1:    900        -7936.858             0.169            0.012
Chain 1:   1000        -8023.896             0.153            0.012
Chain 1:   1100        -8133.447             0.055            0.012
Chain 1:   1200        -8006.754             0.024            0.012
Chain 1:   1300        -7935.466             0.010            0.011
Chain 1:   1400        -7954.084             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002267 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 22.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56950.047             1.000            1.000
Chain 1:    200       -17512.207             1.626            2.252
Chain 1:    300        -8700.575             1.422            1.013
Chain 1:    400        -8275.202             1.079            1.013
Chain 1:    500        -8315.377             0.864            1.000
Chain 1:    600        -8745.228             0.728            1.000
Chain 1:    700        -7741.946             0.643            0.130
Chain 1:    800        -8200.575             0.569            0.130
Chain 1:    900        -7969.831             0.509            0.056
Chain 1:   1000        -7864.558             0.460            0.056
Chain 1:   1100        -7796.277             0.361            0.051
Chain 1:   1200        -7754.712             0.136            0.049
Chain 1:   1300        -7707.049             0.035            0.029
Chain 1:   1400        -7820.614             0.032            0.015
Chain 1:   1500        -7528.460             0.035            0.029
Chain 1:   1600        -7592.674             0.031            0.015
Chain 1:   1700        -7476.283             0.020            0.015
Chain 1:   1800        -7569.539             0.015            0.013
Chain 1:   1900        -7564.186             0.012            0.012
Chain 1:   2000        -7558.706             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003589 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86619.183             1.000            1.000
Chain 1:    200       -13540.719             3.198            5.397
Chain 1:    300        -9913.719             2.254            1.000
Chain 1:    400       -10794.950             1.711            1.000
Chain 1:    500        -8882.536             1.412            0.366
Chain 1:    600        -8466.753             1.185            0.366
Chain 1:    700        -8576.136             1.017            0.215
Chain 1:    800        -9173.988             0.898            0.215
Chain 1:    900        -8687.441             0.805            0.082
Chain 1:   1000        -8493.349             0.727            0.082
Chain 1:   1100        -8736.327             0.629            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8388.358             0.094            0.056
Chain 1:   1300        -8612.823             0.060            0.049
Chain 1:   1400        -8612.195             0.052            0.041
Chain 1:   1500        -8502.722             0.031            0.028
Chain 1:   1600        -8607.333             0.028            0.026
Chain 1:   1700        -8695.314             0.027            0.026
Chain 1:   1800        -8287.368             0.026            0.026
Chain 1:   1900        -8383.974             0.021            0.023
Chain 1:   2000        -8356.219             0.019            0.013
Chain 1:   2100        -8476.944             0.018            0.013
Chain 1:   2200        -8293.893             0.016            0.013
Chain 1:   2300        -8423.657             0.015            0.013
Chain 1:   2400        -8433.678             0.015            0.013
Chain 1:   2500        -8395.970             0.014            0.012
Chain 1:   2600        -8394.703             0.013            0.012
Chain 1:   2700        -8309.544             0.013            0.012
Chain 1:   2800        -8274.414             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003174 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396749.102             1.000            1.000
Chain 1:    200     -1585967.923             2.647            4.294
Chain 1:    300      -892393.465             2.024            1.000
Chain 1:    400      -458374.296             1.755            1.000
Chain 1:    500      -358720.324             1.459            0.947
Chain 1:    600      -233546.408             1.305            0.947
Chain 1:    700      -119522.977             1.255            0.947
Chain 1:    800       -86646.785             1.146            0.947
Chain 1:    900       -66948.867             1.051            0.777
Chain 1:   1000       -51705.208             0.975            0.777
Chain 1:   1100       -39144.744             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38315.554             0.480            0.379
Chain 1:   1300       -26239.573             0.449            0.379
Chain 1:   1400       -25954.655             0.355            0.321
Chain 1:   1500       -22533.289             0.342            0.321
Chain 1:   1600       -21746.671             0.292            0.295
Chain 1:   1700       -20617.051             0.202            0.294
Chain 1:   1800       -20560.203             0.165            0.152
Chain 1:   1900       -20886.172             0.137            0.055
Chain 1:   2000       -19395.667             0.115            0.055
Chain 1:   2100       -19634.191             0.084            0.036
Chain 1:   2200       -19860.798             0.083            0.036
Chain 1:   2300       -19477.900             0.039            0.020
Chain 1:   2400       -19250.013             0.039            0.020
Chain 1:   2500       -19052.102             0.025            0.016
Chain 1:   2600       -18682.424             0.024            0.016
Chain 1:   2700       -18639.399             0.018            0.012
Chain 1:   2800       -18356.354             0.020            0.015
Chain 1:   2900       -18637.588             0.019            0.015
Chain 1:   3000       -18623.783             0.012            0.012
Chain 1:   3100       -18708.738             0.011            0.012
Chain 1:   3200       -18399.523             0.012            0.015
Chain 1:   3300       -18604.157             0.011            0.012
Chain 1:   3400       -18079.273             0.013            0.015
Chain 1:   3500       -18690.906             0.015            0.015
Chain 1:   3600       -17997.951             0.017            0.015
Chain 1:   3700       -18384.498             0.018            0.017
Chain 1:   3800       -17344.768             0.023            0.021
Chain 1:   3900       -17340.936             0.021            0.021
Chain 1:   4000       -17458.225             0.022            0.021
Chain 1:   4100       -17372.019             0.022            0.021
Chain 1:   4200       -17188.375             0.021            0.021
Chain 1:   4300       -17326.682             0.021            0.021
Chain 1:   4400       -17283.620             0.019            0.011
Chain 1:   4500       -17186.174             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49166.557             1.000            1.000
Chain 1:    200       -19052.586             1.290            1.581
Chain 1:    300       -16327.065             0.916            1.000
Chain 1:    400       -23755.488             0.765            1.000
Chain 1:    500       -13722.578             0.758            0.731
Chain 1:    600       -12297.064             0.651            0.731
Chain 1:    700       -16256.964             0.593            0.313
Chain 1:    800       -13010.225             0.550            0.313
Chain 1:    900       -15252.118             0.505            0.250
Chain 1:   1000       -11572.561             0.487            0.313
Chain 1:   1100       -13790.422             0.403            0.250
Chain 1:   1200       -10521.504             0.276            0.250
Chain 1:   1300       -12917.569             0.277            0.250
Chain 1:   1400       -19557.186             0.280            0.250
Chain 1:   1500       -10392.679             0.295            0.250
Chain 1:   1600       -10774.665             0.287            0.250
Chain 1:   1700       -11551.382             0.270            0.250
Chain 1:   1800       -11165.350             0.248            0.185
Chain 1:   1900       -10603.963             0.239            0.185
Chain 1:   2000        -9859.586             0.214            0.161
Chain 1:   2100        -9336.339             0.204            0.075
Chain 1:   2200       -12582.021             0.199            0.075
Chain 1:   2300        -9486.767             0.213            0.075
Chain 1:   2400        -8940.266             0.185            0.067
Chain 1:   2500        -9630.076             0.104            0.067
Chain 1:   2600        -9467.438             0.102            0.067
Chain 1:   2700       -11911.289             0.116            0.072
Chain 1:   2800        -8990.134             0.145            0.075
Chain 1:   2900       -18326.362             0.191            0.205
Chain 1:   3000        -9287.227             0.280            0.258
Chain 1:   3100        -9891.761             0.281            0.258
Chain 1:   3200       -15465.160             0.291            0.325
Chain 1:   3300        -9879.184             0.315            0.325
Chain 1:   3400        -8968.765             0.319            0.325
Chain 1:   3500        -9921.654             0.321            0.325
Chain 1:   3600       -14170.914             0.350            0.325
Chain 1:   3700        -9056.477             0.386            0.360
Chain 1:   3800       -10966.202             0.371            0.360
Chain 1:   3900       -13510.599             0.338            0.300
Chain 1:   4000        -9182.960             0.288            0.300
Chain 1:   4100        -8846.563             0.286            0.300
Chain 1:   4200        -8876.930             0.250            0.188
Chain 1:   4300        -9740.424             0.203            0.174
Chain 1:   4400        -9461.795             0.195            0.174
Chain 1:   4500       -10170.129             0.193            0.174
Chain 1:   4600        -8772.836             0.179            0.159
Chain 1:   4700       -12695.623             0.153            0.159
Chain 1:   4800       -12323.566             0.139            0.089
Chain 1:   4900        -9167.791             0.154            0.089
Chain 1:   5000       -18453.528             0.158            0.089
Chain 1:   5100        -8888.239             0.261            0.159
Chain 1:   5200        -9880.665             0.271            0.159
Chain 1:   5300       -12594.225             0.284            0.215
Chain 1:   5400        -8727.371             0.325            0.309
Chain 1:   5500       -11716.929             0.344            0.309
Chain 1:   5600       -13100.202             0.338            0.309
Chain 1:   5700        -8963.269             0.354            0.344
Chain 1:   5800       -10530.101             0.365            0.344
Chain 1:   5900       -13417.193             0.352            0.255
Chain 1:   6000        -8668.090             0.357            0.255
Chain 1:   6100        -9288.155             0.256            0.215
Chain 1:   6200        -8985.905             0.249            0.215
Chain 1:   6300       -10644.421             0.243            0.215
Chain 1:   6400       -10590.982             0.200            0.156
Chain 1:   6500        -8775.462             0.195            0.156
Chain 1:   6600        -8515.798             0.187            0.156
Chain 1:   6700       -10244.864             0.158            0.156
Chain 1:   6800        -8735.688             0.160            0.169
Chain 1:   6900       -12123.665             0.167            0.169
Chain 1:   7000        -9865.395             0.135            0.169
Chain 1:   7100        -8214.200             0.148            0.173
Chain 1:   7200        -8590.831             0.149            0.173
Chain 1:   7300        -9486.264             0.143            0.173
Chain 1:   7400        -8918.319             0.149            0.173
Chain 1:   7500        -8338.715             0.135            0.169
Chain 1:   7600        -8794.662             0.137            0.169
Chain 1:   7700        -8592.474             0.123            0.094
Chain 1:   7800        -8486.262             0.107            0.070
Chain 1:   7900        -8594.866             0.080            0.064
Chain 1:   8000       -10291.647             0.074            0.064
Chain 1:   8100        -9052.112             0.067            0.064
Chain 1:   8200        -8114.771             0.075            0.070
Chain 1:   8300       -10251.748             0.086            0.070
Chain 1:   8400       -12386.342             0.097            0.116
Chain 1:   8500       -10137.854             0.112            0.137
Chain 1:   8600       -14619.663             0.138            0.165
Chain 1:   8700        -9090.410             0.196            0.172
Chain 1:   8800        -8513.316             0.202            0.172
Chain 1:   8900       -10745.605             0.221            0.208
Chain 1:   9000       -10974.395             0.207            0.208
Chain 1:   9100        -8666.177             0.220            0.208
Chain 1:   9200        -8773.995             0.209            0.208
Chain 1:   9300        -8605.793             0.190            0.208
Chain 1:   9400       -10303.222             0.190            0.208
Chain 1:   9500        -8212.782             0.193            0.208
Chain 1:   9600        -8383.351             0.164            0.165
Chain 1:   9700       -11664.847             0.132            0.165
Chain 1:   9800        -8360.912             0.164            0.208
Chain 1:   9900        -9866.451             0.159            0.165
Chain 1:   10000        -8469.738             0.173            0.165
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62994.751             1.000            1.000
Chain 1:    200       -18103.606             1.740            2.480
Chain 1:    300        -8788.505             1.513            1.060
Chain 1:    400        -8608.885             1.140            1.060
Chain 1:    500        -7974.700             0.928            1.000
Chain 1:    600        -8821.832             0.789            1.000
Chain 1:    700        -7917.223             0.693            0.114
Chain 1:    800        -7863.217             0.607            0.114
Chain 1:    900        -7998.103             0.542            0.096
Chain 1:   1000        -7754.008             0.491            0.096
Chain 1:   1100        -7765.810             0.391            0.080
Chain 1:   1200        -7607.153             0.145            0.031
Chain 1:   1300        -7636.027             0.039            0.021
Chain 1:   1400        -7905.289             0.041            0.031
Chain 1:   1500        -7666.644             0.036            0.031
Chain 1:   1600        -7835.639             0.028            0.022
Chain 1:   1700        -7558.169             0.020            0.022
Chain 1:   1800        -7635.765             0.021            0.022
Chain 1:   1900        -7623.912             0.019            0.022
Chain 1:   2000        -7654.494             0.017            0.021
Chain 1:   2100        -7633.425             0.017            0.021
Chain 1:   2200        -7747.063             0.016            0.015
Chain 1:   2300        -7656.097             0.017            0.015
Chain 1:   2400        -7691.449             0.014            0.012
Chain 1:   2500        -7638.393             0.011            0.010
Chain 1:   2600        -7586.035             0.010            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00289 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86304.934             1.000            1.000
Chain 1:    200       -13494.760             3.198            5.395
Chain 1:    300        -9899.059             2.253            1.000
Chain 1:    400       -10677.273             1.708            1.000
Chain 1:    500        -8866.093             1.407            0.363
Chain 1:    600        -8396.554             1.182            0.363
Chain 1:    700        -8475.290             1.014            0.204
Chain 1:    800        -9202.687             0.898            0.204
Chain 1:    900        -8699.739             0.804            0.079
Chain 1:   1000        -8522.189             0.726            0.079
Chain 1:   1100        -8796.411             0.629            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8450.098             0.094            0.058
Chain 1:   1300        -8622.887             0.059            0.056
Chain 1:   1400        -8628.457             0.052            0.041
Chain 1:   1500        -8488.764             0.033            0.031
Chain 1:   1600        -8599.843             0.029            0.021
Chain 1:   1700        -8686.402             0.029            0.021
Chain 1:   1800        -8285.070             0.026            0.021
Chain 1:   1900        -8383.628             0.021            0.020
Chain 1:   2000        -8355.095             0.020            0.016
Chain 1:   2100        -8474.881             0.018            0.014
Chain 1:   2200        -8265.542             0.016            0.014
Chain 1:   2300        -8416.455             0.016            0.014
Chain 1:   2400        -8295.099             0.017            0.015
Chain 1:   2500        -8359.012             0.017            0.014
Chain 1:   2600        -8381.547             0.016            0.014
Chain 1:   2700        -8300.299             0.016            0.014
Chain 1:   2800        -8273.649             0.011            0.012
Chain 1:   2900        -8329.076             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003363 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414262.358             1.000            1.000
Chain 1:    200     -1583582.632             2.657            4.313
Chain 1:    300      -889939.484             2.031            1.000
Chain 1:    400      -457238.761             1.760            1.000
Chain 1:    500      -357547.450             1.464            0.946
Chain 1:    600      -232491.779             1.309            0.946
Chain 1:    700      -118958.671             1.259            0.946
Chain 1:    800       -86255.039             1.149            0.946
Chain 1:    900       -66636.239             1.054            0.779
Chain 1:   1000       -51466.636             0.978            0.779
Chain 1:   1100       -38981.336             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38159.456             0.481            0.379
Chain 1:   1300       -26151.903             0.449            0.379
Chain 1:   1400       -25873.795             0.355            0.320
Chain 1:   1500       -22471.257             0.342            0.320
Chain 1:   1600       -21691.027             0.292            0.295
Chain 1:   1700       -20568.902             0.202            0.294
Chain 1:   1800       -20514.142             0.165            0.151
Chain 1:   1900       -20840.046             0.137            0.055
Chain 1:   2000       -19354.167             0.115            0.055
Chain 1:   2100       -19592.173             0.084            0.036
Chain 1:   2200       -19818.257             0.083            0.036
Chain 1:   2300       -19435.889             0.039            0.020
Chain 1:   2400       -19208.131             0.039            0.020
Chain 1:   2500       -19010.137             0.025            0.016
Chain 1:   2600       -18640.563             0.023            0.016
Chain 1:   2700       -18597.661             0.018            0.012
Chain 1:   2800       -18314.647             0.020            0.015
Chain 1:   2900       -18595.698             0.019            0.015
Chain 1:   3000       -18581.881             0.012            0.012
Chain 1:   3100       -18666.854             0.011            0.012
Chain 1:   3200       -18357.705             0.012            0.015
Chain 1:   3300       -18562.315             0.011            0.012
Chain 1:   3400       -18037.561             0.013            0.015
Chain 1:   3500       -18648.923             0.015            0.015
Chain 1:   3600       -17956.239             0.017            0.015
Chain 1:   3700       -18342.550             0.019            0.017
Chain 1:   3800       -17303.272             0.023            0.021
Chain 1:   3900       -17299.451             0.021            0.021
Chain 1:   4000       -17416.740             0.022            0.021
Chain 1:   4100       -17330.567             0.022            0.021
Chain 1:   4200       -17147.052             0.022            0.021
Chain 1:   4300       -17285.276             0.021            0.021
Chain 1:   4400       -17242.265             0.019            0.011
Chain 1:   4500       -17144.851             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48412.098             1.000            1.000
Chain 1:    200       -15548.831             1.557            2.114
Chain 1:    300       -21143.201             1.126            1.000
Chain 1:    400       -12798.028             1.008            1.000
Chain 1:    500       -15105.147             0.837            0.652
Chain 1:    600       -13699.119             0.714            0.652
Chain 1:    700       -12745.912             0.623            0.265
Chain 1:    800       -10669.963             0.569            0.265
Chain 1:    900       -10535.977             0.508            0.195
Chain 1:   1000       -13012.724             0.476            0.195
Chain 1:   1100       -24307.931             0.422            0.195
Chain 1:   1200       -11260.337             0.327            0.195
Chain 1:   1300       -10685.399             0.306            0.190
Chain 1:   1400        -9737.006             0.250            0.153
Chain 1:   1500       -10295.144             0.240            0.103
Chain 1:   1600        -9848.837             0.235            0.097
Chain 1:   1700        -9680.316             0.229            0.097
Chain 1:   1800       -14586.842             0.243            0.097
Chain 1:   1900       -10510.893             0.281            0.190
Chain 1:   2000       -13749.419             0.285            0.236
Chain 1:   2100       -11144.403             0.262            0.234
Chain 1:   2200       -10113.196             0.156            0.102
Chain 1:   2300       -11553.995             0.163            0.125
Chain 1:   2400        -8964.284             0.183            0.234
Chain 1:   2500       -13952.568             0.213            0.236
Chain 1:   2600       -13296.765             0.213            0.236
Chain 1:   2700        -9265.543             0.255            0.289
Chain 1:   2800        -8658.948             0.228            0.236
Chain 1:   2900        -9007.579             0.194            0.234
Chain 1:   3000       -15186.482             0.211            0.234
Chain 1:   3100        -9105.893             0.254            0.289
Chain 1:   3200       -10126.997             0.254            0.289
Chain 1:   3300       -15494.233             0.276            0.346
Chain 1:   3400       -11985.898             0.277            0.346
Chain 1:   3500        -9108.117             0.272            0.316
Chain 1:   3600        -9584.696             0.272            0.316
Chain 1:   3700       -16815.984             0.272            0.316
Chain 1:   3800        -8836.957             0.355            0.346
Chain 1:   3900       -11833.204             0.377            0.346
Chain 1:   4000        -8765.315             0.371            0.346
Chain 1:   4100        -8517.299             0.307            0.316
Chain 1:   4200        -9007.725             0.302            0.316
Chain 1:   4300        -8436.274             0.275            0.293
Chain 1:   4400        -8269.944             0.247            0.253
Chain 1:   4500        -8942.272             0.223            0.075
Chain 1:   4600        -8240.221             0.227            0.085
Chain 1:   4700        -9833.604             0.200            0.085
Chain 1:   4800        -8597.795             0.124            0.085
Chain 1:   4900        -9326.784             0.107            0.078
Chain 1:   5000       -12892.407             0.099            0.078
Chain 1:   5100        -8323.562             0.151            0.085
Chain 1:   5200        -9147.855             0.155            0.090
Chain 1:   5300       -11714.641             0.170            0.144
Chain 1:   5400       -12892.588             0.177            0.144
Chain 1:   5500        -8489.872             0.221            0.162
Chain 1:   5600        -8221.936             0.216            0.162
Chain 1:   5700       -12266.323             0.233            0.219
Chain 1:   5800        -8289.994             0.266            0.277
Chain 1:   5900        -8728.701             0.264            0.277
Chain 1:   6000        -9434.626             0.244            0.219
Chain 1:   6100        -9717.380             0.192            0.091
Chain 1:   6200        -8430.772             0.198            0.153
Chain 1:   6300       -13100.151             0.212            0.153
Chain 1:   6400       -12983.570             0.203            0.153
Chain 1:   6500        -9378.345             0.190            0.153
Chain 1:   6600       -10993.067             0.201            0.153
Chain 1:   6700       -10005.616             0.178            0.147
Chain 1:   6800        -9697.630             0.133            0.099
Chain 1:   6900       -12105.205             0.148            0.147
Chain 1:   7000        -8366.224             0.185            0.153
Chain 1:   7100        -8633.937             0.186            0.153
Chain 1:   7200        -8209.007             0.176            0.147
Chain 1:   7300        -9981.457             0.158            0.147
Chain 1:   7400        -7915.311             0.183            0.178
Chain 1:   7500        -9790.710             0.164            0.178
Chain 1:   7600        -8551.687             0.163            0.178
Chain 1:   7700        -8941.007             0.158            0.178
Chain 1:   7800        -8623.246             0.158            0.178
Chain 1:   7900        -8998.206             0.143            0.145
Chain 1:   8000       -10891.733             0.115            0.145
Chain 1:   8100       -11136.191             0.114            0.145
Chain 1:   8200       -10331.753             0.117            0.145
Chain 1:   8300        -7914.707             0.130            0.145
Chain 1:   8400       -10098.914             0.125            0.145
Chain 1:   8500        -7951.994             0.133            0.145
Chain 1:   8600        -9737.301             0.137            0.174
Chain 1:   8700        -8002.661             0.154            0.183
Chain 1:   8800        -8124.438             0.152            0.183
Chain 1:   8900       -10856.879             0.173            0.216
Chain 1:   9000       -11164.121             0.159            0.216
Chain 1:   9100        -8136.579             0.194            0.217
Chain 1:   9200       -11542.557             0.215            0.252
Chain 1:   9300        -8281.580             0.224            0.252
Chain 1:   9400       -11460.856             0.230            0.270
Chain 1:   9500        -7937.358             0.248            0.277
Chain 1:   9600        -9530.804             0.246            0.277
Chain 1:   9700        -7887.937             0.245            0.277
Chain 1:   9800        -8179.235             0.247            0.277
Chain 1:   9900        -9018.166             0.231            0.277
Chain 1:   10000        -7856.386             0.243            0.277
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61348.740             1.000            1.000
Chain 1:    200       -17331.604             1.770            2.540
Chain 1:    300        -8555.626             1.522            1.026
Chain 1:    400        -8068.497             1.156            1.026
Chain 1:    500        -8202.757             0.928            1.000
Chain 1:    600        -8010.361             0.778            1.000
Chain 1:    700        -8008.089             0.667            0.060
Chain 1:    800        -7889.085             0.585            0.060
Chain 1:    900        -7709.759             0.523            0.024
Chain 1:   1000        -7639.639             0.471            0.024
Chain 1:   1100        -7544.589             0.373            0.023
Chain 1:   1200        -7591.091             0.119            0.016
Chain 1:   1300        -7483.290             0.018            0.015
Chain 1:   1400        -7767.413             0.016            0.015
Chain 1:   1500        -7473.541             0.018            0.015
Chain 1:   1600        -7379.026             0.017            0.014
Chain 1:   1700        -7386.799             0.017            0.014
Chain 1:   1800        -7428.939             0.016            0.013
Chain 1:   1900        -7397.313             0.014            0.013
Chain 1:   2000        -7461.197             0.014            0.013
Chain 1:   2100        -7503.270             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002913 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85435.385             1.000            1.000
Chain 1:    200       -12994.297             3.287            5.575
Chain 1:    300        -9509.280             2.314            1.000
Chain 1:    400       -10240.064             1.753            1.000
Chain 1:    500        -8365.887             1.447            0.366
Chain 1:    600        -8502.940             1.209            0.366
Chain 1:    700        -8179.049             1.042            0.224
Chain 1:    800        -8553.743             0.917            0.224
Chain 1:    900        -8419.394             0.817            0.071
Chain 1:   1000        -8157.998             0.738            0.071
Chain 1:   1100        -8439.604             0.642            0.044   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8196.244             0.087            0.040
Chain 1:   1300        -8156.475             0.051            0.033
Chain 1:   1400        -8160.825             0.044            0.032
Chain 1:   1500        -8178.432             0.022            0.030
Chain 1:   1600        -8177.687             0.020            0.030
Chain 1:   1700        -8124.356             0.017            0.016
Chain 1:   1800        -8002.127             0.014            0.015
Chain 1:   1900        -8113.542             0.014            0.014
Chain 1:   2000        -8077.526             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395271.396             1.000            1.000
Chain 1:    200     -1583203.283             2.651            4.303
Chain 1:    300      -890226.533             2.027            1.000
Chain 1:    400      -456782.048             1.758            1.000
Chain 1:    500      -357244.532             1.462            0.949
Chain 1:    600      -232343.030             1.308            0.949
Chain 1:    700      -118644.952             1.258            0.949
Chain 1:    800       -85858.606             1.148            0.949
Chain 1:    900       -66208.051             1.054            0.778
Chain 1:   1000       -50998.543             0.978            0.778
Chain 1:   1100       -38477.545             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37646.309             0.483            0.382
Chain 1:   1300       -25622.568             0.452            0.382
Chain 1:   1400       -25338.507             0.358            0.325
Chain 1:   1500       -21931.705             0.346            0.325
Chain 1:   1600       -21148.505             0.296            0.298
Chain 1:   1700       -20025.810             0.205            0.297
Chain 1:   1800       -19970.189             0.167            0.155
Chain 1:   1900       -20295.384             0.139            0.056
Chain 1:   2000       -18810.138             0.117            0.056
Chain 1:   2100       -19048.199             0.086            0.037
Chain 1:   2200       -19273.760             0.085            0.037
Chain 1:   2300       -18892.032             0.040            0.020
Chain 1:   2400       -18664.521             0.040            0.020
Chain 1:   2500       -18466.456             0.026            0.016
Chain 1:   2600       -18097.708             0.024            0.016
Chain 1:   2700       -18054.988             0.019            0.012
Chain 1:   2800       -17772.236             0.020            0.016
Chain 1:   2900       -18053.047             0.020            0.016
Chain 1:   3000       -18039.295             0.012            0.012
Chain 1:   3100       -18124.127             0.011            0.012
Chain 1:   3200       -17815.474             0.012            0.016
Chain 1:   3300       -18019.684             0.011            0.012
Chain 1:   3400       -17495.754             0.013            0.016
Chain 1:   3500       -18105.869             0.015            0.016
Chain 1:   3600       -17414.890             0.017            0.016
Chain 1:   3700       -17799.945             0.019            0.017
Chain 1:   3800       -16763.236             0.024            0.022
Chain 1:   3900       -16759.481             0.022            0.022
Chain 1:   4000       -16876.777             0.023            0.022
Chain 1:   4100       -16790.703             0.023            0.022
Chain 1:   4200       -16607.755             0.022            0.022
Chain 1:   4300       -16745.588             0.022            0.022
Chain 1:   4400       -16703.060             0.019            0.011
Chain 1:   4500       -16605.720             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001232 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48670.975             1.000            1.000
Chain 1:    200       -20135.743             1.209            1.417
Chain 1:    300       -14610.227             0.932            1.000
Chain 1:    400       -14901.987             0.704            1.000
Chain 1:    500       -12607.729             0.599            0.378
Chain 1:    600       -16839.033             0.541            0.378
Chain 1:    700       -17795.727             0.472            0.251
Chain 1:    800       -12723.818             0.463            0.378
Chain 1:    900       -12646.663             0.412            0.251
Chain 1:   1000       -11006.105             0.386            0.251
Chain 1:   1100       -10922.072             0.286            0.182
Chain 1:   1200       -11107.241             0.146            0.149
Chain 1:   1300       -11510.047             0.112            0.054
Chain 1:   1400        -9737.929             0.128            0.149
Chain 1:   1500       -11765.610             0.127            0.149
Chain 1:   1600        -9857.562             0.121            0.149
Chain 1:   1700        -9880.018             0.116            0.149
Chain 1:   1800       -12299.813             0.096            0.149
Chain 1:   1900       -14844.186             0.113            0.171
Chain 1:   2000       -11990.581             0.122            0.172
Chain 1:   2100       -10647.529             0.133            0.172
Chain 1:   2200       -12093.032             0.144            0.172
Chain 1:   2300       -13564.117             0.151            0.172
Chain 1:   2400       -11432.552             0.151            0.172
Chain 1:   2500       -10023.591             0.148            0.171
Chain 1:   2600        -9435.081             0.135            0.141
Chain 1:   2700       -10830.269             0.148            0.141
Chain 1:   2800       -11044.784             0.130            0.129
Chain 1:   2900        -9674.834             0.127            0.129
Chain 1:   3000        -8967.296             0.111            0.126
Chain 1:   3100        -9242.958             0.102            0.120
Chain 1:   3200        -9050.873             0.092            0.108
Chain 1:   3300        -9714.897             0.088            0.079
Chain 1:   3400        -9173.156             0.075            0.068
Chain 1:   3500        -9332.438             0.063            0.062
Chain 1:   3600        -9135.380             0.059            0.059
Chain 1:   3700       -10608.213             0.060            0.059
Chain 1:   3800       -16879.100             0.095            0.068
Chain 1:   3900       -10016.611             0.149            0.068
Chain 1:   4000       -11555.318             0.155            0.068
Chain 1:   4100        -9114.938             0.178            0.133
Chain 1:   4200       -12456.360             0.203            0.139
Chain 1:   4300       -10165.713             0.219            0.225
Chain 1:   4400       -14499.614             0.243            0.268
Chain 1:   4500        -8533.006             0.311            0.268
Chain 1:   4600       -11925.693             0.337            0.284
Chain 1:   4700        -8941.602             0.357            0.299
Chain 1:   4800        -8782.899             0.321            0.284
Chain 1:   4900       -12633.012             0.283            0.284
Chain 1:   5000       -14638.731             0.284            0.284
Chain 1:   5100       -10174.112             0.301            0.299
Chain 1:   5200        -8729.570             0.291            0.299
Chain 1:   5300       -12512.135             0.298            0.302
Chain 1:   5400       -16974.083             0.295            0.302
Chain 1:   5500       -12543.295             0.260            0.302
Chain 1:   5600        -9378.703             0.265            0.305
Chain 1:   5700       -12951.397             0.260            0.302
Chain 1:   5800       -13815.573             0.264            0.302
Chain 1:   5900        -9179.056             0.284            0.302
Chain 1:   6000       -11611.180             0.291            0.302
Chain 1:   6100        -9318.373             0.272            0.276
Chain 1:   6200        -8947.446             0.260            0.276
Chain 1:   6300        -8883.942             0.230            0.263
Chain 1:   6400       -11929.820             0.229            0.255
Chain 1:   6500        -8544.297             0.234            0.255
Chain 1:   6600        -9315.184             0.208            0.246
Chain 1:   6700        -8866.387             0.186            0.209
Chain 1:   6800       -11360.719             0.201            0.220
Chain 1:   6900        -9129.822             0.175            0.220
Chain 1:   7000       -12390.133             0.181            0.244
Chain 1:   7100       -10873.693             0.170            0.220
Chain 1:   7200        -8293.275             0.197            0.244
Chain 1:   7300        -9437.369             0.208            0.244
Chain 1:   7400        -8554.903             0.193            0.220
Chain 1:   7500        -8270.683             0.157            0.139
Chain 1:   7600        -8532.600             0.152            0.139
Chain 1:   7700        -8645.469             0.148            0.139
Chain 1:   7800        -8946.546             0.129            0.121
Chain 1:   7900        -9267.186             0.108            0.103
Chain 1:   8000       -10012.921             0.090            0.074
Chain 1:   8100        -8562.966             0.093            0.074
Chain 1:   8200        -9137.194             0.068            0.063
Chain 1:   8300        -8863.958             0.059            0.035
Chain 1:   8400       -11654.751             0.072            0.035
Chain 1:   8500        -8310.292             0.109            0.063
Chain 1:   8600        -9355.821             0.117            0.074
Chain 1:   8700        -8375.294             0.128            0.112
Chain 1:   8800        -8407.219             0.125            0.112
Chain 1:   8900       -10375.158             0.140            0.117
Chain 1:   9000        -9779.620             0.139            0.117
Chain 1:   9100        -9902.057             0.123            0.112
Chain 1:   9200        -8417.592             0.134            0.117
Chain 1:   9300       -11835.925             0.160            0.176
Chain 1:   9400        -9164.878             0.165            0.176
Chain 1:   9500       -11847.373             0.148            0.176
Chain 1:   9600        -9564.379             0.161            0.190
Chain 1:   9700       -11468.791             0.165            0.190
Chain 1:   9800        -8430.412             0.201            0.226
Chain 1:   9900        -9105.803             0.190            0.226
Chain 1:   10000        -8997.945             0.185            0.226
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57253.794             1.000            1.000
Chain 1:    200       -17516.815             1.634            2.269
Chain 1:    300        -8820.210             1.418            1.000
Chain 1:    400        -8484.048             1.074            1.000
Chain 1:    500        -8230.510             0.865            0.986
Chain 1:    600        -8623.653             0.728            0.986
Chain 1:    700        -8104.642             0.634            0.064
Chain 1:    800        -8253.193             0.557            0.064
Chain 1:    900        -8086.859             0.497            0.046
Chain 1:   1000        -8349.544             0.450            0.046
Chain 1:   1100        -7893.048             0.356            0.046
Chain 1:   1200        -7818.879             0.130            0.040
Chain 1:   1300        -7840.359             0.032            0.031
Chain 1:   1400        -8013.101             0.030            0.031
Chain 1:   1500        -7726.713             0.031            0.031
Chain 1:   1600        -7929.227             0.029            0.026
Chain 1:   1700        -7640.383             0.026            0.026
Chain 1:   1800        -7717.030             0.025            0.026
Chain 1:   1900        -7640.803             0.024            0.026
Chain 1:   2000        -7736.865             0.022            0.022
Chain 1:   2100        -7751.706             0.017            0.012
Chain 1:   2200        -7829.928             0.017            0.012
Chain 1:   2300        -7719.492             0.018            0.014
Chain 1:   2400        -7774.641             0.017            0.012
Chain 1:   2500        -7653.611             0.014            0.012
Chain 1:   2600        -7673.370             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003285 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86065.728             1.000            1.000
Chain 1:    200       -13509.100             3.185            5.371
Chain 1:    300        -9924.391             2.244            1.000
Chain 1:    400       -10719.797             1.702            1.000
Chain 1:    500        -8890.326             1.402            0.361
Chain 1:    600        -8593.903             1.174            0.361
Chain 1:    700        -8481.039             1.009            0.206
Chain 1:    800        -9275.942             0.893            0.206
Chain 1:    900        -8723.636             0.801            0.086
Chain 1:   1000        -8501.710             0.724            0.086
Chain 1:   1100        -8737.268             0.626            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8414.067             0.093            0.063
Chain 1:   1300        -8628.287             0.059            0.038
Chain 1:   1400        -8626.109             0.052            0.034
Chain 1:   1500        -8520.045             0.033            0.027
Chain 1:   1600        -8623.665             0.030            0.026
Chain 1:   1700        -8712.304             0.030            0.026
Chain 1:   1800        -8309.059             0.026            0.026
Chain 1:   1900        -8408.249             0.021            0.025
Chain 1:   2000        -8379.608             0.019            0.012
Chain 1:   2100        -8499.410             0.018            0.012
Chain 1:   2200        -8290.034             0.016            0.012
Chain 1:   2300        -8440.973             0.016            0.012
Chain 1:   2400        -8319.648             0.017            0.014
Chain 1:   2500        -8383.548             0.017            0.014
Chain 1:   2600        -8406.071             0.016            0.014
Chain 1:   2700        -8324.813             0.016            0.014
Chain 1:   2800        -8298.177             0.011            0.012
Chain 1:   2900        -8353.583             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 51.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407723.101             1.000            1.000
Chain 1:    200     -1586992.629             2.649            4.298
Chain 1:    300      -891987.327             2.026            1.000
Chain 1:    400      -457958.510             1.756            1.000
Chain 1:    500      -357944.520             1.461            0.948
Chain 1:    600      -232795.103             1.307            0.948
Chain 1:    700      -119120.508             1.257            0.948
Chain 1:    800       -86336.377             1.147            0.948
Chain 1:    900       -66708.813             1.052            0.779
Chain 1:   1000       -51527.999             0.976            0.779
Chain 1:   1100       -39024.360             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38202.930             0.481            0.380
Chain 1:   1300       -26184.160             0.449            0.380
Chain 1:   1400       -25904.642             0.355            0.320
Chain 1:   1500       -22498.067             0.342            0.320
Chain 1:   1600       -21716.300             0.292            0.295
Chain 1:   1700       -20593.273             0.202            0.294
Chain 1:   1800       -20538.066             0.165            0.151
Chain 1:   1900       -20863.972             0.137            0.055
Chain 1:   2000       -19377.273             0.115            0.055
Chain 1:   2100       -19615.525             0.084            0.036
Chain 1:   2200       -19841.517             0.083            0.036
Chain 1:   2300       -19459.233             0.039            0.020
Chain 1:   2400       -19231.443             0.039            0.020
Chain 1:   2500       -19033.340             0.025            0.016
Chain 1:   2600       -18663.888             0.023            0.016
Chain 1:   2700       -18621.021             0.018            0.012
Chain 1:   2800       -18337.888             0.020            0.015
Chain 1:   2900       -18619.034             0.019            0.015
Chain 1:   3000       -18605.289             0.012            0.012
Chain 1:   3100       -18690.193             0.011            0.012
Chain 1:   3200       -18381.096             0.012            0.015
Chain 1:   3300       -18585.691             0.011            0.012
Chain 1:   3400       -18060.885             0.013            0.015
Chain 1:   3500       -18672.261             0.015            0.015
Chain 1:   3600       -17979.686             0.017            0.015
Chain 1:   3700       -18365.867             0.018            0.017
Chain 1:   3800       -17326.652             0.023            0.021
Chain 1:   3900       -17322.835             0.021            0.021
Chain 1:   4000       -17440.160             0.022            0.021
Chain 1:   4100       -17353.902             0.022            0.021
Chain 1:   4200       -17170.450             0.021            0.021
Chain 1:   4300       -17308.658             0.021            0.021
Chain 1:   4400       -17265.678             0.019            0.011
Chain 1:   4500       -17168.244             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001424 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12319.013             1.000            1.000
Chain 1:    200        -9238.692             0.667            1.000
Chain 1:    300        -8048.416             0.494            0.333
Chain 1:    400        -8215.664             0.375            0.333
Chain 1:    500        -8082.909             0.304            0.148
Chain 1:    600        -8006.227             0.255            0.148
Chain 1:    700        -7918.430             0.220            0.020
Chain 1:    800        -7960.834             0.193            0.020
Chain 1:    900        -8080.876             0.173            0.016
Chain 1:   1000        -7979.211             0.157            0.016
Chain 1:   1100        -8028.832             0.058            0.015
Chain 1:   1200        -7927.512             0.026            0.013
Chain 1:   1300        -7891.409             0.011            0.013
Chain 1:   1400        -7908.341             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001795 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61857.114             1.000            1.000
Chain 1:    200       -17831.226             1.735            2.469
Chain 1:    300        -8825.003             1.497            1.021
Chain 1:    400        -9342.078             1.136            1.021
Chain 1:    500        -8445.504             0.930            1.000
Chain 1:    600        -8629.644             0.779            1.000
Chain 1:    700        -8321.462             0.673            0.106
Chain 1:    800        -8182.456             0.591            0.106
Chain 1:    900        -7999.921             0.528            0.055
Chain 1:   1000        -7989.540             0.475            0.055
Chain 1:   1100        -7668.200             0.379            0.042
Chain 1:   1200        -7605.346             0.133            0.037
Chain 1:   1300        -7825.480             0.034            0.028
Chain 1:   1400        -7675.618             0.030            0.023
Chain 1:   1500        -7586.704             0.021            0.021
Chain 1:   1600        -7584.086             0.019            0.020
Chain 1:   1700        -7544.664             0.016            0.017
Chain 1:   1800        -7583.432             0.014            0.012
Chain 1:   1900        -7598.367             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003653 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86006.522             1.000            1.000
Chain 1:    200       -13450.599             3.197            5.394
Chain 1:    300        -9854.640             2.253            1.000
Chain 1:    400       -10766.027             1.711            1.000
Chain 1:    500        -8729.624             1.415            0.365
Chain 1:    600        -8334.227             1.187            0.365
Chain 1:    700        -8726.471             1.024            0.233
Chain 1:    800        -9211.040             0.903            0.233
Chain 1:    900        -8658.605             0.810            0.085
Chain 1:   1000        -8445.911             0.731            0.085
Chain 1:   1100        -8721.246             0.634            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8409.479             0.099            0.053
Chain 1:   1300        -8558.195             0.064            0.047
Chain 1:   1400        -8564.234             0.055            0.045
Chain 1:   1500        -8433.634             0.034            0.037
Chain 1:   1600        -8543.485             0.030            0.032
Chain 1:   1700        -8630.485             0.027            0.025
Chain 1:   1800        -8226.231             0.026            0.025
Chain 1:   1900        -8323.903             0.021            0.017
Chain 1:   2000        -8295.677             0.019            0.015
Chain 1:   2100        -8415.626             0.017            0.014
Chain 1:   2200        -8209.337             0.016            0.014
Chain 1:   2300        -8359.159             0.016            0.014
Chain 1:   2400        -8364.627             0.016            0.014
Chain 1:   2500        -8337.823             0.015            0.013
Chain 1:   2600        -8337.228             0.014            0.012
Chain 1:   2700        -8248.287             0.014            0.012
Chain 1:   2800        -8214.792             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003612 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386319.949             1.000            1.000
Chain 1:    200     -1584751.312             2.646            4.292
Chain 1:    300      -891348.989             2.023            1.000
Chain 1:    400      -457792.845             1.754            1.000
Chain 1:    500      -358331.389             1.459            0.947
Chain 1:    600      -233201.453             1.305            0.947
Chain 1:    700      -119303.904             1.255            0.947
Chain 1:    800       -86487.266             1.146            0.947
Chain 1:    900       -66803.582             1.051            0.778
Chain 1:   1000       -51579.912             0.975            0.778
Chain 1:   1100       -39035.898             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38207.018             0.481            0.379
Chain 1:   1300       -26145.785             0.449            0.379
Chain 1:   1400       -25861.885             0.355            0.321
Chain 1:   1500       -22444.781             0.343            0.321
Chain 1:   1600       -21659.509             0.293            0.295
Chain 1:   1700       -20531.610             0.203            0.295
Chain 1:   1800       -20475.164             0.165            0.152
Chain 1:   1900       -20801.079             0.137            0.055
Chain 1:   2000       -19311.633             0.115            0.055
Chain 1:   2100       -19550.064             0.085            0.036
Chain 1:   2200       -19776.542             0.083            0.036
Chain 1:   2300       -19393.767             0.039            0.020
Chain 1:   2400       -19165.909             0.039            0.020
Chain 1:   2500       -18968.007             0.025            0.016
Chain 1:   2600       -18598.441             0.024            0.016
Chain 1:   2700       -18555.426             0.018            0.012
Chain 1:   2800       -18272.437             0.020            0.015
Chain 1:   2900       -18553.601             0.020            0.015
Chain 1:   3000       -18539.762             0.012            0.012
Chain 1:   3100       -18624.739             0.011            0.012
Chain 1:   3200       -18315.587             0.012            0.015
Chain 1:   3300       -18520.160             0.011            0.012
Chain 1:   3400       -17995.404             0.013            0.015
Chain 1:   3500       -18606.886             0.015            0.015
Chain 1:   3600       -17914.063             0.017            0.015
Chain 1:   3700       -18300.523             0.019            0.017
Chain 1:   3800       -17261.071             0.023            0.021
Chain 1:   3900       -17257.238             0.022            0.021
Chain 1:   4000       -17374.517             0.022            0.021
Chain 1:   4100       -17288.362             0.022            0.021
Chain 1:   4200       -17104.750             0.022            0.021
Chain 1:   4300       -17243.031             0.021            0.021
Chain 1:   4400       -17200.004             0.019            0.011
Chain 1:   4500       -17102.562             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002588 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49185.420             1.000            1.000
Chain 1:    200       -18359.123             1.340            1.679
Chain 1:    300       -18881.537             0.902            1.000
Chain 1:    400       -14111.662             0.761            1.000
Chain 1:    500       -20204.698             0.669            0.338
Chain 1:    600       -15611.026             0.607            0.338
Chain 1:    700       -15891.141             0.523            0.302
Chain 1:    800       -15964.636             0.458            0.302
Chain 1:    900       -13211.778             0.430            0.294
Chain 1:   1000       -18743.294             0.417            0.295
Chain 1:   1100       -11997.480             0.373            0.295
Chain 1:   1200       -13073.212             0.213            0.294
Chain 1:   1300       -10654.637             0.233            0.294
Chain 1:   1400       -24930.711             0.257            0.294
Chain 1:   1500       -13477.192             0.311            0.294
Chain 1:   1600       -10088.031             0.316            0.295
Chain 1:   1700       -20056.938             0.364            0.336
Chain 1:   1800       -11529.052             0.437            0.497
Chain 1:   1900       -10436.340             0.427            0.497
Chain 1:   2000       -10792.436             0.400            0.497
Chain 1:   2100        -9677.109             0.356            0.336
Chain 1:   2200       -11503.183             0.363            0.336
Chain 1:   2300       -11827.559             0.343            0.336
Chain 1:   2400        -9379.467             0.312            0.261
Chain 1:   2500        -9486.446             0.228            0.159
Chain 1:   2600        -9353.233             0.196            0.115
Chain 1:   2700        -9431.006             0.147            0.105
Chain 1:   2800       -10038.616             0.079            0.061
Chain 1:   2900        -9250.838             0.077            0.061
Chain 1:   3000        -9662.711             0.078            0.061
Chain 1:   3100        -9242.740             0.071            0.045
Chain 1:   3200        -9378.673             0.057            0.043
Chain 1:   3300        -9762.939             0.058            0.043
Chain 1:   3400       -14884.475             0.067            0.043
Chain 1:   3500        -9735.262             0.118            0.045
Chain 1:   3600        -9632.211             0.118            0.045
Chain 1:   3700        -9954.465             0.120            0.045
Chain 1:   3800        -8802.766             0.127            0.045
Chain 1:   3900       -12936.991             0.151            0.045
Chain 1:   4000       -15923.490             0.165            0.131
Chain 1:   4100        -8825.569             0.241            0.188
Chain 1:   4200        -9601.908             0.248            0.188
Chain 1:   4300        -9966.981             0.248            0.188
Chain 1:   4400       -11455.701             0.226            0.131
Chain 1:   4500        -8796.157             0.204            0.131
Chain 1:   4600       -11334.118             0.225            0.188
Chain 1:   4700        -8864.242             0.249            0.224
Chain 1:   4800        -8915.209             0.237            0.224
Chain 1:   4900        -9227.412             0.208            0.188
Chain 1:   5000        -9122.719             0.191            0.130
Chain 1:   5100        -8788.271             0.114            0.081
Chain 1:   5200       -14030.867             0.143            0.130
Chain 1:   5300       -13183.684             0.146            0.130
Chain 1:   5400        -8562.830             0.187            0.224
Chain 1:   5500       -15739.474             0.203            0.224
Chain 1:   5600       -13114.144             0.200            0.200
Chain 1:   5700       -13510.256             0.175            0.064
Chain 1:   5800       -13848.003             0.177            0.064
Chain 1:   5900        -8920.769             0.229            0.200
Chain 1:   6000       -11629.332             0.251            0.233
Chain 1:   6100        -9132.526             0.275            0.273
Chain 1:   6200        -9028.074             0.238            0.233
Chain 1:   6300        -8202.829             0.242            0.233
Chain 1:   6400        -9416.292             0.201            0.200
Chain 1:   6500       -10149.690             0.163            0.129
Chain 1:   6600       -10458.568             0.146            0.101
Chain 1:   6700        -8416.266             0.167            0.129
Chain 1:   6800        -8374.554             0.165            0.129
Chain 1:   6900       -10706.965             0.131            0.129
Chain 1:   7000        -8735.561             0.131            0.129
Chain 1:   7100        -8616.093             0.105            0.101
Chain 1:   7200        -8814.251             0.106            0.101
Chain 1:   7300       -11360.314             0.118            0.129
Chain 1:   7400       -13337.975             0.120            0.148
Chain 1:   7500       -11379.885             0.130            0.172
Chain 1:   7600       -10111.605             0.140            0.172
Chain 1:   7700       -10928.292             0.123            0.148
Chain 1:   7800        -8473.755             0.151            0.172
Chain 1:   7900        -8570.487             0.131            0.148
Chain 1:   8000        -9105.618             0.114            0.125
Chain 1:   8100        -9540.392             0.117            0.125
Chain 1:   8200       -12271.060             0.137            0.148
Chain 1:   8300        -8602.563             0.157            0.148
Chain 1:   8400        -8510.566             0.144            0.125
Chain 1:   8500       -10847.426             0.148            0.125
Chain 1:   8600        -9311.936             0.152            0.165
Chain 1:   8700       -12020.843             0.167            0.215
Chain 1:   8800        -8241.400             0.184            0.215
Chain 1:   8900        -8732.551             0.188            0.215
Chain 1:   9000        -8241.331             0.189            0.215
Chain 1:   9100       -10682.357             0.207            0.223
Chain 1:   9200       -10835.623             0.186            0.215
Chain 1:   9300        -9257.871             0.160            0.170
Chain 1:   9400        -8764.052             0.165            0.170
Chain 1:   9500       -10417.947             0.159            0.165
Chain 1:   9600       -10495.326             0.144            0.159
Chain 1:   9700        -8419.615             0.146            0.159
Chain 1:   9800       -12488.785             0.132            0.159
Chain 1:   9900        -8261.617             0.178            0.170
Chain 1:   10000       -10933.919             0.196            0.229
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001385 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58578.092             1.000            1.000
Chain 1:    200       -18029.651             1.624            2.249
Chain 1:    300        -8798.145             1.433            1.049
Chain 1:    400        -8134.575             1.095            1.049
Chain 1:    500        -8350.404             0.881            1.000
Chain 1:    600        -8284.243             0.736            1.000
Chain 1:    700        -8077.628             0.634            0.082
Chain 1:    800        -8097.755             0.555            0.082
Chain 1:    900        -8025.754             0.495            0.026
Chain 1:   1000        -7788.908             0.448            0.030
Chain 1:   1100        -7929.637             0.350            0.026
Chain 1:   1200        -7742.079             0.127            0.026
Chain 1:   1300        -7795.319             0.023            0.024
Chain 1:   1400        -7893.458             0.016            0.018
Chain 1:   1500        -7603.075             0.017            0.018
Chain 1:   1600        -7782.918             0.019            0.023
Chain 1:   1700        -7604.217             0.019            0.023
Chain 1:   1800        -7606.691             0.019            0.023
Chain 1:   1900        -7708.222             0.019            0.023
Chain 1:   2000        -7627.951             0.017            0.018
Chain 1:   2100        -7508.648             0.017            0.016
Chain 1:   2200        -7964.350             0.020            0.016
Chain 1:   2300        -7584.244             0.024            0.023
Chain 1:   2400        -7636.332             0.024            0.023
Chain 1:   2500        -7558.568             0.021            0.016
Chain 1:   2600        -7512.997             0.019            0.013
Chain 1:   2700        -7516.167             0.017            0.011
Chain 1:   2800        -7486.917             0.017            0.011
Chain 1:   2900        -7398.346             0.017            0.011
Chain 1:   3000        -7537.364             0.018            0.012
Chain 1:   3100        -7524.090             0.017            0.010
Chain 1:   3200        -7722.217             0.014            0.010
Chain 1:   3300        -7446.221             0.012            0.010
Chain 1:   3400        -7666.221             0.014            0.012
Chain 1:   3500        -7430.944             0.017            0.018
Chain 1:   3600        -7497.067             0.017            0.018
Chain 1:   3700        -7445.780             0.017            0.018
Chain 1:   3800        -7444.651             0.017            0.018
Chain 1:   3900        -7412.026             0.016            0.018
Chain 1:   4000        -7406.218             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003709 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85981.179             1.000            1.000
Chain 1:    200       -13720.651             3.133            5.267
Chain 1:    300        -9971.922             2.214            1.000
Chain 1:    400       -11682.457             1.697            1.000
Chain 1:    500        -8595.492             1.430            0.376
Chain 1:    600        -8352.337             1.196            0.376
Chain 1:    700        -8482.516             1.027            0.359
Chain 1:    800        -8628.517             0.901            0.359
Chain 1:    900        -8922.004             0.805            0.146
Chain 1:   1000        -8727.139             0.726            0.146
Chain 1:   1100        -8567.860             0.628            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8322.184             0.105            0.030
Chain 1:   1300        -8620.901             0.070            0.030
Chain 1:   1400        -8568.664             0.056            0.029
Chain 1:   1500        -8468.102             0.022            0.022
Chain 1:   1600        -8581.803             0.020            0.019
Chain 1:   1700        -8635.626             0.019            0.019
Chain 1:   1800        -8190.738             0.023            0.022
Chain 1:   1900        -8296.205             0.021            0.019
Chain 1:   2000        -8278.989             0.019            0.013
Chain 1:   2100        -8417.438             0.019            0.013
Chain 1:   2200        -8190.434             0.019            0.013
Chain 1:   2300        -8293.088             0.016            0.013
Chain 1:   2400        -8361.147             0.017            0.013
Chain 1:   2500        -8303.654             0.016            0.013
Chain 1:   2600        -8320.314             0.015            0.012
Chain 1:   2700        -8226.333             0.015            0.012
Chain 1:   2800        -8170.713             0.011            0.011
Chain 1:   2900        -8271.249             0.011            0.011
Chain 1:   3000        -8114.653             0.012            0.012
Chain 1:   3100        -8256.115             0.012            0.012
Chain 1:   3200        -8125.037             0.011            0.012
Chain 1:   3300        -8355.436             0.013            0.012
Chain 1:   3400        -8361.109             0.012            0.012
Chain 1:   3500        -8231.588             0.013            0.016
Chain 1:   3600        -8082.276             0.015            0.016
Chain 1:   3700        -8229.383             0.015            0.017
Chain 1:   3800        -8084.919             0.016            0.018
Chain 1:   3900        -8016.707             0.016            0.018
Chain 1:   4000        -8127.910             0.015            0.017
Chain 1:   4100        -8091.985             0.014            0.016
Chain 1:   4200        -8077.835             0.013            0.016
Chain 1:   4300        -8111.290             0.010            0.014
Chain 1:   4400        -8068.218             0.011            0.014
Chain 1:   4500        -8166.227             0.010            0.012
Chain 1:   4600        -8057.745             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390836.533             1.000            1.000
Chain 1:    200     -1584124.140             2.648            4.297
Chain 1:    300      -891981.840             2.024            1.000
Chain 1:    400      -458621.207             1.754            1.000
Chain 1:    500      -358935.387             1.459            0.945
Chain 1:    600      -233732.251             1.305            0.945
Chain 1:    700      -119735.658             1.255            0.945
Chain 1:    800       -86884.580             1.145            0.945
Chain 1:    900       -67187.365             1.050            0.776
Chain 1:   1000       -51957.952             0.975            0.776
Chain 1:   1100       -39399.959             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38579.686             0.479            0.378
Chain 1:   1300       -26490.115             0.447            0.378
Chain 1:   1400       -26208.131             0.354            0.319
Chain 1:   1500       -22782.901             0.341            0.319
Chain 1:   1600       -21996.673             0.291            0.293
Chain 1:   1700       -20864.159             0.201            0.293
Chain 1:   1800       -20807.462             0.164            0.150
Chain 1:   1900       -21134.196             0.136            0.054
Chain 1:   2000       -19641.013             0.114            0.054
Chain 1:   2100       -19879.599             0.083            0.036
Chain 1:   2200       -20107.018             0.083            0.036
Chain 1:   2300       -19723.277             0.039            0.019
Chain 1:   2400       -19495.100             0.039            0.019
Chain 1:   2500       -19297.292             0.025            0.015
Chain 1:   2600       -18926.650             0.023            0.015
Chain 1:   2700       -18883.398             0.018            0.012
Chain 1:   2800       -18600.000             0.019            0.015
Chain 1:   2900       -18881.684             0.019            0.015
Chain 1:   3000       -18867.760             0.012            0.012
Chain 1:   3100       -18952.830             0.011            0.012
Chain 1:   3200       -18643.060             0.012            0.015
Chain 1:   3300       -18848.175             0.011            0.012
Chain 1:   3400       -18322.307             0.012            0.015
Chain 1:   3500       -18935.392             0.015            0.015
Chain 1:   3600       -18240.607             0.016            0.015
Chain 1:   3700       -18628.509             0.018            0.017
Chain 1:   3800       -17585.903             0.023            0.021
Chain 1:   3900       -17582.038             0.021            0.021
Chain 1:   4000       -17699.327             0.022            0.021
Chain 1:   4100       -17612.942             0.022            0.021
Chain 1:   4200       -17428.722             0.021            0.021
Chain 1:   4300       -17567.420             0.021            0.021
Chain 1:   4400       -17523.832             0.018            0.011
Chain 1:   4500       -17426.346             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001568 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12230.190             1.000            1.000
Chain 1:    200        -9237.742             0.662            1.000
Chain 1:    300        -7956.781             0.495            0.324
Chain 1:    400        -8176.774             0.378            0.324
Chain 1:    500        -8039.064             0.306            0.161
Chain 1:    600        -7894.552             0.258            0.161
Chain 1:    700        -7818.435             0.222            0.027
Chain 1:    800        -7846.246             0.195            0.027
Chain 1:    900        -7874.745             0.174            0.018
Chain 1:   1000        -7890.560             0.157            0.018
Chain 1:   1100        -7881.846             0.057            0.017
Chain 1:   1200        -7856.480             0.025            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00159 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61654.246             1.000            1.000
Chain 1:    200       -17753.648             1.736            2.473
Chain 1:    300        -8731.139             1.502            1.033
Chain 1:    400        -9248.909             1.141            1.033
Chain 1:    500        -8159.181             0.939            1.000
Chain 1:    600        -8261.902             0.785            1.000
Chain 1:    700        -7708.860             0.683            0.134
Chain 1:    800        -7924.134             0.601            0.134
Chain 1:    900        -7932.845             0.534            0.072
Chain 1:   1000        -7708.069             0.484            0.072
Chain 1:   1100        -7561.363             0.386            0.056
Chain 1:   1200        -7696.336             0.140            0.029
Chain 1:   1300        -7535.695             0.039            0.027
Chain 1:   1400        -7603.779             0.034            0.021
Chain 1:   1500        -7490.978             0.022            0.019
Chain 1:   1600        -7694.773             0.024            0.021
Chain 1:   1700        -7423.115             0.020            0.021
Chain 1:   1800        -7509.118             0.019            0.019
Chain 1:   1900        -7512.782             0.019            0.019
Chain 1:   2000        -7565.190             0.016            0.018
Chain 1:   2100        -7469.474             0.016            0.015
Chain 1:   2200        -7594.691             0.016            0.015
Chain 1:   2300        -7494.208             0.015            0.013
Chain 1:   2400        -7531.503             0.014            0.013
Chain 1:   2500        -7457.587             0.014            0.013
Chain 1:   2600        -7403.398             0.012            0.011
Chain 1:   2700        -7397.161             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003214 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86322.908             1.000            1.000
Chain 1:    200       -13386.698             3.224            5.448
Chain 1:    300        -9776.427             2.273            1.000
Chain 1:    400       -10618.279             1.724            1.000
Chain 1:    500        -8725.974             1.423            0.369
Chain 1:    600        -8270.842             1.195            0.369
Chain 1:    700        -8420.643             1.027            0.217
Chain 1:    800        -8849.175             0.904            0.217
Chain 1:    900        -8616.668             0.807            0.079
Chain 1:   1000        -8384.161             0.729            0.079
Chain 1:   1100        -8629.081             0.632            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8269.944             0.091            0.048
Chain 1:   1300        -8479.894             0.057            0.043
Chain 1:   1400        -8477.769             0.049            0.028
Chain 1:   1500        -8368.671             0.029            0.028
Chain 1:   1600        -8472.907             0.024            0.027
Chain 1:   1700        -8561.663             0.024            0.027
Chain 1:   1800        -8157.336             0.024            0.027
Chain 1:   1900        -8255.966             0.022            0.025
Chain 1:   2000        -8227.457             0.020            0.013
Chain 1:   2100        -8347.292             0.018            0.013
Chain 1:   2200        -8151.875             0.016            0.013
Chain 1:   2300        -8290.812             0.016            0.013
Chain 1:   2400        -8166.263             0.017            0.014
Chain 1:   2500        -8231.126             0.017            0.014
Chain 1:   2600        -8254.471             0.016            0.014
Chain 1:   2700        -8172.975             0.016            0.014
Chain 1:   2800        -8145.667             0.011            0.012
Chain 1:   2900        -8201.078             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003086 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392866.851             1.000            1.000
Chain 1:    200     -1582213.011             2.652            4.305
Chain 1:    300      -891647.241             2.026            1.000
Chain 1:    400      -458395.000             1.756            1.000
Chain 1:    500      -358994.663             1.460            0.945
Chain 1:    600      -233843.724             1.306            0.945
Chain 1:    700      -119582.561             1.256            0.945
Chain 1:    800       -86679.948             1.146            0.945
Chain 1:    900       -66923.009             1.052            0.774
Chain 1:   1000       -51637.357             0.976            0.774
Chain 1:   1100       -39045.659             0.909            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38209.396             0.480            0.380
Chain 1:   1300       -26099.811             0.449            0.380
Chain 1:   1400       -25811.279             0.356            0.322
Chain 1:   1500       -22382.123             0.343            0.322
Chain 1:   1600       -21593.584             0.294            0.296
Chain 1:   1700       -20459.610             0.204            0.295
Chain 1:   1800       -20401.715             0.166            0.153
Chain 1:   1900       -20727.621             0.138            0.055
Chain 1:   2000       -19235.293             0.116            0.055
Chain 1:   2100       -19473.768             0.085            0.037
Chain 1:   2200       -19700.803             0.084            0.037
Chain 1:   2300       -19317.546             0.040            0.020
Chain 1:   2400       -19089.640             0.040            0.020
Chain 1:   2500       -18892.032             0.025            0.016
Chain 1:   2600       -18522.126             0.024            0.016
Chain 1:   2700       -18479.006             0.018            0.012
Chain 1:   2800       -18196.162             0.020            0.016
Chain 1:   2900       -18477.382             0.020            0.015
Chain 1:   3000       -18463.465             0.012            0.012
Chain 1:   3100       -18548.477             0.011            0.012
Chain 1:   3200       -18239.212             0.012            0.015
Chain 1:   3300       -18443.875             0.011            0.012
Chain 1:   3400       -17919.090             0.013            0.015
Chain 1:   3500       -18530.666             0.015            0.016
Chain 1:   3600       -17837.725             0.017            0.016
Chain 1:   3700       -18224.342             0.019            0.017
Chain 1:   3800       -17184.738             0.023            0.021
Chain 1:   3900       -17180.940             0.022            0.021
Chain 1:   4000       -17298.182             0.022            0.021
Chain 1:   4100       -17212.037             0.022            0.021
Chain 1:   4200       -17028.384             0.022            0.021
Chain 1:   4300       -17166.654             0.021            0.021
Chain 1:   4400       -17123.616             0.019            0.011
Chain 1:   4500       -17026.191             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00134 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13112.522             1.000            1.000
Chain 1:    200        -9884.409             0.663            1.000
Chain 1:    300        -8520.045             0.496            0.327
Chain 1:    400        -8721.507             0.377            0.327
Chain 1:    500        -8659.757             0.303            0.160
Chain 1:    600        -8453.007             0.257            0.160
Chain 1:    700        -8348.842             0.222            0.024
Chain 1:    800        -8379.402             0.195            0.024
Chain 1:    900        -8463.479             0.174            0.023
Chain 1:   1000        -8417.270             0.157            0.023
Chain 1:   1100        -8436.725             0.058            0.012
Chain 1:   1200        -8353.429             0.026            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001651 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62480.971             1.000            1.000
Chain 1:    200       -18639.965             1.676            2.352
Chain 1:    300        -9293.740             1.453            1.006
Chain 1:    400        -9227.899             1.091            1.006
Chain 1:    500        -8512.137             0.890            1.000
Chain 1:    600        -9390.553             0.757            1.000
Chain 1:    700        -8614.647             0.662            0.094
Chain 1:    800        -7880.244             0.591            0.094
Chain 1:    900        -8074.310             0.528            0.093
Chain 1:   1000        -8025.542             0.476            0.093
Chain 1:   1100        -7936.158             0.377            0.090
Chain 1:   1200        -8067.769             0.143            0.084
Chain 1:   1300        -8019.912             0.043            0.024
Chain 1:   1400        -7771.149             0.046            0.032
Chain 1:   1500        -7664.960             0.039            0.024
Chain 1:   1600        -7975.343             0.033            0.024
Chain 1:   1700        -7732.562             0.027            0.024
Chain 1:   1800        -7661.922             0.019            0.016
Chain 1:   1900        -7657.317             0.017            0.014
Chain 1:   2000        -7759.676             0.017            0.014
Chain 1:   2100        -7695.182             0.017            0.014
Chain 1:   2200        -7896.080             0.018            0.014
Chain 1:   2300        -7734.700             0.019            0.021
Chain 1:   2400        -7644.398             0.017            0.014
Chain 1:   2500        -7704.937             0.017            0.013
Chain 1:   2600        -7630.105             0.014            0.012
Chain 1:   2700        -7623.633             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.007439 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 74.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87080.815             1.000            1.000
Chain 1:    200       -14248.498             3.056            5.112
Chain 1:    300       -10494.071             2.156            1.000
Chain 1:    400       -12151.912             1.651            1.000
Chain 1:    500        -9042.422             1.390            0.358
Chain 1:    600        -9491.216             1.166            0.358
Chain 1:    700        -8811.713             1.011            0.344
Chain 1:    800       -10037.628             0.900            0.344
Chain 1:    900        -9345.123             0.808            0.136
Chain 1:   1000        -9091.651             0.730            0.136
Chain 1:   1100        -9260.407             0.632            0.122   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8790.838             0.126            0.077
Chain 1:   1300        -9117.422             0.094            0.074
Chain 1:   1400        -9036.244             0.081            0.053
Chain 1:   1500        -9001.166             0.047            0.047
Chain 1:   1600        -9074.150             0.043            0.036
Chain 1:   1700        -9130.463             0.036            0.028
Chain 1:   1800        -8681.906             0.029            0.028
Chain 1:   1900        -8788.031             0.023            0.018
Chain 1:   2000        -8782.564             0.020            0.012
Chain 1:   2100        -8947.768             0.020            0.012
Chain 1:   2200        -8682.655             0.018            0.012
Chain 1:   2300        -8870.989             0.016            0.012
Chain 1:   2400        -8682.621             0.017            0.018
Chain 1:   2500        -8760.101             0.018            0.018
Chain 1:   2600        -8680.662             0.018            0.018
Chain 1:   2700        -8704.763             0.018            0.018
Chain 1:   2800        -8658.309             0.013            0.012
Chain 1:   2900        -8764.695             0.013            0.012
Chain 1:   3000        -8715.815             0.014            0.012
Chain 1:   3100        -8649.040             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003006 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424436.880             1.000            1.000
Chain 1:    200     -1585500.169             2.657            4.313
Chain 1:    300      -890828.956             2.031            1.000
Chain 1:    400      -458274.332             1.759            1.000
Chain 1:    500      -358488.467             1.463            0.944
Chain 1:    600      -233448.854             1.309            0.944
Chain 1:    700      -119801.319             1.257            0.944
Chain 1:    800       -87080.740             1.147            0.944
Chain 1:    900       -67457.750             1.052            0.780
Chain 1:   1000       -52296.390             0.976            0.780
Chain 1:   1100       -39807.090             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38992.645             0.478            0.376
Chain 1:   1300       -26967.602             0.444            0.376
Chain 1:   1400       -26692.012             0.351            0.314
Chain 1:   1500       -23284.392             0.338            0.314
Chain 1:   1600       -22503.529             0.288            0.291
Chain 1:   1700       -21378.685             0.198            0.290
Chain 1:   1800       -21323.773             0.161            0.146
Chain 1:   1900       -21650.578             0.133            0.053
Chain 1:   2000       -20161.460             0.112            0.053
Chain 1:   2100       -20399.763             0.081            0.035
Chain 1:   2200       -20626.605             0.080            0.035
Chain 1:   2300       -20243.326             0.038            0.019
Chain 1:   2400       -20015.213             0.038            0.019
Chain 1:   2500       -19817.176             0.024            0.015
Chain 1:   2600       -19446.661             0.023            0.015
Chain 1:   2700       -19403.487             0.018            0.012
Chain 1:   2800       -19120.039             0.019            0.015
Chain 1:   2900       -19401.558             0.019            0.015
Chain 1:   3000       -19387.706             0.011            0.012
Chain 1:   3100       -19472.785             0.011            0.011
Chain 1:   3200       -19163.015             0.011            0.015
Chain 1:   3300       -19368.106             0.010            0.011
Chain 1:   3400       -18842.212             0.012            0.015
Chain 1:   3500       -19455.253             0.014            0.015
Chain 1:   3600       -18760.405             0.016            0.015
Chain 1:   3700       -19148.319             0.018            0.016
Chain 1:   3800       -18105.607             0.022            0.020
Chain 1:   3900       -18101.682             0.021            0.020
Chain 1:   4000       -18219.006             0.021            0.020
Chain 1:   4100       -18132.635             0.021            0.020
Chain 1:   4200       -17948.371             0.021            0.020
Chain 1:   4300       -18087.126             0.020            0.020
Chain 1:   4400       -18043.496             0.018            0.010
Chain 1:   4500       -17945.969             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001356 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49074.605             1.000            1.000
Chain 1:    200       -18915.902             1.297            1.594
Chain 1:    300       -20161.255             0.885            1.000
Chain 1:    400       -16078.893             0.728            1.000
Chain 1:    500       -20667.820             0.626            0.254
Chain 1:    600       -12815.739             0.624            0.613
Chain 1:    700       -15736.540             0.561            0.254
Chain 1:    800       -14313.137             0.504            0.254
Chain 1:    900       -12498.457             0.464            0.222
Chain 1:   1000       -14958.633             0.434            0.222
Chain 1:   1100       -10514.553             0.376            0.222
Chain 1:   1200       -18593.116             0.260            0.222
Chain 1:   1300       -15813.478             0.272            0.222
Chain 1:   1400       -14335.263             0.257            0.186
Chain 1:   1500       -14525.066             0.236            0.176
Chain 1:   1600       -11248.357             0.204            0.176
Chain 1:   1700       -12788.405             0.197            0.164
Chain 1:   1800       -11347.181             0.200            0.164
Chain 1:   1900       -10035.066             0.198            0.164
Chain 1:   2000        -9861.171             0.184            0.131
Chain 1:   2100       -22216.395             0.197            0.131
Chain 1:   2200       -12048.251             0.238            0.131
Chain 1:   2300       -25602.025             0.273            0.131
Chain 1:   2400        -9839.678             0.423            0.291
Chain 1:   2500       -11245.931             0.434            0.291
Chain 1:   2600        -9424.851             0.425            0.193
Chain 1:   2700        -9189.404             0.415            0.193
Chain 1:   2800       -11127.665             0.420            0.193
Chain 1:   2900       -17000.721             0.441            0.345
Chain 1:   3000        -9021.866             0.528            0.529   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   3100        -9420.091             0.477            0.345
Chain 1:   3200       -10559.189             0.403            0.193
Chain 1:   3300       -14358.132             0.376            0.193
Chain 1:   3400        -9251.198             0.271            0.193
Chain 1:   3500       -10008.619             0.267            0.193
Chain 1:   3600        -9856.349             0.249            0.174
Chain 1:   3700       -19271.246             0.295            0.265
Chain 1:   3800       -12491.436             0.332            0.345
Chain 1:   3900        -9869.997             0.324            0.266
Chain 1:   4000       -10648.112             0.243            0.265
Chain 1:   4100       -12834.239             0.256            0.265
Chain 1:   4200       -10773.036             0.264            0.265
Chain 1:   4300       -10536.814             0.240            0.191
Chain 1:   4400        -9601.079             0.194            0.170
Chain 1:   4500       -13009.093             0.213            0.191
Chain 1:   4600       -13879.235             0.218            0.191
Chain 1:   4700       -12768.678             0.177            0.170
Chain 1:   4800        -9063.030             0.164            0.170
Chain 1:   4900       -12474.483             0.165            0.170
Chain 1:   5000       -13724.919             0.167            0.170
Chain 1:   5100        -9146.501             0.200            0.191
Chain 1:   5200       -10969.262             0.197            0.166
Chain 1:   5300       -11789.460             0.202            0.166
Chain 1:   5400       -16428.231             0.220            0.262
Chain 1:   5500       -11794.023             0.233            0.273
Chain 1:   5600        -9342.189             0.253            0.273
Chain 1:   5700        -8781.914             0.251            0.273
Chain 1:   5800        -9774.467             0.220            0.262
Chain 1:   5900       -14723.017             0.227            0.262
Chain 1:   6000       -12736.266             0.233            0.262
Chain 1:   6100        -8920.058             0.226            0.262
Chain 1:   6200        -8534.791             0.214            0.262
Chain 1:   6300        -8893.520             0.211            0.262
Chain 1:   6400        -8440.068             0.188            0.156
Chain 1:   6500       -12617.182             0.182            0.156
Chain 1:   6600        -9809.222             0.184            0.156
Chain 1:   6700       -10999.278             0.189            0.156
Chain 1:   6800        -8594.299             0.206            0.280
Chain 1:   6900        -8885.325             0.176            0.156
Chain 1:   7000        -8978.602             0.162            0.108
Chain 1:   7100        -9188.951             0.121            0.054
Chain 1:   7200       -10199.569             0.126            0.099
Chain 1:   7300        -8281.064             0.146            0.108
Chain 1:   7400        -8541.377             0.143            0.108
Chain 1:   7500        -9040.203             0.116            0.099
Chain 1:   7600       -10595.994             0.102            0.099
Chain 1:   7700        -9233.200             0.106            0.099
Chain 1:   7800        -8714.246             0.084            0.060
Chain 1:   7900        -9322.701             0.087            0.065
Chain 1:   8000        -8497.937             0.096            0.097
Chain 1:   8100        -8621.079             0.095            0.097
Chain 1:   8200        -8710.127             0.086            0.065
Chain 1:   8300        -8287.681             0.068            0.060
Chain 1:   8400        -8677.975             0.069            0.060
Chain 1:   8500        -8216.005             0.069            0.060
Chain 1:   8600        -8436.655             0.057            0.056
Chain 1:   8700        -8723.114             0.046            0.051
Chain 1:   8800        -8280.794             0.045            0.051
Chain 1:   8900       -11920.755             0.069            0.051
Chain 1:   9000        -8722.258             0.096            0.051
Chain 1:   9100        -8916.844             0.097            0.051
Chain 1:   9200        -8278.130             0.104            0.053
Chain 1:   9300        -8223.219             0.099            0.053
Chain 1:   9400        -8488.802             0.098            0.053
Chain 1:   9500       -10083.563             0.108            0.053
Chain 1:   9600       -10292.399             0.107            0.053
Chain 1:   9700        -9168.842             0.116            0.077
Chain 1:   9800        -8563.180             0.118            0.077
Chain 1:   9900       -11377.203             0.112            0.077
Chain 1:   10000        -9572.359             0.094            0.077
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58563.441             1.000            1.000
Chain 1:    200       -18003.070             1.626            2.253
Chain 1:    300        -8798.739             1.433            1.046
Chain 1:    400        -8215.280             1.093            1.046
Chain 1:    500        -8237.904             0.875            1.000
Chain 1:    600        -8902.460             0.741            1.000
Chain 1:    700        -8422.667             0.643            0.075
Chain 1:    800        -8222.578             0.566            0.075
Chain 1:    900        -8078.641             0.505            0.071
Chain 1:   1000        -7799.459             0.458            0.071
Chain 1:   1100        -8002.549             0.361            0.057
Chain 1:   1200        -7858.622             0.137            0.036
Chain 1:   1300        -7613.954             0.036            0.032
Chain 1:   1400        -7905.737             0.033            0.032
Chain 1:   1500        -7592.995             0.036            0.036
Chain 1:   1600        -7754.445             0.031            0.032
Chain 1:   1700        -7683.891             0.026            0.025
Chain 1:   1800        -7665.308             0.024            0.025
Chain 1:   1900        -7578.556             0.023            0.025
Chain 1:   2000        -7643.328             0.021            0.021
Chain 1:   2100        -7583.273             0.019            0.018
Chain 1:   2200        -7739.968             0.019            0.020
Chain 1:   2300        -7566.487             0.018            0.020
Chain 1:   2400        -7632.228             0.015            0.011
Chain 1:   2500        -7641.906             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 42.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86190.966             1.000            1.000
Chain 1:    200       -13716.159             3.142            5.284
Chain 1:    300       -10018.455             2.218            1.000
Chain 1:    400       -11170.528             1.689            1.000
Chain 1:    500        -9028.226             1.399            0.369
Chain 1:    600        -8392.816             1.178            0.369
Chain 1:    700        -8531.413             1.012            0.237
Chain 1:    800        -8965.349             0.892            0.237
Chain 1:    900        -8830.991             0.794            0.103
Chain 1:   1000        -8683.545             0.717            0.103
Chain 1:   1100        -8650.774             0.617            0.076   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8401.862             0.092            0.048
Chain 1:   1300        -8673.284             0.058            0.031
Chain 1:   1400        -8651.368             0.048            0.030
Chain 1:   1500        -8547.956             0.025            0.017
Chain 1:   1600        -8655.051             0.019            0.016
Chain 1:   1700        -8726.484             0.018            0.015
Chain 1:   1800        -8293.053             0.018            0.015
Chain 1:   1900        -8397.433             0.018            0.012
Chain 1:   2000        -8372.785             0.017            0.012
Chain 1:   2100        -8510.697             0.018            0.012
Chain 1:   2200        -8303.819             0.018            0.012
Chain 1:   2300        -8442.253             0.016            0.012
Chain 1:   2400        -8301.294             0.017            0.016
Chain 1:   2500        -8371.111             0.017            0.016
Chain 1:   2600        -8284.275             0.017            0.016
Chain 1:   2700        -8317.551             0.016            0.016
Chain 1:   2800        -8278.804             0.012            0.012
Chain 1:   2900        -8370.819             0.012            0.011
Chain 1:   3000        -8197.148             0.013            0.016
Chain 1:   3100        -8360.902             0.014            0.016
Chain 1:   3200        -8233.751             0.013            0.015
Chain 1:   3300        -8242.882             0.011            0.011
Chain 1:   3400        -8394.454             0.011            0.011
Chain 1:   3500        -8385.756             0.011            0.011
Chain 1:   3600        -8191.014             0.012            0.015
Chain 1:   3700        -8334.151             0.013            0.017
Chain 1:   3800        -8197.863             0.014            0.017
Chain 1:   3900        -8133.080             0.014            0.017
Chain 1:   4000        -8207.342             0.013            0.017
Chain 1:   4100        -8198.403             0.011            0.015
Chain 1:   4200        -8186.952             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398252.217             1.000            1.000
Chain 1:    200     -1585752.488             2.648            4.296
Chain 1:    300      -891212.943             2.025            1.000
Chain 1:    400      -457932.084             1.755            1.000
Chain 1:    500      -358230.244             1.460            0.946
Chain 1:    600      -233126.186             1.306            0.946
Chain 1:    700      -119429.769             1.256            0.946
Chain 1:    800       -86634.703             1.146            0.946
Chain 1:    900       -66997.153             1.051            0.779
Chain 1:   1000       -51809.572             0.975            0.779
Chain 1:   1100       -39293.700             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38473.519             0.480            0.379
Chain 1:   1300       -26435.054             0.447            0.379
Chain 1:   1400       -26155.345             0.354            0.319
Chain 1:   1500       -22743.440             0.341            0.319
Chain 1:   1600       -21960.392             0.291            0.293
Chain 1:   1700       -20834.743             0.201            0.293
Chain 1:   1800       -20779.214             0.163            0.150
Chain 1:   1900       -21105.668             0.136            0.054
Chain 1:   2000       -19616.208             0.114            0.054
Chain 1:   2100       -19854.757             0.083            0.036
Chain 1:   2200       -20081.384             0.082            0.036
Chain 1:   2300       -19698.339             0.039            0.019
Chain 1:   2400       -19470.311             0.039            0.019
Chain 1:   2500       -19272.202             0.025            0.015
Chain 1:   2600       -18902.250             0.023            0.015
Chain 1:   2700       -18859.087             0.018            0.012
Chain 1:   2800       -18575.766             0.019            0.015
Chain 1:   2900       -18857.142             0.019            0.015
Chain 1:   3000       -18843.352             0.012            0.012
Chain 1:   3100       -18928.408             0.011            0.012
Chain 1:   3200       -18618.899             0.012            0.015
Chain 1:   3300       -18823.738             0.011            0.012
Chain 1:   3400       -18298.277             0.012            0.015
Chain 1:   3500       -18910.741             0.015            0.015
Chain 1:   3600       -18216.626             0.016            0.015
Chain 1:   3700       -18604.021             0.018            0.017
Chain 1:   3800       -17562.517             0.023            0.021
Chain 1:   3900       -17558.603             0.021            0.021
Chain 1:   4000       -17675.932             0.022            0.021
Chain 1:   4100       -17589.651             0.022            0.021
Chain 1:   4200       -17405.592             0.021            0.021
Chain 1:   4300       -17544.229             0.021            0.021
Chain 1:   4400       -17500.839             0.018            0.011
Chain 1:   4500       -17403.299             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001273 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12412.139             1.000            1.000
Chain 1:    200        -9438.431             0.658            1.000
Chain 1:    300        -8273.389             0.485            0.315
Chain 1:    400        -8454.626             0.369            0.315
Chain 1:    500        -8300.830             0.299            0.141
Chain 1:    600        -8175.848             0.252            0.141
Chain 1:    700        -8111.411             0.217            0.021
Chain 1:    800        -8118.926             0.190            0.021
Chain 1:    900        -8135.430             0.169            0.019
Chain 1:   1000        -8171.453             0.153            0.019
Chain 1:   1100        -8235.263             0.053            0.015
Chain 1:   1200        -8122.431             0.023            0.014
Chain 1:   1300        -8106.648             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001425 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61814.033             1.000            1.000
Chain 1:    200       -17879.878             1.729            2.457
Chain 1:    300        -8860.229             1.492            1.018
Chain 1:    400        -9463.806             1.135            1.018
Chain 1:    500        -8004.298             0.944            1.000
Chain 1:    600        -8729.240             0.801            1.000
Chain 1:    700        -7788.976             0.704            0.182
Chain 1:    800        -7755.478             0.616            0.182
Chain 1:    900        -8025.820             0.551            0.121
Chain 1:   1000        -7683.180             0.501            0.121
Chain 1:   1100        -7712.312             0.401            0.083
Chain 1:   1200        -7708.562             0.155            0.064
Chain 1:   1300        -7758.453             0.054            0.045
Chain 1:   1400        -7656.659             0.049            0.034
Chain 1:   1500        -7636.295             0.031            0.013
Chain 1:   1600        -7777.988             0.025            0.013
Chain 1:   1700        -7549.515             0.016            0.013
Chain 1:   1800        -7650.773             0.017            0.013
Chain 1:   1900        -7588.105             0.014            0.013
Chain 1:   2000        -7632.563             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004008 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86149.671             1.000            1.000
Chain 1:    200       -13529.264             3.184            5.368
Chain 1:    300        -9986.756             2.241            1.000
Chain 1:    400       -10823.306             1.700            1.000
Chain 1:    500        -8892.818             1.403            0.355
Chain 1:    600        -8538.267             1.176            0.355
Chain 1:    700        -8957.313             1.015            0.217
Chain 1:    800        -8816.986             0.890            0.217
Chain 1:    900        -8861.779             0.792            0.077
Chain 1:   1000        -8728.425             0.714            0.077
Chain 1:   1100        -8922.682             0.616            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8641.947             0.083            0.042
Chain 1:   1300        -8689.966             0.048            0.032
Chain 1:   1400        -8759.374             0.041            0.022
Chain 1:   1500        -8616.014             0.021            0.017
Chain 1:   1600        -8719.488             0.018            0.016
Chain 1:   1700        -8803.693             0.014            0.015
Chain 1:   1800        -8419.570             0.017            0.015
Chain 1:   1900        -8521.585             0.018            0.015
Chain 1:   2000        -8491.342             0.017            0.012
Chain 1:   2100        -8625.363             0.016            0.012
Chain 1:   2200        -8410.121             0.015            0.012
Chain 1:   2300        -8551.325             0.016            0.016
Chain 1:   2400        -8562.585             0.016            0.016
Chain 1:   2500        -8530.872             0.015            0.012
Chain 1:   2600        -8529.113             0.013            0.012
Chain 1:   2700        -8438.241             0.013            0.012
Chain 1:   2800        -8416.039             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003665 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8377493.232             1.000            1.000
Chain 1:    200     -1579135.217             2.653            4.305
Chain 1:    300      -890623.990             2.026            1.000
Chain 1:    400      -458042.576             1.756            1.000
Chain 1:    500      -358785.328             1.460            0.944
Chain 1:    600      -233733.567             1.306            0.944
Chain 1:    700      -119603.894             1.255            0.944
Chain 1:    800       -86731.911             1.146            0.944
Chain 1:    900       -66997.802             1.051            0.773
Chain 1:   1000       -51729.343             0.976            0.773
Chain 1:   1100       -39150.637             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38315.613             0.480            0.379
Chain 1:   1300       -26223.296             0.448            0.379
Chain 1:   1400       -25935.065             0.355            0.321
Chain 1:   1500       -22510.702             0.343            0.321
Chain 1:   1600       -21722.841             0.293            0.295
Chain 1:   1700       -20591.319             0.203            0.295
Chain 1:   1800       -20533.857             0.165            0.152
Chain 1:   1900       -20859.479             0.137            0.055
Chain 1:   2000       -19368.979             0.115            0.055
Chain 1:   2100       -19607.291             0.084            0.036
Chain 1:   2200       -19833.917             0.083            0.036
Chain 1:   2300       -19451.120             0.039            0.020
Chain 1:   2400       -19223.374             0.039            0.020
Chain 1:   2500       -19025.673             0.025            0.016
Chain 1:   2600       -18656.160             0.024            0.016
Chain 1:   2700       -18613.187             0.018            0.012
Chain 1:   2800       -18330.456             0.020            0.015
Chain 1:   2900       -18611.531             0.020            0.015
Chain 1:   3000       -18597.640             0.012            0.012
Chain 1:   3100       -18682.575             0.011            0.012
Chain 1:   3200       -18373.561             0.012            0.015
Chain 1:   3300       -18578.045             0.011            0.012
Chain 1:   3400       -18053.651             0.013            0.015
Chain 1:   3500       -18664.598             0.015            0.015
Chain 1:   3600       -17972.517             0.017            0.015
Chain 1:   3700       -18358.488             0.018            0.017
Chain 1:   3800       -17320.152             0.023            0.021
Chain 1:   3900       -17316.397             0.021            0.021
Chain 1:   4000       -17433.643             0.022            0.021
Chain 1:   4100       -17347.544             0.022            0.021
Chain 1:   4200       -17164.202             0.021            0.021
Chain 1:   4300       -17302.262             0.021            0.021
Chain 1:   4400       -17259.444             0.019            0.011
Chain 1:   4500       -17162.086             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00146 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11956.415             1.000            1.000
Chain 1:    200        -8858.715             0.675            1.000
Chain 1:    300        -7843.768             0.493            0.350
Chain 1:    400        -7931.860             0.373            0.350
Chain 1:    500        -7620.268             0.306            0.129
Chain 1:    600        -7643.956             0.256            0.129
Chain 1:    700        -7600.507             0.220            0.041
Chain 1:    800        -7690.480             0.194            0.041
Chain 1:    900        -7649.027             0.173            0.012
Chain 1:   1000        -7652.575             0.156            0.012
Chain 1:   1100        -7700.658             0.056            0.011
Chain 1:   1200        -7633.100             0.022            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001366 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57371.577             1.000            1.000
Chain 1:    200       -17079.360             1.680            2.359
Chain 1:    300        -8360.496             1.467            1.043
Chain 1:    400        -7914.132             1.115            1.043
Chain 1:    500        -8144.591             0.897            1.000
Chain 1:    600        -8532.622             0.755            1.000
Chain 1:    700        -8035.301             0.656            0.062
Chain 1:    800        -7942.007             0.576            0.062
Chain 1:    900        -7752.815             0.514            0.056
Chain 1:   1000        -7689.182             0.464            0.056
Chain 1:   1100        -7581.039             0.365            0.045
Chain 1:   1200        -7455.947             0.131            0.028
Chain 1:   1300        -7450.502             0.027            0.024
Chain 1:   1400        -7691.961             0.024            0.024
Chain 1:   1500        -7459.782             0.025            0.024
Chain 1:   1600        -7456.963             0.020            0.017
Chain 1:   1700        -7337.484             0.016            0.016
Chain 1:   1800        -7400.866             0.015            0.016
Chain 1:   1900        -7418.364             0.013            0.014
Chain 1:   2000        -7424.728             0.012            0.014
Chain 1:   2100        -7449.133             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002989 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86351.306             1.000            1.000
Chain 1:    200       -12956.849             3.332            5.665
Chain 1:    300        -9442.562             2.346            1.000
Chain 1:    400       -10264.146             1.779            1.000
Chain 1:    500        -8295.883             1.471            0.372
Chain 1:    600        -8029.954             1.231            0.372
Chain 1:    700        -8323.633             1.060            0.237
Chain 1:    800        -8590.845             0.932            0.237
Chain 1:    900        -8344.998             0.831            0.080
Chain 1:   1000        -8070.653             0.752            0.080
Chain 1:   1100        -8363.789             0.655            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8112.888             0.092            0.035
Chain 1:   1300        -8038.962             0.056            0.034
Chain 1:   1400        -8040.535             0.048            0.033
Chain 1:   1500        -8075.359             0.024            0.031
Chain 1:   1600        -8080.993             0.021            0.031
Chain 1:   1700        -8018.888             0.018            0.029
Chain 1:   1800        -7899.799             0.017            0.015
Chain 1:   1900        -8014.389             0.015            0.014
Chain 1:   2000        -7975.243             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003165 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8439194.815             1.000            1.000
Chain 1:    200     -1591386.659             2.652            4.303
Chain 1:    300      -890848.374             2.030            1.000
Chain 1:    400      -456581.306             1.760            1.000
Chain 1:    500      -356159.953             1.465            0.951
Chain 1:    600      -231333.463             1.310            0.951
Chain 1:    700      -118096.536             1.260            0.951
Chain 1:    800       -85413.662             1.150            0.951
Chain 1:    900       -65871.359             1.056            0.786
Chain 1:   1000       -50747.837             0.980            0.786
Chain 1:   1100       -38306.112             0.912            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37487.782             0.484            0.383
Chain 1:   1300       -25543.926             0.452            0.383
Chain 1:   1400       -25268.904             0.358            0.325
Chain 1:   1500       -21881.783             0.346            0.325
Chain 1:   1600       -21104.642             0.295            0.298
Chain 1:   1700       -19991.355             0.205            0.297
Chain 1:   1800       -19937.995             0.167            0.155
Chain 1:   1900       -20263.426             0.139            0.056
Chain 1:   2000       -18782.469             0.117            0.056
Chain 1:   2100       -19020.559             0.086            0.037
Chain 1:   2200       -19245.292             0.085            0.037
Chain 1:   2300       -18864.172             0.040            0.020
Chain 1:   2400       -18636.664             0.040            0.020
Chain 1:   2500       -18438.128             0.026            0.016
Chain 1:   2600       -18069.664             0.024            0.016
Chain 1:   2700       -18027.036             0.019            0.013
Chain 1:   2800       -17743.968             0.020            0.016
Chain 1:   2900       -18024.749             0.020            0.016
Chain 1:   3000       -18011.155             0.012            0.013
Chain 1:   3100       -18095.969             0.011            0.012
Chain 1:   3200       -17787.297             0.012            0.016
Chain 1:   3300       -17991.513             0.011            0.012
Chain 1:   3400       -17467.362             0.013            0.016
Chain 1:   3500       -18077.654             0.015            0.016
Chain 1:   3600       -17386.390             0.017            0.016
Chain 1:   3700       -17771.572             0.019            0.017
Chain 1:   3800       -16734.319             0.024            0.022
Chain 1:   3900       -16730.450             0.022            0.022
Chain 1:   4000       -16847.844             0.023            0.022
Chain 1:   4100       -16761.693             0.023            0.022
Chain 1:   4200       -16578.621             0.022            0.022
Chain 1:   4300       -16716.605             0.022            0.022
Chain 1:   4400       -16673.993             0.019            0.011
Chain 1:   4500       -16576.549             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49231.557             1.000            1.000
Chain 1:    200       -20832.210             1.182            1.363
Chain 1:    300       -16114.681             0.885            1.000
Chain 1:    400       -18513.583             0.696            1.000
Chain 1:    500       -19452.348             0.567            0.293
Chain 1:    600       -12674.444             0.561            0.535
Chain 1:    700       -15129.483             0.504            0.293
Chain 1:    800       -15606.907             0.445            0.293
Chain 1:    900       -13628.553             0.412            0.162
Chain 1:   1000       -10853.358             0.396            0.256
Chain 1:   1100       -16484.464             0.330            0.256
Chain 1:   1200       -10523.158             0.251            0.256
Chain 1:   1300       -10147.530             0.225            0.162
Chain 1:   1400       -14910.074             0.244            0.256
Chain 1:   1500       -10908.920             0.276            0.319
Chain 1:   1600       -10400.640             0.227            0.256
Chain 1:   1700       -11532.606             0.221            0.256
Chain 1:   1800       -10532.040             0.227            0.256
Chain 1:   1900       -10835.298             0.216            0.256
Chain 1:   2000       -13018.244             0.207            0.168
Chain 1:   2100       -10754.770             0.194            0.168
Chain 1:   2200        -9613.681             0.149            0.119
Chain 1:   2300       -18322.817             0.193            0.168
Chain 1:   2400       -11603.422             0.219            0.168
Chain 1:   2500       -11647.434             0.183            0.119
Chain 1:   2600        -9525.911             0.200            0.168
Chain 1:   2700        -9570.635             0.191            0.168
Chain 1:   2800        -9425.013             0.183            0.168
Chain 1:   2900       -14488.826             0.215            0.210
Chain 1:   3000       -16475.162             0.210            0.210
Chain 1:   3100        -9025.017             0.272            0.223
Chain 1:   3200       -11692.609             0.282            0.228
Chain 1:   3300       -15490.597             0.259            0.228
Chain 1:   3400        -9718.467             0.261            0.228
Chain 1:   3500        -9829.591             0.262            0.228
Chain 1:   3600       -10562.754             0.246            0.228
Chain 1:   3700       -17306.904             0.285            0.245
Chain 1:   3800        -8821.816             0.380            0.349
Chain 1:   3900       -13261.901             0.378            0.335
Chain 1:   4000        -9044.061             0.413            0.390
Chain 1:   4100        -9210.991             0.332            0.335
Chain 1:   4200        -9936.654             0.316            0.335
Chain 1:   4300       -12883.218             0.315            0.335
Chain 1:   4400       -12081.763             0.262            0.229
Chain 1:   4500       -10472.371             0.276            0.229
Chain 1:   4600        -8753.925             0.289            0.229
Chain 1:   4700        -9197.938             0.255            0.196
Chain 1:   4800       -13901.267             0.192            0.196
Chain 1:   4900        -9233.008             0.209            0.196
Chain 1:   5000       -10964.235             0.179            0.158
Chain 1:   5100       -10657.790             0.180            0.158
Chain 1:   5200        -9217.591             0.188            0.158
Chain 1:   5300       -12068.600             0.189            0.158
Chain 1:   5400        -8539.575             0.223            0.196
Chain 1:   5500       -11742.403             0.235            0.236
Chain 1:   5600       -11480.852             0.218            0.236
Chain 1:   5700        -9771.042             0.231            0.236
Chain 1:   5800        -8578.273             0.211            0.175
Chain 1:   5900       -12469.385             0.191            0.175
Chain 1:   6000        -8550.142             0.221            0.236
Chain 1:   6100        -8498.320             0.219            0.236
Chain 1:   6200        -8685.893             0.206            0.236
Chain 1:   6300       -12701.616             0.214            0.273
Chain 1:   6400        -9212.824             0.210            0.273
Chain 1:   6500        -9056.849             0.185            0.175
Chain 1:   6600        -8526.202             0.189            0.175
Chain 1:   6700       -11760.430             0.199            0.275
Chain 1:   6800        -8627.315             0.221            0.312
Chain 1:   6900        -8376.107             0.193            0.275
Chain 1:   7000        -8561.274             0.149            0.062
Chain 1:   7100        -8409.101             0.150            0.062
Chain 1:   7200        -8884.525             0.154            0.062
Chain 1:   7300        -8846.408             0.122            0.054
Chain 1:   7400        -8831.791             0.085            0.030
Chain 1:   7500       -12644.904             0.113            0.054
Chain 1:   7600        -8788.112             0.151            0.054
Chain 1:   7700       -12267.864             0.152            0.054
Chain 1:   7800        -9698.508             0.142            0.054
Chain 1:   7900        -9475.812             0.141            0.054
Chain 1:   8000        -8438.041             0.151            0.123
Chain 1:   8100        -8395.199             0.150            0.123
Chain 1:   8200        -9226.498             0.154            0.123
Chain 1:   8300        -8445.660             0.162            0.123
Chain 1:   8400        -8411.768             0.163            0.123
Chain 1:   8500        -8737.358             0.136            0.092
Chain 1:   8600       -11004.653             0.113            0.092
Chain 1:   8700        -9013.578             0.107            0.092
Chain 1:   8800       -10958.674             0.098            0.092
Chain 1:   8900       -11922.767             0.104            0.092
Chain 1:   9000        -8448.642             0.133            0.092
Chain 1:   9100        -8503.972             0.133            0.092
Chain 1:   9200        -8898.391             0.128            0.092
Chain 1:   9300       -10062.078             0.130            0.116
Chain 1:   9400        -8399.564             0.150            0.177
Chain 1:   9500        -8468.978             0.147            0.177
Chain 1:   9600       -10461.192             0.145            0.177
Chain 1:   9700       -11316.605             0.131            0.116
Chain 1:   9800        -9305.729             0.135            0.116
Chain 1:   9900        -8265.508             0.139            0.126
Chain 1:   10000        -9391.513             0.110            0.120
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001423 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58272.200             1.000            1.000
Chain 1:    200       -17817.194             1.635            2.271
Chain 1:    300        -8800.349             1.432            1.025
Chain 1:    400        -8211.850             1.092            1.025
Chain 1:    500        -8723.169             0.885            1.000
Chain 1:    600        -8514.297             0.742            1.000
Chain 1:    700        -8052.522             0.644            0.072
Chain 1:    800        -8355.882             0.568            0.072
Chain 1:    900        -7924.999             0.511            0.059
Chain 1:   1000        -8012.010             0.461            0.059
Chain 1:   1100        -7769.171             0.364            0.057
Chain 1:   1200        -7701.493             0.138            0.054
Chain 1:   1300        -7767.802             0.036            0.036
Chain 1:   1400        -7920.133             0.031            0.031
Chain 1:   1500        -7625.219             0.029            0.031
Chain 1:   1600        -7572.119             0.027            0.031
Chain 1:   1700        -7685.645             0.023            0.019
Chain 1:   1800        -7700.967             0.020            0.015
Chain 1:   1900        -7696.986             0.014            0.011
Chain 1:   2000        -7651.010             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003683 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86823.303             1.000            1.000
Chain 1:    200       -13664.731             3.177            5.354
Chain 1:    300       -10001.645             2.240            1.000
Chain 1:    400       -10966.523             1.702            1.000
Chain 1:    500        -8989.777             1.406            0.366
Chain 1:    600        -8518.093             1.181            0.366
Chain 1:    700        -8507.938             1.012            0.220
Chain 1:    800        -8693.234             0.888            0.220
Chain 1:    900        -8701.004             0.790            0.088
Chain 1:   1000        -8707.171             0.711            0.088
Chain 1:   1100        -8825.701             0.612            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8322.739             0.083            0.055
Chain 1:   1300        -8753.182             0.051            0.049
Chain 1:   1400        -8704.400             0.043            0.021
Chain 1:   1500        -8543.757             0.023            0.019
Chain 1:   1600        -8663.678             0.019            0.014
Chain 1:   1700        -8732.330             0.019            0.014
Chain 1:   1800        -8307.906             0.022            0.014
Chain 1:   1900        -8409.466             0.023            0.014
Chain 1:   2000        -8384.094             0.024            0.014
Chain 1:   2100        -8510.517             0.024            0.015
Chain 1:   2200        -8311.015             0.020            0.015
Chain 1:   2300        -8404.472             0.016            0.014
Chain 1:   2400        -8472.873             0.016            0.014
Chain 1:   2500        -8419.123             0.015            0.012
Chain 1:   2600        -8421.011             0.014            0.011
Chain 1:   2700        -8337.492             0.014            0.011
Chain 1:   2800        -8296.668             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424777.068             1.000            1.000
Chain 1:    200     -1588603.705             2.652            4.303
Chain 1:    300      -890497.251             2.029            1.000
Chain 1:    400      -457183.172             1.759            1.000
Chain 1:    500      -357391.558             1.463            0.948
Chain 1:    600      -232462.152             1.309            0.948
Chain 1:    700      -119038.721             1.258            0.948
Chain 1:    800       -86333.288             1.148            0.948
Chain 1:    900       -66750.017             1.053            0.784
Chain 1:   1000       -51609.491             0.977            0.784
Chain 1:   1100       -39140.365             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38323.550             0.481            0.379
Chain 1:   1300       -26334.525             0.448            0.379
Chain 1:   1400       -26058.526             0.354            0.319
Chain 1:   1500       -22659.609             0.341            0.319
Chain 1:   1600       -21880.387             0.291            0.293
Chain 1:   1700       -20760.624             0.201            0.293
Chain 1:   1800       -20706.292             0.163            0.150
Chain 1:   1900       -21032.617             0.136            0.054
Chain 1:   2000       -19546.789             0.114            0.054
Chain 1:   2100       -19785.097             0.083            0.036
Chain 1:   2200       -20011.061             0.082            0.036
Chain 1:   2300       -19628.611             0.039            0.019
Chain 1:   2400       -19400.725             0.039            0.019
Chain 1:   2500       -19202.433             0.025            0.016
Chain 1:   2600       -18832.861             0.023            0.016
Chain 1:   2700       -18789.857             0.018            0.012
Chain 1:   2800       -18506.550             0.019            0.015
Chain 1:   2900       -18787.755             0.019            0.015
Chain 1:   3000       -18774.014             0.012            0.012
Chain 1:   3100       -18859.019             0.011            0.012
Chain 1:   3200       -18549.689             0.012            0.015
Chain 1:   3300       -18754.401             0.011            0.012
Chain 1:   3400       -18229.212             0.012            0.015
Chain 1:   3500       -18841.175             0.015            0.015
Chain 1:   3600       -18147.673             0.016            0.015
Chain 1:   3700       -18534.593             0.018            0.017
Chain 1:   3800       -17493.966             0.023            0.021
Chain 1:   3900       -17490.030             0.021            0.021
Chain 1:   4000       -17607.394             0.022            0.021
Chain 1:   4100       -17521.151             0.022            0.021
Chain 1:   4200       -17337.278             0.021            0.021
Chain 1:   4300       -17475.800             0.021            0.021
Chain 1:   4400       -17432.581             0.018            0.011
Chain 1:   4500       -17335.033             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003979 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13360.520             1.000            1.000
Chain 1:    200       -10013.792             0.667            1.000
Chain 1:    300        -8515.016             0.503            0.334
Chain 1:    400        -8835.081             0.387            0.334
Chain 1:    500        -8526.659             0.317            0.176
Chain 1:    600        -8513.979             0.264            0.176
Chain 1:    700        -8397.537             0.228            0.036
Chain 1:    800        -8495.965             0.201            0.036
Chain 1:    900        -8335.087             0.181            0.036
Chain 1:   1000        -8566.929             0.166            0.036
Chain 1:   1100        -8523.968             0.066            0.027
Chain 1:   1200        -8425.796             0.034            0.019
Chain 1:   1300        -8381.534             0.017            0.014
Chain 1:   1400        -8401.779             0.013            0.012
Chain 1:   1500        -8494.663             0.011            0.012
Chain 1:   1600        -8411.355             0.012            0.012
Chain 1:   1700        -8376.993             0.011            0.011
Chain 1:   1800        -8349.213             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50935.851             1.000            1.000
Chain 1:    200       -17025.791             1.496            1.992
Chain 1:    300        -9234.003             1.279            1.000
Chain 1:    400        -8465.446             0.982            1.000
Chain 1:    500        -8226.414             0.791            0.844
Chain 1:    600        -8377.079             0.662            0.844
Chain 1:    700        -8553.030             0.571            0.091
Chain 1:    800        -8651.705             0.501            0.091
Chain 1:    900        -8124.768             0.452            0.065
Chain 1:   1000        -7968.116             0.409            0.065
Chain 1:   1100        -8037.094             0.310            0.029
Chain 1:   1200        -7959.619             0.112            0.021
Chain 1:   1300        -8170.275             0.030            0.021
Chain 1:   1400        -8010.744             0.023            0.020
Chain 1:   1500        -7686.381             0.024            0.020
Chain 1:   1600        -7912.983             0.025            0.021
Chain 1:   1700        -7940.428             0.023            0.020
Chain 1:   1800        -7790.314             0.024            0.020
Chain 1:   1900        -7695.942             0.019            0.020
Chain 1:   2000        -7874.556             0.019            0.020
Chain 1:   2100        -7685.345             0.021            0.023
Chain 1:   2200        -8022.863             0.024            0.025
Chain 1:   2300        -7810.612             0.024            0.025
Chain 1:   2400        -7738.949             0.023            0.025
Chain 1:   2500        -7730.343             0.019            0.023
Chain 1:   2600        -7671.856             0.017            0.019
Chain 1:   2700        -7664.540             0.017            0.019
Chain 1:   2800        -7554.884             0.016            0.015
Chain 1:   2900        -7512.221             0.016            0.015
Chain 1:   3000        -7687.110             0.016            0.015
Chain 1:   3100        -7650.722             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86553.678             1.000            1.000
Chain 1:    200       -14377.870             3.010            5.020
Chain 1:    300       -10591.747             2.126            1.000
Chain 1:    400       -12412.446             1.631            1.000
Chain 1:    500        -9078.901             1.378            0.367
Chain 1:    600        -9084.172             1.149            0.367
Chain 1:    700        -9122.524             0.985            0.357
Chain 1:    800        -9259.585             0.864            0.357
Chain 1:    900        -9440.281             0.770            0.147
Chain 1:   1000        -9134.259             0.696            0.147
Chain 1:   1100        -9353.836             0.599            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8922.512             0.102            0.034
Chain 1:   1300        -9210.774             0.069            0.031
Chain 1:   1400        -9051.261             0.056            0.023
Chain 1:   1500        -9068.578             0.019            0.019
Chain 1:   1600        -9150.261             0.020            0.019
Chain 1:   1700        -9207.827             0.021            0.019
Chain 1:   1800        -8752.970             0.024            0.023
Chain 1:   1900        -8866.823             0.024            0.023
Chain 1:   2000        -8880.782             0.020            0.018
Chain 1:   2100        -8806.986             0.019            0.013
Chain 1:   2200        -8781.303             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8377613.337             1.000            1.000
Chain 1:    200     -1577323.517             2.656            4.311
Chain 1:    300      -890565.356             2.027            1.000
Chain 1:    400      -458110.491             1.757            1.000
Chain 1:    500      -359126.492             1.460            0.944
Chain 1:    600      -234307.653             1.306            0.944
Chain 1:    700      -120412.684             1.254            0.944
Chain 1:    800       -87575.954             1.144            0.944
Chain 1:    900       -67876.573             1.050            0.771
Chain 1:   1000       -52645.756             0.974            0.771
Chain 1:   1100       -40082.646             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39263.611             0.476            0.375
Chain 1:   1300       -27161.585             0.443            0.375
Chain 1:   1400       -26879.758             0.350            0.313
Chain 1:   1500       -23450.758             0.337            0.313
Chain 1:   1600       -22663.852             0.287            0.290
Chain 1:   1700       -21529.493             0.198            0.289
Chain 1:   1800       -21472.450             0.161            0.146
Chain 1:   1900       -21799.416             0.133            0.053
Chain 1:   2000       -20304.879             0.112            0.053
Chain 1:   2100       -20543.609             0.081            0.035
Chain 1:   2200       -20771.253             0.080            0.035
Chain 1:   2300       -20387.233             0.038            0.019
Chain 1:   2400       -20158.930             0.038            0.019
Chain 1:   2500       -19961.168             0.024            0.015
Chain 1:   2600       -19590.282             0.023            0.015
Chain 1:   2700       -19547.000             0.018            0.012
Chain 1:   2800       -19263.544             0.019            0.015
Chain 1:   2900       -19545.294             0.019            0.014
Chain 1:   3000       -19531.389             0.011            0.012
Chain 1:   3100       -19616.473             0.011            0.011
Chain 1:   3200       -19306.578             0.011            0.014
Chain 1:   3300       -19511.792             0.010            0.011
Chain 1:   3400       -18985.716             0.012            0.014
Chain 1:   3500       -19599.140             0.014            0.015
Chain 1:   3600       -18903.882             0.016            0.015
Chain 1:   3700       -19292.152             0.018            0.016
Chain 1:   3800       -18248.833             0.022            0.020
Chain 1:   3900       -18244.945             0.020            0.020
Chain 1:   4000       -18362.232             0.021            0.020
Chain 1:   4100       -18275.801             0.021            0.020
Chain 1:   4200       -18091.435             0.021            0.020
Chain 1:   4300       -18230.258             0.020            0.020
Chain 1:   4400       -18186.540             0.018            0.010
Chain 1:   4500       -18088.998             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001438 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48735.000             1.000            1.000
Chain 1:    200       -18915.507             1.288            1.576
Chain 1:    300       -12737.526             1.020            1.000
Chain 1:    400       -14161.905             0.791            1.000
Chain 1:    500       -18816.962             0.682            0.485
Chain 1:    600       -12631.917             0.650            0.490
Chain 1:    700       -13029.840             0.561            0.485
Chain 1:    800       -10791.752             0.517            0.485
Chain 1:    900       -10968.079             0.461            0.247
Chain 1:   1000       -13549.938             0.434            0.247
Chain 1:   1100       -10416.202             0.364            0.247
Chain 1:   1200       -10558.560             0.208            0.207
Chain 1:   1300       -11264.289             0.166            0.191
Chain 1:   1400       -16561.206             0.188            0.207
Chain 1:   1500       -11097.615             0.212            0.207
Chain 1:   1600       -13182.692             0.179            0.191
Chain 1:   1700       -21845.457             0.216            0.207
Chain 1:   1800       -10741.624             0.298            0.301
Chain 1:   1900        -9240.594             0.313            0.301
Chain 1:   2000        -9612.677             0.298            0.301
Chain 1:   2100       -10297.317             0.274            0.162
Chain 1:   2200        -9295.421             0.284            0.162
Chain 1:   2300       -12644.256             0.304            0.265
Chain 1:   2400        -9000.840             0.313            0.265
Chain 1:   2500       -15930.372             0.307            0.265
Chain 1:   2600        -9925.798             0.352            0.397
Chain 1:   2700        -9836.421             0.313            0.265
Chain 1:   2800       -15440.300             0.246            0.265
Chain 1:   2900        -9170.001             0.298            0.363
Chain 1:   3000       -10047.876             0.303            0.363
Chain 1:   3100        -8924.979             0.309            0.363
Chain 1:   3200        -8912.151             0.298            0.363
Chain 1:   3300        -8817.410             0.273            0.363
Chain 1:   3400        -8970.216             0.234            0.126
Chain 1:   3500        -9608.431             0.197            0.087
Chain 1:   3600        -9124.134             0.142            0.066
Chain 1:   3700       -16524.384             0.186            0.087
Chain 1:   3800       -15721.728             0.154            0.066
Chain 1:   3900        -9203.976             0.157            0.066
Chain 1:   4000       -11492.911             0.168            0.066
Chain 1:   4100        -9784.938             0.173            0.066
Chain 1:   4200       -14465.988             0.205            0.175
Chain 1:   4300       -12562.365             0.219            0.175
Chain 1:   4400        -8722.532             0.262            0.199
Chain 1:   4500        -9238.250             0.260            0.199
Chain 1:   4600        -8337.127             0.266            0.199
Chain 1:   4700        -9991.842             0.238            0.175
Chain 1:   4800        -8422.658             0.251            0.186
Chain 1:   4900        -8443.034             0.181            0.175
Chain 1:   5000       -10432.491             0.180            0.175
Chain 1:   5100        -8940.869             0.179            0.167
Chain 1:   5200        -9104.727             0.149            0.166
Chain 1:   5300       -12046.594             0.158            0.167
Chain 1:   5400       -10499.554             0.129            0.166
Chain 1:   5500       -10144.859             0.126            0.166
Chain 1:   5600        -8622.028             0.133            0.167
Chain 1:   5700        -9273.933             0.124            0.167
Chain 1:   5800        -8283.203             0.117            0.147
Chain 1:   5900        -8784.171             0.123            0.147
Chain 1:   6000        -9241.092             0.108            0.120
Chain 1:   6100        -8575.096             0.100            0.078
Chain 1:   6200        -8230.791             0.102            0.078
Chain 1:   6300        -8500.842             0.081            0.070
Chain 1:   6400        -9310.095             0.075            0.070
Chain 1:   6500        -8333.274             0.083            0.078
Chain 1:   6600        -8299.105             0.066            0.070
Chain 1:   6700        -8896.538             0.065            0.067
Chain 1:   6800        -9298.974             0.058            0.057
Chain 1:   6900        -9491.327             0.054            0.049
Chain 1:   7000       -10950.613             0.062            0.067
Chain 1:   7100        -8030.838             0.091            0.067
Chain 1:   7200       -12236.728             0.121            0.087
Chain 1:   7300       -10602.399             0.133            0.117
Chain 1:   7400        -8448.236             0.150            0.133
Chain 1:   7500        -9216.038             0.147            0.133
Chain 1:   7600        -8325.492             0.157            0.133
Chain 1:   7700        -8008.232             0.154            0.133
Chain 1:   7800        -8429.736             0.155            0.133
Chain 1:   7900        -8080.401             0.157            0.133
Chain 1:   8000       -12156.791             0.177            0.154
Chain 1:   8100        -8366.743             0.186            0.154
Chain 1:   8200       -10001.638             0.168            0.154
Chain 1:   8300        -8036.615             0.177            0.163
Chain 1:   8400        -7975.716             0.153            0.107
Chain 1:   8500        -8001.952             0.145            0.107
Chain 1:   8600       -10850.953             0.160            0.163
Chain 1:   8700       -10094.530             0.164            0.163
Chain 1:   8800        -8072.076             0.184            0.245
Chain 1:   8900       -11080.755             0.207            0.251
Chain 1:   9000        -8120.839             0.210            0.251
Chain 1:   9100        -8765.675             0.172            0.245
Chain 1:   9200        -8131.469             0.163            0.245
Chain 1:   9300        -8143.596             0.139            0.078
Chain 1:   9400        -9971.526             0.156            0.183
Chain 1:   9500        -7991.865             0.181            0.248
Chain 1:   9600        -8055.609             0.155            0.183
Chain 1:   9700        -9862.483             0.166            0.183
Chain 1:   9800       -10270.329             0.145            0.183
Chain 1:   9900        -8731.462             0.136            0.176
Chain 1:   10000        -8835.010             0.100            0.078
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62940.519             1.000            1.000
Chain 1:    200       -17848.613             1.763            2.526
Chain 1:    300        -8621.136             1.532            1.070
Chain 1:    400        -8207.845             1.162            1.070
Chain 1:    500        -8198.183             0.930            1.000
Chain 1:    600        -8733.572             0.785            1.000
Chain 1:    700        -7874.830             0.688            0.109
Chain 1:    800        -7997.233             0.604            0.109
Chain 1:    900        -7896.406             0.539            0.061
Chain 1:   1000        -7581.033             0.489            0.061
Chain 1:   1100        -7607.087             0.389            0.050
Chain 1:   1200        -7599.416             0.137            0.042
Chain 1:   1300        -7689.635             0.031            0.015
Chain 1:   1400        -7625.376             0.027            0.013
Chain 1:   1500        -7599.771             0.027            0.013
Chain 1:   1600        -7513.281             0.022            0.012
Chain 1:   1700        -7491.246             0.011            0.012
Chain 1:   1800        -7535.855             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003439 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86240.975             1.000            1.000
Chain 1:    200       -13140.632             3.281            5.563
Chain 1:    300        -9580.591             2.312            1.000
Chain 1:    400       -10420.852             1.754            1.000
Chain 1:    500        -8490.680             1.448            0.372
Chain 1:    600        -8113.935             1.215            0.372
Chain 1:    700        -8267.308             1.044            0.227
Chain 1:    800        -8661.831             0.919            0.227
Chain 1:    900        -8446.352             0.820            0.081
Chain 1:   1000        -8168.142             0.741            0.081
Chain 1:   1100        -8321.917             0.643            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8084.468             0.090            0.046
Chain 1:   1300        -8306.148             0.055            0.034
Chain 1:   1400        -8294.477             0.047            0.029
Chain 1:   1500        -8200.923             0.026            0.027
Chain 1:   1600        -8297.446             0.022            0.026
Chain 1:   1700        -8385.716             0.021            0.026
Chain 1:   1800        -7994.838             0.022            0.026
Chain 1:   1900        -8097.312             0.021            0.018
Chain 1:   2000        -8067.286             0.017            0.013
Chain 1:   2100        -8196.091             0.017            0.013
Chain 1:   2200        -7982.601             0.017            0.013
Chain 1:   2300        -8126.138             0.016            0.013
Chain 1:   2400        -8140.318             0.016            0.013
Chain 1:   2500        -8107.379             0.015            0.013
Chain 1:   2600        -8108.574             0.014            0.013
Chain 1:   2700        -8015.961             0.014            0.013
Chain 1:   2800        -7990.327             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002939 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417971.474             1.000            1.000
Chain 1:    200     -1583602.107             2.658            4.316
Chain 1:    300      -889595.476             2.032            1.000
Chain 1:    400      -457117.188             1.760            1.000
Chain 1:    500      -357342.161             1.464            0.946
Chain 1:    600      -232405.851             1.310            0.946
Chain 1:    700      -118713.222             1.259            0.946
Chain 1:    800       -85979.958             1.150            0.946
Chain 1:    900       -66335.619             1.055            0.780
Chain 1:   1000       -51141.532             0.979            0.780
Chain 1:   1100       -38637.071             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37811.474             0.482            0.381
Chain 1:   1300       -25790.672             0.451            0.381
Chain 1:   1400       -25510.478             0.357            0.324
Chain 1:   1500       -22104.946             0.345            0.324
Chain 1:   1600       -21323.241             0.294            0.297
Chain 1:   1700       -20199.859             0.204            0.296
Chain 1:   1800       -20144.634             0.166            0.154
Chain 1:   1900       -20470.317             0.138            0.056
Chain 1:   2000       -18984.242             0.117            0.056
Chain 1:   2100       -19222.219             0.085            0.037
Chain 1:   2200       -19448.241             0.084            0.037
Chain 1:   2300       -19066.031             0.040            0.020
Chain 1:   2400       -18838.327             0.040            0.020
Chain 1:   2500       -18640.404             0.026            0.016
Chain 1:   2600       -18270.934             0.024            0.016
Chain 1:   2700       -18228.085             0.019            0.012
Chain 1:   2800       -17945.155             0.020            0.016
Chain 1:   2900       -18226.223             0.020            0.015
Chain 1:   3000       -18212.357             0.012            0.012
Chain 1:   3100       -18297.274             0.011            0.012
Chain 1:   3200       -17988.254             0.012            0.015
Chain 1:   3300       -18192.786             0.011            0.012
Chain 1:   3400       -17668.257             0.013            0.015
Chain 1:   3500       -18279.241             0.015            0.016
Chain 1:   3600       -17587.123             0.017            0.016
Chain 1:   3700       -17973.021             0.019            0.017
Chain 1:   3800       -16934.526             0.023            0.021
Chain 1:   3900       -16930.739             0.022            0.021
Chain 1:   4000       -17048.039             0.023            0.021
Chain 1:   4100       -16961.857             0.023            0.021
Chain 1:   4200       -16778.533             0.022            0.021
Chain 1:   4300       -16916.605             0.022            0.021
Chain 1:   4400       -16873.741             0.019            0.011
Chain 1:   4500       -16776.363             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48830.719             1.000            1.000
Chain 1:    200       -20109.854             1.214            1.428
Chain 1:    300       -20237.589             0.812            1.000
Chain 1:    400       -17336.391             0.650            1.000
Chain 1:    500       -19521.626             0.543            0.167
Chain 1:    600       -18462.418             0.462            0.167
Chain 1:    700       -15146.106             0.427            0.167
Chain 1:    800       -10876.809             0.423            0.219
Chain 1:    900       -11245.021             0.379            0.167
Chain 1:   1000       -14049.329             0.361            0.200
Chain 1:   1100       -12258.908             0.276            0.167
Chain 1:   1200       -11304.830             0.142            0.146
Chain 1:   1300       -14958.695             0.166            0.167
Chain 1:   1400       -11703.345             0.177            0.200
Chain 1:   1500       -10003.865             0.182            0.200
Chain 1:   1600       -14093.090             0.206            0.219
Chain 1:   1700       -11544.122             0.206            0.221
Chain 1:   1800       -11422.091             0.168            0.200
Chain 1:   1900       -14242.046             0.184            0.200
Chain 1:   2000       -15699.364             0.174            0.198
Chain 1:   2100       -15012.029             0.163            0.198
Chain 1:   2200        -9502.912             0.213            0.221
Chain 1:   2300       -10117.445             0.195            0.198
Chain 1:   2400       -14360.433             0.196            0.198
Chain 1:   2500        -9584.913             0.229            0.221
Chain 1:   2600       -15931.164             0.240            0.221
Chain 1:   2700        -8717.270             0.301            0.295
Chain 1:   2800       -10012.744             0.313            0.295
Chain 1:   2900        -9209.278             0.302            0.295
Chain 1:   3000        -8880.379             0.296            0.295
Chain 1:   3100       -15371.953             0.334            0.398
Chain 1:   3200        -8870.137             0.349            0.398
Chain 1:   3300        -9567.361             0.350            0.398
Chain 1:   3400       -10295.783             0.328            0.398
Chain 1:   3500       -11310.539             0.287            0.129
Chain 1:   3600        -9247.879             0.269            0.129
Chain 1:   3700        -8614.554             0.194            0.090
Chain 1:   3800        -9901.364             0.194            0.090
Chain 1:   3900        -9192.554             0.193            0.090
Chain 1:   4000        -8621.791             0.196            0.090
Chain 1:   4100        -8909.509             0.157            0.077
Chain 1:   4200       -10590.239             0.099            0.077
Chain 1:   4300        -8590.077             0.115            0.090
Chain 1:   4400       -12835.225             0.141            0.130
Chain 1:   4500        -8637.747             0.181            0.159
Chain 1:   4600        -9516.888             0.168            0.130
Chain 1:   4700        -8428.042             0.174            0.130
Chain 1:   4800        -8645.281             0.163            0.129
Chain 1:   4900        -9072.919             0.160            0.129
Chain 1:   5000       -14344.793             0.190            0.159
Chain 1:   5100       -16334.527             0.199            0.159
Chain 1:   5200       -15696.299             0.187            0.129
Chain 1:   5300        -9444.036             0.230            0.129
Chain 1:   5400        -8508.164             0.208            0.122
Chain 1:   5500        -8534.214             0.160            0.110
Chain 1:   5600       -13370.515             0.187            0.122
Chain 1:   5700        -9473.852             0.215            0.122
Chain 1:   5800        -9034.404             0.217            0.122
Chain 1:   5900        -9183.133             0.214            0.122
Chain 1:   6000        -8782.023             0.182            0.110
Chain 1:   6100       -11066.512             0.191            0.110
Chain 1:   6200        -8223.930             0.221            0.206
Chain 1:   6300       -11663.943             0.184            0.206
Chain 1:   6400        -8334.980             0.213            0.295
Chain 1:   6500        -8671.467             0.217            0.295
Chain 1:   6600        -8345.999             0.185            0.206
Chain 1:   6700        -8603.935             0.146            0.049
Chain 1:   6800        -8243.556             0.146            0.046
Chain 1:   6900        -8173.692             0.145            0.046
Chain 1:   7000        -8638.352             0.146            0.054
Chain 1:   7100        -8542.330             0.127            0.044
Chain 1:   7200        -8187.272             0.096            0.043
Chain 1:   7300        -8952.965             0.075            0.043
Chain 1:   7400        -8181.786             0.045            0.043
Chain 1:   7500       -10039.491             0.059            0.044
Chain 1:   7600        -8650.749             0.072            0.054
Chain 1:   7700        -8482.754             0.071            0.054
Chain 1:   7800        -8318.830             0.068            0.054
Chain 1:   7900       -10080.389             0.085            0.086
Chain 1:   8000       -10546.789             0.084            0.086
Chain 1:   8100        -8389.664             0.108            0.094
Chain 1:   8200        -7957.224             0.110            0.094
Chain 1:   8300       -10707.808             0.127            0.161
Chain 1:   8400       -11183.304             0.121            0.161
Chain 1:   8500        -8010.075             0.143            0.161
Chain 1:   8600        -9790.377             0.145            0.175
Chain 1:   8700       -10065.632             0.145            0.175
Chain 1:   8800        -8022.659             0.169            0.182
Chain 1:   8900        -9536.674             0.167            0.182
Chain 1:   9000        -8269.644             0.178            0.182
Chain 1:   9100        -8632.002             0.157            0.159
Chain 1:   9200        -8917.891             0.155            0.159
Chain 1:   9300        -8821.601             0.130            0.153
Chain 1:   9400        -8454.820             0.130            0.153
Chain 1:   9500        -8361.039             0.092            0.043
Chain 1:   9600        -8139.157             0.076            0.042
Chain 1:   9700        -8118.698             0.074            0.042
Chain 1:   9800       -10286.744             0.069            0.042
Chain 1:   9900       -10890.842             0.059            0.042
Chain 1:   10000        -8358.093             0.074            0.042
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001455 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61438.434             1.000            1.000
Chain 1:    200       -17563.367             1.749            2.498
Chain 1:    300        -8739.702             1.503            1.010
Chain 1:    400        -9313.176             1.142            1.010
Chain 1:    500        -8386.523             0.936            1.000
Chain 1:    600        -9044.141             0.792            1.000
Chain 1:    700        -7777.192             0.702            0.163
Chain 1:    800        -8114.129             0.620            0.163
Chain 1:    900        -7799.225             0.555            0.110
Chain 1:   1000        -7867.011             0.501            0.110
Chain 1:   1100        -7645.194             0.403            0.073
Chain 1:   1200        -7560.425             0.155            0.062
Chain 1:   1300        -7783.094             0.057            0.042
Chain 1:   1400        -7693.171             0.052            0.040
Chain 1:   1500        -7625.875             0.042            0.029
Chain 1:   1600        -7559.357             0.035            0.029
Chain 1:   1700        -7544.791             0.019            0.012
Chain 1:   1800        -7610.775             0.016            0.011
Chain 1:   1900        -7502.623             0.013            0.011
Chain 1:   2000        -7589.090             0.013            0.011
Chain 1:   2100        -7678.416             0.012            0.011
Chain 1:   2200        -7697.275             0.011            0.011
Chain 1:   2300        -7591.122             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003403 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85896.369             1.000            1.000
Chain 1:    200       -13238.284             3.244            5.488
Chain 1:    300        -9695.276             2.285            1.000
Chain 1:    400       -10541.539             1.734            1.000
Chain 1:    500        -8588.841             1.432            0.365
Chain 1:    600        -8239.345             1.201            0.365
Chain 1:    700        -8337.766             1.031            0.227
Chain 1:    800        -8955.457             0.911            0.227
Chain 1:    900        -8474.306             0.816            0.080
Chain 1:   1000        -8387.403             0.735            0.080
Chain 1:   1100        -8590.277             0.638            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8299.231             0.092            0.057
Chain 1:   1300        -8241.261             0.056            0.042
Chain 1:   1400        -8304.856             0.049            0.035
Chain 1:   1500        -8287.954             0.027            0.024
Chain 1:   1600        -8286.905             0.022            0.012
Chain 1:   1700        -8218.003             0.022            0.010
Chain 1:   1800        -8099.823             0.017            0.010
Chain 1:   1900        -8218.238             0.012            0.010
Chain 1:   2000        -8178.092             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003461 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410658.041             1.000            1.000
Chain 1:    200     -1582865.650             2.657            4.314
Chain 1:    300      -889389.795             2.031            1.000
Chain 1:    400      -456405.840             1.760            1.000
Chain 1:    500      -356751.462             1.464            0.949
Chain 1:    600      -232007.068             1.310            0.949
Chain 1:    700      -118622.957             1.259            0.949
Chain 1:    800       -85919.453             1.149            0.949
Chain 1:    900       -66329.182             1.055            0.780
Chain 1:   1000       -51170.615             0.979            0.780
Chain 1:   1100       -38693.817             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37873.040             0.482            0.381
Chain 1:   1300       -25879.527             0.450            0.381
Chain 1:   1400       -25602.146             0.356            0.322
Chain 1:   1500       -22202.066             0.344            0.322
Chain 1:   1600       -21422.146             0.294            0.296
Chain 1:   1700       -20302.064             0.204            0.295
Chain 1:   1800       -20247.607             0.166            0.153
Chain 1:   1900       -20573.270             0.138            0.055
Chain 1:   2000       -19088.649             0.116            0.055
Chain 1:   2100       -19326.742             0.085            0.036
Chain 1:   2200       -19552.334             0.084            0.036
Chain 1:   2300       -19170.446             0.040            0.020
Chain 1:   2400       -18942.772             0.040            0.020
Chain 1:   2500       -18744.600             0.025            0.016
Chain 1:   2600       -18375.450             0.024            0.016
Chain 1:   2700       -18332.703             0.019            0.012
Chain 1:   2800       -18049.676             0.020            0.016
Chain 1:   2900       -18330.615             0.020            0.015
Chain 1:   3000       -18316.910             0.012            0.012
Chain 1:   3100       -18401.791             0.011            0.012
Chain 1:   3200       -18092.853             0.012            0.015
Chain 1:   3300       -18297.305             0.011            0.012
Chain 1:   3400       -17772.827             0.013            0.015
Chain 1:   3500       -18383.735             0.015            0.016
Chain 1:   3600       -17691.672             0.017            0.016
Chain 1:   3700       -18077.467             0.019            0.017
Chain 1:   3800       -17039.115             0.023            0.021
Chain 1:   3900       -17035.298             0.022            0.021
Chain 1:   4000       -17152.602             0.022            0.021
Chain 1:   4100       -17066.421             0.022            0.021
Chain 1:   4200       -16883.161             0.022            0.021
Chain 1:   4300       -17021.246             0.022            0.021
Chain 1:   4400       -16978.407             0.019            0.011
Chain 1:   4500       -16881.000             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49556.152             1.000            1.000
Chain 1:    200       -16344.436             1.516            2.032
Chain 1:    300       -21293.539             1.088            1.000
Chain 1:    400       -14781.877             0.926            1.000
Chain 1:    500       -15476.679             0.750            0.441
Chain 1:    600       -14025.760             0.642            0.441
Chain 1:    700       -13728.712             0.554            0.232
Chain 1:    800       -14283.931             0.489            0.232
Chain 1:    900       -16928.228             0.452            0.156
Chain 1:   1000       -23493.615             0.435            0.232
Chain 1:   1100       -10986.717             0.449            0.232
Chain 1:   1200       -11081.415             0.246            0.156
Chain 1:   1300       -20112.532             0.268            0.156
Chain 1:   1400       -16297.183             0.247            0.156
Chain 1:   1500       -18200.500             0.253            0.156
Chain 1:   1600       -10860.828             0.311            0.234
Chain 1:   1700       -22473.391             0.360            0.279
Chain 1:   1800       -16846.539             0.390            0.334
Chain 1:   1900       -10449.303             0.435            0.449
Chain 1:   2000       -21795.363             0.459            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100       -10894.155             0.446            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200       -14150.361             0.468            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300       -10260.144             0.461            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400       -10511.103             0.440            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500       -10561.578             0.430            0.517   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600       -10142.822             0.366            0.379
Chain 1:   2700       -16916.517             0.355            0.379
Chain 1:   2800       -11101.923             0.374            0.400
Chain 1:   2900       -14238.862             0.334            0.379
Chain 1:   3000        -9856.163             0.327            0.379
Chain 1:   3100       -10263.603             0.231            0.230
Chain 1:   3200       -12057.596             0.223            0.220
Chain 1:   3300        -9872.789             0.207            0.220
Chain 1:   3400        -9557.900             0.208            0.220
Chain 1:   3500       -10443.662             0.216            0.220
Chain 1:   3600        -9958.458             0.217            0.220
Chain 1:   3700       -10253.298             0.179            0.149
Chain 1:   3800       -14836.548             0.158            0.149
Chain 1:   3900       -10625.542             0.175            0.149
Chain 1:   4000       -11523.516             0.139            0.085
Chain 1:   4100       -11759.928             0.137            0.085
Chain 1:   4200       -12929.637             0.131            0.085
Chain 1:   4300        -9406.797             0.146            0.085
Chain 1:   4400        -9146.240             0.146            0.085
Chain 1:   4500        -9199.610             0.138            0.078
Chain 1:   4600        -9938.848             0.141            0.078
Chain 1:   4700       -10735.380             0.145            0.078
Chain 1:   4800        -9049.048             0.133            0.078
Chain 1:   4900        -9487.038             0.098            0.074
Chain 1:   5000       -11115.383             0.105            0.074
Chain 1:   5100       -11477.298             0.106            0.074
Chain 1:   5200       -17774.395             0.132            0.074
Chain 1:   5300       -13510.731             0.126            0.074
Chain 1:   5400        -9380.981             0.168            0.146
Chain 1:   5500        -8887.920             0.172            0.146
Chain 1:   5600        -8966.554             0.166            0.146
Chain 1:   5700       -13127.668             0.190            0.186
Chain 1:   5800        -9293.978             0.213            0.316
Chain 1:   5900       -13946.552             0.242            0.317
Chain 1:   6000        -9665.304             0.271            0.334
Chain 1:   6100        -8860.751             0.277            0.334
Chain 1:   6200        -9170.309             0.245            0.317
Chain 1:   6300       -12438.418             0.240            0.317
Chain 1:   6400       -13204.380             0.202            0.263
Chain 1:   6500        -9357.198             0.237            0.317
Chain 1:   6600       -10279.371             0.245            0.317
Chain 1:   6700        -8628.261             0.233            0.263
Chain 1:   6800       -10727.996             0.211            0.196
Chain 1:   6900       -15701.670             0.209            0.196
Chain 1:   7000       -10036.609             0.221            0.196
Chain 1:   7100       -11061.188             0.222            0.196
Chain 1:   7200       -10943.625             0.219            0.196
Chain 1:   7300       -11631.866             0.199            0.191
Chain 1:   7400        -8738.198             0.226            0.196
Chain 1:   7500       -12153.651             0.213            0.196
Chain 1:   7600        -9514.511             0.232            0.277
Chain 1:   7700        -9363.340             0.215            0.277
Chain 1:   7800       -13767.847             0.227            0.281
Chain 1:   7900        -8834.347             0.251            0.281
Chain 1:   8000       -12015.063             0.221            0.277
Chain 1:   8100        -9967.512             0.232            0.277
Chain 1:   8200        -8738.996             0.245            0.277
Chain 1:   8300        -8783.226             0.240            0.277
Chain 1:   8400        -9090.492             0.210            0.265
Chain 1:   8500        -8754.267             0.186            0.205
Chain 1:   8600       -12001.100             0.185            0.205
Chain 1:   8700       -10958.562             0.193            0.205
Chain 1:   8800        -8933.802             0.184            0.205
Chain 1:   8900       -11002.990             0.147            0.188
Chain 1:   9000       -13038.561             0.136            0.156
Chain 1:   9100        -9840.717             0.148            0.156
Chain 1:   9200        -9749.376             0.135            0.156
Chain 1:   9300        -8531.492             0.149            0.156
Chain 1:   9400       -10089.165             0.161            0.156
Chain 1:   9500        -9699.345             0.161            0.156
Chain 1:   9600        -8665.342             0.146            0.154
Chain 1:   9700        -8720.601             0.137            0.154
Chain 1:   9800        -8789.703             0.115            0.143
Chain 1:   9900        -9420.655             0.103            0.119
Chain 1:   10000        -8815.619             0.094            0.069
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62320.132             1.000            1.000
Chain 1:    200       -18620.366             1.673            2.347
Chain 1:    300        -9238.036             1.454            1.016
Chain 1:    400        -8891.401             1.100            1.016
Chain 1:    500        -8381.106             0.892            1.000
Chain 1:    600        -9149.908             0.758            1.000
Chain 1:    700        -8332.820             0.663            0.098
Chain 1:    800        -8324.088             0.581            0.098
Chain 1:    900        -7958.619             0.521            0.084
Chain 1:   1000        -7851.812             0.471            0.084
Chain 1:   1100        -7767.696             0.372            0.061
Chain 1:   1200        -7762.349             0.137            0.046
Chain 1:   1300        -7870.597             0.037            0.039
Chain 1:   1400        -7856.348             0.033            0.014
Chain 1:   1500        -7560.493             0.031            0.014
Chain 1:   1600        -7912.683             0.027            0.014
Chain 1:   1700        -7476.113             0.023            0.014
Chain 1:   1800        -7619.190             0.025            0.019
Chain 1:   1900        -7549.279             0.021            0.014
Chain 1:   2000        -7766.327             0.023            0.019
Chain 1:   2100        -7538.994             0.024            0.028
Chain 1:   2200        -7821.285             0.028            0.030
Chain 1:   2300        -7665.908             0.029            0.030
Chain 1:   2400        -7672.065             0.029            0.030
Chain 1:   2500        -7606.068             0.025            0.028
Chain 1:   2600        -7569.021             0.022            0.020
Chain 1:   2700        -7567.273             0.016            0.019
Chain 1:   2800        -7693.800             0.015            0.016
Chain 1:   2900        -7395.017             0.019            0.020
Chain 1:   3000        -7536.498             0.018            0.019
Chain 1:   3100        -7550.080             0.015            0.016
Chain 1:   3200        -7664.247             0.013            0.015
Chain 1:   3300        -7437.774             0.014            0.015
Chain 1:   3400        -7696.066             0.017            0.016
Chain 1:   3500        -7479.549             0.019            0.019
Chain 1:   3600        -7528.497             0.019            0.019
Chain 1:   3700        -7473.068             0.020            0.019
Chain 1:   3800        -7457.397             0.018            0.019
Chain 1:   3900        -7436.065             0.015            0.015
Chain 1:   4000        -7423.504             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002967 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86313.575             1.000            1.000
Chain 1:    200       -14258.503             3.027            5.053
Chain 1:    300       -10467.517             2.139            1.000
Chain 1:    400       -12498.935             1.645            1.000
Chain 1:    500        -8902.247             1.396            0.404
Chain 1:    600        -8955.222             1.165            0.404
Chain 1:    700        -8770.609             1.001            0.362
Chain 1:    800        -9105.845             0.881            0.362
Chain 1:    900        -9107.402             0.783            0.163
Chain 1:   1000        -9446.224             0.708            0.163
Chain 1:   1100        -9233.750             0.611            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8733.491             0.111            0.037
Chain 1:   1300        -9081.533             0.078            0.037
Chain 1:   1400        -9015.660             0.063            0.036
Chain 1:   1500        -8959.767             0.023            0.023
Chain 1:   1600        -9034.625             0.023            0.023
Chain 1:   1700        -9084.703             0.022            0.023
Chain 1:   1800        -8625.510             0.024            0.023
Chain 1:   1900        -8736.984             0.025            0.023
Chain 1:   2000        -8751.687             0.021            0.013
Chain 1:   2100        -8846.986             0.020            0.011
Chain 1:   2200        -8626.945             0.017            0.011
Chain 1:   2300        -8827.268             0.015            0.011
Chain 1:   2400        -8634.091             0.017            0.013
Chain 1:   2500        -8708.078             0.017            0.013
Chain 1:   2600        -8618.463             0.017            0.013
Chain 1:   2700        -8651.614             0.017            0.013
Chain 1:   2800        -8602.381             0.012            0.011
Chain 1:   2900        -8717.009             0.012            0.011
Chain 1:   3000        -8633.104             0.013            0.011
Chain 1:   3100        -8594.710             0.013            0.010
Chain 1:   3200        -8567.030             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003582 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8374443.937             1.000            1.000
Chain 1:    200     -1579334.845             2.651            4.303
Chain 1:    300      -891783.283             2.024            1.000
Chain 1:    400      -459054.755             1.754            1.000
Chain 1:    500      -359886.026             1.458            0.943
Chain 1:    600      -234796.275             1.304            0.943
Chain 1:    700      -120544.869             1.253            0.943
Chain 1:    800       -87642.035             1.143            0.943
Chain 1:    900       -67891.232             1.049            0.771
Chain 1:   1000       -52619.297             0.973            0.771
Chain 1:   1100       -40022.018             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39198.392             0.476            0.375
Chain 1:   1300       -27059.507             0.444            0.375
Chain 1:   1400       -26774.390             0.351            0.315
Chain 1:   1500       -23336.076             0.338            0.315
Chain 1:   1600       -22546.645             0.288            0.291
Chain 1:   1700       -21407.598             0.199            0.290
Chain 1:   1800       -21349.458             0.161            0.147
Chain 1:   1900       -21676.412             0.134            0.053
Chain 1:   2000       -20179.597             0.112            0.053
Chain 1:   2100       -20418.386             0.082            0.035
Chain 1:   2200       -20646.505             0.081            0.035
Chain 1:   2300       -20262.042             0.038            0.019
Chain 1:   2400       -20033.717             0.038            0.019
Chain 1:   2500       -19836.213             0.024            0.015
Chain 1:   2600       -19465.100             0.023            0.015
Chain 1:   2700       -19421.706             0.018            0.012
Chain 1:   2800       -19138.375             0.019            0.015
Chain 1:   2900       -19420.154             0.019            0.015
Chain 1:   3000       -19406.188             0.011            0.012
Chain 1:   3100       -19491.315             0.011            0.011
Chain 1:   3200       -19181.344             0.011            0.015
Chain 1:   3300       -19386.609             0.010            0.011
Chain 1:   3400       -18860.532             0.012            0.015
Chain 1:   3500       -19474.069             0.014            0.015
Chain 1:   3600       -18778.654             0.016            0.015
Chain 1:   3700       -19167.064             0.018            0.016
Chain 1:   3800       -18123.625             0.022            0.020
Chain 1:   3900       -18119.769             0.021            0.020
Chain 1:   4000       -18236.992             0.021            0.020
Chain 1:   4100       -18150.615             0.021            0.020
Chain 1:   4200       -17966.197             0.021            0.020
Chain 1:   4300       -18105.011             0.020            0.020
Chain 1:   4400       -18061.268             0.018            0.010
Chain 1:   4500       -17963.766             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001342 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12603.006             1.000            1.000
Chain 1:    200        -9183.158             0.686            1.000
Chain 1:    300        -7961.127             0.509            0.372
Chain 1:    400        -8080.881             0.385            0.372
Chain 1:    500        -8053.771             0.309            0.153
Chain 1:    600        -7933.969             0.260            0.153
Chain 1:    700        -7821.817             0.225            0.015
Chain 1:    800        -7822.077             0.197            0.015
Chain 1:    900        -7730.820             0.176            0.015
Chain 1:   1000        -7945.068             0.161            0.015
Chain 1:   1100        -7982.618             0.062            0.015
Chain 1:   1200        -7869.497             0.026            0.014
Chain 1:   1300        -7797.165             0.011            0.014
Chain 1:   1400        -7813.829             0.010            0.012
Chain 1:   1500        -7903.759             0.011            0.012
Chain 1:   1600        -7836.803             0.010            0.011
Chain 1:   1700        -7794.367             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59921.249             1.000            1.000
Chain 1:    200       -18206.662             1.646            2.291
Chain 1:    300        -8790.153             1.454            1.071
Chain 1:    400        -8739.692             1.092            1.071
Chain 1:    500        -8450.987             0.880            1.000
Chain 1:    600        -8697.417             0.738            1.000
Chain 1:    700        -8015.701             0.645            0.085
Chain 1:    800        -8574.614             0.573            0.085
Chain 1:    900        -7951.753             0.518            0.078
Chain 1:   1000        -7599.296             0.471            0.078
Chain 1:   1100        -7845.037             0.374            0.065
Chain 1:   1200        -7721.305             0.146            0.046
Chain 1:   1300        -7808.194             0.040            0.034
Chain 1:   1400        -7677.161             0.041            0.034
Chain 1:   1500        -7553.376             0.040            0.031
Chain 1:   1600        -7777.281             0.040            0.031
Chain 1:   1700        -7429.740             0.036            0.031
Chain 1:   1800        -7584.990             0.031            0.029
Chain 1:   1900        -7532.321             0.024            0.020
Chain 1:   2000        -7622.766             0.021            0.017
Chain 1:   2100        -7565.668             0.018            0.016
Chain 1:   2200        -7729.887             0.019            0.017
Chain 1:   2300        -7553.591             0.020            0.020
Chain 1:   2400        -7635.018             0.019            0.020
Chain 1:   2500        -7541.382             0.019            0.020
Chain 1:   2600        -7503.619             0.017            0.012
Chain 1:   2700        -7464.430             0.012            0.012
Chain 1:   2800        -7477.370             0.011            0.011
Chain 1:   2900        -7359.124             0.012            0.012
Chain 1:   3000        -7499.290             0.012            0.012
Chain 1:   3100        -7495.545             0.011            0.012
Chain 1:   3200        -7699.049             0.012            0.012
Chain 1:   3300        -7426.643             0.013            0.012
Chain 1:   3400        -7639.962             0.015            0.016
Chain 1:   3500        -7409.432             0.017            0.019
Chain 1:   3600        -7474.695             0.017            0.019
Chain 1:   3700        -7423.365             0.017            0.019
Chain 1:   3800        -7425.934             0.017            0.019
Chain 1:   3900        -7391.820             0.016            0.019
Chain 1:   4000        -7386.860             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003773 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86512.094             1.000            1.000
Chain 1:    200       -13634.189             3.173            5.345
Chain 1:    300        -9907.382             2.240            1.000
Chain 1:    400       -11361.683             1.712            1.000
Chain 1:    500        -8703.713             1.431            0.376
Chain 1:    600        -8287.052             1.201            0.376
Chain 1:    700        -8329.444             1.030            0.305
Chain 1:    800        -8585.620             0.905            0.305
Chain 1:    900        -8658.585             0.805            0.128
Chain 1:   1000        -8542.066             0.726            0.128
Chain 1:   1100        -8679.737             0.628            0.050   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8274.782             0.098            0.049
Chain 1:   1300        -8560.491             0.064            0.033
Chain 1:   1400        -8539.062             0.051            0.030
Chain 1:   1500        -8417.491             0.022            0.016
Chain 1:   1600        -8532.703             0.019            0.014
Chain 1:   1700        -8592.491             0.019            0.014
Chain 1:   1800        -8154.648             0.021            0.014
Chain 1:   1900        -8258.718             0.022            0.014
Chain 1:   2000        -8236.878             0.020            0.014
Chain 1:   2100        -8207.138             0.019            0.014
Chain 1:   2200        -8175.921             0.015            0.013
Chain 1:   2300        -8313.343             0.013            0.013
Chain 1:   2400        -8157.040             0.015            0.014
Chain 1:   2500        -8228.110             0.014            0.013
Chain 1:   2600        -8141.383             0.014            0.011
Chain 1:   2700        -8178.196             0.014            0.011
Chain 1:   2800        -8136.421             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8425579.577             1.000            1.000
Chain 1:    200     -1586446.337             2.655            4.311
Chain 1:    300      -891697.516             2.030            1.000
Chain 1:    400      -458191.880             1.759            1.000
Chain 1:    500      -358288.231             1.463            0.946
Chain 1:    600      -233158.520             1.309            0.946
Chain 1:    700      -119374.872             1.258            0.946
Chain 1:    800       -86595.782             1.148            0.946
Chain 1:    900       -66934.598             1.053            0.779
Chain 1:   1000       -51736.881             0.977            0.779
Chain 1:   1100       -39220.642             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38399.934             0.480            0.379
Chain 1:   1300       -26354.929             0.448            0.379
Chain 1:   1400       -26075.776             0.354            0.319
Chain 1:   1500       -22662.944             0.341            0.319
Chain 1:   1600       -21880.413             0.291            0.294
Chain 1:   1700       -20753.579             0.201            0.294
Chain 1:   1800       -20697.972             0.164            0.151
Chain 1:   1900       -21024.507             0.136            0.054
Chain 1:   2000       -19534.840             0.114            0.054
Chain 1:   2100       -19773.122             0.084            0.036
Chain 1:   2200       -19999.997             0.083            0.036
Chain 1:   2300       -19616.785             0.039            0.020
Chain 1:   2400       -19388.739             0.039            0.020
Chain 1:   2500       -19190.798             0.025            0.016
Chain 1:   2600       -18820.426             0.023            0.016
Chain 1:   2700       -18777.298             0.018            0.012
Chain 1:   2800       -18493.975             0.019            0.015
Chain 1:   2900       -18775.433             0.019            0.015
Chain 1:   3000       -18761.535             0.012            0.012
Chain 1:   3100       -18846.598             0.011            0.012
Chain 1:   3200       -18536.968             0.012            0.015
Chain 1:   3300       -18741.963             0.011            0.012
Chain 1:   3400       -18216.328             0.012            0.015
Chain 1:   3500       -18828.993             0.015            0.015
Chain 1:   3600       -18134.666             0.017            0.015
Chain 1:   3700       -18522.212             0.018            0.017
Chain 1:   3800       -17480.322             0.023            0.021
Chain 1:   3900       -17476.441             0.021            0.021
Chain 1:   4000       -17593.744             0.022            0.021
Chain 1:   4100       -17507.404             0.022            0.021
Chain 1:   4200       -17323.328             0.021            0.021
Chain 1:   4300       -17461.940             0.021            0.021
Chain 1:   4400       -17418.474             0.018            0.011
Chain 1:   4500       -17320.974             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001253 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13601.860             1.000            1.000
Chain 1:    200       -10003.497             0.680            1.000
Chain 1:    300        -8801.453             0.499            0.360
Chain 1:    400        -8517.813             0.382            0.360
Chain 1:    500        -8212.525             0.313            0.137
Chain 1:    600        -8264.270             0.262            0.137
Chain 1:    700        -8146.578             0.227            0.037
Chain 1:    800        -8128.238             0.199            0.037
Chain 1:    900        -8155.171             0.177            0.033
Chain 1:   1000        -8226.512             0.160            0.033
Chain 1:   1100        -8480.745             0.063            0.030
Chain 1:   1200        -8199.528             0.031            0.030
Chain 1:   1300        -8116.371             0.018            0.014
Chain 1:   1400        -8139.102             0.015            0.010
Chain 1:   1500        -8250.789             0.013            0.010
Chain 1:   1600        -8137.251             0.013            0.014
Chain 1:   1700        -8115.925             0.012            0.010
Chain 1:   1800        -8088.122             0.012            0.010
Chain 1:   1900        -8115.981             0.012            0.010
Chain 1:   2000        -8048.784             0.012            0.010
Chain 1:   2100        -8056.673             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63974.697             1.000            1.000
Chain 1:    200       -18898.900             1.693            2.385
Chain 1:    300        -9119.474             1.486            1.072
Chain 1:    400        -8101.337             1.146            1.072
Chain 1:    500        -8367.258             0.923            1.000
Chain 1:    600        -9001.580             0.781            1.000
Chain 1:    700        -7859.167             0.690            0.145
Chain 1:    800        -8780.952             0.617            0.145
Chain 1:    900        -8312.794             0.555            0.126
Chain 1:   1000        -7834.964             0.505            0.126
Chain 1:   1100        -7757.673             0.406            0.105
Chain 1:   1200        -7856.117             0.169            0.070
Chain 1:   1300        -7948.296             0.063            0.061
Chain 1:   1400        -8050.247             0.052            0.056
Chain 1:   1500        -7601.095             0.054            0.059
Chain 1:   1600        -7872.064             0.051            0.056
Chain 1:   1700        -7692.587             0.039            0.034
Chain 1:   1800        -7812.673             0.030            0.023
Chain 1:   1900        -7708.656             0.025            0.015
Chain 1:   2000        -7798.811             0.020            0.013
Chain 1:   2100        -7672.960             0.021            0.015
Chain 1:   2200        -7850.044             0.022            0.016
Chain 1:   2300        -7665.391             0.023            0.023
Chain 1:   2400        -7718.472             0.023            0.023
Chain 1:   2500        -7753.503             0.017            0.016
Chain 1:   2600        -7631.662             0.015            0.016
Chain 1:   2700        -7697.619             0.014            0.015
Chain 1:   2800        -7625.563             0.013            0.013
Chain 1:   2900        -7524.747             0.013            0.013
Chain 1:   3000        -7656.706             0.014            0.016
Chain 1:   3100        -7627.474             0.013            0.013
Chain 1:   3200        -7838.162             0.013            0.013
Chain 1:   3300        -7554.506             0.014            0.013
Chain 1:   3400        -7789.747             0.017            0.016
Chain 1:   3500        -7531.488             0.020            0.017
Chain 1:   3600        -7592.162             0.019            0.017
Chain 1:   3700        -7547.518             0.019            0.017
Chain 1:   3800        -7558.433             0.018            0.017
Chain 1:   3900        -7507.864             0.017            0.017
Chain 1:   4000        -7492.308             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002939 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85997.990             1.000            1.000
Chain 1:    200       -14086.215             3.053            5.105
Chain 1:    300       -10317.434             2.157            1.000
Chain 1:    400       -12154.388             1.655            1.000
Chain 1:    500        -8920.743             1.397            0.365
Chain 1:    600        -9890.423             1.180            0.365
Chain 1:    700        -9340.996             1.020            0.362
Chain 1:    800        -9025.628             0.897            0.362
Chain 1:    900        -8917.988             0.799            0.151
Chain 1:   1000        -9203.820             0.722            0.151
Chain 1:   1100        -8910.049             0.625            0.098   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8599.716             0.118            0.059
Chain 1:   1300        -8964.031             0.086            0.041
Chain 1:   1400        -8886.153             0.072            0.036
Chain 1:   1500        -8788.960             0.036            0.035
Chain 1:   1600        -8846.976             0.027            0.033
Chain 1:   1700        -8936.239             0.022            0.031
Chain 1:   1800        -8478.315             0.024            0.031
Chain 1:   1900        -8599.561             0.025            0.031
Chain 1:   2000        -8603.551             0.021            0.014
Chain 1:   2100        -8539.186             0.019            0.011
Chain 1:   2200        -8516.596             0.016            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002955 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411443.720             1.000            1.000
Chain 1:    200     -1585661.894             2.652            4.305
Chain 1:    300      -891954.689             2.027            1.000
Chain 1:    400      -458486.204             1.757            1.000
Chain 1:    500      -358447.623             1.461            0.945
Chain 1:    600      -233527.285             1.307            0.945
Chain 1:    700      -119807.428             1.256            0.945
Chain 1:    800       -87012.559             1.146            0.945
Chain 1:    900       -67377.726             1.051            0.778
Chain 1:   1000       -52199.655             0.975            0.778
Chain 1:   1100       -39688.739             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38876.317             0.478            0.377
Chain 1:   1300       -26831.779             0.445            0.377
Chain 1:   1400       -26556.165             0.352            0.315
Chain 1:   1500       -23141.219             0.339            0.315
Chain 1:   1600       -22358.621             0.289            0.291
Chain 1:   1700       -21231.268             0.199            0.291
Chain 1:   1800       -21175.929             0.162            0.148
Chain 1:   1900       -21502.761             0.134            0.053
Chain 1:   2000       -20011.998             0.112            0.053
Chain 1:   2100       -20250.623             0.082            0.035
Chain 1:   2200       -20477.518             0.081            0.035
Chain 1:   2300       -20094.190             0.038            0.019
Chain 1:   2400       -19865.993             0.038            0.019
Chain 1:   2500       -19667.942             0.024            0.015
Chain 1:   2600       -19297.429             0.023            0.015
Chain 1:   2700       -19254.275             0.018            0.012
Chain 1:   2800       -18970.666             0.019            0.015
Chain 1:   2900       -19252.325             0.019            0.015
Chain 1:   3000       -19238.562             0.012            0.012
Chain 1:   3100       -19323.581             0.011            0.011
Chain 1:   3200       -19013.806             0.011            0.015
Chain 1:   3300       -19218.933             0.010            0.011
Chain 1:   3400       -18692.915             0.012            0.015
Chain 1:   3500       -19306.112             0.014            0.015
Chain 1:   3600       -18611.163             0.016            0.015
Chain 1:   3700       -18999.099             0.018            0.016
Chain 1:   3800       -17956.199             0.022            0.020
Chain 1:   3900       -17952.269             0.021            0.020
Chain 1:   4000       -18069.614             0.021            0.020
Chain 1:   4100       -17983.147             0.021            0.020
Chain 1:   4200       -17798.904             0.021            0.020
Chain 1:   4300       -17937.672             0.021            0.020
Chain 1:   4400       -17894.023             0.018            0.010
Chain 1:   4500       -17796.483             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12546.257             1.000            1.000
Chain 1:    200        -9230.875             0.680            1.000
Chain 1:    300        -8055.331             0.502            0.359
Chain 1:    400        -8236.476             0.382            0.359
Chain 1:    500        -8125.327             0.308            0.146
Chain 1:    600        -7953.480             0.260            0.146
Chain 1:    700        -7903.499             0.224            0.022
Chain 1:    800        -7855.408             0.197            0.022
Chain 1:    900        -7725.703             0.177            0.022
Chain 1:   1000        -7911.020             0.162            0.022
Chain 1:   1100        -7943.622             0.062            0.022
Chain 1:   1200        -7853.195             0.027            0.017
Chain 1:   1300        -7775.297             0.014            0.014
Chain 1:   1400        -7809.558             0.012            0.012
Chain 1:   1500        -7909.276             0.012            0.012
Chain 1:   1600        -7828.572             0.011            0.010
Chain 1:   1700        -7791.665             0.010            0.010
Chain 1:   1800        -7763.043             0.010            0.010
Chain 1:   1900        -7790.093             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001525 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62219.936             1.000            1.000
Chain 1:    200       -18210.901             1.708            2.417
Chain 1:    300        -8996.686             1.480            1.024
Chain 1:    400        -9736.113             1.129            1.024
Chain 1:    500        -8015.872             0.946            1.000
Chain 1:    600        -8255.889             0.793            1.000
Chain 1:    700        -8601.732             0.686            0.215
Chain 1:    800        -8002.026             0.609            0.215
Chain 1:    900        -8109.708             0.543            0.076
Chain 1:   1000        -7819.009             0.493            0.076
Chain 1:   1100        -7822.470             0.393            0.075
Chain 1:   1200        -7649.682             0.153            0.040
Chain 1:   1300        -7676.095             0.051            0.037
Chain 1:   1400        -7869.506             0.046            0.029
Chain 1:   1500        -7579.704             0.028            0.029
Chain 1:   1600        -7746.211             0.028            0.025
Chain 1:   1700        -7536.016             0.026            0.025
Chain 1:   1800        -7545.703             0.019            0.023
Chain 1:   1900        -7578.182             0.018            0.023
Chain 1:   2000        -7713.288             0.016            0.021
Chain 1:   2100        -7590.851             0.018            0.021
Chain 1:   2200        -7696.391             0.017            0.018
Chain 1:   2300        -7577.479             0.018            0.018
Chain 1:   2400        -7619.541             0.016            0.016
Chain 1:   2500        -7553.178             0.013            0.016
Chain 1:   2600        -7511.145             0.012            0.014
Chain 1:   2700        -7504.302             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86419.814             1.000            1.000
Chain 1:    200       -13760.333             3.140            5.280
Chain 1:    300       -10002.501             2.219            1.000
Chain 1:    400       -11807.670             1.702            1.000
Chain 1:    500        -8444.394             1.441            0.398
Chain 1:    600        -8840.466             1.209            0.398
Chain 1:    700        -8524.252             1.041            0.376
Chain 1:    800        -9041.764             0.918            0.376
Chain 1:    900        -8766.762             0.820            0.153
Chain 1:   1000        -8956.275             0.740            0.153
Chain 1:   1100        -8755.628             0.642            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8296.284             0.120            0.055
Chain 1:   1300        -8614.839             0.086            0.045
Chain 1:   1400        -8559.412             0.071            0.037
Chain 1:   1500        -8471.444             0.032            0.037
Chain 1:   1600        -8571.224             0.029            0.031
Chain 1:   1700        -8627.887             0.026            0.023
Chain 1:   1800        -8173.490             0.026            0.023
Chain 1:   1900        -8282.502             0.024            0.021
Chain 1:   2000        -8284.762             0.022            0.013
Chain 1:   2100        -8232.684             0.020            0.012
Chain 1:   2200        -8247.959             0.015            0.010
Chain 1:   2300        -8375.765             0.013            0.010
Chain 1:   2400        -8178.188             0.015            0.012
Chain 1:   2500        -8251.114             0.014            0.012
Chain 1:   2600        -8160.309             0.014            0.011
Chain 1:   2700        -8199.545             0.014            0.011
Chain 1:   2800        -8150.831             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8429990.667             1.000            1.000
Chain 1:    200     -1585170.358             2.659            4.318
Chain 1:    300      -890643.625             2.033            1.000
Chain 1:    400      -457944.973             1.761            1.000
Chain 1:    500      -358167.946             1.464            0.945
Chain 1:    600      -233173.450             1.310            0.945
Chain 1:    700      -119435.086             1.259            0.945
Chain 1:    800       -86728.122             1.148            0.945
Chain 1:    900       -67073.445             1.053            0.780
Chain 1:   1000       -51889.830             0.977            0.780
Chain 1:   1100       -39381.357             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38565.024             0.479            0.377
Chain 1:   1300       -26509.987             0.447            0.377
Chain 1:   1400       -26233.407             0.353            0.318
Chain 1:   1500       -22818.362             0.340            0.318
Chain 1:   1600       -22036.202             0.290            0.293
Chain 1:   1700       -20906.820             0.201            0.293
Chain 1:   1800       -20851.157             0.163            0.150
Chain 1:   1900       -21178.039             0.135            0.054
Chain 1:   2000       -19686.892             0.114            0.054
Chain 1:   2100       -19925.143             0.083            0.035
Chain 1:   2200       -20152.525             0.082            0.035
Chain 1:   2300       -19768.809             0.039            0.019
Chain 1:   2400       -19540.568             0.039            0.019
Chain 1:   2500       -19342.919             0.025            0.015
Chain 1:   2600       -18971.937             0.023            0.015
Chain 1:   2700       -18928.701             0.018            0.012
Chain 1:   2800       -18645.304             0.019            0.015
Chain 1:   2900       -18926.970             0.019            0.015
Chain 1:   3000       -18913.003             0.012            0.012
Chain 1:   3100       -18998.101             0.011            0.012
Chain 1:   3200       -18688.225             0.012            0.015
Chain 1:   3300       -18893.457             0.011            0.012
Chain 1:   3400       -18367.453             0.012            0.015
Chain 1:   3500       -18980.733             0.015            0.015
Chain 1:   3600       -18285.617             0.016            0.015
Chain 1:   3700       -18673.704             0.018            0.017
Chain 1:   3800       -17630.686             0.023            0.021
Chain 1:   3900       -17626.815             0.021            0.021
Chain 1:   4000       -17744.084             0.022            0.021
Chain 1:   4100       -17657.671             0.022            0.021
Chain 1:   4200       -17473.380             0.021            0.021
Chain 1:   4300       -17612.116             0.021            0.021
Chain 1:   4400       -17568.404             0.018            0.011
Chain 1:   4500       -17470.908             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11825.645             1.000            1.000
Chain 1:    200        -8818.314             0.671            1.000
Chain 1:    300        -7797.939             0.491            0.341
Chain 1:    400        -7880.557             0.371            0.341
Chain 1:    500        -7765.437             0.299            0.131
Chain 1:    600        -7622.007             0.253            0.131
Chain 1:    700        -7566.944             0.218            0.019
Chain 1:    800        -7572.871             0.191            0.019
Chain 1:    900        -7491.398             0.171            0.015
Chain 1:   1000        -7619.756             0.155            0.017
Chain 1:   1100        -7662.219             0.056            0.015
Chain 1:   1200        -7586.163             0.023            0.011
Chain 1:   1300        -7544.946             0.010            0.010
Chain 1:   1400        -7560.374             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001449 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61068.593             1.000            1.000
Chain 1:    200       -17221.874             1.773            2.546
Chain 1:    300        -8518.944             1.523            1.022
Chain 1:    400        -8040.380             1.157            1.022
Chain 1:    500        -8136.224             0.928            1.000
Chain 1:    600        -7854.203             0.779            1.000
Chain 1:    700        -7674.277             0.671            0.060
Chain 1:    800        -8559.528             0.600            0.103
Chain 1:    900        -7640.443             0.547            0.103
Chain 1:   1000        -7578.909             0.493            0.103
Chain 1:   1100        -7636.783             0.394            0.060
Chain 1:   1200        -7479.671             0.141            0.036
Chain 1:   1300        -7512.387             0.040            0.023
Chain 1:   1400        -7749.115             0.037            0.023
Chain 1:   1500        -7495.986             0.039            0.031
Chain 1:   1600        -7393.050             0.037            0.023
Chain 1:   1700        -7372.654             0.035            0.021
Chain 1:   1800        -7415.587             0.025            0.014
Chain 1:   1900        -7451.750             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85938.743             1.000            1.000
Chain 1:    200       -12897.146             3.332            5.663
Chain 1:    300        -9394.222             2.345            1.000
Chain 1:    400       -10148.377             1.778            1.000
Chain 1:    500        -8238.675             1.468            0.373
Chain 1:    600        -7999.734             1.229            0.373
Chain 1:    700        -8251.895             1.058            0.232
Chain 1:    800        -8252.174             0.925            0.232
Chain 1:    900        -8286.794             0.823            0.074
Chain 1:   1000        -8146.032             0.742            0.074
Chain 1:   1100        -8332.126             0.645            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8073.798             0.082            0.031
Chain 1:   1300        -8153.396             0.045            0.030
Chain 1:   1400        -8166.087             0.038            0.022
Chain 1:   1500        -8063.761             0.016            0.017
Chain 1:   1600        -8149.994             0.014            0.013
Chain 1:   1700        -8250.842             0.012            0.012
Chain 1:   1800        -7872.153             0.017            0.013
Chain 1:   1900        -7969.942             0.018            0.013
Chain 1:   2000        -7940.545             0.017            0.012
Chain 1:   2100        -8086.302             0.016            0.012
Chain 1:   2200        -7863.626             0.016            0.012
Chain 1:   2300        -7993.259             0.016            0.013
Chain 1:   2400        -7892.627             0.017            0.013
Chain 1:   2500        -7947.280             0.017            0.013
Chain 1:   2600        -7959.927             0.016            0.013
Chain 1:   2700        -7880.996             0.016            0.013
Chain 1:   2800        -7866.632             0.011            0.012
Chain 1:   2900        -7855.226             0.010            0.010
Chain 1:   3000        -7871.376             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003145 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395854.400             1.000            1.000
Chain 1:    200     -1583596.272             2.651            4.302
Chain 1:    300      -890657.718             2.027            1.000
Chain 1:    400      -457249.125             1.757            1.000
Chain 1:    500      -357597.596             1.461            0.948
Chain 1:    600      -232523.368             1.307            0.948
Chain 1:    700      -118669.841             1.258            0.948
Chain 1:    800       -85840.151             1.148            0.948
Chain 1:    900       -66162.484             1.054            0.778
Chain 1:   1000       -50938.204             0.978            0.778
Chain 1:   1100       -38402.792             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37569.539             0.483            0.382
Chain 1:   1300       -25531.620             0.452            0.382
Chain 1:   1400       -25246.604             0.359            0.326
Chain 1:   1500       -21836.193             0.346            0.326
Chain 1:   1600       -21051.935             0.296            0.299
Chain 1:   1700       -19927.542             0.206            0.297
Chain 1:   1800       -19871.477             0.168            0.156
Chain 1:   1900       -20196.774             0.140            0.056
Chain 1:   2000       -18710.391             0.118            0.056
Chain 1:   2100       -18948.557             0.087            0.037
Chain 1:   2200       -19174.321             0.086            0.037
Chain 1:   2300       -18792.361             0.040            0.020
Chain 1:   2400       -18564.788             0.041            0.020
Chain 1:   2500       -18366.758             0.026            0.016
Chain 1:   2600       -17997.878             0.024            0.016
Chain 1:   2700       -17955.086             0.019            0.013
Chain 1:   2800       -17672.335             0.020            0.016
Chain 1:   2900       -17953.180             0.020            0.016
Chain 1:   3000       -17939.436             0.012            0.013
Chain 1:   3100       -18024.288             0.012            0.012
Chain 1:   3200       -17715.537             0.012            0.016
Chain 1:   3300       -17919.803             0.011            0.012
Chain 1:   3400       -17395.752             0.013            0.016
Chain 1:   3500       -18006.064             0.015            0.016
Chain 1:   3600       -17314.803             0.017            0.016
Chain 1:   3700       -17700.099             0.019            0.017
Chain 1:   3800       -16662.952             0.024            0.022
Chain 1:   3900       -16659.174             0.022            0.022
Chain 1:   4000       -16776.478             0.023            0.022
Chain 1:   4100       -16690.404             0.023            0.022
Chain 1:   4200       -16507.325             0.022            0.022
Chain 1:   4300       -16645.243             0.022            0.022
Chain 1:   4400       -16602.644             0.019            0.011
Chain 1:   4500       -16505.279             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00129 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12583.289             1.000            1.000
Chain 1:    200        -9505.027             0.662            1.000
Chain 1:    300        -8256.973             0.492            0.324
Chain 1:    400        -8361.164             0.372            0.324
Chain 1:    500        -8356.773             0.298            0.151
Chain 1:    600        -8151.492             0.252            0.151
Chain 1:    700        -8102.520             0.217            0.025
Chain 1:    800        -8079.625             0.190            0.025
Chain 1:    900        -8126.058             0.170            0.012
Chain 1:   1000        -8136.702             0.153            0.012
Chain 1:   1100        -8193.654             0.054            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58233.882             1.000            1.000
Chain 1:    200       -17840.418             1.632            2.264
Chain 1:    300        -8782.242             1.432            1.031
Chain 1:    400        -8197.513             1.092            1.031
Chain 1:    500        -8527.733             0.881            1.000
Chain 1:    600        -8928.472             0.742            1.000
Chain 1:    700        -8458.289             0.644            0.071
Chain 1:    800        -8348.696             0.565            0.071
Chain 1:    900        -8000.568             0.507            0.056
Chain 1:   1000        -7815.966             0.459            0.056
Chain 1:   1100        -7803.830             0.359            0.045
Chain 1:   1200        -7862.873             0.133            0.044
Chain 1:   1300        -7715.650             0.032            0.039
Chain 1:   1400        -7903.057             0.027            0.024
Chain 1:   1500        -7637.627             0.027            0.024
Chain 1:   1600        -7585.831             0.023            0.024
Chain 1:   1700        -7612.699             0.018            0.019
Chain 1:   1800        -7649.241             0.017            0.019
Chain 1:   1900        -7645.084             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004012 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86928.349             1.000            1.000
Chain 1:    200       -13688.051             3.175            5.351
Chain 1:    300       -10058.370             2.237            1.000
Chain 1:    400       -10835.865             1.696            1.000
Chain 1:    500        -8995.453             1.398            0.361
Chain 1:    600        -8525.228             1.174            0.361
Chain 1:    700        -8887.427             1.012            0.205
Chain 1:    800        -9066.599             0.888            0.205
Chain 1:    900        -8916.056             0.791            0.072
Chain 1:   1000        -8696.646             0.715            0.072
Chain 1:   1100        -8848.916             0.616            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8717.233             0.083            0.041
Chain 1:   1300        -8749.583             0.047            0.025
Chain 1:   1400        -8757.110             0.040            0.020
Chain 1:   1500        -8619.975             0.021            0.017
Chain 1:   1600        -8728.692             0.017            0.017
Chain 1:   1700        -8812.867             0.014            0.016
Chain 1:   1800        -8399.593             0.017            0.016
Chain 1:   1900        -8495.879             0.016            0.015
Chain 1:   2000        -8469.168             0.014            0.012
Chain 1:   2100        -8591.807             0.014            0.012
Chain 1:   2200        -8411.972             0.014            0.012
Chain 1:   2300        -8490.717             0.015            0.012
Chain 1:   2400        -8560.447             0.015            0.012
Chain 1:   2500        -8505.908             0.015            0.011
Chain 1:   2600        -8505.378             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003473 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419740.658             1.000            1.000
Chain 1:    200     -1587386.742             2.652            4.304
Chain 1:    300      -890786.955             2.029            1.000
Chain 1:    400      -458127.419             1.758            1.000
Chain 1:    500      -358104.575             1.462            0.944
Chain 1:    600      -232903.896             1.308            0.944
Chain 1:    700      -119226.463             1.257            0.944
Chain 1:    800       -86478.300             1.147            0.944
Chain 1:    900       -66849.384             1.053            0.782
Chain 1:   1000       -51676.016             0.977            0.782
Chain 1:   1100       -39186.895             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38364.715             0.480            0.379
Chain 1:   1300       -26357.033             0.448            0.379
Chain 1:   1400       -26078.758             0.354            0.319
Chain 1:   1500       -22675.772             0.341            0.319
Chain 1:   1600       -21895.065             0.291            0.294
Chain 1:   1700       -20773.133             0.201            0.294
Chain 1:   1800       -20718.178             0.164            0.150
Chain 1:   1900       -21044.346             0.136            0.054
Chain 1:   2000       -19557.783             0.114            0.054
Chain 1:   2100       -19796.026             0.083            0.036
Chain 1:   2200       -20022.164             0.082            0.036
Chain 1:   2300       -19639.601             0.039            0.019
Chain 1:   2400       -19411.742             0.039            0.019
Chain 1:   2500       -19213.626             0.025            0.015
Chain 1:   2600       -18844.026             0.023            0.015
Chain 1:   2700       -18801.013             0.018            0.012
Chain 1:   2800       -18517.883             0.019            0.015
Chain 1:   2900       -18799.008             0.019            0.015
Chain 1:   3000       -18785.241             0.012            0.012
Chain 1:   3100       -18870.251             0.011            0.012
Chain 1:   3200       -18560.993             0.012            0.015
Chain 1:   3300       -18765.633             0.011            0.012
Chain 1:   3400       -18240.652             0.012            0.015
Chain 1:   3500       -18852.379             0.015            0.015
Chain 1:   3600       -18159.170             0.016            0.015
Chain 1:   3700       -18545.892             0.018            0.017
Chain 1:   3800       -17505.784             0.023            0.021
Chain 1:   3900       -17501.896             0.021            0.021
Chain 1:   4000       -17619.224             0.022            0.021
Chain 1:   4100       -17533.041             0.022            0.021
Chain 1:   4200       -17349.269             0.021            0.021
Chain 1:   4300       -17487.684             0.021            0.021
Chain 1:   4400       -17444.549             0.018            0.011
Chain 1:   4500       -17347.051             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001283 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13004.517             1.000            1.000
Chain 1:    200        -9796.179             0.664            1.000
Chain 1:    300        -8409.578             0.497            0.328
Chain 1:    400        -8626.027             0.379            0.328
Chain 1:    500        -8149.079             0.315            0.165
Chain 1:    600        -8324.951             0.266            0.165
Chain 1:    700        -8419.222             0.230            0.059
Chain 1:    800        -8277.555             0.203            0.059
Chain 1:    900        -8280.351             0.181            0.025
Chain 1:   1000        -8288.752             0.163            0.025
Chain 1:   1100        -8387.481             0.064            0.021
Chain 1:   1200        -8232.975             0.033            0.019
Chain 1:   1300        -8202.932             0.017            0.017
Chain 1:   1400        -8188.873             0.015            0.012
Chain 1:   1500        -8283.937             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59301.004             1.000            1.000
Chain 1:    200       -18579.461             1.596            2.192
Chain 1:    300        -9108.040             1.411            1.040
Chain 1:    400        -8236.200             1.084            1.040
Chain 1:    500        -9421.076             0.893            1.000
Chain 1:    600        -8527.401             0.761            1.000
Chain 1:    700        -8636.897             0.654            0.126
Chain 1:    800        -8312.807             0.577            0.126
Chain 1:    900        -8109.428             0.516            0.106
Chain 1:   1000        -8060.504             0.465            0.106
Chain 1:   1100        -8031.903             0.365            0.105
Chain 1:   1200        -7781.219             0.149            0.039
Chain 1:   1300        -7925.959             0.047            0.032
Chain 1:   1400        -7883.583             0.037            0.025
Chain 1:   1500        -7658.309             0.028            0.025
Chain 1:   1600        -7798.583             0.019            0.018
Chain 1:   1700        -7582.426             0.021            0.025
Chain 1:   1800        -7716.893             0.018            0.018
Chain 1:   1900        -7723.966             0.016            0.018
Chain 1:   2000        -7831.732             0.017            0.018
Chain 1:   2100        -7694.510             0.018            0.018
Chain 1:   2200        -8095.062             0.020            0.018
Chain 1:   2300        -7710.372             0.023            0.018
Chain 1:   2400        -7852.058             0.024            0.018
Chain 1:   2500        -7700.918             0.023            0.018
Chain 1:   2600        -7642.080             0.022            0.018
Chain 1:   2700        -7639.568             0.020            0.018
Chain 1:   2800        -7650.119             0.018            0.018
Chain 1:   2900        -7482.397             0.020            0.018
Chain 1:   3000        -7637.745             0.021            0.020
Chain 1:   3100        -7632.187             0.019            0.020
Chain 1:   3200        -7864.002             0.017            0.020
Chain 1:   3300        -7545.570             0.016            0.020
Chain 1:   3400        -7766.362             0.017            0.020
Chain 1:   3500        -7546.227             0.018            0.022
Chain 1:   3600        -7612.321             0.018            0.022
Chain 1:   3700        -7567.007             0.019            0.022
Chain 1:   3800        -7538.608             0.019            0.022
Chain 1:   3900        -7512.806             0.017            0.020
Chain 1:   4000        -7508.258             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002973 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85976.809             1.000            1.000
Chain 1:    200       -14229.295             3.021            5.042
Chain 1:    300       -10440.245             2.135            1.000
Chain 1:    400       -12361.088             1.640            1.000
Chain 1:    500        -8832.386             1.392            0.400
Chain 1:    600        -9412.280             1.170            0.400
Chain 1:    700        -8976.729             1.010            0.363
Chain 1:    800        -9388.452             0.889            0.363
Chain 1:    900        -9248.263             0.792            0.155
Chain 1:   1000        -9462.439             0.715            0.155
Chain 1:   1100        -9204.832             0.618            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8737.421             0.119            0.053
Chain 1:   1300        -9025.811             0.086            0.049
Chain 1:   1400        -8939.930             0.071            0.044
Chain 1:   1500        -8908.886             0.032            0.032
Chain 1:   1600        -8953.940             0.026            0.028
Chain 1:   1700        -9019.316             0.022            0.023
Chain 1:   1800        -8555.739             0.023            0.023
Chain 1:   1900        -8673.046             0.023            0.023
Chain 1:   2000        -8693.740             0.021            0.014
Chain 1:   2100        -8780.441             0.019            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003479 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8375947.240             1.000            1.000
Chain 1:    200     -1578787.634             2.653            4.305
Chain 1:    300      -890658.734             2.026            1.000
Chain 1:    400      -458906.310             1.755            1.000
Chain 1:    500      -359853.581             1.459            0.941
Chain 1:    600      -234743.138             1.304            0.941
Chain 1:    700      -120485.890             1.254            0.941
Chain 1:    800       -87623.462             1.144            0.941
Chain 1:    900       -67864.043             1.049            0.773
Chain 1:   1000       -52597.987             0.973            0.773
Chain 1:   1100       -40007.812             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39181.879             0.476            0.375
Chain 1:   1300       -27048.656             0.444            0.375
Chain 1:   1400       -26763.545             0.351            0.315
Chain 1:   1500       -23328.357             0.338            0.315
Chain 1:   1600       -22539.793             0.288            0.291
Chain 1:   1700       -21401.298             0.199            0.290
Chain 1:   1800       -21343.430             0.161            0.147
Chain 1:   1900       -21670.452             0.134            0.053
Chain 1:   2000       -20173.867             0.112            0.053
Chain 1:   2100       -20412.481             0.082            0.035
Chain 1:   2200       -20640.804             0.081            0.035
Chain 1:   2300       -20256.168             0.038            0.019
Chain 1:   2400       -20027.798             0.038            0.019
Chain 1:   2500       -19830.370             0.024            0.015
Chain 1:   2600       -19459.079             0.023            0.015
Chain 1:   2700       -19415.552             0.018            0.012
Chain 1:   2800       -19132.269             0.019            0.015
Chain 1:   2900       -19414.074             0.019            0.015
Chain 1:   3000       -19399.999             0.011            0.012
Chain 1:   3100       -19485.216             0.011            0.011
Chain 1:   3200       -19175.136             0.011            0.015
Chain 1:   3300       -19380.440             0.010            0.011
Chain 1:   3400       -18854.262             0.012            0.015
Chain 1:   3500       -19468.019             0.014            0.015
Chain 1:   3600       -18772.223             0.016            0.015
Chain 1:   3700       -19160.967             0.018            0.016
Chain 1:   3800       -18117.027             0.022            0.020
Chain 1:   3900       -18113.154             0.021            0.020
Chain 1:   4000       -18230.376             0.021            0.020
Chain 1:   4100       -18144.044             0.021            0.020
Chain 1:   4200       -17959.434             0.021            0.020
Chain 1:   4300       -18098.358             0.020            0.020
Chain 1:   4400       -18054.495             0.018            0.010
Chain 1:   4500       -17956.994             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48901.564             1.000            1.000
Chain 1:    200       -20605.419             1.187            1.373
Chain 1:    300       -13890.564             0.952            1.000
Chain 1:    400       -17078.045             0.761            1.000
Chain 1:    500       -13528.293             0.661            0.483
Chain 1:    600       -14544.121             0.563            0.483
Chain 1:    700       -10902.701             0.530            0.334
Chain 1:    800       -14639.497             0.496            0.334
Chain 1:    900       -14724.719             0.441            0.262
Chain 1:   1000       -11235.584             0.428            0.311
Chain 1:   1100       -13324.050             0.344            0.262
Chain 1:   1200       -10070.375             0.239            0.262
Chain 1:   1300       -10037.048             0.191            0.255
Chain 1:   1400       -11156.214             0.182            0.255
Chain 1:   1500       -14916.201             0.181            0.252
Chain 1:   1600       -12933.358             0.189            0.252
Chain 1:   1700       -13511.881             0.160            0.157
Chain 1:   1800       -10222.012             0.167            0.157
Chain 1:   1900       -10765.123             0.171            0.157
Chain 1:   2000       -11215.687             0.144            0.153
Chain 1:   2100       -12751.321             0.141            0.120
Chain 1:   2200       -10510.332             0.130            0.120
Chain 1:   2300        -9629.906             0.139            0.120
Chain 1:   2400        -9434.432             0.131            0.120
Chain 1:   2500        -9585.733             0.107            0.091
Chain 1:   2600        -9260.769             0.095            0.050
Chain 1:   2700        -9154.362             0.092            0.050
Chain 1:   2800       -20730.742             0.116            0.050
Chain 1:   2900       -10441.671             0.209            0.091
Chain 1:   3000        -9657.179             0.213            0.091
Chain 1:   3100       -10646.689             0.211            0.091
Chain 1:   3200        -8993.608             0.208            0.091
Chain 1:   3300       -10427.670             0.212            0.093
Chain 1:   3400       -15254.926             0.242            0.138
Chain 1:   3500        -9588.712             0.299            0.184
Chain 1:   3600        -8929.863             0.303            0.184
Chain 1:   3700        -9014.664             0.303            0.184
Chain 1:   3800        -9028.949             0.247            0.138
Chain 1:   3900        -9424.947             0.153            0.093
Chain 1:   4000        -8622.262             0.154            0.093
Chain 1:   4100        -9124.395             0.150            0.093
Chain 1:   4200        -9589.988             0.137            0.074
Chain 1:   4300       -14135.109             0.155            0.074
Chain 1:   4400        -8727.059             0.186            0.074
Chain 1:   4500        -9534.443             0.135            0.074
Chain 1:   4600        -8799.094             0.136            0.084
Chain 1:   4700        -8698.262             0.136            0.084
Chain 1:   4800       -11228.907             0.159            0.085
Chain 1:   4900       -12097.314             0.161            0.085
Chain 1:   5000       -13563.618             0.163            0.085
Chain 1:   5100        -8497.191             0.217            0.108
Chain 1:   5200        -9197.467             0.220            0.108
Chain 1:   5300       -14747.511             0.225            0.108
Chain 1:   5400        -8741.716             0.232            0.108
Chain 1:   5500       -11507.584             0.248            0.225
Chain 1:   5600        -8304.980             0.278            0.240
Chain 1:   5700       -12636.655             0.311            0.343
Chain 1:   5800        -8478.078             0.337            0.376
Chain 1:   5900        -9411.913             0.340            0.376
Chain 1:   6000       -11756.864             0.349            0.376
Chain 1:   6100        -8589.831             0.327            0.369
Chain 1:   6200        -8869.800             0.322            0.369
Chain 1:   6300        -8599.299             0.288            0.343
Chain 1:   6400       -10454.496             0.237            0.240
Chain 1:   6500       -12876.793             0.231            0.199
Chain 1:   6600        -9506.710             0.228            0.199
Chain 1:   6700        -8531.705             0.206            0.188
Chain 1:   6800        -8766.728             0.159            0.177
Chain 1:   6900        -8580.311             0.151            0.177
Chain 1:   7000       -15060.676             0.174            0.177
Chain 1:   7100        -8402.664             0.217            0.177
Chain 1:   7200        -8584.678             0.216            0.177
Chain 1:   7300        -8402.240             0.215            0.177
Chain 1:   7400        -8546.403             0.199            0.114
Chain 1:   7500        -8374.275             0.182            0.027
Chain 1:   7600        -8509.110             0.148            0.022
Chain 1:   7700       -12213.853             0.167            0.022
Chain 1:   7800        -8550.420             0.207            0.022
Chain 1:   7900        -9535.098             0.215            0.103
Chain 1:   8000       -12009.931             0.193            0.103
Chain 1:   8100        -8298.114             0.158            0.103
Chain 1:   8200        -9584.531             0.170            0.134
Chain 1:   8300        -8160.998             0.185            0.174
Chain 1:   8400        -8554.232             0.188            0.174
Chain 1:   8500        -8729.698             0.188            0.174
Chain 1:   8600        -8168.557             0.193            0.174
Chain 1:   8700        -8283.496             0.164            0.134
Chain 1:   8800        -8116.881             0.123            0.103
Chain 1:   8900        -8741.624             0.120            0.071
Chain 1:   9000       -10609.621             0.117            0.071
Chain 1:   9100        -8313.241             0.100            0.071
Chain 1:   9200       -12239.178             0.119            0.071
Chain 1:   9300        -8174.374             0.151            0.071
Chain 1:   9400        -8135.319             0.147            0.071
Chain 1:   9500        -9388.041             0.158            0.133
Chain 1:   9600        -8372.999             0.164            0.133
Chain 1:   9700        -8111.984             0.165            0.133
Chain 1:   9800        -8460.333             0.167            0.133
Chain 1:   9900       -11696.893             0.188            0.176
Chain 1:   10000        -9847.959             0.189            0.188
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57032.540             1.000            1.000
Chain 1:    200       -17466.118             1.633            2.265
Chain 1:    300        -8762.423             1.420            1.000
Chain 1:    400        -8392.265             1.076            1.000
Chain 1:    500        -8489.578             0.863            0.993
Chain 1:    600        -9004.906             0.729            0.993
Chain 1:    700        -7815.594             0.646            0.152
Chain 1:    800        -8139.910             0.570            0.152
Chain 1:    900        -7994.375             0.509            0.057
Chain 1:   1000        -7912.289             0.459            0.057
Chain 1:   1100        -7746.418             0.361            0.044
Chain 1:   1200        -7670.149             0.136            0.040
Chain 1:   1300        -7620.802             0.037            0.021
Chain 1:   1400        -7853.630             0.036            0.021
Chain 1:   1500        -7658.640             0.037            0.025
Chain 1:   1600        -7678.157             0.032            0.021
Chain 1:   1700        -7561.816             0.018            0.018
Chain 1:   1800        -7585.634             0.014            0.015
Chain 1:   1900        -7620.794             0.013            0.010
Chain 1:   2000        -7662.172             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87032.924             1.000            1.000
Chain 1:    200       -13492.159             3.225            5.451
Chain 1:    300        -9834.782             2.274            1.000
Chain 1:    400       -10735.560             1.727            1.000
Chain 1:    500        -8761.150             1.426            0.372
Chain 1:    600        -8310.533             1.198            0.372
Chain 1:    700        -8639.037             1.032            0.225
Chain 1:    800        -9240.140             0.911            0.225
Chain 1:    900        -8679.645             0.817            0.084
Chain 1:   1000        -8334.768             0.740            0.084
Chain 1:   1100        -8654.717             0.643            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8269.216             0.103            0.065
Chain 1:   1300        -8485.725             0.068            0.054
Chain 1:   1400        -8525.739             0.060            0.047
Chain 1:   1500        -8376.541             0.039            0.041
Chain 1:   1600        -8488.052             0.035            0.038
Chain 1:   1700        -8568.382             0.033            0.037
Chain 1:   1800        -8147.425             0.031            0.037
Chain 1:   1900        -8247.063             0.026            0.026
Chain 1:   2000        -8221.289             0.022            0.018
Chain 1:   2100        -8345.935             0.020            0.015
Chain 1:   2200        -8153.172             0.018            0.015
Chain 1:   2300        -8241.709             0.016            0.013
Chain 1:   2400        -8310.847             0.016            0.013
Chain 1:   2500        -8256.969             0.015            0.012
Chain 1:   2600        -8257.820             0.014            0.011
Chain 1:   2700        -8174.796             0.014            0.011
Chain 1:   2800        -8135.429             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002567 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8420309.526             1.000            1.000
Chain 1:    200     -1586523.502             2.654            4.307
Chain 1:    300      -890574.621             2.030            1.000
Chain 1:    400      -457337.806             1.759            1.000
Chain 1:    500      -357504.279             1.463            0.947
Chain 1:    600      -232539.009             1.309            0.947
Chain 1:    700      -118977.781             1.258            0.947
Chain 1:    800       -86246.755             1.148            0.947
Chain 1:    900       -66637.933             1.053            0.781
Chain 1:   1000       -51471.791             0.978            0.781
Chain 1:   1100       -38984.139             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38164.474             0.481            0.380
Chain 1:   1300       -26156.763             0.449            0.380
Chain 1:   1400       -25879.057             0.355            0.320
Chain 1:   1500       -22475.618             0.342            0.320
Chain 1:   1600       -21694.774             0.292            0.295
Chain 1:   1700       -20572.923             0.202            0.294
Chain 1:   1800       -20518.084             0.165            0.151
Chain 1:   1900       -20844.307             0.137            0.055
Chain 1:   2000       -19357.427             0.115            0.055
Chain 1:   2100       -19595.798             0.084            0.036
Chain 1:   2200       -19821.929             0.083            0.036
Chain 1:   2300       -19439.361             0.039            0.020
Chain 1:   2400       -19211.463             0.039            0.020
Chain 1:   2500       -19013.260             0.025            0.016
Chain 1:   2600       -18643.644             0.024            0.016
Chain 1:   2700       -18600.595             0.018            0.012
Chain 1:   2800       -18317.379             0.020            0.015
Chain 1:   2900       -18598.562             0.020            0.015
Chain 1:   3000       -18584.812             0.012            0.012
Chain 1:   3100       -18669.834             0.011            0.012
Chain 1:   3200       -18360.501             0.012            0.015
Chain 1:   3300       -18565.191             0.011            0.012
Chain 1:   3400       -18040.063             0.013            0.015
Chain 1:   3500       -18651.988             0.015            0.015
Chain 1:   3600       -17958.520             0.017            0.015
Chain 1:   3700       -18345.428             0.019            0.017
Chain 1:   3800       -17304.906             0.023            0.021
Chain 1:   3900       -17300.987             0.022            0.021
Chain 1:   4000       -17418.334             0.022            0.021
Chain 1:   4100       -17332.109             0.022            0.021
Chain 1:   4200       -17148.257             0.022            0.021
Chain 1:   4300       -17286.754             0.021            0.021
Chain 1:   4400       -17243.534             0.019            0.011
Chain 1:   4500       -17146.008             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48897.238             1.000            1.000
Chain 1:    200       -20030.611             1.221            1.441
Chain 1:    300       -18870.889             0.834            1.000
Chain 1:    400       -18143.790             0.636            1.000
Chain 1:    500       -13446.225             0.578            0.349
Chain 1:    600       -14102.279             0.490            0.349
Chain 1:    700       -14594.372             0.425            0.061
Chain 1:    800       -19293.643             0.402            0.244
Chain 1:    900       -13130.046             0.409            0.244
Chain 1:   1000       -25068.606             0.416            0.349
Chain 1:   1100       -10110.884             0.464            0.349
Chain 1:   1200       -10430.435             0.323            0.244
Chain 1:   1300       -11795.253             0.328            0.244
Chain 1:   1400       -11117.425             0.331            0.244
Chain 1:   1500       -10236.509             0.304            0.116
Chain 1:   1600       -10879.354             0.305            0.116
Chain 1:   1700       -10180.041             0.309            0.116
Chain 1:   1800       -13804.076             0.311            0.116
Chain 1:   1900       -11239.103             0.287            0.116
Chain 1:   2000       -15742.789             0.268            0.116
Chain 1:   2100       -13164.187             0.139            0.116
Chain 1:   2200       -11902.252             0.147            0.116
Chain 1:   2300       -11083.201             0.143            0.106
Chain 1:   2400        -9448.405             0.154            0.173
Chain 1:   2500       -10859.218             0.158            0.173
Chain 1:   2600        -9085.524             0.172            0.195
Chain 1:   2700        -9860.272             0.173            0.195
Chain 1:   2800        -9177.399             0.154            0.173
Chain 1:   2900       -11535.552             0.152            0.173
Chain 1:   3000       -10920.052             0.129            0.130
Chain 1:   3100       -14079.750             0.132            0.130
Chain 1:   3200       -10001.873             0.162            0.173
Chain 1:   3300       -14763.143             0.187            0.195
Chain 1:   3400        -9160.429             0.231            0.204
Chain 1:   3500        -9051.258             0.219            0.204
Chain 1:   3600        -9409.145             0.203            0.204
Chain 1:   3700       -15833.113             0.236            0.224
Chain 1:   3800        -9018.529             0.304            0.323
Chain 1:   3900        -9771.287             0.291            0.323
Chain 1:   4000        -9704.958             0.286            0.323
Chain 1:   4100        -8762.204             0.274            0.323
Chain 1:   4200       -11542.404             0.258            0.241
Chain 1:   4300        -9947.935             0.242            0.160
Chain 1:   4400       -14149.618             0.210            0.160
Chain 1:   4500        -8880.592             0.268            0.241
Chain 1:   4600        -9415.764             0.270            0.241
Chain 1:   4700       -14780.820             0.266            0.241
Chain 1:   4800        -8788.831             0.258            0.241
Chain 1:   4900        -8715.881             0.252            0.241
Chain 1:   5000       -19377.023             0.306            0.297
Chain 1:   5100        -8517.650             0.423            0.363
Chain 1:   5200        -9088.286             0.405            0.363
Chain 1:   5300       -13891.852             0.423            0.363
Chain 1:   5400        -8376.752             0.460            0.550   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   5500       -12860.302             0.435            0.363
Chain 1:   5600       -10658.946             0.450            0.363
Chain 1:   5700       -10148.166             0.419            0.349
Chain 1:   5800        -8848.596             0.365            0.346
Chain 1:   5900       -10445.316             0.380            0.346
Chain 1:   6000       -10218.126             0.327            0.207
Chain 1:   6100        -8427.204             0.221            0.207
Chain 1:   6200        -8084.198             0.219            0.207
Chain 1:   6300       -13217.706             0.223            0.207
Chain 1:   6400       -13423.810             0.159            0.153
Chain 1:   6500        -9167.407             0.170            0.153
Chain 1:   6600        -8495.712             0.157            0.147
Chain 1:   6700       -12166.535             0.183            0.153
Chain 1:   6800        -8331.524             0.214            0.213
Chain 1:   6900       -10992.189             0.223            0.242
Chain 1:   7000       -10942.422             0.221            0.242
Chain 1:   7100       -14976.370             0.227            0.269
Chain 1:   7200        -8177.021             0.306            0.302
Chain 1:   7300       -11532.616             0.296            0.291
Chain 1:   7400        -8708.659             0.327            0.302
Chain 1:   7500       -10512.448             0.298            0.291
Chain 1:   7600       -11655.296             0.299            0.291
Chain 1:   7700       -12969.950             0.279            0.269
Chain 1:   7800       -11772.790             0.244            0.242
Chain 1:   7900        -8180.407             0.263            0.269
Chain 1:   8000        -8552.459             0.267            0.269
Chain 1:   8100        -8073.678             0.246            0.172
Chain 1:   8200        -9871.998             0.181            0.172
Chain 1:   8300        -8390.489             0.170            0.172
Chain 1:   8400       -11357.995             0.163            0.172
Chain 1:   8500       -12359.786             0.154            0.102
Chain 1:   8600       -10133.508             0.167            0.177
Chain 1:   8700        -9044.978             0.168            0.177
Chain 1:   8800        -8266.042             0.168            0.177
Chain 1:   8900        -8486.010             0.126            0.120
Chain 1:   9000        -8774.397             0.125            0.120
Chain 1:   9100        -8199.868             0.126            0.120
Chain 1:   9200        -8450.737             0.111            0.094
Chain 1:   9300        -9543.551             0.105            0.094
Chain 1:   9400       -11057.445             0.093            0.094
Chain 1:   9500        -8102.423             0.121            0.115
Chain 1:   9600        -9457.088             0.113            0.115
Chain 1:   9700        -8135.695             0.117            0.115
Chain 1:   9800        -8922.948             0.117            0.115
Chain 1:   9900       -12372.930             0.142            0.137
Chain 1:   10000        -9368.053             0.171            0.143
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00137 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58387.689             1.000            1.000
Chain 1:    200       -17798.164             1.640            2.281
Chain 1:    300        -8693.390             1.443            1.047
Chain 1:    400        -8221.844             1.096            1.047
Chain 1:    500        -8302.570             0.879            1.000
Chain 1:    600        -8800.385             0.742            1.000
Chain 1:    700        -7740.896             0.655            0.137
Chain 1:    800        -8113.970             0.579            0.137
Chain 1:    900        -7992.343             0.517            0.057
Chain 1:   1000        -7578.477             0.470            0.057
Chain 1:   1100        -7625.426             0.371            0.057
Chain 1:   1200        -7642.070             0.143            0.055
Chain 1:   1300        -7666.063             0.039            0.046
Chain 1:   1400        -7821.387             0.035            0.020
Chain 1:   1500        -7560.744             0.038            0.034
Chain 1:   1600        -7568.095             0.032            0.020
Chain 1:   1700        -7506.297             0.019            0.015
Chain 1:   1800        -7610.419             0.016            0.014
Chain 1:   1900        -7570.856             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86019.533             1.000            1.000
Chain 1:    200       -13511.465             3.183            5.366
Chain 1:    300        -9808.910             2.248            1.000
Chain 1:    400       -11081.879             1.715            1.000
Chain 1:    500        -8796.153             1.424            0.377
Chain 1:    600        -8417.931             1.194            0.377
Chain 1:    700        -8421.020             1.023            0.260
Chain 1:    800        -8626.486             0.898            0.260
Chain 1:    900        -8651.784             0.799            0.115
Chain 1:   1000        -8220.064             0.724            0.115
Chain 1:   1100        -8571.237             0.628            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8151.488             0.097            0.051
Chain 1:   1300        -8455.343             0.063            0.045
Chain 1:   1400        -8470.100             0.051            0.041
Chain 1:   1500        -8311.853             0.027            0.036
Chain 1:   1600        -8421.564             0.024            0.024
Chain 1:   1700        -8490.851             0.025            0.024
Chain 1:   1800        -8053.197             0.028            0.036
Chain 1:   1900        -8158.839             0.029            0.036
Chain 1:   2000        -8135.446             0.024            0.019
Chain 1:   2100        -8277.673             0.022            0.017
Chain 1:   2200        -8065.550             0.019            0.017
Chain 1:   2300        -8225.736             0.018            0.017
Chain 1:   2400        -8061.342             0.019            0.019
Chain 1:   2500        -8132.881             0.018            0.017
Chain 1:   2600        -8044.892             0.018            0.017
Chain 1:   2700        -8079.141             0.018            0.017
Chain 1:   2800        -8038.879             0.013            0.013
Chain 1:   2900        -8132.531             0.013            0.012
Chain 1:   3000        -7966.506             0.014            0.017
Chain 1:   3100        -8121.781             0.015            0.019
Chain 1:   3200        -7993.415             0.014            0.016
Chain 1:   3300        -8001.398             0.012            0.012
Chain 1:   3400        -8162.908             0.012            0.012
Chain 1:   3500        -8173.397             0.011            0.012
Chain 1:   3600        -7949.829             0.013            0.016
Chain 1:   3700        -8096.380             0.014            0.018
Chain 1:   3800        -7956.241             0.015            0.018
Chain 1:   3900        -7890.615             0.015            0.018
Chain 1:   4000        -7967.038             0.014            0.018
Chain 1:   4100        -7961.978             0.012            0.016
Chain 1:   4200        -7946.022             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003039 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393404.268             1.000            1.000
Chain 1:    200     -1582220.139             2.652            4.305
Chain 1:    300      -890282.216             2.027            1.000
Chain 1:    400      -457641.500             1.757            1.000
Chain 1:    500      -358231.467             1.461            0.945
Chain 1:    600      -233301.347             1.307            0.945
Chain 1:    700      -119457.551             1.256            0.945
Chain 1:    800       -86613.248             1.147            0.945
Chain 1:    900       -66932.036             1.052            0.777
Chain 1:   1000       -51705.712             0.976            0.777
Chain 1:   1100       -39154.611             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38332.931             0.480            0.379
Chain 1:   1300       -26256.046             0.448            0.379
Chain 1:   1400       -25973.920             0.355            0.321
Chain 1:   1500       -22551.766             0.342            0.321
Chain 1:   1600       -21766.088             0.292            0.294
Chain 1:   1700       -20635.354             0.202            0.294
Chain 1:   1800       -20578.833             0.165            0.152
Chain 1:   1900       -20905.410             0.137            0.055
Chain 1:   2000       -19413.442             0.115            0.055
Chain 1:   2100       -19652.057             0.084            0.036
Chain 1:   2200       -19879.126             0.083            0.036
Chain 1:   2300       -19495.696             0.039            0.020
Chain 1:   2400       -19267.589             0.039            0.020
Chain 1:   2500       -19069.706             0.025            0.016
Chain 1:   2600       -18699.382             0.024            0.016
Chain 1:   2700       -18656.215             0.018            0.012
Chain 1:   2800       -18372.864             0.020            0.015
Chain 1:   2900       -18654.430             0.019            0.015
Chain 1:   3000       -18640.552             0.012            0.012
Chain 1:   3100       -18725.585             0.011            0.012
Chain 1:   3200       -18415.992             0.012            0.015
Chain 1:   3300       -18620.963             0.011            0.012
Chain 1:   3400       -18095.363             0.013            0.015
Chain 1:   3500       -18708.039             0.015            0.015
Chain 1:   3600       -18013.757             0.017            0.015
Chain 1:   3700       -18401.282             0.019            0.017
Chain 1:   3800       -17359.455             0.023            0.021
Chain 1:   3900       -17355.588             0.021            0.021
Chain 1:   4000       -17472.884             0.022            0.021
Chain 1:   4100       -17386.531             0.022            0.021
Chain 1:   4200       -17202.469             0.022            0.021
Chain 1:   4300       -17341.084             0.021            0.021
Chain 1:   4400       -17297.654             0.019            0.011
Chain 1:   4500       -17200.144             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001283 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13645.764             1.000            1.000
Chain 1:    200       -10006.194             0.682            1.000
Chain 1:    300        -8787.930             0.501            0.364
Chain 1:    400        -8866.893             0.378            0.364
Chain 1:    500        -9133.957             0.308            0.139
Chain 1:    600        -8673.123             0.266            0.139
Chain 1:    700        -8882.287             0.231            0.053
Chain 1:    800        -8682.365             0.205            0.053
Chain 1:    900        -8581.190             0.184            0.029
Chain 1:   1000        -8740.366             0.167            0.029
Chain 1:   1100        -8980.384             0.070            0.027
Chain 1:   1200        -8628.141             0.037            0.027
Chain 1:   1300        -8583.913             0.024            0.024
Chain 1:   1400        -8594.528             0.023            0.024
Chain 1:   1500        -8676.901             0.021            0.023
Chain 1:   1600        -8578.725             0.017            0.018
Chain 1:   1700        -8557.043             0.015            0.012
Chain 1:   1800        -8530.365             0.013            0.011
Chain 1:   1900        -8549.365             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62658.894             1.000            1.000
Chain 1:    200       -19083.625             1.642            2.283
Chain 1:    300        -9604.858             1.423            1.000
Chain 1:    400       -10034.444             1.078            1.000
Chain 1:    500        -8717.510             0.893            0.987
Chain 1:    600        -8877.699             0.747            0.987
Chain 1:    700        -8942.742             0.641            0.151
Chain 1:    800        -8969.239             0.562            0.151
Chain 1:    900        -8225.933             0.509            0.090
Chain 1:   1000        -7883.014             0.463            0.090
Chain 1:   1100        -8159.815             0.366            0.044
Chain 1:   1200        -7890.724             0.141            0.043
Chain 1:   1300        -7921.565             0.043            0.034
Chain 1:   1400        -8211.095             0.042            0.034
Chain 1:   1500        -7641.456             0.034            0.034
Chain 1:   1600        -7924.474             0.036            0.035
Chain 1:   1700        -7752.985             0.038            0.035
Chain 1:   1800        -7832.542             0.038            0.035
Chain 1:   1900        -7922.687             0.030            0.034
Chain 1:   2000        -7794.621             0.028            0.034
Chain 1:   2100        -7692.785             0.026            0.022
Chain 1:   2200        -8121.727             0.028            0.022
Chain 1:   2300        -7737.197             0.032            0.035
Chain 1:   2400        -7771.188             0.029            0.022
Chain 1:   2500        -7623.444             0.024            0.019
Chain 1:   2600        -7694.453             0.021            0.016
Chain 1:   2700        -7594.920             0.020            0.013
Chain 1:   2800        -7687.263             0.020            0.013
Chain 1:   2900        -7504.900             0.021            0.016
Chain 1:   3000        -7669.962             0.022            0.019
Chain 1:   3100        -7642.496             0.021            0.019
Chain 1:   3200        -7757.887             0.017            0.015
Chain 1:   3300        -7507.398             0.016            0.015
Chain 1:   3400        -7882.239             0.020            0.019
Chain 1:   3500        -7605.551             0.022            0.022
Chain 1:   3600        -7716.842             0.022            0.022
Chain 1:   3700        -7512.199             0.024            0.024
Chain 1:   3800        -7565.848             0.023            0.024
Chain 1:   3900        -7510.461             0.021            0.022
Chain 1:   4000        -7507.071             0.019            0.015
Chain 1:   4100        -7514.956             0.019            0.015
Chain 1:   4200        -7661.945             0.019            0.019
Chain 1:   4300        -7499.799             0.018            0.019
Chain 1:   4400        -7551.786             0.014            0.014
Chain 1:   4500        -7695.044             0.012            0.014
Chain 1:   4600        -7583.457             0.012            0.015
Chain 1:   4700        -7592.636             0.010            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003168 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87860.297             1.000            1.000
Chain 1:    200       -14724.579             2.983            4.967
Chain 1:    300       -10869.033             2.107            1.000
Chain 1:    400       -12943.500             1.620            1.000
Chain 1:    500        -9590.979             1.366            0.355
Chain 1:    600       -10383.416             1.151            0.355
Chain 1:    700        -9634.650             0.998            0.350
Chain 1:    800        -9313.558             0.877            0.350
Chain 1:    900        -9620.124             0.784            0.160
Chain 1:   1000        -9040.108             0.712            0.160
Chain 1:   1100        -9292.853             0.614            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9149.786             0.119            0.076
Chain 1:   1300        -9411.042             0.086            0.064
Chain 1:   1400        -9383.943             0.071            0.034
Chain 1:   1500        -9330.310             0.036            0.032
Chain 1:   1600        -9366.164             0.029            0.028
Chain 1:   1700        -9429.510             0.022            0.027
Chain 1:   1800        -8972.778             0.024            0.027
Chain 1:   1900        -9071.630             0.022            0.016
Chain 1:   2000        -9089.850             0.015            0.011
Chain 1:   2100        -9193.878             0.014            0.011
Chain 1:   2200        -8948.373             0.015            0.011
Chain 1:   2300        -9060.015             0.013            0.011
Chain 1:   2400        -9124.257             0.014            0.011
Chain 1:   2500        -9064.056             0.014            0.011
Chain 1:   2600        -9103.510             0.014            0.011
Chain 1:   2700        -8991.962             0.015            0.011
Chain 1:   2800        -8938.444             0.010            0.011
Chain 1:   2900        -9045.553             0.010            0.011
Chain 1:   3000        -8959.811             0.011            0.011
Chain 1:   3100        -8924.936             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003282 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424409.551             1.000            1.000
Chain 1:    200     -1583758.158             2.660            4.319
Chain 1:    300      -891365.308             2.032            1.000
Chain 1:    400      -458588.598             1.760            1.000
Chain 1:    500      -358785.032             1.464            0.944
Chain 1:    600      -233842.692             1.309            0.944
Chain 1:    700      -120302.369             1.257            0.944
Chain 1:    800       -87564.412             1.146            0.944
Chain 1:    900       -67952.977             1.051            0.777
Chain 1:   1000       -52796.813             0.975            0.777
Chain 1:   1100       -40309.099             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39501.999             0.476            0.374
Chain 1:   1300       -27471.038             0.442            0.374
Chain 1:   1400       -27197.790             0.348            0.310
Chain 1:   1500       -23786.574             0.335            0.310
Chain 1:   1600       -23005.819             0.285            0.289
Chain 1:   1700       -21879.374             0.196            0.287
Chain 1:   1800       -21824.618             0.159            0.143
Chain 1:   1900       -22151.809             0.131            0.051
Chain 1:   2000       -20661.089             0.110            0.051
Chain 1:   2100       -20899.648             0.080            0.034
Chain 1:   2200       -21126.750             0.079            0.034
Chain 1:   2300       -20743.130             0.037            0.018
Chain 1:   2400       -20514.824             0.037            0.018
Chain 1:   2500       -20316.720             0.024            0.015
Chain 1:   2600       -19945.790             0.022            0.015
Chain 1:   2700       -19902.586             0.017            0.011
Chain 1:   2800       -19618.824             0.018            0.014
Chain 1:   2900       -19900.597             0.018            0.014
Chain 1:   3000       -19886.750             0.011            0.011
Chain 1:   3100       -19971.864             0.010            0.011
Chain 1:   3200       -19661.835             0.011            0.014
Chain 1:   3300       -19867.176             0.010            0.011
Chain 1:   3400       -19340.737             0.012            0.014
Chain 1:   3500       -19954.583             0.014            0.014
Chain 1:   3600       -19258.736             0.016            0.014
Chain 1:   3700       -19647.325             0.017            0.016
Chain 1:   3800       -18603.070             0.022            0.020
Chain 1:   3900       -18599.107             0.020            0.020
Chain 1:   4000       -18716.429             0.021            0.020
Chain 1:   4100       -18629.915             0.021            0.020
Chain 1:   4200       -18445.383             0.020            0.020
Chain 1:   4300       -18584.365             0.020            0.020
Chain 1:   4400       -18540.473             0.017            0.010
Chain 1:   4500       -18442.868             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48418.731             1.000            1.000
Chain 1:    200       -17972.330             1.347            1.694
Chain 1:    300       -19686.837             0.927            1.000
Chain 1:    400       -13232.022             0.817            1.000
Chain 1:    500       -17098.856             0.699            0.488
Chain 1:    600       -15769.484             0.597            0.488
Chain 1:    700       -12030.040             0.556            0.311
Chain 1:    800       -12800.379             0.494            0.311
Chain 1:    900       -13813.736             0.447            0.226
Chain 1:   1000       -12947.806             0.409            0.226
Chain 1:   1100       -24911.254             0.357            0.226
Chain 1:   1200       -11650.569             0.302            0.226
Chain 1:   1300       -14467.761             0.312            0.226
Chain 1:   1400        -9528.429             0.315            0.226
Chain 1:   1500       -17715.570             0.339            0.311
Chain 1:   1600       -12084.235             0.377            0.462
Chain 1:   1700       -15671.814             0.369            0.462
Chain 1:   1800       -10567.278             0.411            0.466
Chain 1:   1900       -10575.432             0.404            0.466
Chain 1:   2000       -11070.726             0.402            0.466
Chain 1:   2100        -9531.812             0.370            0.462
Chain 1:   2200        -9215.342             0.259            0.229
Chain 1:   2300        -8722.182             0.246            0.229
Chain 1:   2400        -9732.550             0.204            0.161
Chain 1:   2500       -10002.021             0.161            0.104
Chain 1:   2600        -8791.523             0.128            0.104
Chain 1:   2700        -8901.157             0.106            0.057
Chain 1:   2800       -10008.712             0.069            0.057
Chain 1:   2900       -11737.651             0.084            0.104
Chain 1:   3000       -14522.532             0.098            0.111
Chain 1:   3100        -9370.119             0.137            0.111
Chain 1:   3200        -8590.641             0.143            0.111
Chain 1:   3300       -13174.222             0.172            0.138
Chain 1:   3400       -12372.183             0.168            0.138
Chain 1:   3500       -12767.620             0.168            0.138
Chain 1:   3600       -14531.791             0.167            0.121
Chain 1:   3700        -9221.524             0.223            0.147
Chain 1:   3800        -8613.072             0.219            0.147
Chain 1:   3900        -9872.010             0.217            0.128
Chain 1:   4000        -9006.090             0.208            0.121
Chain 1:   4100       -10709.703             0.169            0.121
Chain 1:   4200        -8513.243             0.185            0.128
Chain 1:   4300        -8827.200             0.154            0.121
Chain 1:   4400        -8620.456             0.150            0.121
Chain 1:   4500        -9470.414             0.156            0.121
Chain 1:   4600        -9008.299             0.149            0.096
Chain 1:   4700        -8231.491             0.101            0.094
Chain 1:   4800        -8469.440             0.096            0.094
Chain 1:   4900       -11556.432             0.110            0.094
Chain 1:   5000        -8951.143             0.130            0.094
Chain 1:   5100       -10441.836             0.128            0.094
Chain 1:   5200       -14611.676             0.131            0.094
Chain 1:   5300        -9884.298             0.175            0.143
Chain 1:   5400        -8455.142             0.190            0.169
Chain 1:   5500        -8713.266             0.184            0.169
Chain 1:   5600       -14897.706             0.220            0.267
Chain 1:   5700        -8245.941             0.291            0.285
Chain 1:   5800       -10656.957             0.311            0.285
Chain 1:   5900        -8866.868             0.305            0.285
Chain 1:   6000        -9966.835             0.287            0.226
Chain 1:   6100        -9419.229             0.278            0.226
Chain 1:   6200        -8107.417             0.266            0.202
Chain 1:   6300        -8982.490             0.228            0.169
Chain 1:   6400       -13252.646             0.243            0.202
Chain 1:   6500        -8453.453             0.297            0.226
Chain 1:   6600        -8548.754             0.256            0.202
Chain 1:   6700        -8985.498             0.181            0.162
Chain 1:   6800        -8488.223             0.164            0.110
Chain 1:   6900       -11581.465             0.170            0.110
Chain 1:   7000        -7962.564             0.205            0.162
Chain 1:   7100        -8248.445             0.202            0.162
Chain 1:   7200        -8833.291             0.193            0.097
Chain 1:   7300        -9141.367             0.186            0.066
Chain 1:   7400        -8854.703             0.157            0.059
Chain 1:   7500       -10166.707             0.114            0.059
Chain 1:   7600       -11658.189             0.125            0.066
Chain 1:   7700       -10380.199             0.133            0.123
Chain 1:   7800       -11078.346             0.133            0.123
Chain 1:   7900       -10851.333             0.109            0.066
Chain 1:   8000        -8109.943             0.097            0.066
Chain 1:   8100       -10628.171             0.117            0.123
Chain 1:   8200       -10629.194             0.111            0.123
Chain 1:   8300        -9707.910             0.117            0.123
Chain 1:   8400        -8044.386             0.134            0.128
Chain 1:   8500       -10268.286             0.143            0.128
Chain 1:   8600        -7996.527             0.158            0.207
Chain 1:   8700        -9024.809             0.158            0.207
Chain 1:   8800       -11160.675             0.170            0.207
Chain 1:   8900        -8394.582             0.201            0.217
Chain 1:   9000       -11171.543             0.192            0.217
Chain 1:   9100        -7931.098             0.209            0.217
Chain 1:   9200       -10306.634             0.232            0.230
Chain 1:   9300       -10366.484             0.224            0.230
Chain 1:   9400        -9924.042             0.207            0.230
Chain 1:   9500        -8200.803             0.207            0.230
Chain 1:   9600        -8128.378             0.179            0.210
Chain 1:   9700        -9973.059             0.186            0.210
Chain 1:   9800        -8146.812             0.190            0.224
Chain 1:   9900       -10915.348             0.182            0.224
Chain 1:   10000        -8074.221             0.192            0.224
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001597 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56201.070             1.000            1.000
Chain 1:    200       -16885.658             1.664            2.328
Chain 1:    300        -8503.722             1.438            1.000
Chain 1:    400        -8708.361             1.084            1.000
Chain 1:    500        -8370.317             0.876            0.986
Chain 1:    600        -9144.072             0.744            0.986
Chain 1:    700        -8079.041             0.656            0.132
Chain 1:    800        -8019.993             0.575            0.132
Chain 1:    900        -7775.524             0.515            0.085
Chain 1:   1000        -7775.873             0.463            0.085
Chain 1:   1100        -7623.602             0.365            0.040
Chain 1:   1200        -7583.003             0.133            0.031
Chain 1:   1300        -7562.753             0.035            0.023
Chain 1:   1400        -7845.784             0.036            0.031
Chain 1:   1500        -7579.195             0.035            0.031
Chain 1:   1600        -7474.338             0.028            0.020
Chain 1:   1700        -7471.454             0.015            0.014
Chain 1:   1800        -7499.025             0.015            0.014
Chain 1:   1900        -7545.297             0.012            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004596 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 45.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86514.769             1.000            1.000
Chain 1:    200       -13053.119             3.314            5.628
Chain 1:    300        -9545.582             2.332            1.000
Chain 1:    400       -10381.276             1.769            1.000
Chain 1:    500        -8415.394             1.462            0.367
Chain 1:    600        -8155.122             1.224            0.367
Chain 1:    700        -8451.356             1.054            0.234
Chain 1:    800        -8545.286             0.923            0.234
Chain 1:    900        -8439.256             0.822            0.081
Chain 1:   1000        -8170.670             0.743            0.081
Chain 1:   1100        -8447.705             0.647            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8155.694             0.087            0.035
Chain 1:   1300        -8331.799             0.053            0.033
Chain 1:   1400        -8255.612             0.046            0.033
Chain 1:   1500        -8206.728             0.023            0.032
Chain 1:   1600        -8205.265             0.020            0.021
Chain 1:   1700        -8147.356             0.017            0.013
Chain 1:   1800        -8025.826             0.017            0.015
Chain 1:   1900        -8138.310             0.017            0.015
Chain 1:   2000        -8100.903             0.015            0.014
Chain 1:   2100        -8244.565             0.013            0.014
Chain 1:   2200        -8026.790             0.012            0.014
Chain 1:   2300        -8163.953             0.012            0.014
Chain 1:   2400        -8052.376             0.012            0.014
Chain 1:   2500        -8109.967             0.012            0.014
Chain 1:   2600        -8122.885             0.012            0.014
Chain 1:   2700        -8044.043             0.013            0.014
Chain 1:   2800        -8029.352             0.011            0.014
Chain 1:   2900        -8017.676             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00366 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411619.761             1.000            1.000
Chain 1:    200     -1588822.960             2.647            4.294
Chain 1:    300      -891159.533             2.026            1.000
Chain 1:    400      -457309.499             1.756            1.000
Chain 1:    500      -357091.324             1.461            0.949
Chain 1:    600      -231934.962             1.308            0.949
Chain 1:    700      -118423.602             1.258            0.949
Chain 1:    800       -85686.462             1.148            0.949
Chain 1:    900       -66089.989             1.054            0.783
Chain 1:   1000       -50929.304             0.978            0.783
Chain 1:   1100       -38455.600             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37629.917             0.483            0.382
Chain 1:   1300       -25655.966             0.452            0.382
Chain 1:   1400       -25377.152             0.358            0.324
Chain 1:   1500       -21982.750             0.345            0.324
Chain 1:   1600       -21203.210             0.295            0.298
Chain 1:   1700       -20086.592             0.205            0.297
Chain 1:   1800       -20032.329             0.167            0.154
Chain 1:   1900       -20357.609             0.139            0.056
Chain 1:   2000       -18875.110             0.117            0.056
Chain 1:   2100       -19113.247             0.086            0.037
Chain 1:   2200       -19338.241             0.085            0.037
Chain 1:   2300       -18956.891             0.040            0.020
Chain 1:   2400       -18729.363             0.040            0.020
Chain 1:   2500       -18531.012             0.026            0.016
Chain 1:   2600       -18162.512             0.024            0.016
Chain 1:   2700       -18119.844             0.019            0.012
Chain 1:   2800       -17836.945             0.020            0.016
Chain 1:   2900       -18117.660             0.020            0.015
Chain 1:   3000       -18104.025             0.012            0.012
Chain 1:   3100       -18188.866             0.011            0.012
Chain 1:   3200       -17880.242             0.012            0.015
Chain 1:   3300       -18084.394             0.011            0.012
Chain 1:   3400       -17560.413             0.013            0.015
Chain 1:   3500       -18170.573             0.015            0.016
Chain 1:   3600       -17479.429             0.017            0.016
Chain 1:   3700       -17864.549             0.019            0.017
Chain 1:   3800       -16827.609             0.024            0.022
Chain 1:   3900       -16823.769             0.022            0.022
Chain 1:   4000       -16941.120             0.023            0.022
Chain 1:   4100       -16855.038             0.023            0.022
Chain 1:   4200       -16671.995             0.022            0.022
Chain 1:   4300       -16809.935             0.022            0.022
Chain 1:   4400       -16767.362             0.019            0.011
Chain 1:   4500       -16669.933             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001287 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48742.270             1.000            1.000
Chain 1:    200       -16745.467             1.455            1.911
Chain 1:    300       -17367.340             0.982            1.000
Chain 1:    400       -28101.654             0.832            1.000
Chain 1:    500       -16192.706             0.813            0.735
Chain 1:    600       -12552.610             0.726            0.735
Chain 1:    700       -10820.647             0.645            0.382
Chain 1:    800       -15708.819             0.603            0.382
Chain 1:    900       -11095.175             0.582            0.382
Chain 1:   1000       -10682.660             0.528            0.382
Chain 1:   1100       -12881.275             0.445            0.311
Chain 1:   1200       -11608.975             0.265            0.290
Chain 1:   1300       -12345.182             0.267            0.290
Chain 1:   1400       -10485.000             0.247            0.177
Chain 1:   1500       -10839.124             0.177            0.171
Chain 1:   1600       -11670.032             0.155            0.160
Chain 1:   1700       -18921.097             0.177            0.171
Chain 1:   1800        -9481.942             0.245            0.171
Chain 1:   1900       -16090.770             0.245            0.171
Chain 1:   2000       -11748.278             0.278            0.177
Chain 1:   2100        -9356.448             0.287            0.256
Chain 1:   2200       -10409.126             0.286            0.256
Chain 1:   2300       -14853.653             0.310            0.299
Chain 1:   2400       -10028.688             0.340            0.370
Chain 1:   2500       -11122.398             0.347            0.370
Chain 1:   2600        -9290.119             0.359            0.370
Chain 1:   2700       -12518.368             0.347            0.299
Chain 1:   2800       -10173.040             0.270            0.258
Chain 1:   2900        -9437.534             0.237            0.256
Chain 1:   3000       -22551.859             0.258            0.256
Chain 1:   3100        -9913.526             0.360            0.258
Chain 1:   3200        -9503.581             0.354            0.258
Chain 1:   3300        -9560.766             0.325            0.231
Chain 1:   3400        -9471.400             0.278            0.197
Chain 1:   3500       -13531.067             0.298            0.231
Chain 1:   3600       -10489.808             0.307            0.258
Chain 1:   3700       -10233.397             0.284            0.231
Chain 1:   3800        -8755.427             0.278            0.169
Chain 1:   3900       -13896.791             0.307            0.290
Chain 1:   4000        -8830.720             0.306            0.290
Chain 1:   4100        -8768.440             0.179            0.169
Chain 1:   4200       -14925.141             0.216            0.290
Chain 1:   4300        -9644.981             0.270            0.300
Chain 1:   4400        -9253.249             0.274            0.300
Chain 1:   4500       -11075.738             0.260            0.290
Chain 1:   4600       -11089.115             0.231            0.169
Chain 1:   4700        -8580.958             0.258            0.292
Chain 1:   4800        -8751.842             0.243            0.292
Chain 1:   4900       -16528.084             0.253            0.292
Chain 1:   5000        -9499.652             0.270            0.292
Chain 1:   5100        -9515.189             0.269            0.292
Chain 1:   5200       -10375.331             0.236            0.165
Chain 1:   5300       -15995.686             0.217            0.165
Chain 1:   5400       -10051.336             0.272            0.292
Chain 1:   5500        -9462.023             0.261            0.292
Chain 1:   5600        -9319.552             0.263            0.292
Chain 1:   5700       -12775.001             0.261            0.270
Chain 1:   5800        -8639.634             0.306            0.351
Chain 1:   5900       -14869.407             0.301            0.351
Chain 1:   6000       -10809.160             0.265            0.351
Chain 1:   6100        -8634.028             0.290            0.351
Chain 1:   6200        -8693.164             0.282            0.351
Chain 1:   6300        -8873.020             0.249            0.270
Chain 1:   6400       -13659.714             0.225            0.270
Chain 1:   6500        -9797.861             0.258            0.350
Chain 1:   6600        -8345.468             0.274            0.350
Chain 1:   6700        -8285.256             0.248            0.350
Chain 1:   6800       -12884.418             0.236            0.350
Chain 1:   6900        -8615.062             0.243            0.350
Chain 1:   7000        -8337.703             0.209            0.252
Chain 1:   7100        -8650.877             0.187            0.174
Chain 1:   7200        -8338.441             0.191            0.174
Chain 1:   7300        -8255.303             0.190            0.174
Chain 1:   7400        -8268.877             0.155            0.037
Chain 1:   7500       -10763.715             0.138            0.037
Chain 1:   7600        -8436.607             0.149            0.037
Chain 1:   7700       -10351.808             0.166            0.185
Chain 1:   7800        -9728.672             0.137            0.064
Chain 1:   7900        -9683.450             0.088            0.037
Chain 1:   8000        -8480.259             0.099            0.064
Chain 1:   8100        -8379.715             0.096            0.064
Chain 1:   8200        -8495.118             0.094            0.064
Chain 1:   8300        -8177.357             0.097            0.064
Chain 1:   8400        -8474.714             0.100            0.064
Chain 1:   8500        -8254.143             0.080            0.039
Chain 1:   8600        -8667.127             0.057            0.039
Chain 1:   8700        -9059.951             0.043            0.039
Chain 1:   8800        -8917.339             0.038            0.035
Chain 1:   8900       -10817.411             0.055            0.039
Chain 1:   9000        -9691.123             0.053            0.039
Chain 1:   9100        -8962.009             0.059            0.043
Chain 1:   9200        -9154.848             0.060            0.043
Chain 1:   9300        -8802.887             0.060            0.043
Chain 1:   9400        -8785.695             0.057            0.043
Chain 1:   9500       -12962.504             0.087            0.048
Chain 1:   9600        -8342.549             0.137            0.081
Chain 1:   9700        -9270.323             0.143            0.100
Chain 1:   9800        -8404.942             0.152            0.103
Chain 1:   9900        -9532.413             0.146            0.103
Chain 1:   10000        -8756.688             0.143            0.100
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45891.032             1.000            1.000
Chain 1:    200       -15390.549             1.491            1.982
Chain 1:    300        -8634.010             1.255            1.000
Chain 1:    400        -8617.416             0.942            1.000
Chain 1:    500        -8169.787             0.764            0.783
Chain 1:    600        -8061.230             0.639            0.783
Chain 1:    700        -7869.196             0.551            0.055
Chain 1:    800        -8141.606             0.487            0.055
Chain 1:    900        -7873.326             0.436            0.034
Chain 1:   1000        -7691.625             0.395            0.034
Chain 1:   1100        -7820.310             0.297            0.033
Chain 1:   1200        -7694.528             0.100            0.024
Chain 1:   1300        -7538.210             0.024            0.024
Chain 1:   1400        -7626.418             0.025            0.024
Chain 1:   1500        -7568.916             0.020            0.021
Chain 1:   1600        -7721.620             0.021            0.021
Chain 1:   1700        -7467.897             0.022            0.021
Chain 1:   1800        -7557.455             0.020            0.020
Chain 1:   1900        -7520.850             0.017            0.016
Chain 1:   2000        -7561.839             0.015            0.016
Chain 1:   2100        -7548.129             0.013            0.012
Chain 1:   2200        -7652.674             0.013            0.012
Chain 1:   2300        -7560.611             0.012            0.012
Chain 1:   2400        -7584.877             0.011            0.012
Chain 1:   2500        -7591.039             0.011            0.012
Chain 1:   2600        -7486.673             0.010            0.012
Chain 1:   2700        -7506.451             0.007            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003038 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86631.519             1.000            1.000
Chain 1:    200       -13405.682             3.231            5.462
Chain 1:    300        -9819.092             2.276            1.000
Chain 1:    400       -10647.944             1.726            1.000
Chain 1:    500        -8765.902             1.424            0.365
Chain 1:    600        -8692.463             1.188            0.365
Chain 1:    700        -8440.730             1.023            0.215
Chain 1:    800        -8933.278             0.902            0.215
Chain 1:    900        -8643.812             0.805            0.078
Chain 1:   1000        -8433.290             0.727            0.078
Chain 1:   1100        -8648.956             0.630            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8313.562             0.087            0.040
Chain 1:   1300        -8607.102             0.054            0.034
Chain 1:   1400        -8532.707             0.047            0.033
Chain 1:   1500        -8435.633             0.027            0.030
Chain 1:   1600        -8535.856             0.027            0.030
Chain 1:   1700        -8624.496             0.026            0.025
Chain 1:   1800        -8229.398             0.025            0.025
Chain 1:   1900        -8331.216             0.023            0.025
Chain 1:   2000        -8301.728             0.021            0.012
Chain 1:   2100        -8425.309             0.020            0.012
Chain 1:   2200        -8208.434             0.018            0.012
Chain 1:   2300        -8359.940             0.017            0.012
Chain 1:   2400        -8374.372             0.016            0.012
Chain 1:   2500        -8342.924             0.015            0.012
Chain 1:   2600        -8345.293             0.014            0.012
Chain 1:   2700        -8251.692             0.014            0.012
Chain 1:   2800        -8223.472             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003215 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407419.195             1.000            1.000
Chain 1:    200     -1590160.357             2.644            4.287
Chain 1:    300      -892641.564             2.023            1.000
Chain 1:    400      -458065.329             1.754            1.000
Chain 1:    500      -357831.166             1.459            0.949
Chain 1:    600      -232640.669             1.306            0.949
Chain 1:    700      -118981.447             1.256            0.949
Chain 1:    800       -86187.267             1.146            0.949
Chain 1:    900       -66563.350             1.052            0.781
Chain 1:   1000       -51382.958             0.976            0.781
Chain 1:   1100       -38881.486             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38058.304             0.482            0.380
Chain 1:   1300       -26049.343             0.450            0.380
Chain 1:   1400       -25769.543             0.356            0.322
Chain 1:   1500       -22365.084             0.343            0.322
Chain 1:   1600       -21583.037             0.293            0.295
Chain 1:   1700       -20461.879             0.203            0.295
Chain 1:   1800       -20406.830             0.165            0.152
Chain 1:   1900       -20732.538             0.137            0.055
Chain 1:   2000       -19246.723             0.115            0.055
Chain 1:   2100       -19485.140             0.084            0.036
Chain 1:   2200       -19710.741             0.083            0.036
Chain 1:   2300       -19328.769             0.039            0.020
Chain 1:   2400       -19101.030             0.039            0.020
Chain 1:   2500       -18902.731             0.025            0.016
Chain 1:   2600       -18533.680             0.024            0.016
Chain 1:   2700       -18490.868             0.018            0.012
Chain 1:   2800       -18207.730             0.020            0.016
Chain 1:   2900       -18488.759             0.020            0.015
Chain 1:   3000       -18475.121             0.012            0.012
Chain 1:   3100       -18559.990             0.011            0.012
Chain 1:   3200       -18251.029             0.012            0.015
Chain 1:   3300       -18455.466             0.011            0.012
Chain 1:   3400       -17930.862             0.013            0.015
Chain 1:   3500       -18541.928             0.015            0.016
Chain 1:   3600       -17849.709             0.017            0.016
Chain 1:   3700       -18235.616             0.019            0.017
Chain 1:   3800       -17196.941             0.023            0.021
Chain 1:   3900       -17193.076             0.022            0.021
Chain 1:   4000       -17310.439             0.022            0.021
Chain 1:   4100       -17224.218             0.022            0.021
Chain 1:   4200       -17040.839             0.022            0.021
Chain 1:   4300       -17179.022             0.021            0.021
Chain 1:   4400       -17136.147             0.019            0.011
Chain 1:   4500       -17038.679             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13301.279             1.000            1.000
Chain 1:    200       -10003.706             0.665            1.000
Chain 1:    300        -8471.952             0.503            0.330
Chain 1:    400        -8189.501             0.386            0.330
Chain 1:    500        -8117.989             0.311            0.181
Chain 1:    600        -7960.891             0.262            0.181
Chain 1:    700        -8143.499             0.228            0.034
Chain 1:    800        -7932.091             0.203            0.034
Chain 1:    900        -8043.577             0.182            0.027
Chain 1:   1000        -8027.159             0.164            0.027
Chain 1:   1100        -8062.606             0.064            0.022
Chain 1:   1200        -7971.298             0.032            0.020
Chain 1:   1300        -7917.082             0.015            0.014
Chain 1:   1400        -7950.689             0.012            0.011
Chain 1:   1500        -8053.911             0.012            0.013
Chain 1:   1600        -7973.043             0.011            0.011
Chain 1:   1700        -7928.715             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001424 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57216.752             1.000            1.000
Chain 1:    200       -17775.353             1.609            2.219
Chain 1:    300        -8751.451             1.417            1.031
Chain 1:    400        -7919.324             1.089            1.031
Chain 1:    500        -8678.653             0.889            1.000
Chain 1:    600        -9234.456             0.750            1.000
Chain 1:    700        -8353.480             0.658            0.105
Chain 1:    800        -8116.208             0.580            0.105
Chain 1:    900        -7956.402             0.518            0.105
Chain 1:   1000        -7761.436             0.468            0.105
Chain 1:   1100        -7713.799             0.369            0.087
Chain 1:   1200        -7617.058             0.148            0.060
Chain 1:   1300        -7596.155             0.045            0.029
Chain 1:   1400        -7700.713             0.036            0.025
Chain 1:   1500        -7579.040             0.029            0.020
Chain 1:   1600        -7725.110             0.025            0.019
Chain 1:   1700        -7604.168             0.016            0.016
Chain 1:   1800        -7681.873             0.014            0.016
Chain 1:   1900        -7567.506             0.014            0.015
Chain 1:   2000        -7614.914             0.012            0.014
Chain 1:   2100        -7565.075             0.012            0.014
Chain 1:   2200        -7736.229             0.013            0.015
Chain 1:   2300        -7548.065             0.015            0.016
Chain 1:   2400        -7610.853             0.014            0.016
Chain 1:   2500        -7627.098             0.013            0.015
Chain 1:   2600        -7519.545             0.013            0.014
Chain 1:   2700        -7539.856             0.011            0.010
Chain 1:   2800        -7505.354             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003314 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86030.187             1.000            1.000
Chain 1:    200       -13761.441             3.126            5.252
Chain 1:    300       -10055.570             2.207            1.000
Chain 1:    400       -11294.174             1.682            1.000
Chain 1:    500        -9019.530             1.396            0.369
Chain 1:    600        -8397.861             1.176            0.369
Chain 1:    700        -8464.428             1.009            0.252
Chain 1:    800        -8902.786             0.889            0.252
Chain 1:    900        -8752.344             0.792            0.110
Chain 1:   1000        -8897.838             0.715            0.110
Chain 1:   1100        -8613.299             0.618            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8416.208             0.095            0.049
Chain 1:   1300        -8780.555             0.062            0.041
Chain 1:   1400        -8671.227             0.053            0.033
Chain 1:   1500        -8554.868             0.029            0.023
Chain 1:   1600        -8668.190             0.023            0.017
Chain 1:   1700        -8732.939             0.023            0.017
Chain 1:   1800        -8292.511             0.023            0.017
Chain 1:   1900        -8399.204             0.023            0.016
Chain 1:   2000        -8377.457             0.021            0.014
Chain 1:   2100        -8517.361             0.020            0.014
Chain 1:   2200        -8305.657             0.020            0.014
Chain 1:   2300        -8464.930             0.018            0.014
Chain 1:   2400        -8302.793             0.018            0.016
Chain 1:   2500        -8374.064             0.018            0.016
Chain 1:   2600        -8286.036             0.018            0.016
Chain 1:   2700        -8319.575             0.017            0.016
Chain 1:   2800        -8279.107             0.012            0.013
Chain 1:   2900        -8373.264             0.012            0.011
Chain 1:   3000        -8208.843             0.014            0.016
Chain 1:   3100        -8362.131             0.014            0.018
Chain 1:   3200        -8233.606             0.013            0.016
Chain 1:   3300        -8243.524             0.011            0.011
Chain 1:   3400        -8407.542             0.011            0.011
Chain 1:   3500        -8419.124             0.011            0.011
Chain 1:   3600        -8189.947             0.012            0.016
Chain 1:   3700        -8336.996             0.014            0.018
Chain 1:   3800        -8196.095             0.015            0.018
Chain 1:   3900        -8130.263             0.015            0.018
Chain 1:   4000        -8208.401             0.014            0.017
Chain 1:   4100        -8201.681             0.012            0.016
Chain 1:   4200        -8186.334             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395046.857             1.000            1.000
Chain 1:    200     -1580839.141             2.655            4.311
Chain 1:    300      -890913.924             2.028            1.000
Chain 1:    400      -458420.723             1.757            1.000
Chain 1:    500      -358988.493             1.461            0.943
Chain 1:    600      -233821.132             1.307            0.943
Chain 1:    700      -119823.165             1.256            0.943
Chain 1:    800       -86946.229             1.146            0.943
Chain 1:    900       -67233.515             1.051            0.774
Chain 1:   1000       -51990.964             0.976            0.774
Chain 1:   1100       -39426.113             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38602.844             0.479            0.378
Chain 1:   1300       -26513.590             0.447            0.378
Chain 1:   1400       -26229.595             0.353            0.319
Chain 1:   1500       -22804.829             0.341            0.319
Chain 1:   1600       -22018.346             0.291            0.293
Chain 1:   1700       -20886.505             0.201            0.293
Chain 1:   1800       -20829.649             0.164            0.150
Chain 1:   1900       -21156.232             0.136            0.054
Chain 1:   2000       -19663.777             0.114            0.054
Chain 1:   2100       -19902.269             0.083            0.036
Chain 1:   2200       -20129.464             0.082            0.036
Chain 1:   2300       -19745.942             0.039            0.019
Chain 1:   2400       -19517.879             0.039            0.019
Chain 1:   2500       -19319.977             0.025            0.015
Chain 1:   2600       -18949.521             0.023            0.015
Chain 1:   2700       -18906.380             0.018            0.012
Chain 1:   2800       -18623.036             0.019            0.015
Chain 1:   2900       -18904.644             0.019            0.015
Chain 1:   3000       -18890.708             0.012            0.012
Chain 1:   3100       -18975.746             0.011            0.012
Chain 1:   3200       -18666.098             0.012            0.015
Chain 1:   3300       -18871.129             0.011            0.012
Chain 1:   3400       -18345.454             0.012            0.015
Chain 1:   3500       -18958.184             0.015            0.015
Chain 1:   3600       -18263.880             0.016            0.015
Chain 1:   3700       -18651.452             0.018            0.017
Chain 1:   3800       -17609.493             0.023            0.021
Chain 1:   3900       -17605.650             0.021            0.021
Chain 1:   4000       -17722.940             0.022            0.021
Chain 1:   4100       -17636.568             0.022            0.021
Chain 1:   4200       -17452.519             0.021            0.021
Chain 1:   4300       -17591.111             0.021            0.021
Chain 1:   4400       -17547.667             0.018            0.011
Chain 1:   4500       -17450.189             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001254 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49085.493             1.000            1.000
Chain 1:    200       -22705.702             1.081            1.162
Chain 1:    300       -13133.774             0.964            1.000
Chain 1:    400       -25404.495             0.843            1.000
Chain 1:    500       -20362.301             0.724            0.729
Chain 1:    600       -17160.694             0.635            0.729
Chain 1:    700       -13076.635             0.589            0.483
Chain 1:    800       -11870.645             0.528            0.483
Chain 1:    900       -18347.119             0.508            0.353
Chain 1:   1000       -12360.800             0.506            0.483
Chain 1:   1100       -11822.869             0.410            0.353
Chain 1:   1200       -13844.568             0.309            0.312
Chain 1:   1300       -11752.475             0.254            0.248
Chain 1:   1400        -9805.838             0.225            0.199
Chain 1:   1500       -17396.442             0.244            0.199
Chain 1:   1600       -11976.400             0.271            0.312
Chain 1:   1700        -9970.027             0.260            0.201
Chain 1:   1800       -10942.637             0.258            0.201
Chain 1:   1900       -10310.326             0.229            0.199
Chain 1:   2000        -9554.778             0.189            0.178
Chain 1:   2100        -9683.487             0.186            0.178
Chain 1:   2200        -9406.003             0.174            0.178
Chain 1:   2300       -10198.106             0.164            0.089
Chain 1:   2400        -9504.654             0.151            0.079
Chain 1:   2500        -9826.096             0.111            0.078
Chain 1:   2600        -9248.598             0.072            0.073
Chain 1:   2700       -10263.339             0.062            0.073
Chain 1:   2800       -10588.462             0.056            0.062
Chain 1:   2900        -9413.915             0.062            0.073
Chain 1:   3000        -8645.489             0.063            0.073
Chain 1:   3100       -13738.201             0.099            0.078
Chain 1:   3200        -8904.592             0.150            0.089
Chain 1:   3300        -9082.606             0.144            0.089
Chain 1:   3400        -8861.309             0.140            0.089
Chain 1:   3500        -9249.083             0.141            0.089
Chain 1:   3600        -8646.892             0.141            0.089
Chain 1:   3700        -9619.729             0.142            0.089
Chain 1:   3800       -10115.342             0.143            0.089
Chain 1:   3900        -8704.564             0.147            0.089
Chain 1:   4000        -9673.314             0.148            0.100
Chain 1:   4100        -8791.024             0.121            0.100
Chain 1:   4200       -14955.001             0.108            0.100
Chain 1:   4300        -9132.134             0.170            0.100
Chain 1:   4400        -8952.579             0.169            0.100
Chain 1:   4500        -8983.552             0.166            0.100
Chain 1:   4600       -13495.809             0.192            0.101
Chain 1:   4700       -12076.863             0.194            0.117
Chain 1:   4800       -10885.220             0.200            0.117
Chain 1:   4900        -9072.145             0.203            0.117
Chain 1:   5000       -10881.729             0.210            0.166
Chain 1:   5100       -10039.895             0.208            0.166
Chain 1:   5200        -8513.418             0.185            0.166
Chain 1:   5300        -9323.336             0.130            0.117
Chain 1:   5400       -10183.696             0.137            0.117
Chain 1:   5500        -8618.811             0.154            0.166
Chain 1:   5600        -8245.644             0.125            0.117
Chain 1:   5700        -9780.586             0.129            0.157
Chain 1:   5800        -9194.742             0.125            0.157
Chain 1:   5900        -8171.714             0.117            0.125
Chain 1:   6000        -8192.296             0.101            0.087
Chain 1:   6100        -9294.375             0.104            0.119
Chain 1:   6200       -11871.421             0.108            0.119
Chain 1:   6300        -9179.989             0.129            0.125
Chain 1:   6400       -13165.781             0.151            0.157
Chain 1:   6500        -8847.466             0.181            0.157
Chain 1:   6600        -8474.925             0.181            0.157
Chain 1:   6700       -14023.237             0.205            0.217
Chain 1:   6800        -9142.221             0.252            0.293
Chain 1:   6900        -8286.712             0.250            0.293
Chain 1:   7000        -8947.605             0.257            0.293
Chain 1:   7100        -9443.073             0.250            0.293
Chain 1:   7200        -9356.761             0.230            0.293
Chain 1:   7300        -8776.527             0.207            0.103
Chain 1:   7400        -8537.984             0.179            0.074
Chain 1:   7500        -8093.552             0.136            0.066
Chain 1:   7600        -8318.948             0.134            0.066
Chain 1:   7700        -8553.526             0.098            0.055
Chain 1:   7800       -11131.889             0.067            0.055
Chain 1:   7900        -8047.308             0.095            0.055
Chain 1:   8000        -8219.406             0.090            0.052
Chain 1:   8100        -8685.298             0.090            0.054
Chain 1:   8200        -9276.517             0.096            0.055
Chain 1:   8300       -10693.919             0.102            0.055
Chain 1:   8400       -10479.918             0.102            0.055
Chain 1:   8500        -8030.834             0.127            0.064
Chain 1:   8600        -8254.761             0.127            0.064
Chain 1:   8700        -9323.674             0.135            0.115
Chain 1:   8800       -10031.900             0.119            0.071
Chain 1:   8900       -11027.467             0.090            0.071
Chain 1:   9000        -8552.285             0.117            0.090
Chain 1:   9100        -9877.766             0.125            0.115
Chain 1:   9200       -10398.053             0.123            0.115
Chain 1:   9300        -8207.771             0.137            0.115
Chain 1:   9400       -11126.395             0.161            0.134
Chain 1:   9500        -8976.336             0.154            0.134
Chain 1:   9600        -9807.313             0.160            0.134
Chain 1:   9700        -9535.736             0.152            0.134
Chain 1:   9800       -10228.507             0.151            0.134
Chain 1:   9900       -10200.385             0.143            0.134
Chain 1:   10000        -9320.665             0.123            0.094
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57813.935             1.000            1.000
Chain 1:    200       -17328.039             1.668            2.336
Chain 1:    300        -8552.347             1.454            1.026
Chain 1:    400        -8144.677             1.103            1.026
Chain 1:    500        -8372.166             0.888            1.000
Chain 1:    600        -8149.659             0.745            1.000
Chain 1:    700        -7942.240             0.642            0.050
Chain 1:    800        -8053.124             0.563            0.050
Chain 1:    900        -7911.190             0.503            0.027
Chain 1:   1000        -7787.415             0.454            0.027
Chain 1:   1100        -7687.772             0.355            0.027
Chain 1:   1200        -7673.445             0.122            0.026
Chain 1:   1300        -7662.640             0.019            0.018
Chain 1:   1400        -7879.522             0.017            0.018
Chain 1:   1500        -7601.540             0.018            0.018
Chain 1:   1600        -7532.712             0.016            0.016
Chain 1:   1700        -7503.698             0.014            0.014
Chain 1:   1800        -7556.910             0.013            0.013
Chain 1:   1900        -7596.860             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86089.957             1.000            1.000
Chain 1:    200       -13209.002             3.259            5.518
Chain 1:    300        -9647.021             2.296            1.000
Chain 1:    400       -10459.284             1.741            1.000
Chain 1:    500        -8575.648             1.437            0.369
Chain 1:    600        -8205.888             1.205            0.369
Chain 1:    700        -8548.415             1.038            0.220
Chain 1:    800        -9076.112             0.916            0.220
Chain 1:    900        -8467.472             0.822            0.078
Chain 1:   1000        -8219.509             0.743            0.078
Chain 1:   1100        -8484.195             0.646            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8153.991             0.098            0.058
Chain 1:   1300        -8213.495             0.062            0.045
Chain 1:   1400        -8211.217             0.054            0.040
Chain 1:   1500        -8242.570             0.033            0.040
Chain 1:   1600        -8249.144             0.028            0.031
Chain 1:   1700        -8173.314             0.025            0.030
Chain 1:   1800        -8060.751             0.021            0.014
Chain 1:   1900        -8179.609             0.015            0.014
Chain 1:   2000        -8139.726             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003743 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431527.670             1.000            1.000
Chain 1:    200     -1589867.553             2.652            4.303
Chain 1:    300      -890583.834             2.029            1.000
Chain 1:    400      -456825.890             1.759            1.000
Chain 1:    500      -356650.015             1.464            0.950
Chain 1:    600      -231650.362             1.310            0.950
Chain 1:    700      -118404.817             1.259            0.950
Chain 1:    800       -85715.998             1.150            0.950
Chain 1:    900       -66166.161             1.055            0.785
Chain 1:   1000       -51042.374             0.979            0.785
Chain 1:   1100       -38597.759             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37779.772             0.483            0.381
Chain 1:   1300       -25826.077             0.451            0.381
Chain 1:   1400       -25551.167             0.357            0.322
Chain 1:   1500       -22161.475             0.344            0.322
Chain 1:   1600       -21384.136             0.294            0.296
Chain 1:   1700       -20269.331             0.204            0.295
Chain 1:   1800       -20215.837             0.166            0.153
Chain 1:   1900       -20541.541             0.138            0.055
Chain 1:   2000       -19059.384             0.116            0.055
Chain 1:   2100       -19297.448             0.085            0.036
Chain 1:   2200       -19522.556             0.084            0.036
Chain 1:   2300       -19141.053             0.040            0.020
Chain 1:   2400       -18913.419             0.040            0.020
Chain 1:   2500       -18714.977             0.025            0.016
Chain 1:   2600       -18346.139             0.024            0.016
Chain 1:   2700       -18303.423             0.019            0.012
Chain 1:   2800       -18020.280             0.020            0.016
Chain 1:   2900       -18301.171             0.020            0.015
Chain 1:   3000       -18287.541             0.012            0.012
Chain 1:   3100       -18372.412             0.011            0.012
Chain 1:   3200       -18063.554             0.012            0.015
Chain 1:   3300       -18267.920             0.011            0.012
Chain 1:   3400       -17743.440             0.013            0.015
Chain 1:   3500       -18354.266             0.015            0.016
Chain 1:   3600       -17662.291             0.017            0.016
Chain 1:   3700       -18047.995             0.019            0.017
Chain 1:   3800       -17009.705             0.023            0.021
Chain 1:   3900       -17005.835             0.022            0.021
Chain 1:   4000       -17123.206             0.022            0.021
Chain 1:   4100       -17037.015             0.022            0.021
Chain 1:   4200       -16853.725             0.022            0.021
Chain 1:   4300       -16991.864             0.022            0.021
Chain 1:   4400       -16949.052             0.019            0.011
Chain 1:   4500       -16851.588             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001241 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48724.464             1.000            1.000
Chain 1:    200       -14012.020             1.739            2.477
Chain 1:    300       -20634.344             1.266            1.000
Chain 1:    400       -13536.375             1.081            1.000
Chain 1:    500       -23152.863             0.948            0.524
Chain 1:    600       -15896.730             0.866            0.524
Chain 1:    700       -15586.985             0.745            0.456
Chain 1:    800       -10643.039             0.710            0.465
Chain 1:    900       -10524.277             0.632            0.456
Chain 1:   1000       -12129.317             0.582            0.456
Chain 1:   1100       -17638.024             0.513            0.415   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12375.043             0.308            0.415
Chain 1:   1300       -11377.597             0.285            0.415
Chain 1:   1400       -10117.010             0.245            0.312
Chain 1:   1500       -11932.368             0.219            0.152
Chain 1:   1600       -12287.279             0.176            0.132
Chain 1:   1700       -10236.898             0.194            0.152
Chain 1:   1800       -17450.763             0.189            0.152
Chain 1:   1900        -9769.623             0.266            0.200
Chain 1:   2000       -10222.070             0.258            0.200
Chain 1:   2100       -13836.652             0.252            0.200
Chain 1:   2200       -11110.917             0.234            0.200
Chain 1:   2300        -9000.290             0.249            0.235
Chain 1:   2400        -9111.798             0.238            0.235
Chain 1:   2500       -11060.942             0.240            0.235
Chain 1:   2600        -9668.778             0.252            0.235
Chain 1:   2700        -9267.216             0.236            0.235
Chain 1:   2800        -9444.722             0.197            0.176
Chain 1:   2900        -9216.679             0.120            0.144
Chain 1:   3000        -9878.488             0.123            0.144
Chain 1:   3100        -8866.210             0.108            0.114
Chain 1:   3200        -8837.775             0.084            0.067
Chain 1:   3300        -9532.696             0.068            0.067
Chain 1:   3400        -9765.850             0.069            0.067
Chain 1:   3500        -8847.501             0.062            0.067
Chain 1:   3600        -9267.685             0.052            0.045
Chain 1:   3700        -9156.756             0.049            0.045
Chain 1:   3800        -8636.656             0.053            0.060
Chain 1:   3900       -11035.817             0.072            0.067
Chain 1:   4000        -8625.954             0.093            0.073
Chain 1:   4100        -9542.182             0.091            0.073
Chain 1:   4200       -13498.589             0.120            0.096
Chain 1:   4300       -10111.849             0.147            0.104
Chain 1:   4400        -8863.469             0.158            0.141
Chain 1:   4500       -12809.716             0.179            0.217
Chain 1:   4600       -11490.903             0.186            0.217
Chain 1:   4700        -9238.730             0.209            0.244
Chain 1:   4800        -8482.247             0.212            0.244
Chain 1:   4900        -9158.531             0.197            0.244
Chain 1:   5000        -9796.873             0.176            0.141
Chain 1:   5100        -8754.985             0.178            0.141
Chain 1:   5200       -11513.798             0.173            0.141
Chain 1:   5300       -10345.829             0.151            0.119
Chain 1:   5400       -10014.879             0.140            0.115
Chain 1:   5500       -10944.940             0.118            0.113
Chain 1:   5600        -8912.263             0.129            0.113
Chain 1:   5700       -11441.845             0.127            0.113
Chain 1:   5800        -8920.582             0.146            0.119
Chain 1:   5900        -8229.349             0.147            0.119
Chain 1:   6000       -11392.188             0.168            0.221
Chain 1:   6100        -8849.608             0.185            0.228
Chain 1:   6200       -10359.922             0.176            0.221
Chain 1:   6300        -8392.641             0.188            0.228
Chain 1:   6400       -12583.275             0.218            0.234
Chain 1:   6500       -10113.898             0.234            0.244
Chain 1:   6600        -8300.094             0.233            0.244
Chain 1:   6700        -8632.383             0.215            0.244
Chain 1:   6800       -10769.428             0.206            0.234
Chain 1:   6900        -9717.061             0.209            0.234
Chain 1:   7000       -10385.848             0.187            0.219
Chain 1:   7100        -9250.108             0.171            0.198
Chain 1:   7200        -9695.435             0.161            0.198
Chain 1:   7300        -9243.188             0.142            0.123
Chain 1:   7400        -8135.149             0.123            0.123
Chain 1:   7500       -10578.540             0.121            0.123
Chain 1:   7600        -9636.617             0.109            0.108
Chain 1:   7700        -8290.392             0.122            0.123
Chain 1:   7800        -8373.623             0.103            0.108
Chain 1:   7900        -9700.051             0.106            0.123
Chain 1:   8000       -10887.122             0.110            0.123
Chain 1:   8100        -8181.769             0.131            0.136
Chain 1:   8200       -11148.912             0.153            0.137
Chain 1:   8300       -11285.882             0.149            0.137
Chain 1:   8400        -8202.410             0.173            0.162
Chain 1:   8500        -8173.258             0.150            0.137
Chain 1:   8600        -8525.857             0.145            0.137
Chain 1:   8700        -7925.046             0.136            0.109
Chain 1:   8800        -8110.388             0.137            0.109
Chain 1:   8900        -8418.564             0.127            0.076
Chain 1:   9000       -11297.842             0.142            0.076
Chain 1:   9100        -8987.821             0.135            0.076
Chain 1:   9200        -7974.552             0.121            0.076
Chain 1:   9300        -9399.264             0.135            0.127
Chain 1:   9400        -8529.718             0.107            0.102
Chain 1:   9500        -8187.997             0.111            0.102
Chain 1:   9600        -8418.594             0.110            0.102
Chain 1:   9700        -8135.891             0.106            0.102
Chain 1:   9800        -8520.826             0.108            0.102
Chain 1:   9900        -8422.287             0.105            0.102
Chain 1:   10000        -9187.916             0.088            0.083
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006654 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 66.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61614.571             1.000            1.000
Chain 1:    200       -17451.381             1.765            2.531
Chain 1:    300        -8673.858             1.514            1.012
Chain 1:    400        -8140.441             1.152            1.012
Chain 1:    500        -8116.295             0.922            1.000
Chain 1:    600        -8698.664             0.780            1.000
Chain 1:    700        -7682.207             0.687            0.132
Chain 1:    800        -7997.035             0.606            0.132
Chain 1:    900        -7638.857             0.544            0.067
Chain 1:   1000        -7686.800             0.490            0.067
Chain 1:   1100        -7810.262             0.392            0.066
Chain 1:   1200        -7494.414             0.143            0.047
Chain 1:   1300        -7650.608             0.044            0.042
Chain 1:   1400        -7784.231             0.039            0.039
Chain 1:   1500        -7534.383             0.042            0.039
Chain 1:   1600        -7679.106             0.037            0.033
Chain 1:   1700        -7455.052             0.027            0.030
Chain 1:   1800        -7544.064             0.024            0.020
Chain 1:   1900        -7550.571             0.020            0.019
Chain 1:   2000        -7576.172             0.019            0.019
Chain 1:   2100        -7551.247             0.018            0.019
Chain 1:   2200        -7634.552             0.015            0.017
Chain 1:   2300        -7498.811             0.015            0.017
Chain 1:   2400        -7560.835             0.014            0.012
Chain 1:   2500        -7398.727             0.013            0.012
Chain 1:   2600        -7452.654             0.012            0.011
Chain 1:   2700        -7544.145             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003056 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85422.829             1.000            1.000
Chain 1:    200       -13170.864             3.243            5.486
Chain 1:    300        -9664.845             2.283            1.000
Chain 1:    400       -10487.505             1.732            1.000
Chain 1:    500        -8530.989             1.431            0.363
Chain 1:    600        -8287.039             1.198            0.363
Chain 1:    700        -8448.622             1.029            0.229
Chain 1:    800        -8512.945             0.902            0.229
Chain 1:    900        -8514.815             0.801            0.078
Chain 1:   1000        -8305.451             0.724            0.078
Chain 1:   1100        -8535.013             0.626            0.029   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8216.477             0.082            0.029
Chain 1:   1300        -8269.075             0.046            0.027
Chain 1:   1400        -8343.402             0.039            0.025
Chain 1:   1500        -8288.916             0.017            0.019
Chain 1:   1600        -8296.048             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002969 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392090.914             1.000            1.000
Chain 1:    200     -1580458.786             2.655            4.310
Chain 1:    300      -889385.575             2.029            1.000
Chain 1:    400      -456722.959             1.759            1.000
Chain 1:    500      -357244.199             1.463            0.947
Chain 1:    600      -232504.854             1.308            0.947
Chain 1:    700      -118836.395             1.258            0.947
Chain 1:    800       -86083.297             1.148            0.947
Chain 1:    900       -66432.394             1.054            0.777
Chain 1:   1000       -51220.026             0.978            0.777
Chain 1:   1100       -38698.186             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37868.733             0.481            0.380
Chain 1:   1300       -25832.010             0.450            0.380
Chain 1:   1400       -25549.485             0.357            0.324
Chain 1:   1500       -22139.205             0.344            0.324
Chain 1:   1600       -21356.024             0.294            0.297
Chain 1:   1700       -20230.846             0.204            0.296
Chain 1:   1800       -20175.024             0.166            0.154
Chain 1:   1900       -20500.505             0.138            0.056
Chain 1:   2000       -19013.738             0.117            0.056
Chain 1:   2100       -19251.840             0.085            0.037
Chain 1:   2200       -19477.835             0.084            0.037
Chain 1:   2300       -19095.661             0.040            0.020
Chain 1:   2400       -18868.003             0.040            0.020
Chain 1:   2500       -18670.164             0.026            0.016
Chain 1:   2600       -18300.955             0.024            0.016
Chain 1:   2700       -18258.134             0.019            0.012
Chain 1:   2800       -17975.349             0.020            0.016
Chain 1:   2900       -18256.280             0.020            0.015
Chain 1:   3000       -18242.480             0.012            0.012
Chain 1:   3100       -18327.357             0.011            0.012
Chain 1:   3200       -18018.513             0.012            0.015
Chain 1:   3300       -18222.892             0.011            0.012
Chain 1:   3400       -17698.678             0.013            0.015
Chain 1:   3500       -18309.282             0.015            0.016
Chain 1:   3600       -17617.634             0.017            0.016
Chain 1:   3700       -18003.192             0.019            0.017
Chain 1:   3800       -16965.540             0.023            0.021
Chain 1:   3900       -16961.781             0.022            0.021
Chain 1:   4000       -17079.049             0.022            0.021
Chain 1:   4100       -16992.942             0.023            0.021
Chain 1:   4200       -16809.786             0.022            0.021
Chain 1:   4300       -16947.744             0.022            0.021
Chain 1:   4400       -16905.032             0.019            0.011
Chain 1:   4500       -16807.676             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001207 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49382.723             1.000            1.000
Chain 1:    200       -23061.420             1.071            1.141
Chain 1:    300       -14932.986             0.895            1.000
Chain 1:    400       -13535.912             0.697            1.000
Chain 1:    500       -13679.795             0.560            0.544
Chain 1:    600       -14482.923             0.476            0.544
Chain 1:    700       -15540.643             0.418            0.103
Chain 1:    800       -14744.450             0.372            0.103
Chain 1:    900       -19972.429             0.360            0.103
Chain 1:   1000       -13312.698             0.374            0.262
Chain 1:   1100       -10768.860             0.298            0.236
Chain 1:   1200       -11541.824             0.190            0.103
Chain 1:   1300       -12414.880             0.143            0.070
Chain 1:   1400       -12984.990             0.137            0.068
Chain 1:   1500       -10859.806             0.155            0.070
Chain 1:   1600       -10992.891             0.151            0.070
Chain 1:   1700       -13026.477             0.160            0.156
Chain 1:   1800       -10138.793             0.183            0.196
Chain 1:   1900       -10845.291             0.163            0.156
Chain 1:   2000       -10293.724             0.118            0.070
Chain 1:   2100       -18043.543             0.138            0.070
Chain 1:   2200       -10305.179             0.206            0.156
Chain 1:   2300       -16857.320             0.238            0.196
Chain 1:   2400        -9931.359             0.303            0.285
Chain 1:   2500       -10302.206             0.287            0.285
Chain 1:   2600       -13098.829             0.308            0.285
Chain 1:   2700       -15282.203             0.306            0.285
Chain 1:   2800       -12188.962             0.303            0.254
Chain 1:   2900       -14392.701             0.312            0.254
Chain 1:   3000        -9624.000             0.356            0.389
Chain 1:   3100       -10152.519             0.318            0.254
Chain 1:   3200       -10588.058             0.247            0.214
Chain 1:   3300       -12140.576             0.221            0.153
Chain 1:   3400       -13904.701             0.164            0.143
Chain 1:   3500       -10075.362             0.199            0.153
Chain 1:   3600        -9220.912             0.187            0.143
Chain 1:   3700       -20394.862             0.227            0.153
Chain 1:   3800        -9917.494             0.307            0.153
Chain 1:   3900       -14000.482             0.321            0.292
Chain 1:   4000        -9804.677             0.314            0.292
Chain 1:   4100        -9041.154             0.318            0.292
Chain 1:   4200       -10102.149             0.324            0.292
Chain 1:   4300       -16487.518             0.350            0.380
Chain 1:   4400        -8976.391             0.421            0.387
Chain 1:   4500       -10065.232             0.394            0.387
Chain 1:   4600        -8836.423             0.398            0.387
Chain 1:   4700       -12380.651             0.372            0.292
Chain 1:   4800       -17221.753             0.295            0.286
Chain 1:   4900       -12294.279             0.306            0.286
Chain 1:   5000       -13635.660             0.273            0.281
Chain 1:   5100        -8628.088             0.322            0.286
Chain 1:   5200       -11009.482             0.333            0.286
Chain 1:   5300       -13425.331             0.313            0.281
Chain 1:   5400       -13389.952             0.229            0.216
Chain 1:   5500        -8737.951             0.272            0.281
Chain 1:   5600        -8887.847             0.260            0.281
Chain 1:   5700        -8848.652             0.231            0.216
Chain 1:   5800        -8975.801             0.205            0.180
Chain 1:   5900       -16490.060             0.210            0.180
Chain 1:   6000        -8925.877             0.285            0.216
Chain 1:   6100       -13416.710             0.260            0.216
Chain 1:   6200        -9181.207             0.285            0.335
Chain 1:   6300        -8664.363             0.273            0.335
Chain 1:   6400       -12518.314             0.303            0.335
Chain 1:   6500        -9176.187             0.287            0.335
Chain 1:   6600       -11663.044             0.306            0.335
Chain 1:   6700       -13730.329             0.321            0.335
Chain 1:   6800        -8528.447             0.380            0.364
Chain 1:   6900        -8736.595             0.337            0.335
Chain 1:   7000        -8720.128             0.253            0.308
Chain 1:   7100        -8457.883             0.222            0.213
Chain 1:   7200        -8957.160             0.182            0.151
Chain 1:   7300        -8515.788             0.181            0.151
Chain 1:   7400        -8421.115             0.151            0.056
Chain 1:   7500        -9641.490             0.128            0.056
Chain 1:   7600       -11445.479             0.122            0.056
Chain 1:   7700       -12177.259             0.113            0.056
Chain 1:   7800        -9154.230             0.085            0.056
Chain 1:   7900        -8569.772             0.089            0.060
Chain 1:   8000        -8272.036             0.093            0.060
Chain 1:   8100       -11434.129             0.117            0.068
Chain 1:   8200       -10105.265             0.125            0.127
Chain 1:   8300       -13325.961             0.144            0.132
Chain 1:   8400        -8978.247             0.191            0.158
Chain 1:   8500       -11399.357             0.200            0.212
Chain 1:   8600        -9920.623             0.199            0.212
Chain 1:   8700       -10978.896             0.203            0.212
Chain 1:   8800        -8194.481             0.204            0.212
Chain 1:   8900        -8911.493             0.205            0.212
Chain 1:   9000       -12291.405             0.229            0.242
Chain 1:   9100        -8561.238             0.245            0.242
Chain 1:   9200        -9559.824             0.242            0.242
Chain 1:   9300        -8261.458             0.233            0.212
Chain 1:   9400        -8516.642             0.188            0.157
Chain 1:   9500        -8224.027             0.170            0.149
Chain 1:   9600        -9391.828             0.168            0.124
Chain 1:   9700        -8405.798             0.170            0.124
Chain 1:   9800        -8369.992             0.136            0.117
Chain 1:   9900       -10374.573             0.148            0.124
Chain 1:   10000       -12786.403             0.139            0.124
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002403 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58011.869             1.000            1.000
Chain 1:    200       -18199.900             1.594            2.187
Chain 1:    300        -9070.225             1.398            1.007
Chain 1:    400        -8178.182             1.076            1.007
Chain 1:    500        -8489.024             0.868            1.000
Chain 1:    600        -8588.651             0.725            1.000
Chain 1:    700        -8860.573             0.626            0.109
Chain 1:    800        -8307.387             0.556            0.109
Chain 1:    900        -8221.978             0.495            0.067
Chain 1:   1000        -8129.397             0.447            0.067
Chain 1:   1100        -7669.848             0.353            0.060
Chain 1:   1200        -7590.521             0.135            0.037
Chain 1:   1300        -7795.181             0.037            0.031
Chain 1:   1400        -7676.558             0.028            0.026
Chain 1:   1500        -7702.242             0.025            0.015
Chain 1:   1600        -7717.459             0.024            0.015
Chain 1:   1700        -7577.498             0.022            0.015
Chain 1:   1800        -7570.491             0.016            0.011
Chain 1:   1900        -7626.983             0.016            0.011
Chain 1:   2000        -7768.470             0.016            0.015
Chain 1:   2100        -7558.155             0.013            0.015
Chain 1:   2200        -7793.070             0.015            0.018
Chain 1:   2300        -7633.418             0.014            0.018
Chain 1:   2400        -7707.342             0.014            0.018
Chain 1:   2500        -7620.603             0.015            0.018
Chain 1:   2600        -7534.179             0.016            0.018
Chain 1:   2700        -7527.087             0.014            0.011
Chain 1:   2800        -7665.428             0.016            0.018
Chain 1:   2900        -7420.188             0.018            0.018
Chain 1:   3000        -7547.661             0.018            0.018
Chain 1:   3100        -7544.223             0.015            0.017
Chain 1:   3200        -7731.147             0.015            0.017
Chain 1:   3300        -7466.650             0.016            0.017
Chain 1:   3400        -7668.259             0.018            0.018
Chain 1:   3500        -7451.827             0.020            0.024
Chain 1:   3600        -7504.162             0.019            0.024
Chain 1:   3700        -7467.297             0.020            0.024
Chain 1:   3800        -7438.339             0.018            0.024
Chain 1:   3900        -7409.742             0.015            0.017
Chain 1:   4000        -7405.251             0.014            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002884 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85579.034             1.000            1.000
Chain 1:    200       -14118.015             3.031            5.062
Chain 1:    300       -10232.892             2.147            1.000
Chain 1:    400       -12727.028             1.659            1.000
Chain 1:    500        -9934.995             1.384            0.380
Chain 1:    600        -8536.387             1.180            0.380
Chain 1:    700        -8287.031             1.016            0.281
Chain 1:    800        -8726.196             0.895            0.281
Chain 1:    900        -8832.531             0.797            0.196
Chain 1:   1000        -8785.632             0.718            0.196
Chain 1:   1100        -8981.527             0.620            0.164   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8328.245             0.122            0.078
Chain 1:   1300        -8846.250             0.090            0.059
Chain 1:   1400        -8541.397             0.074            0.050
Chain 1:   1500        -8612.384             0.046            0.036
Chain 1:   1600        -8690.941             0.031            0.030
Chain 1:   1700        -8738.763             0.028            0.022
Chain 1:   1800        -8275.575             0.029            0.022
Chain 1:   1900        -8365.096             0.029            0.022
Chain 1:   2000        -8378.629             0.029            0.022
Chain 1:   2100        -8520.011             0.028            0.017
Chain 1:   2200        -8234.966             0.024            0.017
Chain 1:   2300        -8323.412             0.019            0.011
Chain 1:   2400        -8417.110             0.016            0.011
Chain 1:   2500        -8313.586             0.017            0.011
Chain 1:   2600        -8364.275             0.017            0.011
Chain 1:   2700        -8271.068             0.017            0.011
Chain 1:   2800        -8239.627             0.012            0.011
Chain 1:   2900        -8325.325             0.012            0.011
Chain 1:   3000        -8256.593             0.013            0.011
Chain 1:   3100        -8211.361             0.011            0.011
Chain 1:   3200        -8170.160             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003371 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8363565.627             1.000            1.000
Chain 1:    200     -1578638.019             2.649            4.298
Chain 1:    300      -890983.448             2.023            1.000
Chain 1:    400      -458169.604             1.754            1.000
Chain 1:    500      -359337.241             1.458            0.945
Chain 1:    600      -234363.919             1.304            0.945
Chain 1:    700      -120310.754             1.253            0.945
Chain 1:    800       -87473.915             1.143            0.945
Chain 1:    900       -67753.322             1.049            0.772
Chain 1:   1000       -52507.628             0.973            0.772
Chain 1:   1100       -39925.273             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39107.595             0.477            0.375
Chain 1:   1300       -26970.179             0.444            0.375
Chain 1:   1400       -26687.261             0.351            0.315
Chain 1:   1500       -23249.427             0.338            0.315
Chain 1:   1600       -22461.009             0.288            0.291
Chain 1:   1700       -21321.537             0.199            0.290
Chain 1:   1800       -21263.651             0.162            0.148
Chain 1:   1900       -21591.227             0.134            0.053
Chain 1:   2000       -20093.136             0.113            0.053
Chain 1:   2100       -20331.938             0.082            0.035
Chain 1:   2200       -20560.591             0.081            0.035
Chain 1:   2300       -20175.530             0.038            0.019
Chain 1:   2400       -19947.004             0.038            0.019
Chain 1:   2500       -19749.518             0.024            0.015
Chain 1:   2600       -19377.801             0.023            0.015
Chain 1:   2700       -19334.227             0.018            0.012
Chain 1:   2800       -19050.675             0.019            0.015
Chain 1:   2900       -19332.696             0.019            0.015
Chain 1:   3000       -19318.670             0.012            0.012
Chain 1:   3100       -19403.902             0.011            0.011
Chain 1:   3200       -19093.540             0.011            0.015
Chain 1:   3300       -19299.114             0.010            0.011
Chain 1:   3400       -18772.338             0.012            0.015
Chain 1:   3500       -19386.927             0.014            0.015
Chain 1:   3600       -18690.134             0.016            0.015
Chain 1:   3700       -19079.576             0.018            0.016
Chain 1:   3800       -18034.002             0.022            0.020
Chain 1:   3900       -18030.103             0.021            0.020
Chain 1:   4000       -18147.327             0.021            0.020
Chain 1:   4100       -18060.860             0.021            0.020
Chain 1:   4200       -17875.971             0.021            0.020
Chain 1:   4300       -18015.119             0.020            0.020
Chain 1:   4400       -17970.981             0.018            0.010
Chain 1:   4500       -17873.423             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48441.371             1.000            1.000
Chain 1:    200       -16668.682             1.453            1.906
Chain 1:    300       -17301.461             0.981            1.000
Chain 1:    400       -15911.646             0.758            1.000
Chain 1:    500       -11309.083             0.687            0.407
Chain 1:    600       -11756.919             0.579            0.407
Chain 1:    700       -11365.311             0.501            0.087
Chain 1:    800       -10930.840             0.444            0.087
Chain 1:    900       -10474.852             0.399            0.044
Chain 1:   1000       -12776.234             0.377            0.087
Chain 1:   1100       -21221.489             0.317            0.087
Chain 1:   1200       -13431.879             0.184            0.087
Chain 1:   1300       -12702.967             0.187            0.087
Chain 1:   1400       -18918.281             0.211            0.180
Chain 1:   1500       -10619.265             0.248            0.180
Chain 1:   1600       -10784.591             0.246            0.180
Chain 1:   1700       -10181.661             0.248            0.180
Chain 1:   1800       -12814.772             0.265            0.205
Chain 1:   1900       -10547.024             0.282            0.215
Chain 1:   2000       -11012.828             0.268            0.215
Chain 1:   2100       -13402.681             0.246            0.205
Chain 1:   2200       -10146.780             0.220            0.205
Chain 1:   2300       -10965.107             0.222            0.205
Chain 1:   2400        -8547.669             0.218            0.205
Chain 1:   2500        -8933.601             0.144            0.178
Chain 1:   2600        -9373.685             0.147            0.178
Chain 1:   2700        -8782.252             0.148            0.178
Chain 1:   2800       -10363.751             0.142            0.153
Chain 1:   2900       -10342.590             0.121            0.075
Chain 1:   3000       -10117.827             0.119            0.075
Chain 1:   3100       -15069.216             0.134            0.075
Chain 1:   3200       -17206.932             0.114            0.075
Chain 1:   3300        -9578.216             0.187            0.124
Chain 1:   3400        -8851.956             0.167            0.082
Chain 1:   3500       -11028.533             0.182            0.124
Chain 1:   3600        -8740.631             0.203            0.153
Chain 1:   3700        -9102.837             0.201            0.153
Chain 1:   3800        -9597.471             0.191            0.124
Chain 1:   3900       -11207.488             0.205            0.144
Chain 1:   4000       -13870.032             0.222            0.192
Chain 1:   4100        -9337.021             0.237            0.192
Chain 1:   4200        -8869.832             0.230            0.192
Chain 1:   4300        -8494.237             0.155            0.144
Chain 1:   4400       -12663.693             0.180            0.192
Chain 1:   4500       -14605.687             0.173            0.144
Chain 1:   4600        -8394.495             0.221            0.144
Chain 1:   4700       -10623.064             0.238            0.192
Chain 1:   4800        -8432.961             0.259            0.210
Chain 1:   4900        -8844.714             0.249            0.210
Chain 1:   5000       -10690.408             0.247            0.210
Chain 1:   5100        -8837.858             0.220            0.210
Chain 1:   5200       -12924.533             0.246            0.210
Chain 1:   5300       -12633.034             0.244            0.210
Chain 1:   5400        -8668.621             0.257            0.210
Chain 1:   5500       -12896.802             0.276            0.260
Chain 1:   5600       -10023.398             0.231            0.260
Chain 1:   5700        -8474.266             0.228            0.260
Chain 1:   5800        -8563.690             0.203            0.210
Chain 1:   5900        -8848.854             0.202            0.210
Chain 1:   6000        -8400.121             0.190            0.210
Chain 1:   6100        -8378.566             0.169            0.183
Chain 1:   6200        -8558.955             0.140            0.053
Chain 1:   6300        -8277.858             0.141            0.053
Chain 1:   6400        -9090.159             0.104            0.053
Chain 1:   6500        -9085.180             0.071            0.034
Chain 1:   6600        -8261.818             0.053            0.034
Chain 1:   6700       -10376.341             0.055            0.034
Chain 1:   6800        -8758.740             0.072            0.053
Chain 1:   6900        -8733.387             0.069            0.053
Chain 1:   7000        -8809.874             0.065            0.034
Chain 1:   7100        -8001.470             0.075            0.089
Chain 1:   7200        -8086.571             0.074            0.089
Chain 1:   7300       -10379.995             0.092            0.100
Chain 1:   7400        -9161.501             0.097            0.101
Chain 1:   7500        -7956.804             0.112            0.133
Chain 1:   7600        -8268.972             0.105            0.133
Chain 1:   7700        -8550.494             0.088            0.101
Chain 1:   7800        -8320.146             0.073            0.038
Chain 1:   7900        -8184.657             0.074            0.038
Chain 1:   8000        -7968.407             0.076            0.038
Chain 1:   8100        -8267.619             0.069            0.036
Chain 1:   8200       -10045.889             0.086            0.038
Chain 1:   8300        -8079.995             0.088            0.038
Chain 1:   8400        -8353.288             0.078            0.036
Chain 1:   8500        -8102.879             0.066            0.033
Chain 1:   8600        -8759.152             0.070            0.033
Chain 1:   8700        -8312.585             0.072            0.036
Chain 1:   8800       -10000.748             0.086            0.054
Chain 1:   8900       -10901.373             0.093            0.075
Chain 1:   9000        -9106.112             0.110            0.083
Chain 1:   9100        -8074.552             0.119            0.128
Chain 1:   9200        -7749.374             0.105            0.083
Chain 1:   9300       -10208.536             0.105            0.083
Chain 1:   9400        -7931.955             0.131            0.128
Chain 1:   9500        -8091.765             0.129            0.128
Chain 1:   9600        -8010.593             0.123            0.128
Chain 1:   9700        -9713.517             0.135            0.169
Chain 1:   9800       -11581.287             0.134            0.161
Chain 1:   9900        -8578.745             0.161            0.175
Chain 1:   10000        -8997.507             0.146            0.161
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56450.752             1.000            1.000
Chain 1:    200       -17027.907             1.658            2.315
Chain 1:    300        -8561.071             1.435            1.000
Chain 1:    400        -8841.531             1.084            1.000
Chain 1:    500        -8284.690             0.881            0.989
Chain 1:    600        -8699.590             0.742            0.989
Chain 1:    700        -7797.164             0.652            0.116
Chain 1:    800        -8526.126             0.582            0.116
Chain 1:    900        -7899.868             0.526            0.085
Chain 1:   1000        -7893.125             0.473            0.085
Chain 1:   1100        -7835.403             0.374            0.079
Chain 1:   1200        -7592.271             0.146            0.067
Chain 1:   1300        -7635.794             0.047            0.048
Chain 1:   1400        -7925.844             0.048            0.048
Chain 1:   1500        -7607.189             0.045            0.042
Chain 1:   1600        -7496.312             0.042            0.037
Chain 1:   1700        -7482.819             0.031            0.032
Chain 1:   1800        -7545.292             0.023            0.015
Chain 1:   1900        -7589.030             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0036 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86432.738             1.000            1.000
Chain 1:    200       -13137.471             3.290            5.579
Chain 1:    300        -9588.342             2.316            1.000
Chain 1:    400       -10482.004             1.759            1.000
Chain 1:    500        -8491.741             1.454            0.370
Chain 1:    600        -8142.833             1.219            0.370
Chain 1:    700        -8243.785             1.046            0.234
Chain 1:    800        -8848.327             0.924            0.234
Chain 1:    900        -8411.385             0.827            0.085
Chain 1:   1000        -8292.492             0.746            0.085
Chain 1:   1100        -8486.527             0.648            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8201.082             0.094            0.052
Chain 1:   1300        -8351.900             0.059            0.043
Chain 1:   1400        -8319.651             0.050            0.035
Chain 1:   1500        -8207.723             0.028            0.023
Chain 1:   1600        -8309.361             0.025            0.018
Chain 1:   1700        -8396.341             0.025            0.018
Chain 1:   1800        -8009.105             0.023            0.018
Chain 1:   1900        -8111.686             0.019            0.014
Chain 1:   2000        -8081.558             0.018            0.014
Chain 1:   2100        -8213.338             0.017            0.014
Chain 1:   2200        -7999.263             0.017            0.014
Chain 1:   2300        -8140.988             0.017            0.014
Chain 1:   2400        -8153.336             0.016            0.014
Chain 1:   2500        -8121.414             0.015            0.013
Chain 1:   2600        -8121.159             0.014            0.013
Chain 1:   2700        -8029.398             0.014            0.013
Chain 1:   2800        -8005.426             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003016 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408926.555             1.000            1.000
Chain 1:    200     -1584872.277             2.653            4.306
Chain 1:    300      -890182.543             2.029            1.000
Chain 1:    400      -457166.132             1.758            1.000
Chain 1:    500      -357406.127             1.462            0.947
Chain 1:    600      -232359.228             1.308            0.947
Chain 1:    700      -118691.200             1.258            0.947
Chain 1:    800       -85941.877             1.149            0.947
Chain 1:    900       -66307.831             1.054            0.780
Chain 1:   1000       -51121.184             0.978            0.780
Chain 1:   1100       -38620.866             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37795.035             0.482            0.381
Chain 1:   1300       -25781.444             0.451            0.381
Chain 1:   1400       -25500.860             0.357            0.324
Chain 1:   1500       -22096.643             0.345            0.324
Chain 1:   1600       -21315.087             0.295            0.297
Chain 1:   1700       -20192.806             0.204            0.296
Chain 1:   1800       -20137.569             0.166            0.154
Chain 1:   1900       -20463.212             0.138            0.056
Chain 1:   2000       -18977.560             0.117            0.056
Chain 1:   2100       -19215.681             0.085            0.037
Chain 1:   2200       -19441.490             0.084            0.037
Chain 1:   2300       -19059.384             0.040            0.020
Chain 1:   2400       -18831.701             0.040            0.020
Chain 1:   2500       -18633.661             0.026            0.016
Chain 1:   2600       -18264.452             0.024            0.016
Chain 1:   2700       -18221.622             0.019            0.012
Chain 1:   2800       -17938.716             0.020            0.016
Chain 1:   2900       -18219.648             0.020            0.015
Chain 1:   3000       -18205.896             0.012            0.012
Chain 1:   3100       -18290.803             0.011            0.012
Chain 1:   3200       -17981.858             0.012            0.015
Chain 1:   3300       -18186.298             0.011            0.012
Chain 1:   3400       -17661.886             0.013            0.015
Chain 1:   3500       -18272.737             0.015            0.016
Chain 1:   3600       -17580.733             0.017            0.016
Chain 1:   3700       -17966.530             0.019            0.017
Chain 1:   3800       -16928.287             0.023            0.021
Chain 1:   3900       -16924.479             0.022            0.021
Chain 1:   4000       -17041.780             0.023            0.021
Chain 1:   4100       -16955.636             0.023            0.021
Chain 1:   4200       -16772.346             0.022            0.021
Chain 1:   4300       -16910.426             0.022            0.021
Chain 1:   4400       -16867.611             0.019            0.011
Chain 1:   4500       -16770.200             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49438.146             1.000            1.000
Chain 1:    200       -23925.020             1.033            1.066
Chain 1:    300       -19014.166             0.775            1.000
Chain 1:    400       -14689.822             0.655            1.000
Chain 1:    500       -16196.567             0.542            0.294
Chain 1:    600       -11914.768             0.512            0.359
Chain 1:    700       -15724.745             0.473            0.294
Chain 1:    800       -14730.842             0.423            0.294
Chain 1:    900       -14966.643             0.377            0.258
Chain 1:   1000       -13249.129             0.353            0.258
Chain 1:   1100       -14025.189             0.258            0.242
Chain 1:   1200       -27335.242             0.200            0.242
Chain 1:   1300       -21511.803             0.201            0.242
Chain 1:   1400       -11841.417             0.254            0.242
Chain 1:   1500       -12228.840             0.248            0.242
Chain 1:   1600       -10580.968             0.227            0.156
Chain 1:   1700       -15950.768             0.237            0.156
Chain 1:   1800       -10547.342             0.281            0.271
Chain 1:   1900       -10064.308             0.284            0.271
Chain 1:   2000       -11127.141             0.281            0.271
Chain 1:   2100       -10214.701             0.284            0.271
Chain 1:   2200       -11379.308             0.246            0.156
Chain 1:   2300       -11010.074             0.222            0.102
Chain 1:   2400       -16265.468             0.173            0.102
Chain 1:   2500       -10896.746             0.219            0.156
Chain 1:   2600       -16183.417             0.236            0.323
Chain 1:   2700       -15335.410             0.208            0.102
Chain 1:   2800        -9425.680             0.219            0.102
Chain 1:   2900        -9227.487             0.217            0.102
Chain 1:   3000        -9621.037             0.211            0.102
Chain 1:   3100       -10141.267             0.207            0.102
Chain 1:   3200        -9412.809             0.205            0.077
Chain 1:   3300       -10439.522             0.211            0.098
Chain 1:   3400       -10290.068             0.181            0.077
Chain 1:   3500       -10001.697             0.134            0.055
Chain 1:   3600       -10316.594             0.105            0.051
Chain 1:   3700        -9703.709             0.105            0.051
Chain 1:   3800        -9927.647             0.045            0.041
Chain 1:   3900       -13499.745             0.069            0.051
Chain 1:   4000       -16738.259             0.084            0.063
Chain 1:   4100       -14537.475             0.094            0.077
Chain 1:   4200        -9196.811             0.145            0.098
Chain 1:   4300       -10451.889             0.147            0.120
Chain 1:   4400       -12735.627             0.163            0.151
Chain 1:   4500        -9371.189             0.196            0.179
Chain 1:   4600        -9334.669             0.194            0.179
Chain 1:   4700        -9515.995             0.189            0.179
Chain 1:   4800       -12347.941             0.210            0.193
Chain 1:   4900       -13683.711             0.193            0.179
Chain 1:   5000       -10325.723             0.207            0.179
Chain 1:   5100        -8816.706             0.209            0.179
Chain 1:   5200        -9038.327             0.153            0.171
Chain 1:   5300       -10054.486             0.151            0.171
Chain 1:   5400        -8668.472             0.149            0.160
Chain 1:   5500       -12688.793             0.145            0.160
Chain 1:   5600       -12510.688             0.146            0.160
Chain 1:   5700        -9817.387             0.171            0.171
Chain 1:   5800        -9833.258             0.149            0.160
Chain 1:   5900       -11612.199             0.154            0.160
Chain 1:   6000        -8741.642             0.155            0.160
Chain 1:   6100       -12494.399             0.167            0.160
Chain 1:   6200        -9099.313             0.202            0.274
Chain 1:   6300        -8855.560             0.195            0.274
Chain 1:   6400        -8857.908             0.179            0.274
Chain 1:   6500        -8717.096             0.149            0.153
Chain 1:   6600        -9632.127             0.157            0.153
Chain 1:   6700        -8867.083             0.138            0.095
Chain 1:   6800        -9089.801             0.140            0.095
Chain 1:   6900        -8964.987             0.127            0.086
Chain 1:   7000       -15774.308             0.137            0.086
Chain 1:   7100        -9546.583             0.172            0.086
Chain 1:   7200        -8593.684             0.146            0.086
Chain 1:   7300        -8787.664             0.145            0.086
Chain 1:   7400        -8936.738             0.147            0.086
Chain 1:   7500        -9505.040             0.151            0.086
Chain 1:   7600       -11760.043             0.161            0.086
Chain 1:   7700        -8830.618             0.186            0.111
Chain 1:   7800       -11703.041             0.208            0.192
Chain 1:   7900        -8471.318             0.244            0.245
Chain 1:   8000        -8594.170             0.203            0.192
Chain 1:   8100       -10022.099             0.152            0.142
Chain 1:   8200       -12081.181             0.158            0.170
Chain 1:   8300        -8912.263             0.191            0.192
Chain 1:   8400       -13229.450             0.222            0.245
Chain 1:   8500        -8679.354             0.268            0.326
Chain 1:   8600       -11361.234             0.273            0.326
Chain 1:   8700        -9808.584             0.255            0.245
Chain 1:   8800       -12228.427             0.251            0.236
Chain 1:   8900       -10829.467             0.225            0.198
Chain 1:   9000        -9387.034             0.239            0.198
Chain 1:   9100        -8585.100             0.235            0.198
Chain 1:   9200       -11453.670             0.243            0.236
Chain 1:   9300       -11312.607             0.208            0.198
Chain 1:   9400        -8518.150             0.208            0.198
Chain 1:   9500        -8821.439             0.159            0.158
Chain 1:   9600        -9178.088             0.140            0.154
Chain 1:   9700       -11216.867             0.142            0.154
Chain 1:   9800        -8485.659             0.154            0.154
Chain 1:   9900        -9663.004             0.154            0.154
Chain 1:   10000        -8396.149             0.153            0.151
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57305.506             1.000            1.000
Chain 1:    200       -17756.809             1.614            2.227
Chain 1:    300        -8927.202             1.405            1.000
Chain 1:    400        -8284.818             1.073            1.000
Chain 1:    500        -8794.728             0.870            0.989
Chain 1:    600        -8896.861             0.727            0.989
Chain 1:    700        -8326.607             0.633            0.078
Chain 1:    800        -8294.510             0.554            0.078
Chain 1:    900        -8111.916             0.495            0.068
Chain 1:   1000        -7670.113             0.452            0.068
Chain 1:   1100        -7662.201             0.352            0.058
Chain 1:   1200        -7685.143             0.129            0.058
Chain 1:   1300        -7804.349             0.032            0.023
Chain 1:   1400        -7935.643             0.026            0.017
Chain 1:   1500        -7656.586             0.024            0.017
Chain 1:   1600        -7827.433             0.025            0.022
Chain 1:   1700        -7578.357             0.021            0.022
Chain 1:   1800        -7791.116             0.023            0.023
Chain 1:   1900        -7692.205             0.022            0.022
Chain 1:   2000        -7760.890             0.018            0.017
Chain 1:   2100        -7586.697             0.020            0.022
Chain 1:   2200        -7997.353             0.025            0.023
Chain 1:   2300        -7647.248             0.028            0.027
Chain 1:   2400        -7741.947             0.027            0.027
Chain 1:   2500        -7652.795             0.025            0.023
Chain 1:   2600        -7621.515             0.023            0.023
Chain 1:   2700        -7618.693             0.020            0.013
Chain 1:   2800        -7610.474             0.017            0.012
Chain 1:   2900        -7492.798             0.017            0.012
Chain 1:   3000        -7636.149             0.018            0.016
Chain 1:   3100        -7625.217             0.016            0.012
Chain 1:   3200        -7823.835             0.014            0.012
Chain 1:   3300        -7549.338             0.013            0.012
Chain 1:   3400        -7768.391             0.014            0.016
Chain 1:   3500        -7532.534             0.016            0.019
Chain 1:   3600        -7598.764             0.017            0.019
Chain 1:   3700        -7547.526             0.017            0.019
Chain 1:   3800        -7546.243             0.017            0.019
Chain 1:   3900        -7513.562             0.016            0.019
Chain 1:   4000        -7507.821             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003182 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86919.484             1.000            1.000
Chain 1:    200       -13854.217             3.137            5.274
Chain 1:    300       -10155.561             2.213            1.000
Chain 1:    400       -11165.339             1.682            1.000
Chain 1:    500        -9073.143             1.392            0.364
Chain 1:    600        -9307.770             1.164            0.364
Chain 1:    700        -9509.913             1.001            0.231
Chain 1:    800        -8528.390             0.890            0.231
Chain 1:    900        -8478.437             0.792            0.115
Chain 1:   1000        -9243.970             0.721            0.115
Chain 1:   1100        -8693.817             0.627            0.090   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9067.520             0.104            0.083
Chain 1:   1300        -8578.624             0.073            0.063
Chain 1:   1400        -8648.513             0.065            0.057
Chain 1:   1500        -8604.389             0.042            0.041
Chain 1:   1600        -8619.631             0.040            0.041
Chain 1:   1700        -8505.567             0.039            0.041
Chain 1:   1800        -8559.605             0.028            0.013
Chain 1:   1900        -8434.070             0.029            0.015
Chain 1:   2000        -8497.773             0.022            0.013
Chain 1:   2100        -8637.075             0.017            0.013
Chain 1:   2200        -8438.718             0.015            0.013
Chain 1:   2300        -8590.555             0.011            0.013
Chain 1:   2400        -8430.895             0.013            0.015
Chain 1:   2500        -8500.110             0.013            0.015
Chain 1:   2600        -8414.155             0.014            0.015
Chain 1:   2700        -8446.580             0.013            0.015
Chain 1:   2800        -8407.454             0.013            0.015
Chain 1:   2900        -8499.618             0.012            0.011
Chain 1:   3000        -8325.726             0.013            0.016
Chain 1:   3100        -8489.580             0.014            0.018
Chain 1:   3200        -8362.447             0.013            0.015
Chain 1:   3300        -8371.550             0.011            0.011
Chain 1:   3400        -8523.250             0.011            0.011
Chain 1:   3500        -8514.747             0.010            0.011
Chain 1:   3600        -8319.708             0.012            0.015
Chain 1:   3700        -8462.863             0.013            0.017
Chain 1:   3800        -8326.552             0.014            0.017
Chain 1:   3900        -8261.765             0.014            0.017
Chain 1:   4000        -8336.019             0.013            0.016
Chain 1:   4100        -8327.094             0.011            0.015
Chain 1:   4200        -8315.639             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003125 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402658.006             1.000            1.000
Chain 1:    200     -1582330.340             2.655            4.310
Chain 1:    300      -889862.549             2.029            1.000
Chain 1:    400      -457363.365             1.759            1.000
Chain 1:    500      -357921.058             1.462            0.946
Chain 1:    600      -232960.427             1.308            0.946
Chain 1:    700      -119397.964             1.257            0.946
Chain 1:    800       -86690.562             1.147            0.946
Chain 1:    900       -67068.289             1.052            0.778
Chain 1:   1000       -51897.830             0.976            0.778
Chain 1:   1100       -39404.490             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38586.743             0.479            0.377
Chain 1:   1300       -26557.615             0.446            0.377
Chain 1:   1400       -26280.221             0.353            0.317
Chain 1:   1500       -22871.095             0.340            0.317
Chain 1:   1600       -22089.845             0.290            0.293
Chain 1:   1700       -20964.312             0.200            0.292
Chain 1:   1800       -20909.119             0.163            0.149
Chain 1:   1900       -21235.625             0.135            0.054
Chain 1:   2000       -19746.824             0.113            0.054
Chain 1:   2100       -19985.125             0.083            0.035
Chain 1:   2200       -20211.825             0.082            0.035
Chain 1:   2300       -19828.714             0.038            0.019
Chain 1:   2400       -19600.677             0.039            0.019
Chain 1:   2500       -19402.752             0.025            0.015
Chain 1:   2600       -19032.534             0.023            0.015
Chain 1:   2700       -18989.456             0.018            0.012
Chain 1:   2800       -18706.210             0.019            0.015
Chain 1:   2900       -18987.537             0.019            0.015
Chain 1:   3000       -18973.685             0.012            0.012
Chain 1:   3100       -19058.742             0.011            0.012
Chain 1:   3200       -18749.207             0.011            0.015
Chain 1:   3300       -18954.123             0.011            0.012
Chain 1:   3400       -18428.681             0.012            0.015
Chain 1:   3500       -19041.150             0.014            0.015
Chain 1:   3600       -18346.999             0.016            0.015
Chain 1:   3700       -18734.387             0.018            0.017
Chain 1:   3800       -17692.913             0.023            0.021
Chain 1:   3900       -17689.037             0.021            0.021
Chain 1:   4000       -17806.309             0.022            0.021
Chain 1:   4100       -17720.030             0.022            0.021
Chain 1:   4200       -17536.038             0.021            0.021
Chain 1:   4300       -17674.610             0.021            0.021
Chain 1:   4400       -17631.196             0.018            0.010
Chain 1:   4500       -17533.695             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001334 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12503.258             1.000            1.000
Chain 1:    200        -9165.087             0.682            1.000
Chain 1:    300        -8037.208             0.502            0.364
Chain 1:    400        -8191.162             0.381            0.364
Chain 1:    500        -7775.799             0.315            0.140
Chain 1:    600        -7913.410             0.266            0.140
Chain 1:    700        -7849.238             0.229            0.053
Chain 1:    800        -7882.590             0.201            0.053
Chain 1:    900        -7956.655             0.180            0.019
Chain 1:   1000        -7923.690             0.162            0.019
Chain 1:   1100        -7865.457             0.063            0.017
Chain 1:   1200        -7848.960             0.027            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001578 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61904.587             1.000            1.000
Chain 1:    200       -18130.276             1.707            2.414
Chain 1:    300        -8952.805             1.480            1.025
Chain 1:    400        -9398.276             1.122            1.025
Chain 1:    500        -7859.425             0.937            1.000
Chain 1:    600        -8756.813             0.798            1.000
Chain 1:    700        -7867.561             0.700            0.196
Chain 1:    800        -7810.125             0.613            0.196
Chain 1:    900        -7654.680             0.547            0.113
Chain 1:   1000        -7857.304             0.495            0.113
Chain 1:   1100        -7874.013             0.395            0.102
Chain 1:   1200        -7764.527             0.155            0.047
Chain 1:   1300        -7840.689             0.054            0.026
Chain 1:   1400        -7654.545             0.052            0.024
Chain 1:   1500        -7542.832             0.033            0.020
Chain 1:   1600        -7770.390             0.026            0.020
Chain 1:   1700        -7575.146             0.017            0.020
Chain 1:   1800        -7640.315             0.017            0.020
Chain 1:   1900        -7575.915             0.016            0.015
Chain 1:   2000        -7657.161             0.015            0.014
Chain 1:   2100        -7571.831             0.016            0.014
Chain 1:   2200        -7705.241             0.016            0.015
Chain 1:   2300        -7551.613             0.017            0.017
Chain 1:   2400        -7574.085             0.015            0.015
Chain 1:   2500        -7599.582             0.014            0.011
Chain 1:   2600        -7506.396             0.012            0.011
Chain 1:   2700        -7530.686             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003147 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86841.842             1.000            1.000
Chain 1:    200       -13695.343             3.170            5.341
Chain 1:    300        -9952.147             2.239            1.000
Chain 1:    400       -11540.348             1.714            1.000
Chain 1:    500        -8784.775             1.434            0.376
Chain 1:    600        -8488.284             1.201            0.376
Chain 1:    700        -8471.070             1.029            0.314
Chain 1:    800        -8554.860             0.902            0.314
Chain 1:    900        -8652.246             0.803            0.138
Chain 1:   1000        -8520.411             0.724            0.138
Chain 1:   1100        -8727.800             0.627            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8255.502             0.098            0.035
Chain 1:   1300        -8584.203             0.064            0.035
Chain 1:   1400        -8591.925             0.051            0.024
Chain 1:   1500        -8436.621             0.021            0.018
Chain 1:   1600        -8549.983             0.019            0.015
Chain 1:   1700        -8604.608             0.019            0.015
Chain 1:   1800        -8155.874             0.024            0.018
Chain 1:   1900        -8265.157             0.024            0.018
Chain 1:   2000        -8249.356             0.023            0.018
Chain 1:   2100        -8387.793             0.022            0.017
Chain 1:   2200        -8160.354             0.019            0.017
Chain 1:   2300        -8260.475             0.017            0.013
Chain 1:   2400        -8332.709             0.017            0.013
Chain 1:   2500        -8272.159             0.016            0.013
Chain 1:   2600        -8288.903             0.015            0.012
Chain 1:   2700        -8195.382             0.016            0.012
Chain 1:   2800        -8141.282             0.011            0.011
Chain 1:   2900        -8247.148             0.011            0.011
Chain 1:   3000        -8084.895             0.013            0.012
Chain 1:   3100        -8225.829             0.013            0.012
Chain 1:   3200        -8095.029             0.011            0.012
Chain 1:   3300        -8320.894             0.013            0.013
Chain 1:   3400        -8354.985             0.012            0.013
Chain 1:   3500        -8194.971             0.014            0.016
Chain 1:   3600        -8051.851             0.015            0.017
Chain 1:   3700        -8198.353             0.016            0.018
Chain 1:   3800        -8052.634             0.017            0.018
Chain 1:   3900        -7986.902             0.017            0.018
Chain 1:   4000        -8096.980             0.016            0.018
Chain 1:   4100        -8062.015             0.015            0.018
Chain 1:   4200        -8047.947             0.013            0.018
Chain 1:   4300        -8081.344             0.011            0.014
Chain 1:   4400        -8038.165             0.011            0.014
Chain 1:   4500        -8136.159             0.010            0.012
Chain 1:   4600        -8027.902             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003454 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8446687.498             1.000            1.000
Chain 1:    200     -1595137.162             2.648            4.295
Chain 1:    300      -893397.093             2.027            1.000
Chain 1:    400      -458485.401             1.757            1.000
Chain 1:    500      -357892.930             1.462            0.949
Chain 1:    600      -232413.406             1.308            0.949
Chain 1:    700      -118957.079             1.258            0.949
Chain 1:    800       -86263.115             1.148            0.949
Chain 1:    900       -66696.998             1.053            0.785
Chain 1:   1000       -51593.899             0.977            0.785
Chain 1:   1100       -39153.798             0.909            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38346.577             0.481            0.379
Chain 1:   1300       -26378.619             0.448            0.379
Chain 1:   1400       -26107.478             0.354            0.318
Chain 1:   1500       -22714.097             0.341            0.318
Chain 1:   1600       -21937.151             0.291            0.293
Chain 1:   1700       -20819.470             0.201            0.293
Chain 1:   1800       -20766.054             0.163            0.149
Chain 1:   1900       -21092.750             0.135            0.054
Chain 1:   2000       -19607.336             0.114            0.054
Chain 1:   2100       -19845.558             0.083            0.035
Chain 1:   2200       -20071.678             0.082            0.035
Chain 1:   2300       -19689.066             0.039            0.019
Chain 1:   2400       -19461.054             0.039            0.019
Chain 1:   2500       -19262.661             0.025            0.015
Chain 1:   2600       -18892.607             0.023            0.015
Chain 1:   2700       -18849.572             0.018            0.012
Chain 1:   2800       -18565.954             0.019            0.015
Chain 1:   2900       -18847.380             0.019            0.015
Chain 1:   3000       -18833.637             0.012            0.012
Chain 1:   3100       -18918.678             0.011            0.012
Chain 1:   3200       -18609.049             0.012            0.015
Chain 1:   3300       -18814.041             0.011            0.012
Chain 1:   3400       -18288.225             0.012            0.015
Chain 1:   3500       -18901.028             0.015            0.015
Chain 1:   3600       -18206.528             0.016            0.015
Chain 1:   3700       -18594.082             0.018            0.017
Chain 1:   3800       -17551.839             0.023            0.021
Chain 1:   3900       -17547.883             0.021            0.021
Chain 1:   4000       -17665.258             0.022            0.021
Chain 1:   4100       -17578.863             0.022            0.021
Chain 1:   4200       -17394.722             0.021            0.021
Chain 1:   4300       -17533.431             0.021            0.021
Chain 1:   4400       -17489.893             0.018            0.011
Chain 1:   4500       -17392.340             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48816.265             1.000            1.000
Chain 1:    200       -12451.151             1.960            2.921
Chain 1:    300       -15531.262             1.373            1.000
Chain 1:    400       -12756.651             1.084            1.000
Chain 1:    500       -16199.588             0.910            0.218
Chain 1:    600       -11799.595             0.820            0.373
Chain 1:    700       -14037.206             0.726            0.218
Chain 1:    800       -23643.783             0.686            0.373
Chain 1:    900       -19934.581             0.630            0.218
Chain 1:   1000       -12080.750             0.632            0.373
Chain 1:   1100       -13972.837             0.546            0.218   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12354.984             0.267            0.213
Chain 1:   1300       -12555.335             0.249            0.213
Chain 1:   1400       -11376.823             0.237            0.186
Chain 1:   1500       -23314.941             0.267            0.186
Chain 1:   1600       -25163.195             0.237            0.159
Chain 1:   1700        -9848.878             0.377            0.186
Chain 1:   1800       -12327.805             0.356            0.186
Chain 1:   1900       -17246.427             0.366            0.201
Chain 1:   2000       -10753.185             0.362            0.201
Chain 1:   2100        -9927.481             0.356            0.201
Chain 1:   2200       -16780.196             0.384            0.285
Chain 1:   2300       -10597.358             0.441            0.408
Chain 1:   2400        -9306.241             0.444            0.408
Chain 1:   2500       -16837.985             0.438            0.408
Chain 1:   2600        -9318.077             0.511            0.447   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2700        -9780.762             0.361            0.408
Chain 1:   2800       -11442.995             0.355            0.408
Chain 1:   2900        -9550.834             0.346            0.408
Chain 1:   3000        -9671.460             0.287            0.198
Chain 1:   3100       -13777.380             0.309            0.298
Chain 1:   3200       -11022.525             0.293            0.250
Chain 1:   3300       -10184.288             0.243            0.198
Chain 1:   3400        -9227.887             0.239            0.198
Chain 1:   3500        -9554.756             0.198            0.145
Chain 1:   3600       -10566.839             0.127            0.104
Chain 1:   3700       -17040.915             0.160            0.145
Chain 1:   3800       -11280.661             0.197            0.198
Chain 1:   3900       -10782.522             0.181            0.104
Chain 1:   4000       -10091.488             0.187            0.104
Chain 1:   4100       -10071.690             0.157            0.096
Chain 1:   4200       -10622.033             0.137            0.082
Chain 1:   4300        -9641.281             0.139            0.096
Chain 1:   4400        -8776.184             0.139            0.096
Chain 1:   4500        -8571.640             0.138            0.096
Chain 1:   4600        -8945.512             0.132            0.068
Chain 1:   4700        -8487.016             0.100            0.054
Chain 1:   4800        -8906.736             0.054            0.052
Chain 1:   4900        -9721.799             0.057            0.054
Chain 1:   5000        -9753.909             0.051            0.052
Chain 1:   5100        -8826.179             0.061            0.054
Chain 1:   5200        -9053.663             0.058            0.054
Chain 1:   5300        -9072.834             0.048            0.047
Chain 1:   5400        -8691.802             0.043            0.044
Chain 1:   5500       -12262.659             0.070            0.047
Chain 1:   5600       -10448.029             0.083            0.054
Chain 1:   5700       -13439.915             0.100            0.084
Chain 1:   5800        -8718.742             0.149            0.105
Chain 1:   5900        -8813.585             0.142            0.105
Chain 1:   6000        -9171.501             0.145            0.105
Chain 1:   6100        -8829.740             0.139            0.044
Chain 1:   6200        -9426.044             0.143            0.063
Chain 1:   6300        -9814.339             0.146            0.063
Chain 1:   6400        -9599.394             0.144            0.063
Chain 1:   6500        -8564.460             0.127            0.063
Chain 1:   6600        -9275.588             0.118            0.063
Chain 1:   6700        -8480.452             0.105            0.063
Chain 1:   6800       -12325.034             0.082            0.063
Chain 1:   6900       -10457.137             0.098            0.077
Chain 1:   7000        -8748.523             0.114            0.094
Chain 1:   7100        -9312.030             0.116            0.094
Chain 1:   7200        -8732.504             0.117            0.094
Chain 1:   7300       -10452.470             0.129            0.121
Chain 1:   7400        -8653.400             0.148            0.165
Chain 1:   7500        -8881.510             0.138            0.165
Chain 1:   7600        -8674.342             0.133            0.165
Chain 1:   7700       -10401.222             0.140            0.166
Chain 1:   7800        -8702.188             0.128            0.166
Chain 1:   7900        -8590.708             0.112            0.165
Chain 1:   8000        -8901.597             0.096            0.066
Chain 1:   8100        -8290.691             0.097            0.074
Chain 1:   8200       -10856.519             0.114            0.165
Chain 1:   8300        -8318.820             0.128            0.166
Chain 1:   8400       -11999.072             0.138            0.166
Chain 1:   8500        -9175.208             0.166            0.195
Chain 1:   8600       -12590.779             0.191            0.236
Chain 1:   8700       -10801.213             0.191            0.236
Chain 1:   8800        -8504.580             0.198            0.270
Chain 1:   8900        -9608.012             0.209            0.270
Chain 1:   9000        -8516.031             0.218            0.270
Chain 1:   9100        -9453.155             0.221            0.270
Chain 1:   9200        -9206.698             0.200            0.270
Chain 1:   9300       -10746.693             0.183            0.166
Chain 1:   9400       -10267.244             0.157            0.143
Chain 1:   9500        -8492.458             0.147            0.143
Chain 1:   9600        -8327.928             0.122            0.128
Chain 1:   9700        -9243.014             0.116            0.115
Chain 1:   9800        -8697.737             0.095            0.099
Chain 1:   9900        -9190.017             0.089            0.099
Chain 1:   10000        -9103.655             0.077            0.063
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001426 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57240.094             1.000            1.000
Chain 1:    200       -17489.689             1.636            2.273
Chain 1:    300        -8783.631             1.421            1.000
Chain 1:    400        -8447.645             1.076            1.000
Chain 1:    500        -8469.382             0.861            0.991
Chain 1:    600        -8555.395             0.719            0.991
Chain 1:    700        -8692.961             0.619            0.040
Chain 1:    800        -8103.730             0.551            0.073
Chain 1:    900        -7952.789             0.492            0.040
Chain 1:   1000        -7871.018             0.443            0.040
Chain 1:   1100        -7796.606             0.344            0.019
Chain 1:   1200        -7704.750             0.118            0.016
Chain 1:   1300        -7628.024             0.020            0.012
Chain 1:   1400        -7696.219             0.017            0.010
Chain 1:   1500        -7675.372             0.017            0.010
Chain 1:   1600        -7729.835             0.017            0.010
Chain 1:   1700        -7610.463             0.017            0.010
Chain 1:   1800        -7644.665             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003167 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86057.766             1.000            1.000
Chain 1:    200       -13549.242             3.176            5.351
Chain 1:    300        -9963.598             2.237            1.000
Chain 1:    400       -10863.043             1.699            1.000
Chain 1:    500        -8888.042             1.403            0.360
Chain 1:    600        -8448.805             1.178            0.360
Chain 1:    700        -8676.520             1.014            0.222
Chain 1:    800        -8958.693             0.891            0.222
Chain 1:    900        -8759.664             0.794            0.083
Chain 1:   1000        -8552.153             0.717            0.083
Chain 1:   1100        -8808.213             0.620            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8444.556             0.089            0.043
Chain 1:   1300        -8648.129             0.056            0.031
Chain 1:   1400        -8655.930             0.048            0.029
Chain 1:   1500        -8551.000             0.027            0.026
Chain 1:   1600        -8653.247             0.023            0.024
Chain 1:   1700        -8741.824             0.021            0.024
Chain 1:   1800        -8337.051             0.023            0.024
Chain 1:   1900        -8435.542             0.022            0.024
Chain 1:   2000        -8407.158             0.019            0.012
Chain 1:   2100        -8526.986             0.018            0.012
Chain 1:   2200        -8335.572             0.016            0.012
Chain 1:   2300        -8470.998             0.015            0.012
Chain 1:   2400        -8345.914             0.017            0.014
Chain 1:   2500        -8410.743             0.016            0.014
Chain 1:   2600        -8434.436             0.015            0.014
Chain 1:   2700        -8352.725             0.015            0.014
Chain 1:   2800        -8325.294             0.011            0.012
Chain 1:   2900        -8380.697             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388983.106             1.000            1.000
Chain 1:    200     -1580933.491             2.653            4.306
Chain 1:    300      -890589.087             2.027            1.000
Chain 1:    400      -457534.681             1.757            1.000
Chain 1:    500      -358340.048             1.461            0.946
Chain 1:    600      -233244.121             1.307            0.946
Chain 1:    700      -119392.818             1.256            0.946
Chain 1:    800       -86592.670             1.147            0.946
Chain 1:    900       -66909.990             1.052            0.775
Chain 1:   1000       -51684.648             0.976            0.775
Chain 1:   1100       -39143.203             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38315.545             0.480            0.379
Chain 1:   1300       -26251.930             0.448            0.379
Chain 1:   1400       -25968.058             0.355            0.320
Chain 1:   1500       -22550.717             0.342            0.320
Chain 1:   1600       -21766.012             0.292            0.295
Chain 1:   1700       -20637.222             0.202            0.294
Chain 1:   1800       -20580.787             0.165            0.152
Chain 1:   1900       -20906.758             0.137            0.055
Chain 1:   2000       -19417.239             0.115            0.055
Chain 1:   2100       -19655.475             0.084            0.036
Chain 1:   2200       -19882.121             0.083            0.036
Chain 1:   2300       -19499.224             0.039            0.020
Chain 1:   2400       -19271.385             0.039            0.020
Chain 1:   2500       -19073.594             0.025            0.016
Chain 1:   2600       -18703.806             0.023            0.016
Chain 1:   2700       -18660.807             0.018            0.012
Chain 1:   2800       -18377.853             0.020            0.015
Chain 1:   2900       -18659.038             0.019            0.015
Chain 1:   3000       -18645.139             0.012            0.012
Chain 1:   3100       -18730.124             0.011            0.012
Chain 1:   3200       -18420.918             0.012            0.015
Chain 1:   3300       -18625.581             0.011            0.012
Chain 1:   3400       -18100.773             0.012            0.015
Chain 1:   3500       -18712.325             0.015            0.015
Chain 1:   3600       -18019.443             0.017            0.015
Chain 1:   3700       -18405.940             0.018            0.017
Chain 1:   3800       -17366.407             0.023            0.021
Chain 1:   3900       -17362.618             0.021            0.021
Chain 1:   4000       -17479.861             0.022            0.021
Chain 1:   4100       -17393.695             0.022            0.021
Chain 1:   4200       -17210.117             0.021            0.021
Chain 1:   4300       -17348.361             0.021            0.021
Chain 1:   4400       -17305.318             0.018            0.011
Chain 1:   4500       -17207.914             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12576.392             1.000            1.000
Chain 1:    200        -9529.530             0.660            1.000
Chain 1:    300        -8204.424             0.494            0.320
Chain 1:    400        -8351.999             0.375            0.320
Chain 1:    500        -8308.928             0.301            0.162
Chain 1:    600        -8174.525             0.253            0.162
Chain 1:    700        -8122.361             0.218            0.018
Chain 1:    800        -8142.739             0.191            0.018
Chain 1:    900        -8084.679             0.171            0.016
Chain 1:   1000        -8155.276             0.155            0.016
Chain 1:   1100        -8334.157             0.057            0.016
Chain 1:   1200        -8129.942             0.027            0.016
Chain 1:   1300        -8064.864             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001493 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62497.285             1.000            1.000
Chain 1:    200       -17993.145             1.737            2.473
Chain 1:    300        -8942.501             1.495            1.012
Chain 1:    400        -8486.971             1.135            1.012
Chain 1:    500        -8702.907             0.913            1.000
Chain 1:    600        -9203.822             0.770            1.000
Chain 1:    700        -7797.943             0.686            0.180
Chain 1:    800        -8160.899             0.605            0.180
Chain 1:    900        -7950.010             0.541            0.054
Chain 1:   1000        -7775.460             0.489            0.054
Chain 1:   1100        -7813.703             0.390            0.054
Chain 1:   1200        -7706.001             0.144            0.044
Chain 1:   1300        -7763.000             0.043            0.027
Chain 1:   1400        -7880.514             0.039            0.025
Chain 1:   1500        -7566.628             0.041            0.027
Chain 1:   1600        -7752.919             0.038            0.024
Chain 1:   1700        -7545.652             0.023            0.024
Chain 1:   1800        -7634.849             0.019            0.022
Chain 1:   1900        -7650.915             0.017            0.015
Chain 1:   2000        -7643.904             0.015            0.014
Chain 1:   2100        -7616.732             0.015            0.014
Chain 1:   2200        -7724.575             0.015            0.014
Chain 1:   2300        -7568.587             0.016            0.015
Chain 1:   2400        -7678.754             0.016            0.014
Chain 1:   2500        -7495.800             0.014            0.014
Chain 1:   2600        -7540.772             0.013            0.014
Chain 1:   2700        -7566.433             0.010            0.012
Chain 1:   2800        -7606.417             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003063 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86032.085             1.000            1.000
Chain 1:    200       -13687.114             3.143            5.286
Chain 1:    300       -10070.181             2.215            1.000
Chain 1:    400       -10814.230             1.678            1.000
Chain 1:    500        -9036.318             1.382            0.359
Chain 1:    600        -8748.877             1.157            0.359
Chain 1:    700        -8697.474             0.993            0.197
Chain 1:    800        -9350.017             0.877            0.197
Chain 1:    900        -8805.601             0.787            0.070
Chain 1:   1000        -8614.565             0.710            0.070
Chain 1:   1100        -8940.315             0.614            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8560.976             0.090            0.062
Chain 1:   1300        -8776.907             0.056            0.044
Chain 1:   1400        -8784.365             0.050            0.036
Chain 1:   1500        -8635.818             0.032            0.033
Chain 1:   1600        -8748.380             0.030            0.025
Chain 1:   1700        -8834.763             0.030            0.025
Chain 1:   1800        -8425.818             0.028            0.025
Chain 1:   1900        -8521.498             0.023            0.022
Chain 1:   2000        -8494.235             0.021            0.017
Chain 1:   2100        -8615.600             0.019            0.014
Chain 1:   2200        -8456.879             0.016            0.014
Chain 1:   2300        -8519.908             0.014            0.013
Chain 1:   2400        -8586.471             0.015            0.013
Chain 1:   2500        -8531.938             0.014            0.011
Chain 1:   2600        -8530.257             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003362 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8364101.875             1.000            1.000
Chain 1:    200     -1577777.771             2.651            4.301
Chain 1:    300      -890142.093             2.025            1.000
Chain 1:    400      -457366.784             1.755            1.000
Chain 1:    500      -358570.628             1.459            0.946
Chain 1:    600      -233577.297             1.305            0.946
Chain 1:    700      -119676.199             1.255            0.946
Chain 1:    800       -86831.905             1.145            0.946
Chain 1:    900       -67128.571             1.050            0.773
Chain 1:   1000       -51884.960             0.975            0.773
Chain 1:   1100       -39318.727             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38490.839             0.479            0.378
Chain 1:   1300       -26402.727             0.447            0.378
Chain 1:   1400       -26117.169             0.354            0.320
Chain 1:   1500       -22692.696             0.341            0.320
Chain 1:   1600       -21905.635             0.291            0.294
Chain 1:   1700       -20774.084             0.202            0.294
Chain 1:   1800       -20717.086             0.164            0.151
Chain 1:   1900       -21043.133             0.136            0.054
Chain 1:   2000       -19551.665             0.115            0.054
Chain 1:   2100       -19790.157             0.084            0.036
Chain 1:   2200       -20017.016             0.083            0.036
Chain 1:   2300       -19633.889             0.039            0.020
Chain 1:   2400       -19405.981             0.039            0.020
Chain 1:   2500       -19208.164             0.025            0.015
Chain 1:   2600       -18838.341             0.023            0.015
Chain 1:   2700       -18795.271             0.018            0.012
Chain 1:   2800       -18512.274             0.019            0.015
Chain 1:   2900       -18793.523             0.019            0.015
Chain 1:   3000       -18779.658             0.012            0.012
Chain 1:   3100       -18864.666             0.011            0.012
Chain 1:   3200       -18555.371             0.012            0.015
Chain 1:   3300       -18760.062             0.011            0.012
Chain 1:   3400       -18235.141             0.012            0.015
Chain 1:   3500       -18846.899             0.015            0.015
Chain 1:   3600       -18153.740             0.016            0.015
Chain 1:   3700       -18540.490             0.018            0.017
Chain 1:   3800       -17500.514             0.023            0.021
Chain 1:   3900       -17496.705             0.021            0.021
Chain 1:   4000       -17613.955             0.022            0.021
Chain 1:   4100       -17527.778             0.022            0.021
Chain 1:   4200       -17344.096             0.021            0.021
Chain 1:   4300       -17482.424             0.021            0.021
Chain 1:   4400       -17439.313             0.018            0.011
Chain 1:   4500       -17341.895             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001306 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12198.221             1.000            1.000
Chain 1:    200        -9103.807             0.670            1.000
Chain 1:    300        -7999.445             0.493            0.340
Chain 1:    400        -8111.977             0.373            0.340
Chain 1:    500        -8039.396             0.300            0.138
Chain 1:    600        -7907.164             0.253            0.138
Chain 1:    700        -7824.757             0.218            0.017
Chain 1:    800        -7835.541             0.191            0.017
Chain 1:    900        -7739.298             0.171            0.014
Chain 1:   1000        -7885.616             0.156            0.017
Chain 1:   1100        -7944.269             0.057            0.014
Chain 1:   1200        -7842.105             0.024            0.013
Chain 1:   1300        -7793.812             0.011            0.012
Chain 1:   1400        -7821.269             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61480.139             1.000            1.000
Chain 1:    200       -17744.099             1.732            2.465
Chain 1:    300        -8751.701             1.497            1.028
Chain 1:    400        -9236.393             1.136            1.028
Chain 1:    500        -8338.696             0.930            1.000
Chain 1:    600        -8520.005             0.779            1.000
Chain 1:    700        -7773.648             0.681            0.108
Chain 1:    800        -8134.593             0.602            0.108
Chain 1:    900        -7983.250             0.537            0.096
Chain 1:   1000        -7656.783             0.488            0.096
Chain 1:   1100        -7624.794             0.388            0.052
Chain 1:   1200        -7565.356             0.142            0.044
Chain 1:   1300        -7564.384             0.040            0.043
Chain 1:   1400        -7812.694             0.037            0.032
Chain 1:   1500        -7544.434             0.030            0.032
Chain 1:   1600        -7561.649             0.028            0.032
Chain 1:   1700        -7452.105             0.020            0.019
Chain 1:   1800        -7528.524             0.017            0.015
Chain 1:   1900        -7493.688             0.015            0.010
Chain 1:   2000        -7541.451             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003048 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86046.092             1.000            1.000
Chain 1:    200       -13358.881             3.221            5.441
Chain 1:    300        -9766.278             2.270            1.000
Chain 1:    400       -10745.902             1.725            1.000
Chain 1:    500        -8687.595             1.427            0.368
Chain 1:    600        -8255.130             1.198            0.368
Chain 1:    700        -8418.534             1.030            0.237
Chain 1:    800        -8723.866             0.905            0.237
Chain 1:    900        -8662.060             0.806            0.091
Chain 1:   1000        -8428.124             0.728            0.091
Chain 1:   1100        -8656.746             0.631            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8300.219             0.091            0.043
Chain 1:   1300        -8550.289             0.057            0.035
Chain 1:   1400        -8495.221             0.048            0.029
Chain 1:   1500        -8350.804             0.026            0.028
Chain 1:   1600        -8460.797             0.022            0.026
Chain 1:   1700        -8548.188             0.022            0.026
Chain 1:   1800        -8146.155             0.023            0.026
Chain 1:   1900        -8243.672             0.023            0.026
Chain 1:   2000        -8215.364             0.021            0.017
Chain 1:   2100        -8335.152             0.020            0.014
Chain 1:   2200        -8141.136             0.018            0.014
Chain 1:   2300        -8278.857             0.017            0.014
Chain 1:   2400        -8154.084             0.018            0.015
Chain 1:   2500        -8218.968             0.017            0.014
Chain 1:   2600        -8242.414             0.016            0.014
Chain 1:   2700        -8160.862             0.016            0.014
Chain 1:   2800        -8133.510             0.011            0.012
Chain 1:   2900        -8188.930             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003308 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8404371.337             1.000            1.000
Chain 1:    200     -1584506.346             2.652            4.304
Chain 1:    300      -891135.561             2.027            1.000
Chain 1:    400      -457835.227             1.757            1.000
Chain 1:    500      -358356.987             1.461            0.946
Chain 1:    600      -233128.201             1.307            0.946
Chain 1:    700      -119197.982             1.257            0.946
Chain 1:    800       -86398.919             1.147            0.946
Chain 1:    900       -66705.892             1.053            0.778
Chain 1:   1000       -51483.210             0.977            0.778
Chain 1:   1100       -38943.981             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38114.310             0.481            0.380
Chain 1:   1300       -26055.609             0.449            0.380
Chain 1:   1400       -25771.216             0.356            0.322
Chain 1:   1500       -22356.347             0.343            0.322
Chain 1:   1600       -21571.826             0.293            0.296
Chain 1:   1700       -20443.992             0.203            0.295
Chain 1:   1800       -20387.616             0.166            0.153
Chain 1:   1900       -20713.551             0.138            0.055
Chain 1:   2000       -19224.631             0.116            0.055
Chain 1:   2100       -19462.694             0.085            0.036
Chain 1:   2200       -19689.351             0.084            0.036
Chain 1:   2300       -19306.497             0.039            0.020
Chain 1:   2400       -19078.700             0.040            0.020
Chain 1:   2500       -18880.884             0.025            0.016
Chain 1:   2600       -18511.083             0.024            0.016
Chain 1:   2700       -18468.071             0.018            0.012
Chain 1:   2800       -18185.157             0.020            0.016
Chain 1:   2900       -18466.318             0.020            0.015
Chain 1:   3000       -18452.395             0.012            0.012
Chain 1:   3100       -18537.383             0.011            0.012
Chain 1:   3200       -18228.167             0.012            0.015
Chain 1:   3300       -18432.819             0.011            0.012
Chain 1:   3400       -17908.016             0.013            0.015
Chain 1:   3500       -18519.536             0.015            0.016
Chain 1:   3600       -17826.715             0.017            0.016
Chain 1:   3700       -18213.179             0.019            0.017
Chain 1:   3800       -17173.679             0.023            0.021
Chain 1:   3900       -17169.894             0.022            0.021
Chain 1:   4000       -17287.148             0.022            0.021
Chain 1:   4100       -17200.998             0.022            0.021
Chain 1:   4200       -17017.407             0.022            0.021
Chain 1:   4300       -17155.639             0.021            0.021
Chain 1:   4400       -17112.595             0.019            0.011
Chain 1:   4500       -17015.218             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12186.113             1.000            1.000
Chain 1:    200        -9048.808             0.673            1.000
Chain 1:    300        -8060.294             0.490            0.347
Chain 1:    400        -8088.847             0.368            0.347
Chain 1:    500        -7940.541             0.298            0.123
Chain 1:    600        -7866.506             0.250            0.123
Chain 1:    700        -7784.465             0.216            0.019
Chain 1:    800        -7817.171             0.189            0.019
Chain 1:    900        -7982.887             0.171            0.019
Chain 1:   1000        -7824.468             0.156            0.020
Chain 1:   1100        -7865.454             0.056            0.019
Chain 1:   1200        -7819.068             0.022            0.011
Chain 1:   1300        -7758.166             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45988.717             1.000            1.000
Chain 1:    200       -15243.040             1.509            2.017
Chain 1:    300        -8567.788             1.265            1.000
Chain 1:    400        -8519.585             0.950            1.000
Chain 1:    500        -8353.289             0.764            0.779
Chain 1:    600        -8176.948             0.641            0.779
Chain 1:    700        -7795.179             0.556            0.049
Chain 1:    800        -8028.163             0.490            0.049
Chain 1:    900        -7747.833             0.440            0.036
Chain 1:   1000        -7793.879             0.396            0.036
Chain 1:   1100        -7658.304             0.298            0.029
Chain 1:   1200        -7623.695             0.097            0.022
Chain 1:   1300        -7608.454             0.019            0.020
Chain 1:   1400        -7719.527             0.020            0.020
Chain 1:   1500        -7604.472             0.020            0.018
Chain 1:   1600        -7504.287             0.019            0.015
Chain 1:   1700        -7516.572             0.014            0.014
Chain 1:   1800        -7561.404             0.012            0.013
Chain 1:   1900        -7599.247             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002587 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86041.841             1.000            1.000
Chain 1:    200       -13256.019             3.245            5.491
Chain 1:    300        -9687.171             2.286            1.000
Chain 1:    400       -10565.338             1.736            1.000
Chain 1:    500        -8623.327             1.434            0.368
Chain 1:    600        -8219.452             1.203            0.368
Chain 1:    700        -8357.351             1.033            0.225
Chain 1:    800        -8939.114             0.912            0.225
Chain 1:    900        -8504.630             0.817            0.083
Chain 1:   1000        -8356.438             0.737            0.083
Chain 1:   1100        -8581.674             0.639            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8263.320             0.094            0.051
Chain 1:   1300        -8413.801             0.059            0.049
Chain 1:   1400        -8420.468             0.051            0.039
Chain 1:   1500        -8287.952             0.030            0.026
Chain 1:   1600        -8396.069             0.026            0.018
Chain 1:   1700        -8480.862             0.026            0.018
Chain 1:   1800        -8087.625             0.024            0.018
Chain 1:   1900        -8188.477             0.020            0.018
Chain 1:   2000        -8159.103             0.019            0.016
Chain 1:   2100        -8281.903             0.018            0.015
Chain 1:   2200        -8063.738             0.016            0.015
Chain 1:   2300        -8217.267             0.016            0.015
Chain 1:   2400        -8231.288             0.017            0.015
Chain 1:   2500        -8200.471             0.015            0.013
Chain 1:   2600        -8203.029             0.014            0.012
Chain 1:   2700        -8109.308             0.014            0.012
Chain 1:   2800        -8080.648             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003189 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391273.839             1.000            1.000
Chain 1:    200     -1584712.971             2.648            4.295
Chain 1:    300      -890591.336             2.025            1.000
Chain 1:    400      -457177.332             1.756            1.000
Chain 1:    500      -357531.970             1.460            0.948
Chain 1:    600      -232498.888             1.307            0.948
Chain 1:    700      -118847.633             1.256            0.948
Chain 1:    800       -86081.412             1.147            0.948
Chain 1:    900       -66447.355             1.052            0.779
Chain 1:   1000       -51262.602             0.977            0.779
Chain 1:   1100       -38758.009             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37935.195             0.482            0.381
Chain 1:   1300       -25917.362             0.450            0.381
Chain 1:   1400       -25637.292             0.356            0.323
Chain 1:   1500       -22231.109             0.344            0.323
Chain 1:   1600       -21448.820             0.294            0.296
Chain 1:   1700       -20326.140             0.204            0.295
Chain 1:   1800       -20270.982             0.166            0.153
Chain 1:   1900       -20596.743             0.138            0.055
Chain 1:   2000       -19110.373             0.116            0.055
Chain 1:   2100       -19348.674             0.085            0.036
Chain 1:   2200       -19574.499             0.084            0.036
Chain 1:   2300       -19192.351             0.040            0.020
Chain 1:   2400       -18964.593             0.040            0.020
Chain 1:   2500       -18766.469             0.025            0.016
Chain 1:   2600       -18397.221             0.024            0.016
Chain 1:   2700       -18354.393             0.019            0.012
Chain 1:   2800       -18071.329             0.020            0.016
Chain 1:   2900       -18352.389             0.020            0.015
Chain 1:   3000       -18338.653             0.012            0.012
Chain 1:   3100       -18423.541             0.011            0.012
Chain 1:   3200       -18114.556             0.012            0.015
Chain 1:   3300       -18319.038             0.011            0.012
Chain 1:   3400       -17794.447             0.013            0.015
Chain 1:   3500       -18405.549             0.015            0.016
Chain 1:   3600       -17713.275             0.017            0.016
Chain 1:   3700       -18099.243             0.019            0.017
Chain 1:   3800       -17060.525             0.023            0.021
Chain 1:   3900       -17056.698             0.022            0.021
Chain 1:   4000       -17174.020             0.022            0.021
Chain 1:   4100       -17087.810             0.022            0.021
Chain 1:   4200       -16904.443             0.022            0.021
Chain 1:   4300       -17042.590             0.022            0.021
Chain 1:   4400       -16999.691             0.019            0.011
Chain 1:   4500       -16902.267             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12337.952             1.000            1.000
Chain 1:    200        -9206.745             0.670            1.000
Chain 1:    300        -8051.006             0.495            0.340
Chain 1:    400        -8201.963             0.376            0.340
Chain 1:    500        -8084.805             0.303            0.144
Chain 1:    600        -8009.428             0.254            0.144
Chain 1:    700        -7923.271             0.220            0.018
Chain 1:    800        -7962.844             0.193            0.018
Chain 1:    900        -8084.625             0.173            0.015
Chain 1:   1000        -7977.376             0.157            0.015
Chain 1:   1100        -8025.820             0.058            0.014
Chain 1:   1200        -7961.674             0.024            0.013
Chain 1:   1300        -7893.986             0.011            0.011
Chain 1:   1400        -7907.744             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46188.706             1.000            1.000
Chain 1:    200       -15443.582             1.495            1.991
Chain 1:    300        -8678.136             1.257            1.000
Chain 1:    400        -8657.491             0.943            1.000
Chain 1:    500        -8170.648             0.766            0.780
Chain 1:    600        -8833.670             0.651            0.780
Chain 1:    700        -8082.209             0.571            0.093
Chain 1:    800        -8234.639             0.502            0.093
Chain 1:    900        -7978.422             0.450            0.075
Chain 1:   1000        -7957.675             0.405            0.075
Chain 1:   1100        -7664.302             0.309            0.060
Chain 1:   1200        -7679.498             0.110            0.038
Chain 1:   1300        -7642.104             0.033            0.032
Chain 1:   1400        -7927.616             0.036            0.036
Chain 1:   1500        -7636.195             0.034            0.036
Chain 1:   1600        -7617.072             0.027            0.032
Chain 1:   1700        -7577.532             0.018            0.019
Chain 1:   1800        -7623.846             0.017            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86578.137             1.000            1.000
Chain 1:    200       -13438.878             3.221            5.442
Chain 1:    300        -9849.843             2.269            1.000
Chain 1:    400       -10821.073             1.724            1.000
Chain 1:    500        -8771.551             1.426            0.364
Chain 1:    600        -8525.567             1.193            0.364
Chain 1:    700        -8587.541             1.024            0.234
Chain 1:    800        -8836.418             0.899            0.234
Chain 1:    900        -8660.903             0.802            0.090
Chain 1:   1000        -8438.246             0.724            0.090
Chain 1:   1100        -8701.149             0.627            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8245.296             0.088            0.030
Chain 1:   1300        -8568.486             0.056            0.030
Chain 1:   1400        -8564.173             0.047            0.029
Chain 1:   1500        -8443.515             0.025            0.028
Chain 1:   1600        -8549.611             0.023            0.026
Chain 1:   1700        -8635.533             0.024            0.026
Chain 1:   1800        -8235.238             0.026            0.026
Chain 1:   1900        -8334.862             0.025            0.026
Chain 1:   2000        -8306.058             0.022            0.014
Chain 1:   2100        -8425.985             0.021            0.014
Chain 1:   2200        -8214.837             0.018            0.014
Chain 1:   2300        -8366.080             0.016            0.014
Chain 1:   2400        -8247.544             0.017            0.014
Chain 1:   2500        -8310.414             0.017            0.014
Chain 1:   2600        -8331.722             0.016            0.014
Chain 1:   2700        -8250.813             0.016            0.014
Chain 1:   2800        -8224.895             0.011            0.012
Chain 1:   2900        -8280.268             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003072 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414310.013             1.000            1.000
Chain 1:    200     -1586898.513             2.651            4.302
Chain 1:    300      -891645.896             2.027            1.000
Chain 1:    400      -458068.176             1.757            1.000
Chain 1:    500      -358297.326             1.461            0.947
Chain 1:    600      -233011.873             1.307            0.947
Chain 1:    700      -119162.163             1.257            0.947
Chain 1:    800       -86377.292             1.147            0.947
Chain 1:    900       -66705.822             1.053            0.780
Chain 1:   1000       -51497.276             0.977            0.780
Chain 1:   1100       -38976.865             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38149.388             0.481            0.380
Chain 1:   1300       -26113.181             0.449            0.380
Chain 1:   1400       -25831.517             0.356            0.321
Chain 1:   1500       -22421.691             0.343            0.321
Chain 1:   1600       -21638.559             0.293            0.295
Chain 1:   1700       -20513.476             0.203            0.295
Chain 1:   1800       -20457.718             0.165            0.152
Chain 1:   1900       -20783.568             0.137            0.055
Chain 1:   2000       -19295.951             0.115            0.055
Chain 1:   2100       -19534.209             0.084            0.036
Chain 1:   2200       -19760.436             0.083            0.036
Chain 1:   2300       -19377.911             0.039            0.020
Chain 1:   2400       -19150.096             0.039            0.020
Chain 1:   2500       -18952.139             0.025            0.016
Chain 1:   2600       -18582.626             0.024            0.016
Chain 1:   2700       -18539.665             0.018            0.012
Chain 1:   2800       -18256.686             0.020            0.015
Chain 1:   2900       -18537.771             0.020            0.015
Chain 1:   3000       -18523.953             0.012            0.012
Chain 1:   3100       -18608.917             0.011            0.012
Chain 1:   3200       -18299.796             0.012            0.015
Chain 1:   3300       -18504.355             0.011            0.012
Chain 1:   3400       -17979.660             0.013            0.015
Chain 1:   3500       -18590.994             0.015            0.015
Chain 1:   3600       -17898.341             0.017            0.015
Chain 1:   3700       -18284.646             0.019            0.017
Chain 1:   3800       -17245.438             0.023            0.021
Chain 1:   3900       -17241.601             0.022            0.021
Chain 1:   4000       -17358.896             0.022            0.021
Chain 1:   4100       -17272.737             0.022            0.021
Chain 1:   4200       -17089.185             0.022            0.021
Chain 1:   4300       -17227.423             0.021            0.021
Chain 1:   4400       -17184.431             0.019            0.011
Chain 1:   4500       -17086.990             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001234 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12323.632             1.000            1.000
Chain 1:    200        -9147.413             0.674            1.000
Chain 1:    300        -8265.272             0.485            0.347
Chain 1:    400        -8280.936             0.364            0.347
Chain 1:    500        -8175.918             0.294            0.107
Chain 1:    600        -8053.899             0.247            0.107
Chain 1:    700        -7981.156             0.213            0.015
Chain 1:    800        -7991.011             0.187            0.015
Chain 1:    900        -7888.298             0.167            0.013
Chain 1:   1000        -8035.189             0.153            0.015
Chain 1:   1100        -8044.652             0.053            0.013
Chain 1:   1200        -8013.001             0.018            0.013
Chain 1:   1300        -7949.563             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001408 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61766.091             1.000            1.000
Chain 1:    200       -17787.911             1.736            2.472
Chain 1:    300        -8810.943             1.497            1.019
Chain 1:    400        -9189.460             1.133            1.019
Chain 1:    500        -7780.355             0.943            1.000
Chain 1:    600        -8483.967             0.799            1.000
Chain 1:    700        -8118.159             0.692            0.181
Chain 1:    800        -8175.084             0.606            0.181
Chain 1:    900        -7913.246             0.542            0.083
Chain 1:   1000        -7777.909             0.490            0.083
Chain 1:   1100        -7563.487             0.393            0.045
Chain 1:   1200        -7721.353             0.148            0.041
Chain 1:   1300        -7701.817             0.046            0.033
Chain 1:   1400        -7820.451             0.043            0.028
Chain 1:   1500        -7586.376             0.028            0.028
Chain 1:   1600        -7615.750             0.020            0.020
Chain 1:   1700        -7508.418             0.017            0.017
Chain 1:   1800        -7544.450             0.017            0.017
Chain 1:   1900        -7558.964             0.014            0.015
Chain 1:   2000        -7572.111             0.012            0.014
Chain 1:   2100        -7575.416             0.010            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002715 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86338.579             1.000            1.000
Chain 1:    200       -13454.211             3.209            5.417
Chain 1:    300        -9886.928             2.259            1.000
Chain 1:    400       -10858.416             1.717            1.000
Chain 1:    500        -8813.909             1.420            0.361
Chain 1:    600        -8658.766             1.186            0.361
Chain 1:    700        -8712.121             1.018            0.232
Chain 1:    800        -8647.772             0.891            0.232
Chain 1:    900        -8678.344             0.793            0.089
Chain 1:   1000        -8511.714             0.715            0.089
Chain 1:   1100        -8762.143             0.618            0.029   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8470.120             0.080            0.029
Chain 1:   1300        -8627.641             0.046            0.020
Chain 1:   1400        -8628.864             0.037            0.018
Chain 1:   1500        -8495.942             0.015            0.018
Chain 1:   1600        -8602.130             0.015            0.016
Chain 1:   1700        -8689.067             0.015            0.016
Chain 1:   1800        -8298.153             0.019            0.018
Chain 1:   1900        -8399.632             0.020            0.018
Chain 1:   2000        -8370.088             0.018            0.016
Chain 1:   2100        -8496.723             0.017            0.015
Chain 1:   2200        -8282.618             0.016            0.015
Chain 1:   2300        -8428.560             0.016            0.015
Chain 1:   2400        -8444.002             0.016            0.015
Chain 1:   2500        -8410.461             0.015            0.012
Chain 1:   2600        -8412.300             0.014            0.012
Chain 1:   2700        -8319.260             0.014            0.012
Chain 1:   2800        -8292.530             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003107 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416505.773             1.000            1.000
Chain 1:    200     -1588000.943             2.650            4.300
Chain 1:    300      -891011.528             2.027            1.000
Chain 1:    400      -457655.907             1.757            1.000
Chain 1:    500      -357553.092             1.462            0.947
Chain 1:    600      -232541.271             1.308            0.947
Chain 1:    700      -118926.160             1.257            0.947
Chain 1:    800       -86205.859             1.148            0.947
Chain 1:    900       -66589.267             1.053            0.782
Chain 1:   1000       -51419.381             0.977            0.782
Chain 1:   1100       -38928.152             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38107.492             0.481            0.380
Chain 1:   1300       -26098.193             0.449            0.380
Chain 1:   1400       -25819.830             0.356            0.321
Chain 1:   1500       -22416.980             0.343            0.321
Chain 1:   1600       -21636.219             0.293            0.295
Chain 1:   1700       -20514.223             0.203            0.295
Chain 1:   1800       -20459.359             0.165            0.152
Chain 1:   1900       -20785.111             0.137            0.055
Chain 1:   2000       -19299.626             0.115            0.055
Chain 1:   2100       -19537.626             0.084            0.036
Chain 1:   2200       -19763.501             0.083            0.036
Chain 1:   2300       -19381.394             0.039            0.020
Chain 1:   2400       -19153.697             0.039            0.020
Chain 1:   2500       -18955.701             0.025            0.016
Chain 1:   2600       -18586.244             0.024            0.016
Chain 1:   2700       -18543.439             0.018            0.012
Chain 1:   2800       -18260.409             0.020            0.015
Chain 1:   2900       -18541.515             0.020            0.015
Chain 1:   3000       -18527.695             0.012            0.012
Chain 1:   3100       -18612.582             0.011            0.012
Chain 1:   3200       -18303.542             0.012            0.015
Chain 1:   3300       -18508.123             0.011            0.012
Chain 1:   3400       -17983.491             0.013            0.015
Chain 1:   3500       -18594.557             0.015            0.015
Chain 1:   3600       -17902.391             0.017            0.015
Chain 1:   3700       -18288.281             0.019            0.017
Chain 1:   3800       -17249.654             0.023            0.021
Chain 1:   3900       -17245.871             0.022            0.021
Chain 1:   4000       -17363.176             0.022            0.021
Chain 1:   4100       -17276.939             0.022            0.021
Chain 1:   4200       -17093.647             0.022            0.021
Chain 1:   4300       -17231.710             0.021            0.021
Chain 1:   4400       -17188.820             0.019            0.011
Chain 1:   4500       -17091.454             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48562.613             1.000            1.000
Chain 1:    200       -13238.117             1.834            2.668
Chain 1:    300       -28193.560             1.400            1.000
Chain 1:    400       -14272.521             1.294            1.000
Chain 1:    500       -13123.666             1.052            0.975
Chain 1:    600       -15507.390             0.903            0.975
Chain 1:    700       -19215.853             0.801            0.530
Chain 1:    800       -14381.624             0.743            0.530
Chain 1:    900       -12556.415             0.677            0.336
Chain 1:   1000       -12059.603             0.613            0.336
Chain 1:   1100        -9633.149             0.538            0.252   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11599.818             0.288            0.193
Chain 1:   1300       -11913.702             0.238            0.170
Chain 1:   1400        -9800.061             0.162            0.170
Chain 1:   1500       -10284.863             0.158            0.170
Chain 1:   1600       -26840.674             0.204            0.193
Chain 1:   1700       -16007.481             0.253            0.216
Chain 1:   1800       -12141.759             0.251            0.216
Chain 1:   1900       -14515.240             0.253            0.216
Chain 1:   2000       -11961.943             0.270            0.216
Chain 1:   2100        -9114.666             0.276            0.216
Chain 1:   2200       -10195.211             0.270            0.216
Chain 1:   2300        -9386.550             0.276            0.216
Chain 1:   2400        -9445.429             0.255            0.213
Chain 1:   2500        -9453.767             0.250            0.213
Chain 1:   2600        -8920.384             0.194            0.164
Chain 1:   2700       -11936.942             0.152            0.164
Chain 1:   2800        -8839.355             0.155            0.164
Chain 1:   2900        -8759.940             0.140            0.106
Chain 1:   3000        -8805.625             0.119            0.086
Chain 1:   3100        -8983.784             0.090            0.060
Chain 1:   3200       -13840.456             0.114            0.060
Chain 1:   3300        -9285.945             0.155            0.060
Chain 1:   3400       -12025.017             0.177            0.228
Chain 1:   3500        -9226.499             0.207            0.253
Chain 1:   3600        -9293.774             0.202            0.253
Chain 1:   3700        -9043.523             0.179            0.228
Chain 1:   3800        -8480.585             0.151            0.066
Chain 1:   3900       -12210.871             0.180            0.228
Chain 1:   4000        -9990.046             0.202            0.228
Chain 1:   4100       -12678.501             0.221            0.228
Chain 1:   4200       -10216.236             0.210            0.228
Chain 1:   4300        -9510.393             0.169            0.222
Chain 1:   4400        -8444.800             0.159            0.212
Chain 1:   4500       -13729.700             0.167            0.212
Chain 1:   4600        -8232.999             0.233            0.222
Chain 1:   4700        -8976.902             0.238            0.222
Chain 1:   4800        -8263.081             0.240            0.222
Chain 1:   4900        -8429.568             0.212            0.212
Chain 1:   5000        -9660.216             0.202            0.127
Chain 1:   5100        -8423.375             0.196            0.127
Chain 1:   5200       -13410.808             0.209            0.127
Chain 1:   5300        -8065.809             0.268            0.147
Chain 1:   5400       -14966.322             0.301            0.372
Chain 1:   5500       -11710.424             0.290            0.278
Chain 1:   5600       -15358.411             0.247            0.238
Chain 1:   5700       -11029.069             0.278            0.278
Chain 1:   5800        -8382.686             0.301            0.316
Chain 1:   5900       -12747.000             0.334            0.342
Chain 1:   6000        -8825.151             0.365            0.372
Chain 1:   6100        -8822.422             0.351            0.372
Chain 1:   6200        -8350.402             0.319            0.342
Chain 1:   6300        -8114.237             0.256            0.316
Chain 1:   6400        -8244.842             0.211            0.278
Chain 1:   6500       -11584.825             0.212            0.288
Chain 1:   6600        -8156.493             0.231            0.316
Chain 1:   6700        -8180.674             0.192            0.288
Chain 1:   6800       -11571.664             0.189            0.288
Chain 1:   6900        -9744.267             0.174            0.188
Chain 1:   7000        -8191.489             0.148            0.188
Chain 1:   7100       -14430.671             0.192            0.190
Chain 1:   7200        -8424.981             0.257            0.288
Chain 1:   7300        -9057.486             0.261            0.288
Chain 1:   7400        -7920.375             0.274            0.288
Chain 1:   7500        -8839.469             0.256            0.190
Chain 1:   7600        -8575.883             0.217            0.188
Chain 1:   7700        -8183.368             0.221            0.188
Chain 1:   7800       -10042.023             0.210            0.185
Chain 1:   7900        -9232.632             0.200            0.144
Chain 1:   8000       -10941.404             0.197            0.144
Chain 1:   8100        -8244.832             0.186            0.144
Chain 1:   8200       -10967.910             0.140            0.144
Chain 1:   8300        -7923.679             0.171            0.156
Chain 1:   8400       -10679.973             0.183            0.185
Chain 1:   8500        -7890.022             0.208            0.248
Chain 1:   8600        -8512.935             0.212            0.248
Chain 1:   8700        -7908.016             0.215            0.248
Chain 1:   8800        -8327.848             0.202            0.248
Chain 1:   8900       -12251.098             0.225            0.258
Chain 1:   9000       -10319.660             0.228            0.258
Chain 1:   9100        -9899.123             0.199            0.248
Chain 1:   9200       -10266.031             0.178            0.187
Chain 1:   9300        -9263.685             0.151            0.108
Chain 1:   9400        -8247.227             0.137            0.108
Chain 1:   9500       -10982.144             0.127            0.108
Chain 1:   9600        -7961.013             0.157            0.123
Chain 1:   9700        -8223.651             0.153            0.123
Chain 1:   9800        -8444.983             0.150            0.123
Chain 1:   9900        -9981.406             0.134            0.123
Chain 1:   10000        -8267.572             0.136            0.123
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001385 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57627.260             1.000            1.000
Chain 1:    200       -17189.176             1.676            2.353
Chain 1:    300        -8519.582             1.457            1.018
Chain 1:    400        -7863.205             1.113            1.018
Chain 1:    500        -8344.085             0.902            1.000
Chain 1:    600        -8193.824             0.755            1.000
Chain 1:    700        -8316.445             0.649            0.083
Chain 1:    800        -8846.598             0.576            0.083
Chain 1:    900        -7767.570             0.527            0.083
Chain 1:   1000        -7740.801             0.475            0.083
Chain 1:   1100        -7770.177             0.375            0.060
Chain 1:   1200        -7733.959             0.140            0.058
Chain 1:   1300        -7746.314             0.039            0.018
Chain 1:   1400        -7889.812             0.032            0.018
Chain 1:   1500        -7651.186             0.029            0.018
Chain 1:   1600        -7557.530             0.029            0.015
Chain 1:   1700        -7543.246             0.028            0.012
Chain 1:   1800        -7576.395             0.022            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002967 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85658.970             1.000            1.000
Chain 1:    200       -13010.821             3.292            5.584
Chain 1:    300        -9487.398             2.318            1.000
Chain 1:    400       -10416.526             1.761            1.000
Chain 1:    500        -8398.016             1.457            0.371
Chain 1:    600        -8046.580             1.221            0.371
Chain 1:    700        -8340.741             1.052            0.240
Chain 1:    800        -8566.446             0.924            0.240
Chain 1:    900        -8313.410             0.824            0.089
Chain 1:   1000        -8103.370             0.745            0.089
Chain 1:   1100        -8363.407             0.648            0.044   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8032.934             0.093            0.041
Chain 1:   1300        -8230.835             0.059            0.035
Chain 1:   1400        -8229.023             0.050            0.031
Chain 1:   1500        -8130.444             0.027            0.030
Chain 1:   1600        -8219.068             0.024            0.026
Chain 1:   1700        -8309.578             0.021            0.026
Chain 1:   1800        -7925.348             0.024            0.026
Chain 1:   1900        -8027.584             0.022            0.024
Chain 1:   2000        -7997.103             0.020            0.013
Chain 1:   2100        -8133.396             0.018            0.013
Chain 1:   2200        -7916.593             0.017            0.013
Chain 1:   2300        -8058.129             0.016            0.013
Chain 1:   2400        -8067.603             0.016            0.013
Chain 1:   2500        -8035.978             0.015            0.013
Chain 1:   2600        -8033.431             0.014            0.013
Chain 1:   2700        -7943.380             0.014            0.013
Chain 1:   2800        -7922.305             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003075 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8430273.267             1.000            1.000
Chain 1:    200     -1589523.542             2.652            4.304
Chain 1:    300      -890319.270             2.030            1.000
Chain 1:    400      -457010.656             1.759            1.000
Chain 1:    500      -356906.065             1.464            0.948
Chain 1:    600      -231985.581             1.309            0.948
Chain 1:    700      -118438.503             1.259            0.948
Chain 1:    800       -85708.749             1.150            0.948
Chain 1:    900       -66097.944             1.055            0.785
Chain 1:   1000       -50934.958             0.979            0.785
Chain 1:   1100       -38456.536             0.912            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37632.591             0.483            0.382
Chain 1:   1300       -25645.415             0.452            0.382
Chain 1:   1400       -25367.223             0.358            0.324
Chain 1:   1500       -21969.508             0.345            0.324
Chain 1:   1600       -21189.652             0.295            0.298
Chain 1:   1700       -20070.662             0.205            0.297
Chain 1:   1800       -20016.164             0.167            0.155
Chain 1:   1900       -20341.748             0.139            0.056
Chain 1:   2000       -18857.766             0.117            0.056
Chain 1:   2100       -19095.901             0.086            0.037
Chain 1:   2200       -19321.315             0.085            0.037
Chain 1:   2300       -18939.560             0.040            0.020
Chain 1:   2400       -18711.907             0.040            0.020
Chain 1:   2500       -18513.721             0.026            0.016
Chain 1:   2600       -18144.771             0.024            0.016
Chain 1:   2700       -18101.987             0.019            0.012
Chain 1:   2800       -17818.997             0.020            0.016
Chain 1:   2900       -18099.899             0.020            0.016
Chain 1:   3000       -18086.206             0.012            0.012
Chain 1:   3100       -18171.094             0.011            0.012
Chain 1:   3200       -17862.243             0.012            0.016
Chain 1:   3300       -18066.586             0.011            0.012
Chain 1:   3400       -17542.266             0.013            0.016
Chain 1:   3500       -18152.944             0.015            0.016
Chain 1:   3600       -17461.130             0.017            0.016
Chain 1:   3700       -17846.765             0.019            0.017
Chain 1:   3800       -16808.802             0.024            0.022
Chain 1:   3900       -16804.955             0.022            0.022
Chain 1:   4000       -16922.297             0.023            0.022
Chain 1:   4100       -16836.159             0.023            0.022
Chain 1:   4200       -16652.905             0.022            0.022
Chain 1:   4300       -16790.979             0.022            0.022
Chain 1:   4400       -16748.215             0.019            0.011
Chain 1:   4500       -16650.784             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48673.352             1.000            1.000
Chain 1:    200       -16441.553             1.480            1.960
Chain 1:    300       -18603.029             1.026            1.000
Chain 1:    400       -19564.135             0.781            1.000
Chain 1:    500       -30591.174             0.697            0.360
Chain 1:    600       -11097.829             0.874            1.000
Chain 1:    700       -16023.352             0.793            0.360
Chain 1:    800       -15153.850             0.701            0.360
Chain 1:    900       -15845.824             0.628            0.307
Chain 1:   1000       -12263.950             0.594            0.307
Chain 1:   1100       -10620.903             0.510            0.292   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12034.503             0.325            0.155
Chain 1:   1300        -9926.036             0.335            0.212
Chain 1:   1400       -10200.213             0.333            0.212
Chain 1:   1500       -11595.408             0.309            0.155
Chain 1:   1600       -10922.343             0.139            0.120
Chain 1:   1700        -9608.632             0.122            0.120
Chain 1:   1800       -10987.132             0.129            0.125
Chain 1:   1900       -10634.278             0.128            0.125
Chain 1:   2000       -19280.119             0.144            0.125
Chain 1:   2100       -18919.592             0.130            0.120
Chain 1:   2200       -10044.518             0.207            0.125
Chain 1:   2300        -9327.997             0.193            0.120
Chain 1:   2400        -9956.554             0.197            0.120
Chain 1:   2500       -11153.030             0.196            0.107
Chain 1:   2600        -9329.905             0.209            0.125
Chain 1:   2700        -9856.349             0.201            0.107
Chain 1:   2800       -16417.612             0.228            0.107
Chain 1:   2900       -10130.178             0.287            0.195
Chain 1:   3000       -16310.323             0.280            0.195
Chain 1:   3100        -9096.628             0.357            0.379
Chain 1:   3200        -9326.231             0.271            0.195
Chain 1:   3300        -9420.885             0.265            0.195
Chain 1:   3400       -14235.337             0.292            0.338
Chain 1:   3500        -9146.750             0.337            0.379
Chain 1:   3600        -9922.780             0.325            0.379
Chain 1:   3700        -8886.830             0.332            0.379
Chain 1:   3800        -8580.454             0.295            0.338
Chain 1:   3900        -9378.308             0.242            0.117
Chain 1:   4000        -8646.541             0.212            0.085
Chain 1:   4100        -9201.588             0.139            0.085
Chain 1:   4200        -8792.703             0.141            0.085
Chain 1:   4300       -14849.545             0.181            0.085
Chain 1:   4400        -9739.005             0.200            0.085
Chain 1:   4500        -9552.805             0.146            0.085
Chain 1:   4600       -13010.183             0.165            0.085
Chain 1:   4700        -8380.739             0.208            0.085
Chain 1:   4800        -8746.871             0.209            0.085
Chain 1:   4900        -8620.269             0.202            0.085
Chain 1:   5000       -10594.810             0.212            0.186
Chain 1:   5100        -8805.233             0.226            0.203
Chain 1:   5200        -9935.878             0.233            0.203
Chain 1:   5300        -9494.123             0.197            0.186
Chain 1:   5400       -11115.004             0.159            0.146
Chain 1:   5500        -8544.987             0.187            0.186
Chain 1:   5600        -8694.889             0.162            0.146
Chain 1:   5700        -8559.883             0.109            0.114
Chain 1:   5800        -8828.219             0.107            0.114
Chain 1:   5900       -11620.306             0.130            0.146
Chain 1:   6000        -8692.954             0.145            0.146
Chain 1:   6100        -8889.434             0.127            0.114
Chain 1:   6200       -12310.368             0.143            0.146
Chain 1:   6300       -14352.972             0.153            0.146
Chain 1:   6400       -12545.976             0.153            0.144
Chain 1:   6500        -9377.721             0.156            0.144
Chain 1:   6600        -8547.276             0.164            0.144
Chain 1:   6700        -8864.024             0.166            0.144
Chain 1:   6800        -9663.156             0.172            0.144
Chain 1:   6900       -11619.569             0.164            0.144
Chain 1:   7000        -8885.251             0.162            0.144
Chain 1:   7100        -8838.567             0.160            0.144
Chain 1:   7200        -8588.276             0.135            0.142
Chain 1:   7300        -8724.287             0.122            0.097
Chain 1:   7400        -8393.050             0.112            0.083
Chain 1:   7500        -9021.582             0.085            0.070
Chain 1:   7600        -8359.940             0.083            0.070
Chain 1:   7700        -9732.840             0.094            0.079
Chain 1:   7800       -11848.649             0.103            0.079
Chain 1:   7900        -8637.216             0.124            0.079
Chain 1:   8000        -8432.058             0.095            0.070
Chain 1:   8100        -9337.338             0.105            0.079
Chain 1:   8200       -10151.699             0.110            0.080
Chain 1:   8300        -8197.075             0.132            0.097
Chain 1:   8400       -12025.513             0.160            0.141
Chain 1:   8500        -8275.970             0.198            0.179
Chain 1:   8600       -10059.428             0.208            0.179
Chain 1:   8700       -10880.840             0.201            0.179
Chain 1:   8800        -8128.596             0.217            0.238
Chain 1:   8900        -9392.178             0.194            0.177
Chain 1:   9000        -8891.091             0.197            0.177
Chain 1:   9100        -8538.557             0.191            0.177
Chain 1:   9200        -8917.163             0.188            0.177
Chain 1:   9300       -12949.932             0.195            0.177
Chain 1:   9400        -8412.878             0.217            0.177
Chain 1:   9500        -8693.583             0.175            0.135
Chain 1:   9600        -8190.902             0.163            0.075
Chain 1:   9700        -8241.217             0.156            0.061
Chain 1:   9800        -8848.460             0.129            0.061
Chain 1:   9900        -9042.123             0.118            0.056
Chain 1:   10000        -8945.883             0.114            0.042
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001572 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56881.433             1.000            1.000
Chain 1:    200       -17365.583             1.638            2.276
Chain 1:    300        -8678.740             1.425            1.001
Chain 1:    400        -8333.557             1.079            1.001
Chain 1:    500        -8178.570             0.867            1.000
Chain 1:    600        -8078.881             0.725            1.000
Chain 1:    700        -7994.686             0.623            0.041
Chain 1:    800        -7628.038             0.551            0.048
Chain 1:    900        -7932.468             0.494            0.041
Chain 1:   1000        -7623.800             0.449            0.041
Chain 1:   1100        -7755.261             0.350            0.040
Chain 1:   1200        -7693.874             0.124            0.038
Chain 1:   1300        -7708.840             0.024            0.019
Chain 1:   1400        -7806.531             0.021            0.017
Chain 1:   1500        -7577.219             0.022            0.017
Chain 1:   1600        -7695.943             0.022            0.017
Chain 1:   1700        -7456.625             0.024            0.030
Chain 1:   1800        -7579.695             0.021            0.017
Chain 1:   1900        -7500.413             0.018            0.016
Chain 1:   2000        -7519.243             0.015            0.015
Chain 1:   2100        -7537.899             0.013            0.013
Chain 1:   2200        -7657.851             0.014            0.015
Chain 1:   2300        -7552.409             0.015            0.015
Chain 1:   2400        -7599.153             0.015            0.015
Chain 1:   2500        -7449.721             0.014            0.015
Chain 1:   2600        -7486.612             0.012            0.014
Chain 1:   2700        -7519.134             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003155 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86346.189             1.000            1.000
Chain 1:    200       -13435.867             3.213            5.427
Chain 1:    300        -9829.821             2.264            1.000
Chain 1:    400       -10553.812             1.715            1.000
Chain 1:    500        -8779.630             1.413            0.367
Chain 1:    600        -8307.345             1.187            0.367
Chain 1:    700        -8353.122             1.018            0.202
Chain 1:    800        -8862.650             0.898            0.202
Chain 1:    900        -8624.190             0.801            0.069
Chain 1:   1000        -8434.415             0.723            0.069
Chain 1:   1100        -8685.375             0.626            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8208.100             0.089            0.057
Chain 1:   1300        -8414.489             0.055            0.057
Chain 1:   1400        -8547.166             0.050            0.029
Chain 1:   1500        -8413.562             0.031            0.028
Chain 1:   1600        -8525.638             0.027            0.025
Chain 1:   1700        -8608.617             0.027            0.025
Chain 1:   1800        -8202.493             0.027            0.025
Chain 1:   1900        -8300.874             0.025            0.023
Chain 1:   2000        -8272.543             0.023            0.016
Chain 1:   2100        -8392.676             0.022            0.016
Chain 1:   2200        -8185.253             0.018            0.016
Chain 1:   2300        -8336.540             0.018            0.016
Chain 1:   2400        -8343.646             0.016            0.014
Chain 1:   2500        -8314.347             0.015            0.013
Chain 1:   2600        -8313.250             0.014            0.012
Chain 1:   2700        -8225.369             0.014            0.012
Chain 1:   2800        -8191.485             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003268 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409996.152             1.000            1.000
Chain 1:    200     -1584936.413             2.653            4.306
Chain 1:    300      -892201.643             2.028            1.000
Chain 1:    400      -458387.009             1.757            1.000
Chain 1:    500      -358674.894             1.461            0.946
Chain 1:    600      -233441.261             1.307            0.946
Chain 1:    700      -119406.133             1.257            0.946
Chain 1:    800       -86556.854             1.147            0.946
Chain 1:    900       -66846.945             1.053            0.776
Chain 1:   1000       -51598.756             0.977            0.776
Chain 1:   1100       -39042.188             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38209.561             0.481            0.380
Chain 1:   1300       -26135.957             0.449            0.380
Chain 1:   1400       -25850.215             0.356            0.322
Chain 1:   1500       -22430.511             0.343            0.322
Chain 1:   1600       -21644.620             0.293            0.296
Chain 1:   1700       -20515.198             0.203            0.295
Chain 1:   1800       -20458.293             0.165            0.152
Chain 1:   1900       -20784.246             0.137            0.055
Chain 1:   2000       -19294.108             0.116            0.055
Chain 1:   2100       -19532.437             0.085            0.036
Chain 1:   2200       -19759.152             0.084            0.036
Chain 1:   2300       -19376.182             0.039            0.020
Chain 1:   2400       -19148.321             0.039            0.020
Chain 1:   2500       -18950.483             0.025            0.016
Chain 1:   2600       -18580.726             0.024            0.016
Chain 1:   2700       -18537.661             0.018            0.012
Chain 1:   2800       -18254.739             0.020            0.015
Chain 1:   2900       -18535.884             0.020            0.015
Chain 1:   3000       -18522.027             0.012            0.012
Chain 1:   3100       -18607.042             0.011            0.012
Chain 1:   3200       -18297.787             0.012            0.015
Chain 1:   3300       -18502.428             0.011            0.012
Chain 1:   3400       -17977.590             0.013            0.015
Chain 1:   3500       -18589.178             0.015            0.015
Chain 1:   3600       -17896.205             0.017            0.015
Chain 1:   3700       -18282.816             0.019            0.017
Chain 1:   3800       -17243.112             0.023            0.021
Chain 1:   3900       -17239.289             0.022            0.021
Chain 1:   4000       -17356.565             0.022            0.021
Chain 1:   4100       -17270.412             0.022            0.021
Chain 1:   4200       -17086.739             0.022            0.021
Chain 1:   4300       -17225.048             0.021            0.021
Chain 1:   4400       -17181.992             0.019            0.011
Chain 1:   4500       -17084.545             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00121 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12453.394             1.000            1.000
Chain 1:    200        -9366.825             0.665            1.000
Chain 1:    300        -8200.627             0.491            0.330
Chain 1:    400        -8359.819             0.373            0.330
Chain 1:    500        -8278.915             0.300            0.142
Chain 1:    600        -8142.026             0.253            0.142
Chain 1:    700        -8068.748             0.218            0.019
Chain 1:    800        -8078.501             0.191            0.019
Chain 1:    900        -7982.615             0.171            0.017
Chain 1:   1000        -8193.716             0.157            0.019
Chain 1:   1100        -8103.529             0.058            0.017
Chain 1:   1200        -8087.554             0.025            0.012
Chain 1:   1300        -8039.654             0.011            0.011
Chain 1:   1400        -8061.212             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58180.389             1.000            1.000
Chain 1:    200       -17747.734             1.639            2.278
Chain 1:    300        -8672.743             1.442            1.046
Chain 1:    400        -8195.234             1.096            1.046
Chain 1:    500        -8075.956             0.880            1.000
Chain 1:    600        -8611.426             0.743            1.000
Chain 1:    700        -8218.887             0.644            0.062
Chain 1:    800        -8390.601             0.566            0.062
Chain 1:    900        -7813.918             0.511            0.062
Chain 1:   1000        -7799.361             0.460            0.062
Chain 1:   1100        -7641.037             0.362            0.058
Chain 1:   1200        -7621.331             0.135            0.048
Chain 1:   1300        -7743.169             0.032            0.021
Chain 1:   1400        -7811.209             0.027            0.020
Chain 1:   1500        -7566.175             0.029            0.021
Chain 1:   1600        -7761.033             0.025            0.021
Chain 1:   1700        -7479.547             0.024            0.021
Chain 1:   1800        -7576.624             0.023            0.021
Chain 1:   1900        -7615.359             0.016            0.016
Chain 1:   2000        -7529.776             0.017            0.016
Chain 1:   2100        -7523.340             0.015            0.013
Chain 1:   2200        -7697.423             0.017            0.016
Chain 1:   2300        -7517.625             0.018            0.023
Chain 1:   2400        -7603.533             0.018            0.023
Chain 1:   2500        -7542.120             0.016            0.013
Chain 1:   2600        -7492.613             0.014            0.011
Chain 1:   2700        -7531.930             0.011            0.011
Chain 1:   2800        -7473.350             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002969 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86566.336             1.000            1.000
Chain 1:    200       -13534.186             3.198            5.396
Chain 1:    300        -9971.358             2.251            1.000
Chain 1:    400       -10721.610             1.706            1.000
Chain 1:    500        -8861.390             1.407            0.357
Chain 1:    600        -8499.966             1.179            0.357
Chain 1:    700        -8534.448             1.011            0.210
Chain 1:    800        -8955.630             0.891            0.210
Chain 1:    900        -8715.473             0.795            0.070
Chain 1:   1000        -8478.845             0.718            0.070
Chain 1:   1100        -8858.654             0.623            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8490.326             0.087            0.043
Chain 1:   1300        -8574.218             0.052            0.043
Chain 1:   1400        -8526.276             0.046            0.043
Chain 1:   1500        -8559.436             0.025            0.028
Chain 1:   1600        -8559.150             0.021            0.028
Chain 1:   1700        -8482.099             0.022            0.028
Chain 1:   1800        -8366.452             0.018            0.014
Chain 1:   1900        -8485.677             0.017            0.014
Chain 1:   2000        -8445.817             0.015            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003689 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410896.174             1.000            1.000
Chain 1:    200     -1586266.110             2.651            4.302
Chain 1:    300      -890793.399             2.028            1.000
Chain 1:    400      -457865.545             1.757            1.000
Chain 1:    500      -357948.685             1.462            0.946
Chain 1:    600      -232893.380             1.307            0.946
Chain 1:    700      -119159.759             1.257            0.946
Chain 1:    800       -86398.622             1.147            0.946
Chain 1:    900       -66746.878             1.053            0.781
Chain 1:   1000       -51550.170             0.977            0.781
Chain 1:   1100       -39040.541             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38212.715             0.481            0.379
Chain 1:   1300       -26190.788             0.449            0.379
Chain 1:   1400       -25909.491             0.355            0.320
Chain 1:   1500       -22503.643             0.342            0.320
Chain 1:   1600       -21721.520             0.292            0.295
Chain 1:   1700       -20598.309             0.202            0.294
Chain 1:   1800       -20542.928             0.165            0.151
Chain 1:   1900       -20868.627             0.137            0.055
Chain 1:   2000       -19382.284             0.115            0.055
Chain 1:   2100       -19620.393             0.084            0.036
Chain 1:   2200       -19846.423             0.083            0.036
Chain 1:   2300       -19464.139             0.039            0.020
Chain 1:   2400       -19236.399             0.039            0.020
Chain 1:   2500       -19038.411             0.025            0.016
Chain 1:   2600       -18669.063             0.023            0.016
Chain 1:   2700       -18626.132             0.018            0.012
Chain 1:   2800       -18343.212             0.020            0.015
Chain 1:   2900       -18624.231             0.019            0.015
Chain 1:   3000       -18610.416             0.012            0.012
Chain 1:   3100       -18695.365             0.011            0.012
Chain 1:   3200       -18386.335             0.012            0.015
Chain 1:   3300       -18590.816             0.011            0.012
Chain 1:   3400       -18066.281             0.013            0.015
Chain 1:   3500       -18677.337             0.015            0.015
Chain 1:   3600       -17985.051             0.017            0.015
Chain 1:   3700       -18371.091             0.018            0.017
Chain 1:   3800       -17332.419             0.023            0.021
Chain 1:   3900       -17328.597             0.021            0.021
Chain 1:   4000       -17445.909             0.022            0.021
Chain 1:   4100       -17359.771             0.022            0.021
Chain 1:   4200       -17176.336             0.021            0.021
Chain 1:   4300       -17314.499             0.021            0.021
Chain 1:   4400       -17271.603             0.019            0.011
Chain 1:   4500       -17174.185             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11944.269             1.000            1.000
Chain 1:    200        -8868.762             0.673            1.000
Chain 1:    300        -7915.829             0.489            0.347
Chain 1:    400        -8014.219             0.370            0.347
Chain 1:    500        -7729.971             0.303            0.120
Chain 1:    600        -7732.574             0.253            0.120
Chain 1:    700        -7713.156             0.217            0.037
Chain 1:    800        -7693.714             0.190            0.037
Chain 1:    900        -7884.390             0.172            0.024
Chain 1:   1000        -7731.534             0.157            0.024
Chain 1:   1100        -7899.920             0.059            0.021
Chain 1:   1200        -7696.169             0.027            0.021
Chain 1:   1300        -7671.056             0.015            0.020
Chain 1:   1400        -7680.948             0.014            0.020
Chain 1:   1500        -7764.618             0.011            0.011
Chain 1:   1600        -7751.784             0.011            0.011
Chain 1:   1700        -7663.010             0.012            0.012
Chain 1:   1800        -7649.537             0.012            0.012
Chain 1:   1900        -7631.721             0.010            0.011
Chain 1:   2000        -7613.000             0.008            0.003   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001457 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56679.778             1.000            1.000
Chain 1:    200       -16959.910             1.671            2.342
Chain 1:    300        -8495.207             1.446            1.000
Chain 1:    400        -8629.280             1.088            1.000
Chain 1:    500        -8011.803             0.886            0.996
Chain 1:    600        -8658.146             0.751            0.996
Chain 1:    700        -7831.140             0.659            0.106
Chain 1:    800        -8253.351             0.583            0.106
Chain 1:    900        -7616.539             0.527            0.084
Chain 1:   1000        -7684.773             0.475            0.084
Chain 1:   1100        -7648.642             0.376            0.077
Chain 1:   1200        -7531.646             0.143            0.075
Chain 1:   1300        -7635.108             0.045            0.051
Chain 1:   1400        -7749.179             0.045            0.051
Chain 1:   1500        -7559.295             0.040            0.025
Chain 1:   1600        -7464.487             0.034            0.016
Chain 1:   1700        -7446.234             0.023            0.015
Chain 1:   1800        -7481.437             0.019            0.014
Chain 1:   1900        -7529.011             0.011            0.013
Chain 1:   2000        -7524.334             0.010            0.013
Chain 1:   2100        -7558.086             0.010            0.013
Chain 1:   2200        -7621.608             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003236 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86001.309             1.000            1.000
Chain 1:    200       -12994.597             3.309            5.618
Chain 1:    300        -9496.030             2.329            1.000
Chain 1:    400       -10253.843             1.765            1.000
Chain 1:    500        -8378.362             1.457            0.368
Chain 1:    600        -8208.636             1.218            0.368
Chain 1:    700        -8356.080             1.046            0.224
Chain 1:    800        -8674.070             0.920            0.224
Chain 1:    900        -8355.240             0.822            0.074
Chain 1:   1000        -8128.906             0.743            0.074
Chain 1:   1100        -8346.509             0.645            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8115.256             0.086            0.037
Chain 1:   1300        -8278.535             0.051            0.028
Chain 1:   1400        -8203.296             0.045            0.028
Chain 1:   1500        -8153.873             0.023            0.026
Chain 1:   1600        -8152.299             0.021            0.026
Chain 1:   1700        -8095.338             0.020            0.026
Chain 1:   1800        -7973.841             0.018            0.020
Chain 1:   1900        -8086.060             0.015            0.015
Chain 1:   2000        -8048.960             0.013            0.014
Chain 1:   2100        -8193.321             0.012            0.014
Chain 1:   2200        -7974.932             0.012            0.014
Chain 1:   2300        -8107.819             0.012            0.014
Chain 1:   2400        -8002.753             0.012            0.014
Chain 1:   2500        -8057.641             0.012            0.014
Chain 1:   2600        -8070.359             0.012            0.014
Chain 1:   2700        -7991.461             0.013            0.014
Chain 1:   2800        -7977.406             0.011            0.013
Chain 1:   2900        -7967.176             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413935.315             1.000            1.000
Chain 1:    200     -1585650.750             2.653            4.306
Chain 1:    300      -890743.751             2.029            1.000
Chain 1:    400      -457690.354             1.758            1.000
Chain 1:    500      -357778.777             1.462            0.946
Chain 1:    600      -232544.012             1.308            0.946
Chain 1:    700      -118674.021             1.259            0.946
Chain 1:    800       -85879.467             1.149            0.946
Chain 1:    900       -66208.535             1.054            0.780
Chain 1:   1000       -50995.166             0.979            0.780
Chain 1:   1100       -38478.004             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37644.897             0.483            0.382
Chain 1:   1300       -25624.919             0.452            0.382
Chain 1:   1400       -25341.381             0.358            0.325
Chain 1:   1500       -21936.252             0.346            0.325
Chain 1:   1600       -21153.639             0.296            0.298
Chain 1:   1700       -20031.230             0.205            0.297
Chain 1:   1800       -19975.668             0.167            0.155
Chain 1:   1900       -20300.927             0.139            0.056
Chain 1:   2000       -18815.849             0.117            0.056
Chain 1:   2100       -19053.854             0.086            0.037
Chain 1:   2200       -19279.480             0.085            0.037
Chain 1:   2300       -18897.654             0.040            0.020
Chain 1:   2400       -18670.098             0.040            0.020
Chain 1:   2500       -18472.091             0.026            0.016
Chain 1:   2600       -18103.222             0.024            0.016
Chain 1:   2700       -18060.434             0.019            0.012
Chain 1:   2800       -17777.717             0.020            0.016
Chain 1:   2900       -18058.511             0.020            0.016
Chain 1:   3000       -18044.729             0.012            0.012
Chain 1:   3100       -18129.602             0.011            0.012
Chain 1:   3200       -17820.888             0.012            0.016
Chain 1:   3300       -18025.119             0.011            0.012
Chain 1:   3400       -17501.143             0.013            0.016
Chain 1:   3500       -18111.374             0.015            0.016
Chain 1:   3600       -17420.166             0.017            0.016
Chain 1:   3700       -17805.399             0.019            0.017
Chain 1:   3800       -16768.404             0.024            0.022
Chain 1:   3900       -16764.627             0.022            0.022
Chain 1:   4000       -16881.923             0.023            0.022
Chain 1:   4100       -16795.877             0.023            0.022
Chain 1:   4200       -16612.811             0.022            0.022
Chain 1:   4300       -16750.711             0.022            0.022
Chain 1:   4400       -16708.116             0.019            0.011
Chain 1:   4500       -16610.758             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001406 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48174.848             1.000            1.000
Chain 1:    200       -16233.881             1.484            1.968
Chain 1:    300       -14909.097             1.019            1.000
Chain 1:    400       -24214.038             0.860            1.000
Chain 1:    500       -10987.648             0.929            1.000
Chain 1:    600       -16260.093             0.828            1.000
Chain 1:    700       -14408.737             0.728            0.384
Chain 1:    800       -10325.070             0.687            0.396
Chain 1:    900       -10606.091             0.613            0.384
Chain 1:   1000       -11904.928             0.563            0.384
Chain 1:   1100       -12409.018             0.467            0.324
Chain 1:   1200       -10271.960             0.291            0.208
Chain 1:   1300       -11908.658             0.296            0.208
Chain 1:   1400       -20884.468             0.300            0.208
Chain 1:   1500       -20471.766             0.182            0.137
Chain 1:   1600       -12094.020             0.219            0.137
Chain 1:   1700       -10363.996             0.223            0.167
Chain 1:   1800        -9485.807             0.192            0.137
Chain 1:   1900       -10574.770             0.200            0.137
Chain 1:   2000        -9366.498             0.202            0.137
Chain 1:   2100        -9588.481             0.200            0.137
Chain 1:   2200        -9244.118             0.183            0.129
Chain 1:   2300       -12036.485             0.193            0.129
Chain 1:   2400        -8734.625             0.187            0.129
Chain 1:   2500       -11221.654             0.208            0.167
Chain 1:   2600        -9409.872             0.158            0.167
Chain 1:   2700        -8883.564             0.147            0.129
Chain 1:   2800        -9714.937             0.146            0.129
Chain 1:   2900       -17067.588             0.179            0.193
Chain 1:   3000        -8878.812             0.258            0.222
Chain 1:   3100        -8963.433             0.257            0.222
Chain 1:   3200        -8627.840             0.257            0.222
Chain 1:   3300        -8708.510             0.235            0.193
Chain 1:   3400        -8641.477             0.198            0.086
Chain 1:   3500        -8709.348             0.176            0.059
Chain 1:   3600       -12590.949             0.188            0.059
Chain 1:   3700       -10394.618             0.203            0.086
Chain 1:   3800       -14099.620             0.221            0.211
Chain 1:   3900        -8654.554             0.241            0.211
Chain 1:   4000        -9611.105             0.158            0.100
Chain 1:   4100        -8798.223             0.167            0.100
Chain 1:   4200       -12174.619             0.191            0.211
Chain 1:   4300       -13516.039             0.200            0.211
Chain 1:   4400       -10144.091             0.232            0.263
Chain 1:   4500        -8660.681             0.248            0.263
Chain 1:   4600        -8178.896             0.223            0.211
Chain 1:   4700        -8708.907             0.208            0.171
Chain 1:   4800       -11165.817             0.204            0.171
Chain 1:   4900        -8483.173             0.173            0.171
Chain 1:   5000       -14297.337             0.204            0.220
Chain 1:   5100        -9346.359             0.247            0.277
Chain 1:   5200       -12470.494             0.245            0.251
Chain 1:   5300        -9460.266             0.266            0.316
Chain 1:   5400       -12503.453             0.258            0.251
Chain 1:   5500        -8581.298             0.286            0.316
Chain 1:   5600        -8266.587             0.284            0.316
Chain 1:   5700       -11595.367             0.307            0.316
Chain 1:   5800        -8541.984             0.320            0.318
Chain 1:   5900        -8207.137             0.293            0.318
Chain 1:   6000        -9504.174             0.266            0.287
Chain 1:   6100        -8833.965             0.220            0.251
Chain 1:   6200        -8071.694             0.205            0.243
Chain 1:   6300        -8683.551             0.180            0.136
Chain 1:   6400       -12632.953             0.187            0.136
Chain 1:   6500        -8262.801             0.194            0.136
Chain 1:   6600        -8246.163             0.191            0.136
Chain 1:   6700       -12628.854             0.197            0.136
Chain 1:   6800        -9080.415             0.200            0.136
Chain 1:   6900       -11782.939             0.219            0.229
Chain 1:   7000        -8760.550             0.240            0.313
Chain 1:   7100        -8354.704             0.237            0.313
Chain 1:   7200        -9770.987             0.242            0.313
Chain 1:   7300       -11645.565             0.251            0.313
Chain 1:   7400        -8568.922             0.256            0.345
Chain 1:   7500       -12211.765             0.233            0.298
Chain 1:   7600        -8503.079             0.276            0.345
Chain 1:   7700        -8219.105             0.245            0.298
Chain 1:   7800        -9254.456             0.217            0.229
Chain 1:   7900        -9794.268             0.199            0.161
Chain 1:   8000       -10968.868             0.176            0.145
Chain 1:   8100       -11953.111             0.179            0.145
Chain 1:   8200       -10695.338             0.176            0.118
Chain 1:   8300       -10355.036             0.163            0.112
Chain 1:   8400        -8158.562             0.155            0.112
Chain 1:   8500        -8224.486             0.125            0.107
Chain 1:   8600        -9992.367             0.100            0.107
Chain 1:   8700        -8847.338             0.109            0.112
Chain 1:   8800        -8043.648             0.108            0.107
Chain 1:   8900        -8559.170             0.108            0.107
Chain 1:   9000        -9023.296             0.103            0.100
Chain 1:   9100        -8563.038             0.100            0.100
Chain 1:   9200        -9101.462             0.094            0.060
Chain 1:   9300       -10677.758             0.106            0.100
Chain 1:   9400        -8090.947             0.111            0.100
Chain 1:   9500        -8131.745             0.110            0.100
Chain 1:   9600        -8327.354             0.095            0.060
Chain 1:   9700        -8262.234             0.083            0.059
Chain 1:   9800        -9106.498             0.082            0.059
Chain 1:   9900        -8502.235             0.083            0.059
Chain 1:   10000        -8960.693             0.083            0.059
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61108.482             1.000            1.000
Chain 1:    200       -17202.205             1.776            2.552
Chain 1:    300        -8601.256             1.517            1.000
Chain 1:    400        -8103.716             1.153            1.000
Chain 1:    500        -7887.134             0.928            1.000
Chain 1:    600        -8543.565             0.786            1.000
Chain 1:    700        -7855.603             0.687            0.088
Chain 1:    800        -7989.643             0.603            0.088
Chain 1:    900        -7836.999             0.538            0.077
Chain 1:   1000        -7789.775             0.485            0.077
Chain 1:   1100        -7646.447             0.387            0.061
Chain 1:   1200        -7616.379             0.132            0.027
Chain 1:   1300        -7681.632             0.033            0.019
Chain 1:   1400        -7707.954             0.027            0.019
Chain 1:   1500        -7588.779             0.026            0.017
Chain 1:   1600        -7515.475             0.019            0.016
Chain 1:   1700        -7499.345             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002932 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85935.374             1.000            1.000
Chain 1:    200       -12951.214             3.318            5.635
Chain 1:    300        -9532.354             2.331            1.000
Chain 1:    400        -9957.011             1.759            1.000
Chain 1:    500        -8431.151             1.444            0.359
Chain 1:    600        -8458.033             1.203            0.359
Chain 1:    700        -8474.502             1.032            0.181
Chain 1:    800        -8628.387             0.905            0.181
Chain 1:    900        -8428.519             0.807            0.043
Chain 1:   1000        -8251.506             0.729            0.043
Chain 1:   1100        -8461.637             0.631            0.025   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8217.521             0.070            0.025
Chain 1:   1300        -8371.893             0.036            0.024
Chain 1:   1400        -8268.037             0.033            0.021
Chain 1:   1500        -8263.198             0.015            0.018
Chain 1:   1600        -8369.022             0.016            0.018
Chain 1:   1700        -8438.507             0.017            0.018
Chain 1:   1800        -8105.573             0.019            0.021
Chain 1:   1900        -8197.965             0.018            0.018
Chain 1:   2000        -8172.224             0.016            0.013
Chain 1:   2100        -8323.999             0.016            0.013
Chain 1:   2200        -8099.310             0.015            0.013
Chain 1:   2300        -8175.112             0.014            0.013
Chain 1:   2400        -8230.856             0.014            0.011
Chain 1:   2500        -8200.485             0.014            0.011
Chain 1:   2600        -8191.707             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002582 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412452.902             1.000            1.000
Chain 1:    200     -1587839.376             2.649            4.298
Chain 1:    300      -891332.084             2.026            1.000
Chain 1:    400      -457297.443             1.757            1.000
Chain 1:    500      -357156.442             1.462            0.949
Chain 1:    600      -232005.754             1.308            0.949
Chain 1:    700      -118410.918             1.258            0.949
Chain 1:    800       -85649.545             1.149            0.949
Chain 1:    900       -66031.526             1.054            0.781
Chain 1:   1000       -50846.072             0.979            0.781
Chain 1:   1100       -38352.634             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37521.195             0.484            0.383
Chain 1:   1300       -25534.958             0.452            0.383
Chain 1:   1400       -25252.087             0.359            0.326
Chain 1:   1500       -21855.685             0.346            0.326
Chain 1:   1600       -21074.910             0.296            0.299
Chain 1:   1700       -19957.356             0.206            0.297
Chain 1:   1800       -19902.553             0.168            0.155
Chain 1:   1900       -20227.342             0.139            0.056
Chain 1:   2000       -18745.638             0.117            0.056
Chain 1:   2100       -18983.414             0.086            0.037
Chain 1:   2200       -19208.267             0.085            0.037
Chain 1:   2300       -18827.279             0.040            0.020
Chain 1:   2400       -18600.002             0.040            0.020
Chain 1:   2500       -18401.771             0.026            0.016
Chain 1:   2600       -18033.569             0.024            0.016
Chain 1:   2700       -17991.062             0.019            0.013
Chain 1:   2800       -17708.446             0.020            0.016
Chain 1:   2900       -17989.002             0.020            0.016
Chain 1:   3000       -17975.300             0.012            0.013
Chain 1:   3100       -18060.047             0.011            0.012
Chain 1:   3200       -17751.720             0.012            0.016
Chain 1:   3300       -17955.701             0.011            0.012
Chain 1:   3400       -17432.275             0.013            0.016
Chain 1:   3500       -18041.530             0.015            0.016
Chain 1:   3600       -17351.702             0.017            0.016
Chain 1:   3700       -17735.871             0.019            0.017
Chain 1:   3800       -16700.855             0.024            0.022
Chain 1:   3900       -16697.139             0.022            0.022
Chain 1:   4000       -16814.450             0.023            0.022
Chain 1:   4100       -16728.432             0.023            0.022
Chain 1:   4200       -16545.882             0.022            0.022
Chain 1:   4300       -16683.446             0.022            0.022
Chain 1:   4400       -16641.226             0.019            0.011
Chain 1:   4500       -16543.936             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49513.390             1.000            1.000
Chain 1:    200       -15323.327             1.616            2.231
Chain 1:    300       -13305.073             1.128            1.000
Chain 1:    400       -15923.696             0.887            1.000
Chain 1:    500       -13109.407             0.752            0.215
Chain 1:    600       -15221.886             0.650            0.215
Chain 1:    700       -16505.980             0.568            0.164
Chain 1:    800       -12334.658             0.540            0.215
Chain 1:    900       -13301.797             0.488            0.164
Chain 1:   1000       -12219.506             0.448            0.164
Chain 1:   1100       -11565.462             0.353            0.152
Chain 1:   1200       -12535.925             0.138            0.139
Chain 1:   1300       -10806.416             0.139            0.139
Chain 1:   1400       -13305.231             0.141            0.139
Chain 1:   1500       -10679.338             0.144            0.139
Chain 1:   1600       -13036.682             0.149            0.160
Chain 1:   1700        -9912.687             0.172            0.181
Chain 1:   1800       -16814.392             0.180            0.181
Chain 1:   1900       -11319.465             0.221            0.188
Chain 1:   2000       -10173.846             0.223            0.188
Chain 1:   2100       -10080.863             0.218            0.188
Chain 1:   2200        -9863.387             0.213            0.188
Chain 1:   2300        -9823.135             0.197            0.188
Chain 1:   2400       -14838.558             0.212            0.246
Chain 1:   2500       -16388.044             0.197            0.181
Chain 1:   2600       -10065.857             0.242            0.315
Chain 1:   2700       -11013.964             0.219            0.113
Chain 1:   2800       -16928.863             0.213            0.113
Chain 1:   2900        -9950.277             0.235            0.113
Chain 1:   3000       -16067.632             0.261            0.338
Chain 1:   3100        -8976.487             0.339            0.349
Chain 1:   3200       -12477.002             0.365            0.349
Chain 1:   3300       -10492.422             0.384            0.349
Chain 1:   3400        -9322.266             0.363            0.349
Chain 1:   3500       -10917.864             0.368            0.349
Chain 1:   3600        -9284.251             0.322            0.281
Chain 1:   3700        -9601.840             0.317            0.281
Chain 1:   3800       -10027.289             0.286            0.189
Chain 1:   3900        -9531.787             0.222            0.176
Chain 1:   4000       -13233.650             0.211            0.176
Chain 1:   4100       -14492.298             0.141            0.146
Chain 1:   4200        -9964.141             0.159            0.146
Chain 1:   4300       -10467.542             0.144            0.126
Chain 1:   4400        -9728.494             0.139            0.087
Chain 1:   4500       -10667.863             0.134            0.087
Chain 1:   4600        -8883.180             0.136            0.087
Chain 1:   4700       -13880.951             0.169            0.088
Chain 1:   4800        -9254.862             0.215            0.201
Chain 1:   4900       -19699.354             0.262            0.280
Chain 1:   5000       -11811.435             0.301            0.360
Chain 1:   5100        -8895.009             0.325            0.360
Chain 1:   5200        -9155.285             0.283            0.328
Chain 1:   5300       -11868.439             0.301            0.328
Chain 1:   5400       -12615.334             0.299            0.328
Chain 1:   5500        -8868.913             0.333            0.360
Chain 1:   5600       -13714.990             0.348            0.360
Chain 1:   5700        -9926.151             0.350            0.382
Chain 1:   5800        -9838.388             0.301            0.353
Chain 1:   5900       -16645.848             0.289            0.353
Chain 1:   6000       -11640.442             0.265            0.353
Chain 1:   6100        -9638.278             0.253            0.353
Chain 1:   6200        -8649.316             0.262            0.353
Chain 1:   6300        -9023.132             0.243            0.353
Chain 1:   6400        -8492.229             0.243            0.353
Chain 1:   6500        -9450.961             0.211            0.208
Chain 1:   6600       -14027.682             0.208            0.208
Chain 1:   6700       -14130.912             0.171            0.114
Chain 1:   6800        -8881.560             0.229            0.208
Chain 1:   6900        -8509.433             0.193            0.114
Chain 1:   7000        -9964.407             0.164            0.114
Chain 1:   7100        -8508.152             0.161            0.114
Chain 1:   7200       -11589.766             0.176            0.146
Chain 1:   7300        -8427.640             0.209            0.171
Chain 1:   7400       -10371.604             0.222            0.187
Chain 1:   7500        -9968.112             0.215            0.187
Chain 1:   7600        -9151.949             0.192            0.171
Chain 1:   7700       -12896.836             0.220            0.187
Chain 1:   7800        -8873.643             0.206            0.187
Chain 1:   7900        -8432.560             0.207            0.187
Chain 1:   8000        -8465.966             0.193            0.187
Chain 1:   8100        -8624.431             0.178            0.187
Chain 1:   8200        -9565.558             0.161            0.098
Chain 1:   8300       -12388.985             0.146            0.098
Chain 1:   8400        -9951.984             0.152            0.098
Chain 1:   8500        -8345.076             0.167            0.193
Chain 1:   8600        -8719.772             0.163            0.193
Chain 1:   8700        -9093.528             0.138            0.098
Chain 1:   8800        -8911.604             0.094            0.052
Chain 1:   8900        -9733.074             0.097            0.084
Chain 1:   9000        -8574.356             0.111            0.098
Chain 1:   9100        -8459.151             0.110            0.098
Chain 1:   9200        -9458.774             0.111            0.106
Chain 1:   9300        -9272.331             0.090            0.084
Chain 1:   9400        -8718.055             0.072            0.064
Chain 1:   9500       -11456.653             0.077            0.064
Chain 1:   9600       -11753.418             0.075            0.064
Chain 1:   9700        -8872.997             0.103            0.084
Chain 1:   9800       -11087.523             0.121            0.106
Chain 1:   9900        -8534.941             0.143            0.135
Chain 1:   10000        -8276.007             0.132            0.106
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63462.180             1.000            1.000
Chain 1:    200       -18573.960             1.708            2.417
Chain 1:    300        -8919.542             1.500            1.082
Chain 1:    400        -8512.394             1.137            1.082
Chain 1:    500        -8505.488             0.910            1.000
Chain 1:    600        -8909.192             0.766            1.000
Chain 1:    700        -7948.379             0.673            0.121
Chain 1:    800        -7652.641             0.594            0.121
Chain 1:    900        -7767.014             0.530            0.048
Chain 1:   1000        -8016.635             0.480            0.048
Chain 1:   1100        -7596.637             0.385            0.048
Chain 1:   1200        -7942.416             0.148            0.045
Chain 1:   1300        -7714.198             0.043            0.044
Chain 1:   1400        -7861.378             0.040            0.039
Chain 1:   1500        -7471.880             0.045            0.044
Chain 1:   1600        -7691.244             0.043            0.039
Chain 1:   1700        -7549.879             0.033            0.031
Chain 1:   1800        -7661.566             0.031            0.030
Chain 1:   1900        -7540.334             0.031            0.030
Chain 1:   2000        -7627.737             0.029            0.029
Chain 1:   2100        -7489.277             0.025            0.019
Chain 1:   2200        -7705.473             0.024            0.019
Chain 1:   2300        -7471.064             0.024            0.019
Chain 1:   2400        -7498.301             0.022            0.019
Chain 1:   2500        -7538.730             0.018            0.018
Chain 1:   2600        -7462.870             0.016            0.016
Chain 1:   2700        -7532.325             0.015            0.015
Chain 1:   2800        -7470.311             0.014            0.011
Chain 1:   2900        -7355.784             0.014            0.011
Chain 1:   3000        -7498.711             0.015            0.016
Chain 1:   3100        -7467.016             0.013            0.010
Chain 1:   3200        -7672.207             0.013            0.010
Chain 1:   3300        -7386.809             0.014            0.010
Chain 1:   3400        -7623.870             0.017            0.016
Chain 1:   3500        -7370.102             0.020            0.019
Chain 1:   3600        -7436.043             0.020            0.019
Chain 1:   3700        -7386.616             0.019            0.019
Chain 1:   3800        -7387.307             0.019            0.019
Chain 1:   3900        -7344.335             0.018            0.019
Chain 1:   4000        -7337.086             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002629 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86699.910             1.000            1.000
Chain 1:    200       -13862.283             3.127            5.254
Chain 1:    300       -10135.439             2.207            1.000
Chain 1:    400       -11474.890             1.685            1.000
Chain 1:    500        -9025.201             1.402            0.368
Chain 1:    600        -9349.285             1.174            0.368
Chain 1:    700        -8738.162             1.016            0.271
Chain 1:    800        -8411.547             0.894            0.271
Chain 1:    900        -8432.660             0.795            0.117
Chain 1:   1000        -8735.428             0.719            0.117
Chain 1:   1100        -8865.830             0.621            0.070   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8527.447             0.099            0.040
Chain 1:   1300        -8802.557             0.065            0.039
Chain 1:   1400        -8715.796             0.055            0.035
Chain 1:   1500        -8636.208             0.029            0.035
Chain 1:   1600        -8744.362             0.026            0.031
Chain 1:   1700        -8805.708             0.020            0.015
Chain 1:   1800        -8365.026             0.021            0.015
Chain 1:   1900        -8469.865             0.022            0.015
Chain 1:   2000        -8451.397             0.019            0.012
Chain 1:   2100        -8574.951             0.019            0.012
Chain 1:   2200        -8369.592             0.018            0.012
Chain 1:   2300        -8462.642             0.016            0.012
Chain 1:   2400        -8529.669             0.015            0.012
Chain 1:   2500        -8478.538             0.015            0.012
Chain 1:   2600        -8490.266             0.014            0.011
Chain 1:   2700        -8399.204             0.014            0.011
Chain 1:   2800        -8348.214             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410172.768             1.000            1.000
Chain 1:    200     -1584657.498             2.654            4.307
Chain 1:    300      -889977.897             2.029            1.000
Chain 1:    400      -457118.625             1.759            1.000
Chain 1:    500      -357573.771             1.463            0.947
Chain 1:    600      -232786.764             1.308            0.947
Chain 1:    700      -119352.080             1.257            0.947
Chain 1:    800       -86623.031             1.147            0.947
Chain 1:    900       -67026.318             1.052            0.781
Chain 1:   1000       -51871.135             0.976            0.781
Chain 1:   1100       -39386.792             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38573.558             0.479            0.378
Chain 1:   1300       -26561.835             0.446            0.378
Chain 1:   1400       -26286.433             0.353            0.317
Chain 1:   1500       -22880.953             0.340            0.317
Chain 1:   1600       -22100.363             0.290            0.292
Chain 1:   1700       -20977.373             0.200            0.292
Chain 1:   1800       -20922.670             0.163            0.149
Chain 1:   1900       -21249.332             0.135            0.054
Chain 1:   2000       -19761.178             0.113            0.054
Chain 1:   2100       -19999.687             0.083            0.035
Chain 1:   2200       -20226.097             0.082            0.035
Chain 1:   2300       -19843.192             0.038            0.019
Chain 1:   2400       -19615.119             0.039            0.019
Chain 1:   2500       -19416.911             0.025            0.015
Chain 1:   2600       -19046.868             0.023            0.015
Chain 1:   2700       -19003.783             0.018            0.012
Chain 1:   2800       -18720.301             0.019            0.015
Chain 1:   2900       -19001.729             0.019            0.015
Chain 1:   3000       -18987.983             0.012            0.012
Chain 1:   3100       -19073.015             0.011            0.012
Chain 1:   3200       -18763.446             0.011            0.015
Chain 1:   3300       -18968.368             0.011            0.012
Chain 1:   3400       -18442.727             0.012            0.015
Chain 1:   3500       -19055.368             0.014            0.015
Chain 1:   3600       -18361.027             0.016            0.015
Chain 1:   3700       -18748.551             0.018            0.016
Chain 1:   3800       -17706.613             0.023            0.021
Chain 1:   3900       -17702.665             0.021            0.021
Chain 1:   4000       -17820.027             0.022            0.021
Chain 1:   4100       -17733.669             0.022            0.021
Chain 1:   4200       -17549.563             0.021            0.021
Chain 1:   4300       -17688.253             0.021            0.021
Chain 1:   4400       -17644.792             0.018            0.010
Chain 1:   4500       -17547.221             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12245.194             1.000            1.000
Chain 1:    200        -9165.530             0.668            1.000
Chain 1:    300        -7885.490             0.499            0.336
Chain 1:    400        -8052.409             0.380            0.336
Chain 1:    500        -8009.044             0.305            0.162
Chain 1:    600        -7829.858             0.258            0.162
Chain 1:    700        -7751.301             0.222            0.023
Chain 1:    800        -7774.900             0.195            0.023
Chain 1:    900        -7804.190             0.174            0.021
Chain 1:   1000        -7807.429             0.156            0.021
Chain 1:   1100        -7847.512             0.057            0.010
Chain 1:   1200        -7751.866             0.025            0.010
Chain 1:   1300        -7748.379             0.008            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61032.511             1.000            1.000
Chain 1:    200       -17494.612             1.744            2.489
Chain 1:    300        -8742.313             1.497            1.001
Chain 1:    400        -8277.910             1.136            1.001
Chain 1:    500        -7948.130             0.917            1.000
Chain 1:    600        -8772.139             0.780            1.000
Chain 1:    700        -8332.287             0.676            0.094
Chain 1:    800        -8253.573             0.593            0.094
Chain 1:    900        -7948.557             0.531            0.056
Chain 1:   1000        -7772.601             0.480            0.056
Chain 1:   1100        -7562.864             0.383            0.053
Chain 1:   1200        -7895.899             0.139            0.042
Chain 1:   1300        -7543.085             0.043            0.042
Chain 1:   1400        -7767.222             0.040            0.041
Chain 1:   1500        -7538.569             0.039            0.038
Chain 1:   1600        -7729.439             0.032            0.030
Chain 1:   1700        -7495.160             0.030            0.030
Chain 1:   1800        -7583.561             0.030            0.030
Chain 1:   1900        -7596.409             0.027            0.029
Chain 1:   2000        -7623.188             0.025            0.029
Chain 1:   2100        -7552.060             0.023            0.029
Chain 1:   2200        -7679.810             0.020            0.025
Chain 1:   2300        -7585.300             0.017            0.017
Chain 1:   2400        -7616.484             0.015            0.012
Chain 1:   2500        -7559.983             0.012            0.012
Chain 1:   2600        -7525.321             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002595 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85537.720             1.000            1.000
Chain 1:    200       -13432.789             3.184            5.368
Chain 1:    300        -9767.941             2.248            1.000
Chain 1:    400       -10706.965             1.708            1.000
Chain 1:    500        -8747.455             1.411            0.375
Chain 1:    600        -8414.498             1.182            0.375
Chain 1:    700        -8332.457             1.015            0.224
Chain 1:    800        -8617.295             0.892            0.224
Chain 1:    900        -8646.480             0.793            0.088
Chain 1:   1000        -8340.560             0.718            0.088
Chain 1:   1100        -8613.120             0.621            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8221.191             0.089            0.040
Chain 1:   1300        -8445.560             0.054            0.037
Chain 1:   1400        -8461.902             0.045            0.033
Chain 1:   1500        -8309.371             0.025            0.032
Chain 1:   1600        -8424.567             0.022            0.027
Chain 1:   1700        -8499.552             0.022            0.027
Chain 1:   1800        -8074.383             0.024            0.027
Chain 1:   1900        -8176.454             0.025            0.027
Chain 1:   2000        -8151.046             0.022            0.018
Chain 1:   2100        -8277.470             0.020            0.015
Chain 1:   2200        -8077.855             0.018            0.015
Chain 1:   2300        -8171.418             0.016            0.014
Chain 1:   2400        -8239.782             0.017            0.014
Chain 1:   2500        -8185.981             0.016            0.012
Chain 1:   2600        -8187.929             0.014            0.011
Chain 1:   2700        -8104.412             0.015            0.011
Chain 1:   2800        -8063.500             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003225 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380158.712             1.000            1.000
Chain 1:    200     -1579931.446             2.652            4.304
Chain 1:    300      -891751.445             2.025            1.000
Chain 1:    400      -458668.145             1.755            1.000
Chain 1:    500      -359175.962             1.459            0.944
Chain 1:    600      -233881.823             1.305            0.944
Chain 1:    700      -119650.329             1.255            0.944
Chain 1:    800       -86720.940             1.146            0.944
Chain 1:    900       -66972.866             1.051            0.772
Chain 1:   1000       -51702.778             0.976            0.772
Chain 1:   1100       -39114.526             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38286.828             0.480            0.380
Chain 1:   1300       -26178.367             0.449            0.380
Chain 1:   1400       -25891.968             0.355            0.322
Chain 1:   1500       -22462.012             0.343            0.322
Chain 1:   1600       -21673.443             0.293            0.295
Chain 1:   1700       -20539.559             0.203            0.295
Chain 1:   1800       -20482.136             0.165            0.153
Chain 1:   1900       -20808.357             0.138            0.055
Chain 1:   2000       -19315.365             0.116            0.055
Chain 1:   2100       -19553.880             0.085            0.036
Chain 1:   2200       -19781.031             0.084            0.036
Chain 1:   2300       -19397.654             0.039            0.020
Chain 1:   2400       -19169.643             0.040            0.020
Chain 1:   2500       -18971.846             0.025            0.016
Chain 1:   2600       -18601.578             0.024            0.016
Chain 1:   2700       -18558.521             0.018            0.012
Chain 1:   2800       -18275.305             0.020            0.015
Chain 1:   2900       -18556.813             0.020            0.015
Chain 1:   3000       -18542.935             0.012            0.012
Chain 1:   3100       -18627.890             0.011            0.012
Chain 1:   3200       -18318.429             0.012            0.015
Chain 1:   3300       -18523.325             0.011            0.012
Chain 1:   3400       -17997.974             0.013            0.015
Chain 1:   3500       -18610.257             0.015            0.015
Chain 1:   3600       -17916.596             0.017            0.015
Chain 1:   3700       -18303.637             0.019            0.017
Chain 1:   3800       -17262.722             0.023            0.021
Chain 1:   3900       -17258.930             0.022            0.021
Chain 1:   4000       -17376.198             0.022            0.021
Chain 1:   4100       -17289.847             0.022            0.021
Chain 1:   4200       -17106.068             0.022            0.021
Chain 1:   4300       -17244.460             0.021            0.021
Chain 1:   4400       -17201.180             0.019            0.011
Chain 1:   4500       -17103.777             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001284 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12737.958             1.000            1.000
Chain 1:    200        -9688.400             0.657            1.000
Chain 1:    300        -8410.786             0.489            0.315
Chain 1:    400        -8573.972             0.371            0.315
Chain 1:    500        -8444.043             0.300            0.152
Chain 1:    600        -8371.534             0.252            0.152
Chain 1:    700        -8283.379             0.217            0.019
Chain 1:    800        -8326.471             0.191            0.019
Chain 1:    900        -8446.337             0.171            0.015
Chain 1:   1000        -8348.490             0.155            0.015
Chain 1:   1100        -8358.883             0.055            0.014
Chain 1:   1200        -8295.319             0.025            0.012
Chain 1:   1300        -8257.818             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46695.960             1.000            1.000
Chain 1:    200       -15885.488             1.470            1.940
Chain 1:    300        -8824.179             1.247            1.000
Chain 1:    400        -8194.045             0.954            1.000
Chain 1:    500        -9102.582             0.783            0.800
Chain 1:    600        -8316.074             0.669            0.800
Chain 1:    700        -7828.303             0.582            0.100
Chain 1:    800        -8147.484             0.514            0.100
Chain 1:    900        -8123.133             0.457            0.095
Chain 1:   1000        -7800.543             0.416            0.095
Chain 1:   1100        -7764.885             0.316            0.077
Chain 1:   1200        -7713.630             0.123            0.062
Chain 1:   1300        -8090.492             0.047            0.047
Chain 1:   1400        -8027.502             0.041            0.041
Chain 1:   1500        -7535.017             0.037            0.041
Chain 1:   1600        -7693.799             0.030            0.039
Chain 1:   1700        -7571.267             0.025            0.021
Chain 1:   1800        -7587.450             0.021            0.016
Chain 1:   1900        -7582.022             0.021            0.016
Chain 1:   2000        -7624.485             0.018            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002666 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86784.015             1.000            1.000
Chain 1:    200       -13837.750             3.136            5.272
Chain 1:    300       -10228.406             2.208            1.000
Chain 1:    400       -10960.776             1.673            1.000
Chain 1:    500        -9172.159             1.377            0.353
Chain 1:    600        -8829.347             1.154            0.353
Chain 1:    700        -8804.460             0.990            0.195
Chain 1:    800        -9290.868             0.873            0.195
Chain 1:    900        -8969.071             0.780            0.067
Chain 1:   1000        -8910.133             0.702            0.067
Chain 1:   1100        -9078.621             0.604            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8629.679             0.082            0.052
Chain 1:   1300        -8943.302             0.050            0.039
Chain 1:   1400        -8938.969             0.044            0.036
Chain 1:   1500        -8813.169             0.026            0.035
Chain 1:   1600        -8920.811             0.023            0.019
Chain 1:   1700        -9006.991             0.024            0.019
Chain 1:   1800        -8602.618             0.023            0.019
Chain 1:   1900        -8700.429             0.021            0.014
Chain 1:   2000        -8672.261             0.020            0.014
Chain 1:   2100        -8792.188             0.020            0.014
Chain 1:   2200        -8584.148             0.017            0.014
Chain 1:   2300        -8735.696             0.015            0.014
Chain 1:   2400        -8742.417             0.015            0.014
Chain 1:   2500        -8714.228             0.014            0.012
Chain 1:   2600        -8713.482             0.013            0.011
Chain 1:   2700        -8624.891             0.013            0.011
Chain 1:   2800        -8591.331             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003118 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8371106.381             1.000            1.000
Chain 1:    200     -1576583.652             2.655            4.310
Chain 1:    300      -889244.269             2.028            1.000
Chain 1:    400      -456848.083             1.757            1.000
Chain 1:    500      -357888.777             1.461            0.946
Chain 1:    600      -233259.668             1.307            0.946
Chain 1:    700      -119601.881             1.256            0.946
Chain 1:    800       -86801.742             1.146            0.946
Chain 1:    900       -67148.261             1.051            0.773
Chain 1:   1000       -51936.616             0.975            0.773
Chain 1:   1100       -39400.036             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38578.622             0.478            0.378
Chain 1:   1300       -26520.709             0.447            0.378
Chain 1:   1400       -26238.407             0.353            0.318
Chain 1:   1500       -22821.053             0.340            0.318
Chain 1:   1600       -22036.112             0.290            0.293
Chain 1:   1700       -20907.976             0.201            0.293
Chain 1:   1800       -20851.846             0.163            0.150
Chain 1:   1900       -21177.851             0.136            0.054
Chain 1:   2000       -19688.383             0.114            0.054
Chain 1:   2100       -19926.835             0.083            0.036
Chain 1:   2200       -20153.220             0.082            0.036
Chain 1:   2300       -19770.537             0.039            0.019
Chain 1:   2400       -19542.685             0.039            0.019
Chain 1:   2500       -19344.699             0.025            0.015
Chain 1:   2600       -18975.052             0.023            0.015
Chain 1:   2700       -18932.147             0.018            0.012
Chain 1:   2800       -18649.053             0.019            0.015
Chain 1:   2900       -18930.294             0.019            0.015
Chain 1:   3000       -18916.472             0.012            0.012
Chain 1:   3100       -19001.394             0.011            0.012
Chain 1:   3200       -18692.217             0.011            0.015
Chain 1:   3300       -18896.882             0.011            0.012
Chain 1:   3400       -18372.033             0.012            0.015
Chain 1:   3500       -18983.559             0.015            0.015
Chain 1:   3600       -18290.780             0.016            0.015
Chain 1:   3700       -18677.184             0.018            0.017
Chain 1:   3800       -17637.669             0.023            0.021
Chain 1:   3900       -17633.863             0.021            0.021
Chain 1:   4000       -17751.146             0.022            0.021
Chain 1:   4100       -17664.892             0.022            0.021
Chain 1:   4200       -17481.394             0.021            0.021
Chain 1:   4300       -17619.632             0.021            0.021
Chain 1:   4400       -17576.616             0.018            0.010
Chain 1:   4500       -17479.187             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001135 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50444.865             1.000            1.000
Chain 1:    200       -14988.381             1.683            2.366
Chain 1:    300       -19979.418             1.205            1.000
Chain 1:    400       -21882.507             0.926            1.000
Chain 1:    500       -16109.538             0.812            0.358
Chain 1:    600       -20654.035             0.713            0.358
Chain 1:    700       -15743.844             0.656            0.312
Chain 1:    800       -16195.469             0.578            0.312
Chain 1:    900       -16084.293             0.514            0.250
Chain 1:   1000       -25521.634             0.500            0.312
Chain 1:   1100       -11800.046             0.516            0.312   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12453.558             0.285            0.250
Chain 1:   1300       -13620.155             0.268            0.220
Chain 1:   1400       -13352.019             0.262            0.220
Chain 1:   1500       -12886.658             0.229            0.086
Chain 1:   1600       -10768.039             0.227            0.086
Chain 1:   1700       -17477.346             0.234            0.086
Chain 1:   1800       -12624.742             0.270            0.197
Chain 1:   1900       -12912.069             0.271            0.197
Chain 1:   2000       -10353.004             0.259            0.197
Chain 1:   2100       -11273.814             0.151            0.086
Chain 1:   2200       -12258.886             0.154            0.086
Chain 1:   2300       -17460.574             0.175            0.197
Chain 1:   2400       -10346.637             0.242            0.247
Chain 1:   2500       -10756.220             0.242            0.247
Chain 1:   2600       -13582.414             0.243            0.247
Chain 1:   2700       -14462.660             0.211            0.208
Chain 1:   2800       -10357.527             0.212            0.208
Chain 1:   2900       -10228.915             0.211            0.208
Chain 1:   3000       -10389.973             0.188            0.082
Chain 1:   3100       -11394.775             0.189            0.088
Chain 1:   3200        -9923.755             0.195            0.148
Chain 1:   3300       -11536.681             0.180            0.140
Chain 1:   3400       -11027.707             0.115            0.088
Chain 1:   3500       -10514.942             0.116            0.088
Chain 1:   3600       -14887.296             0.125            0.088
Chain 1:   3700       -16140.087             0.127            0.088
Chain 1:   3800       -10430.252             0.142            0.088
Chain 1:   3900        -9954.708             0.145            0.088
Chain 1:   4000       -19854.604             0.194            0.140
Chain 1:   4100       -10506.878             0.274            0.148
Chain 1:   4200        -9920.338             0.265            0.140
Chain 1:   4300       -11357.598             0.264            0.127
Chain 1:   4400       -10392.811             0.268            0.127
Chain 1:   4500        -9793.512             0.269            0.127
Chain 1:   4600       -13986.201             0.270            0.127
Chain 1:   4700       -14714.332             0.267            0.127
Chain 1:   4800       -14693.562             0.213            0.093
Chain 1:   4900       -10797.450             0.244            0.127
Chain 1:   5000       -17415.694             0.232            0.127
Chain 1:   5100        -9635.113             0.224            0.127
Chain 1:   5200       -10075.622             0.222            0.127
Chain 1:   5300        -9280.858             0.218            0.093
Chain 1:   5400       -11770.054             0.230            0.211
Chain 1:   5500       -10876.170             0.232            0.211
Chain 1:   5600       -10162.994             0.209            0.086
Chain 1:   5700       -10475.352             0.207            0.086
Chain 1:   5800        -9457.599             0.218            0.108
Chain 1:   5900       -12675.210             0.207            0.108
Chain 1:   6000        -9301.780             0.205            0.108
Chain 1:   6100       -16478.928             0.168            0.108
Chain 1:   6200        -9518.783             0.237            0.211
Chain 1:   6300       -13761.320             0.259            0.254
Chain 1:   6400       -14867.341             0.246            0.254
Chain 1:   6500       -10312.138             0.282            0.308
Chain 1:   6600        -9461.373             0.284            0.308
Chain 1:   6700        -9276.303             0.283            0.308
Chain 1:   6800        -9282.628             0.272            0.308
Chain 1:   6900       -11147.289             0.263            0.308
Chain 1:   7000        -9609.944             0.243            0.167
Chain 1:   7100        -9103.419             0.205            0.160
Chain 1:   7200        -9991.926             0.141            0.090
Chain 1:   7300        -9652.485             0.113            0.089
Chain 1:   7400       -12642.458             0.130            0.090
Chain 1:   7500       -12886.126             0.087            0.089
Chain 1:   7600       -14717.456             0.091            0.089
Chain 1:   7700        -9727.605             0.140            0.124
Chain 1:   7800       -16700.362             0.182            0.160
Chain 1:   7900       -11404.872             0.211            0.160
Chain 1:   8000        -9489.066             0.216            0.202
Chain 1:   8100       -12256.772             0.233            0.226
Chain 1:   8200       -10078.909             0.245            0.226
Chain 1:   8300        -9723.453             0.245            0.226
Chain 1:   8400       -13820.305             0.251            0.226
Chain 1:   8500       -10931.510             0.276            0.264
Chain 1:   8600        -8935.888             0.286            0.264
Chain 1:   8700       -10085.652             0.246            0.226
Chain 1:   8800        -9813.650             0.207            0.223
Chain 1:   8900       -14313.934             0.192            0.223
Chain 1:   9000        -9045.462             0.230            0.226
Chain 1:   9100        -9156.800             0.209            0.223
Chain 1:   9200       -14144.159             0.222            0.264
Chain 1:   9300       -11901.196             0.238            0.264
Chain 1:   9400        -9322.093             0.236            0.264
Chain 1:   9500       -10303.973             0.219            0.223
Chain 1:   9600        -9191.501             0.208            0.188
Chain 1:   9700        -9144.665             0.198            0.188
Chain 1:   9800       -12710.475             0.223            0.277
Chain 1:   9900       -10909.438             0.208            0.188
Chain 1:   10000        -9210.830             0.168            0.184
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47316.161             1.000            1.000
Chain 1:    200       -16886.350             1.401            1.802
Chain 1:    300        -9516.029             1.192            1.000
Chain 1:    400        -8388.512             0.928            1.000
Chain 1:    500        -9127.470             0.758            0.775
Chain 1:    600        -9424.306             0.637            0.775
Chain 1:    700        -8068.040             0.570            0.168
Chain 1:    800        -8624.520             0.507            0.168
Chain 1:    900        -7758.741             0.463            0.134
Chain 1:   1000        -7876.270             0.418            0.134
Chain 1:   1100        -7814.527             0.319            0.112
Chain 1:   1200        -7674.664             0.141            0.081
Chain 1:   1300        -7731.100             0.064            0.065
Chain 1:   1400        -7881.056             0.052            0.031
Chain 1:   1500        -7792.701             0.045            0.019
Chain 1:   1600        -7819.444             0.043            0.018
Chain 1:   1700        -7652.598             0.028            0.018
Chain 1:   1800        -7673.020             0.022            0.015
Chain 1:   1900        -7786.481             0.012            0.015
Chain 1:   2000        -7792.345             0.011            0.011
Chain 1:   2100        -7652.884             0.012            0.015
Chain 1:   2200        -7884.591             0.013            0.015
Chain 1:   2300        -7704.186             0.014            0.018
Chain 1:   2400        -7692.124             0.013            0.015
Chain 1:   2500        -7668.011             0.012            0.015
Chain 1:   2600        -7595.463             0.013            0.015
Chain 1:   2700        -7562.691             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002511 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87900.975             1.000            1.000
Chain 1:    200       -14884.908             2.953            4.905
Chain 1:    300       -10941.576             2.089            1.000
Chain 1:    400       -13282.447             1.611            1.000
Chain 1:    500        -9318.855             1.373            0.425
Chain 1:    600        -9157.878             1.147            0.425
Chain 1:    700        -9200.771             0.984            0.360
Chain 1:    800        -9419.799             0.864            0.360
Chain 1:    900        -9712.657             0.771            0.176
Chain 1:   1000        -9962.704             0.697            0.176
Chain 1:   1100        -9573.731             0.601            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8976.486             0.117            0.041
Chain 1:   1300        -9645.716             0.088            0.041
Chain 1:   1400        -9214.367             0.075            0.041
Chain 1:   1500        -9216.402             0.032            0.030
Chain 1:   1600        -9134.235             0.032            0.030
Chain 1:   1700        -9000.455             0.033            0.030
Chain 1:   1800        -9042.290             0.031            0.030
Chain 1:   1900        -9075.445             0.028            0.025
Chain 1:   2000        -9250.436             0.027            0.019
Chain 1:   2100        -9033.070             0.026            0.019
Chain 1:   2200        -8986.694             0.020            0.015
Chain 1:   2300        -9224.880             0.015            0.015
Chain 1:   2400        -8943.139             0.014            0.015
Chain 1:   2500        -9015.379             0.015            0.015
Chain 1:   2600        -8920.153             0.015            0.015
Chain 1:   2700        -8932.810             0.013            0.011
Chain 1:   2800        -8799.258             0.014            0.015
Chain 1:   2900        -8985.051             0.016            0.019
Chain 1:   3000        -8883.612             0.015            0.015
Chain 1:   3100        -8988.976             0.014            0.012
Chain 1:   3200        -8850.925             0.015            0.015
Chain 1:   3300        -9112.430             0.015            0.015
Chain 1:   3400        -9076.551             0.013            0.012
Chain 1:   3500        -8947.090             0.013            0.014
Chain 1:   3600        -9034.719             0.013            0.014
Chain 1:   3700        -8882.737             0.015            0.015
Chain 1:   3800        -8786.319             0.014            0.014
Chain 1:   3900        -9030.093             0.015            0.014
Chain 1:   4000        -9046.325             0.014            0.014
Chain 1:   4100        -8818.971             0.016            0.016
Chain 1:   4200        -8803.745             0.014            0.014
Chain 1:   4300        -8804.584             0.011            0.011
Chain 1:   4400        -8757.753             0.011            0.011
Chain 1:   4500        -8902.799             0.012            0.011
Chain 1:   4600        -8931.601             0.011            0.011
Chain 1:   4700        -9055.415             0.011            0.011
Chain 1:   4800        -8868.473             0.012            0.014
Chain 1:   4900        -8901.323             0.009            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401033.193             1.000            1.000
Chain 1:    200     -1585399.050             2.650            4.299
Chain 1:    300      -893057.617             2.025            1.000
Chain 1:    400      -459790.275             1.754            1.000
Chain 1:    500      -359779.029             1.459            0.942
Chain 1:    600      -234676.200             1.305            0.942
Chain 1:    700      -120782.916             1.253            0.942
Chain 1:    800       -87954.178             1.143            0.942
Chain 1:    900       -68290.257             1.048            0.775
Chain 1:   1000       -53095.609             0.972            0.775
Chain 1:   1100       -40564.466             0.903            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39755.808             0.475            0.373
Chain 1:   1300       -27679.929             0.441            0.373
Chain 1:   1400       -27403.933             0.348            0.309
Chain 1:   1500       -23980.492             0.334            0.309
Chain 1:   1600       -23196.105             0.284            0.288
Chain 1:   1700       -22064.367             0.195            0.286
Chain 1:   1800       -22008.334             0.158            0.143
Chain 1:   1900       -22335.876             0.131            0.051
Chain 1:   2000       -20841.356             0.109            0.051
Chain 1:   2100       -21080.318             0.079            0.034
Chain 1:   2200       -21308.050             0.079            0.034
Chain 1:   2300       -20923.731             0.037            0.018
Chain 1:   2400       -20695.201             0.037            0.018
Chain 1:   2500       -20497.207             0.024            0.015
Chain 1:   2600       -20125.880             0.022            0.015
Chain 1:   2700       -20082.461             0.017            0.011
Chain 1:   2800       -19798.579             0.018            0.014
Chain 1:   2900       -20080.609             0.018            0.014
Chain 1:   3000       -20066.754             0.011            0.011
Chain 1:   3100       -20151.903             0.010            0.011
Chain 1:   3200       -19841.622             0.011            0.014
Chain 1:   3300       -20047.131             0.010            0.011
Chain 1:   3400       -19520.251             0.012            0.014
Chain 1:   3500       -20134.813             0.014            0.014
Chain 1:   3600       -19438.056             0.015            0.014
Chain 1:   3700       -19827.366             0.017            0.016
Chain 1:   3800       -18781.682             0.021            0.020
Chain 1:   3900       -18777.668             0.020            0.020
Chain 1:   4000       -18895.021             0.021            0.020
Chain 1:   4100       -18808.436             0.021            0.020
Chain 1:   4200       -18623.532             0.020            0.020
Chain 1:   4300       -18762.761             0.020            0.020
Chain 1:   4400       -18718.621             0.017            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001248 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12381.320             1.000            1.000
Chain 1:    200        -9303.125             0.665            1.000
Chain 1:    300        -7995.752             0.498            0.331
Chain 1:    400        -8186.505             0.379            0.331
Chain 1:    500        -8052.047             0.307            0.164
Chain 1:    600        -7878.909             0.259            0.164
Chain 1:    700        -7824.114             0.223            0.023
Chain 1:    800        -7835.833             0.196            0.023
Chain 1:    900        -7885.211             0.175            0.022
Chain 1:   1000        -7925.388             0.158            0.022
Chain 1:   1100        -8005.889             0.059            0.017
Chain 1:   1200        -7860.359             0.027            0.017
Chain 1:   1300        -7783.007             0.012            0.010
Chain 1:   1400        -7804.532             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45898.751             1.000            1.000
Chain 1:    200       -15585.800             1.472            1.945
Chain 1:    300        -8707.367             1.245            1.000
Chain 1:    400        -8492.461             0.940            1.000
Chain 1:    500        -8355.831             0.755            0.790
Chain 1:    600        -8262.517             0.631            0.790
Chain 1:    700        -7798.071             0.550            0.060
Chain 1:    800        -7938.521             0.483            0.060
Chain 1:    900        -7862.583             0.431            0.025
Chain 1:   1000        -7724.882             0.389            0.025
Chain 1:   1100        -7735.839             0.289            0.018
Chain 1:   1200        -7675.173             0.096            0.018
Chain 1:   1300        -7836.461             0.019            0.018
Chain 1:   1400        -7827.782             0.016            0.016
Chain 1:   1500        -7598.745             0.018            0.018
Chain 1:   1600        -7714.965             0.018            0.018
Chain 1:   1700        -7542.488             0.014            0.018
Chain 1:   1800        -7598.099             0.013            0.015
Chain 1:   1900        -7601.725             0.012            0.015
Chain 1:   2000        -7642.007             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86430.272             1.000            1.000
Chain 1:    200       -13544.729             3.191            5.381
Chain 1:    300        -9862.578             2.251            1.000
Chain 1:    400       -10753.461             1.709            1.000
Chain 1:    500        -8863.486             1.410            0.373
Chain 1:    600        -8519.715             1.182            0.373
Chain 1:    700        -8229.149             1.018            0.213
Chain 1:    800        -8747.405             0.898            0.213
Chain 1:    900        -8579.332             0.801            0.083
Chain 1:   1000        -8593.304             0.721            0.083
Chain 1:   1100        -8646.189             0.621            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8159.217             0.089            0.059
Chain 1:   1300        -8463.739             0.055            0.040
Chain 1:   1400        -8515.533             0.048            0.036
Chain 1:   1500        -8395.299             0.028            0.035
Chain 1:   1600        -8507.395             0.025            0.020
Chain 1:   1700        -8575.208             0.022            0.014
Chain 1:   1800        -8144.288             0.022            0.014
Chain 1:   1900        -8248.286             0.021            0.013
Chain 1:   2000        -8223.423             0.021            0.013
Chain 1:   2100        -8359.056             0.022            0.014
Chain 1:   2200        -8152.789             0.019            0.014
Chain 1:   2300        -8248.936             0.016            0.013
Chain 1:   2400        -8312.369             0.016            0.013
Chain 1:   2500        -8256.740             0.016            0.013
Chain 1:   2600        -8260.882             0.014            0.012
Chain 1:   2700        -8176.203             0.015            0.012
Chain 1:   2800        -8132.852             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378214.347             1.000            1.000
Chain 1:    200     -1581036.305             2.650            4.299
Chain 1:    300      -891083.007             2.024            1.000
Chain 1:    400      -457910.712             1.755            1.000
Chain 1:    500      -358587.722             1.459            0.946
Chain 1:    600      -233416.042             1.305            0.946
Chain 1:    700      -119505.911             1.255            0.946
Chain 1:    800       -86652.206             1.146            0.946
Chain 1:    900       -66961.555             1.051            0.774
Chain 1:   1000       -51728.411             0.975            0.774
Chain 1:   1100       -39174.782             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38352.393             0.480            0.379
Chain 1:   1300       -26275.792             0.448            0.379
Chain 1:   1400       -25992.861             0.355            0.320
Chain 1:   1500       -22571.151             0.342            0.320
Chain 1:   1600       -21785.239             0.292            0.294
Chain 1:   1700       -20654.948             0.202            0.294
Chain 1:   1800       -20598.462             0.165            0.152
Chain 1:   1900       -20924.839             0.137            0.055
Chain 1:   2000       -19433.464             0.115            0.055
Chain 1:   2100       -19671.992             0.084            0.036
Chain 1:   2200       -19898.878             0.083            0.036
Chain 1:   2300       -19515.669             0.039            0.020
Chain 1:   2400       -19287.640             0.039            0.020
Chain 1:   2500       -19089.704             0.025            0.016
Chain 1:   2600       -18719.506             0.023            0.016
Chain 1:   2700       -18676.463             0.018            0.012
Chain 1:   2800       -18393.153             0.020            0.015
Chain 1:   2900       -18674.664             0.019            0.015
Chain 1:   3000       -18660.783             0.012            0.012
Chain 1:   3100       -18745.754             0.011            0.012
Chain 1:   3200       -18436.283             0.012            0.015
Chain 1:   3300       -18641.195             0.011            0.012
Chain 1:   3400       -18115.756             0.012            0.015
Chain 1:   3500       -18728.136             0.015            0.015
Chain 1:   3600       -18034.302             0.017            0.015
Chain 1:   3700       -18421.459             0.018            0.017
Chain 1:   3800       -17380.253             0.023            0.021
Chain 1:   3900       -17376.418             0.021            0.021
Chain 1:   4000       -17493.717             0.022            0.021
Chain 1:   4100       -17407.348             0.022            0.021
Chain 1:   4200       -17223.476             0.021            0.021
Chain 1:   4300       -17361.956             0.021            0.021
Chain 1:   4400       -17318.630             0.019            0.011
Chain 1:   4500       -17221.159             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001337 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12082.677             1.000            1.000
Chain 1:    200        -9009.930             0.671            1.000
Chain 1:    300        -7818.843             0.498            0.341
Chain 1:    400        -8017.684             0.380            0.341
Chain 1:    500        -7953.215             0.305            0.152
Chain 1:    600        -7919.437             0.255            0.152
Chain 1:    700        -7701.442             0.223            0.028
Chain 1:    800        -7656.855             0.196            0.028
Chain 1:    900        -7601.724             0.175            0.025
Chain 1:   1000        -7704.100             0.159            0.025
Chain 1:   1100        -7845.026             0.060            0.018
Chain 1:   1200        -7712.302             0.028            0.017
Chain 1:   1300        -7655.054             0.013            0.013
Chain 1:   1400        -7681.788             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00142 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56460.467             1.000            1.000
Chain 1:    200       -17103.001             1.651            2.301
Chain 1:    300        -8574.926             1.432            1.000
Chain 1:    400        -8257.359             1.084            1.000
Chain 1:    500        -8045.082             0.872            0.995
Chain 1:    600        -8561.227             0.737            0.995
Chain 1:    700        -7797.524             0.646            0.098
Chain 1:    800        -8190.937             0.571            0.098
Chain 1:    900        -7972.323             0.510            0.060
Chain 1:   1000        -7640.417             0.464            0.060
Chain 1:   1100        -7694.040             0.364            0.048
Chain 1:   1200        -7773.334             0.135            0.043
Chain 1:   1300        -7685.324             0.037            0.038
Chain 1:   1400        -7628.356             0.034            0.027
Chain 1:   1500        -7564.390             0.032            0.027
Chain 1:   1600        -7494.669             0.027            0.011
Chain 1:   1700        -7488.670             0.017            0.010
Chain 1:   1800        -7557.456             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003656 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86167.441             1.000            1.000
Chain 1:    200       -13178.924             3.269            5.538
Chain 1:    300        -9598.782             2.304            1.000
Chain 1:    400       -10388.394             1.747            1.000
Chain 1:    500        -8519.268             1.441            0.373
Chain 1:    600        -8231.526             1.207            0.373
Chain 1:    700        -8257.236             1.035            0.219
Chain 1:    800        -8532.080             0.910            0.219
Chain 1:    900        -8423.943             0.810            0.076
Chain 1:   1000        -8214.824             0.732            0.076
Chain 1:   1100        -8443.380             0.634            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8075.247             0.085            0.035
Chain 1:   1300        -8138.128             0.048            0.032
Chain 1:   1400        -8246.972             0.042            0.027
Chain 1:   1500        -8174.186             0.021            0.025
Chain 1:   1600        -8171.516             0.018            0.013
Chain 1:   1700        -8091.657             0.018            0.013
Chain 1:   1800        -7981.152             0.016            0.013
Chain 1:   1900        -8101.590             0.017            0.014
Chain 1:   2000        -8062.057             0.015            0.013
Chain 1:   2100        -8186.409             0.013            0.013
Chain 1:   2200        -7963.523             0.012            0.013
Chain 1:   2300        -8123.571             0.013            0.014
Chain 1:   2400        -8007.777             0.013            0.014
Chain 1:   2500        -8069.918             0.013            0.014
Chain 1:   2600        -8089.982             0.013            0.014
Chain 1:   2700        -8009.559             0.013            0.014
Chain 1:   2800        -7984.917             0.012            0.014
Chain 1:   2900        -8039.492             0.011            0.010
Chain 1:   3000        -7924.992             0.012            0.014
Chain 1:   3100        -8061.735             0.012            0.014
Chain 1:   3200        -7942.343             0.011            0.014
Chain 1:   3300        -7963.187             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002604 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402285.469             1.000            1.000
Chain 1:    200     -1588182.626             2.645            4.291
Chain 1:    300      -891721.084             2.024            1.000
Chain 1:    400      -457423.094             1.755            1.000
Chain 1:    500      -357340.512             1.460            0.949
Chain 1:    600      -232237.271             1.307            0.949
Chain 1:    700      -118687.396             1.257            0.949
Chain 1:    800       -85921.946             1.147            0.949
Chain 1:    900       -66318.270             1.053            0.781
Chain 1:   1000       -51152.208             0.977            0.781
Chain 1:   1100       -38659.578             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37842.264             0.482            0.381
Chain 1:   1300       -25836.485             0.451            0.381
Chain 1:   1400       -25558.322             0.357            0.323
Chain 1:   1500       -22154.849             0.344            0.323
Chain 1:   1600       -21373.665             0.294            0.296
Chain 1:   1700       -20252.411             0.204            0.296
Chain 1:   1800       -20197.727             0.166            0.154
Chain 1:   1900       -20523.523             0.138            0.055
Chain 1:   2000       -19037.832             0.116            0.055
Chain 1:   2100       -19276.109             0.085            0.037
Chain 1:   2200       -19501.795             0.084            0.037
Chain 1:   2300       -19119.783             0.040            0.020
Chain 1:   2400       -18892.050             0.040            0.020
Chain 1:   2500       -18693.829             0.026            0.016
Chain 1:   2600       -18324.564             0.024            0.016
Chain 1:   2700       -18281.799             0.019            0.012
Chain 1:   2800       -17998.600             0.020            0.016
Chain 1:   2900       -18279.755             0.020            0.015
Chain 1:   3000       -18266.006             0.012            0.012
Chain 1:   3100       -18350.867             0.011            0.012
Chain 1:   3200       -18041.866             0.012            0.015
Chain 1:   3300       -18246.420             0.011            0.012
Chain 1:   3400       -17721.708             0.013            0.015
Chain 1:   3500       -18332.882             0.015            0.016
Chain 1:   3600       -17640.603             0.017            0.016
Chain 1:   3700       -18026.542             0.019            0.017
Chain 1:   3800       -16987.699             0.023            0.021
Chain 1:   3900       -16983.883             0.022            0.021
Chain 1:   4000       -17101.223             0.022            0.021
Chain 1:   4100       -17014.931             0.023            0.021
Chain 1:   4200       -16831.610             0.022            0.021
Chain 1:   4300       -16969.742             0.022            0.021
Chain 1:   4400       -16926.837             0.019            0.011
Chain 1:   4500       -16829.412             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00131 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49488.964             1.000            1.000
Chain 1:    200       -20214.651             1.224            1.448
Chain 1:    300       -24098.287             0.870            1.000
Chain 1:    400       -22951.851             0.665            1.000
Chain 1:    500       -13390.652             0.675            0.714
Chain 1:    600       -35648.257             0.666            0.714
Chain 1:    700       -14071.805             0.790            0.714
Chain 1:    800       -14425.204             0.694            0.714
Chain 1:    900       -12917.700             0.630            0.624
Chain 1:   1000       -15490.824             0.584            0.624
Chain 1:   1100       -15513.975             0.484            0.166
Chain 1:   1200       -12771.056             0.361            0.166
Chain 1:   1300       -11283.095             0.358            0.166
Chain 1:   1400       -15205.338             0.379            0.215
Chain 1:   1500       -10643.967             0.350            0.215
Chain 1:   1600       -11659.693             0.296            0.166
Chain 1:   1700       -21208.831             0.188            0.166
Chain 1:   1800       -13683.361             0.240            0.215
Chain 1:   1900       -10666.471             0.257            0.258
Chain 1:   2000       -11393.990             0.247            0.258
Chain 1:   2100       -10532.090             0.255            0.258
Chain 1:   2200       -11758.202             0.244            0.258
Chain 1:   2300        -9950.604             0.249            0.258
Chain 1:   2400       -10494.510             0.228            0.182
Chain 1:   2500       -10039.124             0.190            0.104
Chain 1:   2600       -10137.421             0.182            0.104
Chain 1:   2700        -9410.079             0.145            0.082
Chain 1:   2800       -11272.858             0.106            0.082
Chain 1:   2900       -10318.765             0.087            0.082
Chain 1:   3000        -9755.998             0.087            0.082
Chain 1:   3100        -9417.822             0.082            0.077
Chain 1:   3200        -9891.406             0.077            0.058
Chain 1:   3300        -9586.272             0.062            0.052
Chain 1:   3400        -9589.792             0.056            0.048
Chain 1:   3500        -9831.240             0.054            0.048
Chain 1:   3600       -12139.815             0.072            0.058
Chain 1:   3700       -10213.395             0.083            0.058
Chain 1:   3800       -10350.912             0.068            0.048
Chain 1:   3900       -14255.328             0.086            0.048
Chain 1:   4000        -9498.022             0.131            0.048
Chain 1:   4100        -9612.554             0.128            0.048
Chain 1:   4200        -9445.473             0.125            0.032
Chain 1:   4300       -10423.585             0.132            0.094
Chain 1:   4400        -9137.588             0.146            0.141
Chain 1:   4500        -9673.932             0.149            0.141
Chain 1:   4600        -9975.308             0.133            0.094
Chain 1:   4700        -9507.390             0.119            0.055
Chain 1:   4800        -9493.348             0.118            0.055
Chain 1:   4900       -11441.178             0.107            0.055
Chain 1:   5000       -13853.539             0.074            0.055
Chain 1:   5100        -9683.687             0.116            0.094
Chain 1:   5200        -9441.324             0.117            0.094
Chain 1:   5300        -9704.487             0.110            0.055
Chain 1:   5400       -16035.103             0.136            0.055
Chain 1:   5500       -12840.429             0.155            0.170
Chain 1:   5600       -14967.915             0.166            0.170
Chain 1:   5700        -9343.034             0.222            0.174
Chain 1:   5800       -14717.200             0.258            0.249
Chain 1:   5900       -17429.632             0.257            0.249
Chain 1:   6000        -9630.491             0.320            0.365
Chain 1:   6100       -12412.367             0.300            0.249
Chain 1:   6200       -12042.471             0.300            0.249
Chain 1:   6300       -10051.957             0.317            0.249
Chain 1:   6400        -9032.940             0.289            0.224
Chain 1:   6500       -10210.675             0.276            0.198
Chain 1:   6600        -9169.563             0.273            0.198
Chain 1:   6700        -9157.613             0.213            0.156
Chain 1:   6800       -12778.073             0.204            0.156
Chain 1:   6900        -9210.805             0.228            0.198
Chain 1:   7000       -12896.347             0.175            0.198
Chain 1:   7100       -10500.429             0.176            0.198
Chain 1:   7200        -8984.735             0.189            0.198
Chain 1:   7300       -12153.784             0.196            0.228
Chain 1:   7400        -8824.845             0.222            0.261
Chain 1:   7500       -10394.168             0.226            0.261
Chain 1:   7600        -9217.300             0.227            0.261
Chain 1:   7700        -9524.361             0.230            0.261
Chain 1:   7800       -10510.875             0.211            0.228
Chain 1:   7900        -9543.270             0.183            0.169
Chain 1:   8000        -9250.863             0.157            0.151
Chain 1:   8100        -8803.992             0.140            0.128
Chain 1:   8200       -11464.868             0.146            0.128
Chain 1:   8300        -8809.254             0.150            0.128
Chain 1:   8400        -9988.524             0.124            0.118
Chain 1:   8500        -8762.515             0.123            0.118
Chain 1:   8600        -8704.010             0.111            0.101
Chain 1:   8700        -9134.068             0.112            0.101
Chain 1:   8800        -8656.599             0.108            0.101
Chain 1:   8900        -9845.873             0.110            0.118
Chain 1:   9000        -9796.503             0.108            0.118
Chain 1:   9100       -11607.364             0.118            0.121
Chain 1:   9200        -9136.147             0.122            0.121
Chain 1:   9300       -10315.164             0.103            0.118
Chain 1:   9400        -8688.569             0.110            0.121
Chain 1:   9500       -11226.819             0.119            0.121
Chain 1:   9600        -8884.167             0.145            0.156
Chain 1:   9700       -13452.202             0.174            0.187
Chain 1:   9800        -9506.619             0.210            0.226
Chain 1:   9900       -11037.145             0.212            0.226
Chain 1:   10000       -10939.543             0.212            0.226
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57873.154             1.000            1.000
Chain 1:    200       -18129.796             1.596            2.192
Chain 1:    300        -9060.965             1.398            1.001
Chain 1:    400        -8223.357             1.074            1.001
Chain 1:    500        -8639.347             0.869            1.000
Chain 1:    600        -8052.382             0.736            1.000
Chain 1:    700        -8498.527             0.638            0.102
Chain 1:    800        -8405.059             0.560            0.102
Chain 1:    900        -8162.946             0.501            0.073
Chain 1:   1000        -7728.952             0.457            0.073
Chain 1:   1100        -8008.787             0.360            0.056
Chain 1:   1200        -7787.247             0.144            0.052
Chain 1:   1300        -7594.292             0.046            0.048
Chain 1:   1400        -7930.398             0.040            0.042
Chain 1:   1500        -7597.545             0.040            0.042
Chain 1:   1600        -7767.089             0.035            0.035
Chain 1:   1700        -7670.677             0.031            0.030
Chain 1:   1800        -7663.063             0.030            0.030
Chain 1:   1900        -7632.164             0.027            0.028
Chain 1:   2000        -7714.509             0.023            0.025
Chain 1:   2100        -7646.604             0.020            0.022
Chain 1:   2200        -8012.203             0.022            0.022
Chain 1:   2300        -7644.956             0.024            0.022
Chain 1:   2400        -7539.159             0.021            0.014
Chain 1:   2500        -7605.271             0.018            0.013
Chain 1:   2600        -7558.935             0.016            0.011
Chain 1:   2700        -7456.710             0.016            0.011
Chain 1:   2800        -7699.285             0.019            0.014
Chain 1:   2900        -7399.247             0.023            0.014
Chain 1:   3000        -7550.222             0.024            0.020
Chain 1:   3100        -7552.497             0.023            0.020
Chain 1:   3200        -7767.406             0.021            0.020
Chain 1:   3300        -7465.993             0.020            0.020
Chain 1:   3400        -7703.387             0.022            0.028
Chain 1:   3500        -7453.140             0.024            0.031
Chain 1:   3600        -7529.491             0.025            0.031
Chain 1:   3700        -7475.993             0.024            0.031
Chain 1:   3800        -7587.367             0.023            0.028
Chain 1:   3900        -7440.277             0.020            0.020
Chain 1:   4000        -7422.574             0.019            0.020
Chain 1:   4100        -7438.918             0.019            0.020
Chain 1:   4200        -7517.575             0.017            0.015
Chain 1:   4300        -7419.728             0.014            0.013
Chain 1:   4400        -7458.952             0.012            0.010
Chain 1:   4500        -7563.324             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86218.803             1.000            1.000
Chain 1:    200       -14139.649             3.049            5.098
Chain 1:    300       -10479.543             2.149            1.000
Chain 1:    400       -11482.825             1.634            1.000
Chain 1:    500        -9466.922             1.349            0.349
Chain 1:    600        -9494.455             1.125            0.349
Chain 1:    700        -8919.615             0.974            0.213
Chain 1:    800        -9482.861             0.859            0.213
Chain 1:    900        -9311.731             0.766            0.087
Chain 1:   1000        -8980.388             0.693            0.087
Chain 1:   1100        -9305.019             0.596            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8814.093             0.092            0.059
Chain 1:   1300        -9053.854             0.060            0.056
Chain 1:   1400        -9189.248             0.053            0.037
Chain 1:   1500        -9030.219             0.033            0.035
Chain 1:   1600        -9140.919             0.034            0.035
Chain 1:   1700        -9215.657             0.028            0.026
Chain 1:   1800        -8792.575             0.027            0.026
Chain 1:   1900        -8893.010             0.027            0.026
Chain 1:   2000        -8867.639             0.023            0.018
Chain 1:   2100        -8993.471             0.021            0.015
Chain 1:   2200        -8795.386             0.018            0.015
Chain 1:   2300        -8887.908             0.016            0.014
Chain 1:   2400        -8956.578             0.015            0.012
Chain 1:   2500        -8902.904             0.014            0.011
Chain 1:   2600        -8904.469             0.013            0.010
Chain 1:   2700        -8821.045             0.013            0.010
Chain 1:   2800        -8780.682             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002945 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8381214.541             1.000            1.000
Chain 1:    200     -1582513.063             2.648            4.296
Chain 1:    300      -892322.125             2.023            1.000
Chain 1:    400      -459541.828             1.753            1.000
Chain 1:    500      -360105.827             1.458            0.942
Chain 1:    600      -234735.201             1.304            0.942
Chain 1:    700      -120394.838             1.253            0.942
Chain 1:    800       -87494.773             1.143            0.942
Chain 1:    900       -67726.461             1.049            0.773
Chain 1:   1000       -52442.478             0.973            0.773
Chain 1:   1100       -39847.210             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39014.427             0.477            0.376
Chain 1:   1300       -26894.086             0.445            0.376
Chain 1:   1400       -26606.407             0.352            0.316
Chain 1:   1500       -23174.716             0.339            0.316
Chain 1:   1600       -22385.848             0.289            0.292
Chain 1:   1700       -21250.137             0.200            0.291
Chain 1:   1800       -21192.292             0.162            0.148
Chain 1:   1900       -21518.491             0.135            0.053
Chain 1:   2000       -20024.712             0.113            0.053
Chain 1:   2100       -20263.197             0.082            0.035
Chain 1:   2200       -20490.685             0.081            0.035
Chain 1:   2300       -20106.966             0.038            0.019
Chain 1:   2400       -19878.922             0.038            0.019
Chain 1:   2500       -19681.402             0.024            0.015
Chain 1:   2600       -19311.044             0.023            0.015
Chain 1:   2700       -19267.776             0.018            0.012
Chain 1:   2800       -18984.808             0.019            0.015
Chain 1:   2900       -19266.226             0.019            0.015
Chain 1:   3000       -19252.241             0.012            0.012
Chain 1:   3100       -19337.322             0.011            0.011
Chain 1:   3200       -19027.802             0.011            0.015
Chain 1:   3300       -19232.659             0.010            0.011
Chain 1:   3400       -18707.449             0.012            0.015
Chain 1:   3500       -19319.708             0.014            0.015
Chain 1:   3600       -18625.884             0.016            0.015
Chain 1:   3700       -19013.155             0.018            0.016
Chain 1:   3800       -17972.217             0.022            0.020
Chain 1:   3900       -17968.410             0.021            0.020
Chain 1:   4000       -18085.641             0.021            0.020
Chain 1:   4100       -17999.443             0.021            0.020
Chain 1:   4200       -17815.500             0.021            0.020
Chain 1:   4300       -17953.957             0.020            0.020
Chain 1:   4400       -17910.653             0.018            0.010
Chain 1:   4500       -17813.233             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12108.124             1.000            1.000
Chain 1:    200        -8932.600             0.678            1.000
Chain 1:    300        -7858.792             0.497            0.355
Chain 1:    400        -7950.060             0.376            0.355
Chain 1:    500        -7923.273             0.301            0.137
Chain 1:    600        -7785.681             0.254            0.137
Chain 1:    700        -7707.903             0.219            0.018
Chain 1:    800        -7719.650             0.192            0.018
Chain 1:    900        -7686.234             0.171            0.011
Chain 1:   1000        -7751.919             0.155            0.011
Chain 1:   1100        -7831.673             0.056            0.010
Chain 1:   1200        -7742.622             0.022            0.010
Chain 1:   1300        -7683.110             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57855.287             1.000            1.000
Chain 1:    200       -17332.009             1.669            2.338
Chain 1:    300        -8543.757             1.456            1.029
Chain 1:    400        -8142.880             1.104            1.029
Chain 1:    500        -8363.505             0.888            1.000
Chain 1:    600        -8708.784             0.747            1.000
Chain 1:    700        -8052.964             0.652            0.081
Chain 1:    800        -8226.728             0.573            0.081
Chain 1:    900        -7877.866             0.514            0.049
Chain 1:   1000        -7889.871             0.463            0.049
Chain 1:   1100        -7755.926             0.365            0.044
Chain 1:   1200        -7758.424             0.131            0.040
Chain 1:   1300        -7604.599             0.030            0.026
Chain 1:   1400        -7859.294             0.028            0.026
Chain 1:   1500        -7599.795             0.029            0.032
Chain 1:   1600        -7501.969             0.027            0.021
Chain 1:   1700        -7502.019             0.018            0.020
Chain 1:   1800        -7548.360             0.017            0.017
Chain 1:   1900        -7559.711             0.013            0.013
Chain 1:   2000        -7556.875             0.013            0.013
Chain 1:   2100        -7580.023             0.011            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002568 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85938.915             1.000            1.000
Chain 1:    200       -13172.522             3.262            5.524
Chain 1:    300        -9618.347             2.298            1.000
Chain 1:    400       -10661.230             1.748            1.000
Chain 1:    500        -8544.794             1.448            0.370
Chain 1:    600        -8475.280             1.208            0.370
Chain 1:    700        -8134.410             1.041            0.248
Chain 1:    800        -8639.651             0.918            0.248
Chain 1:    900        -8581.248             0.817            0.098
Chain 1:   1000        -8339.407             0.738            0.098
Chain 1:   1100        -8498.549             0.640            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8058.343             0.093            0.055
Chain 1:   1300        -8314.143             0.059            0.042
Chain 1:   1400        -8339.327             0.050            0.031
Chain 1:   1500        -8222.405             0.027            0.029
Chain 1:   1600        -8327.428             0.027            0.029
Chain 1:   1700        -8413.639             0.024            0.019
Chain 1:   1800        -8020.700             0.023            0.019
Chain 1:   1900        -8122.705             0.023            0.019
Chain 1:   2000        -8093.092             0.021            0.014
Chain 1:   2100        -8218.100             0.021            0.014
Chain 1:   2200        -8002.790             0.018            0.014
Chain 1:   2300        -8151.452             0.017            0.014
Chain 1:   2400        -8166.553             0.016            0.014
Chain 1:   2500        -8133.924             0.015            0.013
Chain 1:   2600        -8136.050             0.014            0.013
Chain 1:   2700        -8042.710             0.014            0.013
Chain 1:   2800        -8015.127             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003312 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8425083.892             1.000            1.000
Chain 1:    200     -1585931.368             2.656            4.312
Chain 1:    300      -889471.718             2.032            1.000
Chain 1:    400      -456802.919             1.761            1.000
Chain 1:    500      -356980.499             1.464            0.947
Chain 1:    600      -231947.427             1.310            0.947
Chain 1:    700      -118502.469             1.260            0.947
Chain 1:    800       -85839.467             1.150            0.947
Chain 1:    900       -66242.282             1.055            0.783
Chain 1:   1000       -51091.595             0.979            0.783
Chain 1:   1100       -38624.796             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37802.117             0.482            0.381
Chain 1:   1300       -25816.987             0.450            0.381
Chain 1:   1400       -25539.176             0.357            0.323
Chain 1:   1500       -22143.498             0.344            0.323
Chain 1:   1600       -21364.918             0.294            0.297
Chain 1:   1700       -20245.888             0.204            0.296
Chain 1:   1800       -20191.656             0.166            0.153
Chain 1:   1900       -20517.423             0.138            0.055
Chain 1:   2000       -19033.513             0.116            0.055
Chain 1:   2100       -19271.284             0.085            0.036
Chain 1:   2200       -19497.078             0.084            0.036
Chain 1:   2300       -19115.026             0.040            0.020
Chain 1:   2400       -18887.388             0.040            0.020
Chain 1:   2500       -18689.316             0.025            0.016
Chain 1:   2600       -18319.930             0.024            0.016
Chain 1:   2700       -18277.080             0.019            0.012
Chain 1:   2800       -17994.132             0.020            0.016
Chain 1:   2900       -18275.096             0.020            0.015
Chain 1:   3000       -18261.248             0.012            0.012
Chain 1:   3100       -18346.209             0.011            0.012
Chain 1:   3200       -18037.164             0.012            0.015
Chain 1:   3300       -18241.698             0.011            0.012
Chain 1:   3400       -17717.106             0.013            0.015
Chain 1:   3500       -18328.168             0.015            0.016
Chain 1:   3600       -17635.883             0.017            0.016
Chain 1:   3700       -18021.896             0.019            0.017
Chain 1:   3800       -16983.184             0.023            0.021
Chain 1:   3900       -16979.384             0.022            0.021
Chain 1:   4000       -17096.679             0.022            0.021
Chain 1:   4100       -17010.533             0.023            0.021
Chain 1:   4200       -16827.148             0.022            0.021
Chain 1:   4300       -16965.281             0.022            0.021
Chain 1:   4400       -16922.367             0.019            0.011
Chain 1:   4500       -16824.980             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001283 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11996.548             1.000            1.000
Chain 1:    200        -8937.644             0.671            1.000
Chain 1:    300        -7946.911             0.489            0.342
Chain 1:    400        -8028.599             0.369            0.342
Chain 1:    500        -7958.498             0.297            0.125
Chain 1:    600        -7952.670             0.248            0.125
Chain 1:    700        -7739.838             0.216            0.027
Chain 1:    800        -7756.074             0.190            0.027
Chain 1:    900        -7935.694             0.171            0.023
Chain 1:   1000        -7763.226             0.156            0.023
Chain 1:   1100        -7825.630             0.057            0.022
Chain 1:   1200        -7760.565             0.024            0.010
Chain 1:   1300        -7721.084             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001558 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61137.556             1.000            1.000
Chain 1:    200       -17361.721             1.761            2.521
Chain 1:    300        -8638.130             1.510            1.010
Chain 1:    400        -8124.823             1.149            1.010
Chain 1:    500        -8304.478             0.923            1.000
Chain 1:    600        -8410.731             0.771            1.000
Chain 1:    700        -7837.827             0.672            0.073
Chain 1:    800        -7937.162             0.589            0.073
Chain 1:    900        -7906.801             0.524            0.063
Chain 1:   1000        -7835.273             0.473            0.063
Chain 1:   1100        -7757.470             0.374            0.022
Chain 1:   1200        -7591.793             0.124            0.022
Chain 1:   1300        -7627.898             0.023            0.013
Chain 1:   1400        -7871.783             0.020            0.013
Chain 1:   1500        -7591.016             0.022            0.013
Chain 1:   1600        -7500.050             0.022            0.013
Chain 1:   1700        -7480.906             0.014            0.012
Chain 1:   1800        -7513.213             0.014            0.010
Chain 1:   1900        -7552.509             0.014            0.010
Chain 1:   2000        -7554.781             0.013            0.010
Chain 1:   2100        -7570.674             0.012            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86156.288             1.000            1.000
Chain 1:    200       -13057.232             3.299            5.598
Chain 1:    300        -9554.166             2.322            1.000
Chain 1:    400       -10405.174             1.762            1.000
Chain 1:    500        -8405.953             1.457            0.367
Chain 1:    600        -8168.617             1.219            0.367
Chain 1:    700        -8416.572             1.049            0.238
Chain 1:    800        -8444.778             0.918            0.238
Chain 1:    900        -8437.191             0.816            0.082
Chain 1:   1000        -8295.909             0.736            0.082
Chain 1:   1100        -8485.995             0.639            0.029   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8177.913             0.083            0.029
Chain 1:   1300        -8327.813             0.048            0.029
Chain 1:   1400        -8321.585             0.040            0.022
Chain 1:   1500        -8227.163             0.017            0.018
Chain 1:   1600        -8311.298             0.015            0.017
Chain 1:   1700        -8416.025             0.013            0.012
Chain 1:   1800        -8036.462             0.018            0.017
Chain 1:   1900        -8134.304             0.019            0.017
Chain 1:   2000        -8104.985             0.018            0.012
Chain 1:   2100        -8250.709             0.017            0.012
Chain 1:   2200        -8028.017             0.016            0.012
Chain 1:   2300        -8158.096             0.016            0.012
Chain 1:   2400        -8056.668             0.017            0.013
Chain 1:   2500        -8111.796             0.017            0.013
Chain 1:   2600        -8124.422             0.016            0.013
Chain 1:   2700        -8045.477             0.015            0.013
Chain 1:   2800        -8031.053             0.011            0.012
Chain 1:   2900        -8019.528             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003919 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426774.144             1.000            1.000
Chain 1:    200     -1587593.858             2.654            4.308
Chain 1:    300      -890732.592             2.030            1.000
Chain 1:    400      -457263.389             1.760            1.000
Chain 1:    500      -357256.570             1.464            0.948
Chain 1:    600      -232296.258             1.309            0.948
Chain 1:    700      -118599.838             1.259            0.948
Chain 1:    800       -85844.107             1.150            0.948
Chain 1:    900       -66208.150             1.055            0.782
Chain 1:   1000       -51017.011             0.979            0.782
Chain 1:   1100       -38514.754             0.912            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37684.589             0.483            0.382
Chain 1:   1300       -25676.405             0.451            0.382
Chain 1:   1400       -25395.047             0.358            0.325
Chain 1:   1500       -21992.709             0.345            0.325
Chain 1:   1600       -21210.942             0.295            0.298
Chain 1:   1700       -20089.989             0.205            0.297
Chain 1:   1800       -20034.719             0.167            0.155
Chain 1:   1900       -20360.040             0.139            0.056
Chain 1:   2000       -18875.535             0.117            0.056
Chain 1:   2100       -19113.561             0.086            0.037
Chain 1:   2200       -19339.084             0.085            0.037
Chain 1:   2300       -18957.340             0.040            0.020
Chain 1:   2400       -18729.788             0.040            0.020
Chain 1:   2500       -18531.712             0.026            0.016
Chain 1:   2600       -18162.877             0.024            0.016
Chain 1:   2700       -18120.118             0.019            0.012
Chain 1:   2800       -17837.358             0.020            0.016
Chain 1:   2900       -18118.138             0.020            0.015
Chain 1:   3000       -18104.415             0.012            0.012
Chain 1:   3100       -18189.268             0.011            0.012
Chain 1:   3200       -17880.532             0.012            0.015
Chain 1:   3300       -18084.779             0.011            0.012
Chain 1:   3400       -17560.748             0.013            0.015
Chain 1:   3500       -18171.008             0.015            0.016
Chain 1:   3600       -17479.774             0.017            0.016
Chain 1:   3700       -17865.025             0.019            0.017
Chain 1:   3800       -16827.928             0.024            0.022
Chain 1:   3900       -16824.132             0.022            0.022
Chain 1:   4000       -16941.445             0.023            0.022
Chain 1:   4100       -16855.378             0.023            0.022
Chain 1:   4200       -16672.301             0.022            0.022
Chain 1:   4300       -16810.218             0.022            0.022
Chain 1:   4400       -16767.610             0.019            0.011
Chain 1:   4500       -16670.238             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001242 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48890.782             1.000            1.000
Chain 1:    200       -15118.039             1.617            2.234
Chain 1:    300       -15660.528             1.090            1.000
Chain 1:    400       -13604.445             0.855            1.000
Chain 1:    500       -22917.842             0.765            0.406
Chain 1:    600       -12576.285             0.775            0.822
Chain 1:    700       -13138.139             0.670            0.406
Chain 1:    800       -21105.914             0.634            0.406
Chain 1:    900       -16783.319             0.592            0.378
Chain 1:   1000       -18642.729             0.543            0.378
Chain 1:   1100       -10933.473             0.513            0.378   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11510.219             0.295            0.258
Chain 1:   1300       -12626.375             0.300            0.258
Chain 1:   1400       -11651.175             0.293            0.258
Chain 1:   1500       -13551.148             0.267            0.140
Chain 1:   1600       -11470.870             0.203            0.140
Chain 1:   1700       -13054.426             0.210            0.140
Chain 1:   1800       -17492.574             0.198            0.140
Chain 1:   1900       -10394.083             0.241            0.140
Chain 1:   2000        -9880.190             0.236            0.140
Chain 1:   2100       -15500.022             0.202            0.140
Chain 1:   2200       -11545.860             0.231            0.181
Chain 1:   2300        -9332.201             0.246            0.237
Chain 1:   2400       -22052.546             0.295            0.254
Chain 1:   2500       -13174.564             0.348            0.342
Chain 1:   2600        -9403.371             0.370            0.363
Chain 1:   2700        -8948.328             0.363            0.363
Chain 1:   2800       -10369.992             0.352            0.363
Chain 1:   2900        -9196.197             0.296            0.342
Chain 1:   3000        -9301.885             0.292            0.342
Chain 1:   3100        -8837.895             0.261            0.237
Chain 1:   3200       -16495.852             0.273            0.237
Chain 1:   3300       -17526.711             0.255            0.137
Chain 1:   3400        -8990.170             0.293            0.137
Chain 1:   3500       -12500.796             0.253            0.137
Chain 1:   3600       -20094.656             0.251            0.137
Chain 1:   3700        -8732.617             0.376            0.281
Chain 1:   3800        -8476.464             0.365            0.281
Chain 1:   3900        -8704.788             0.355            0.281
Chain 1:   4000        -8990.341             0.357            0.281
Chain 1:   4100        -9241.603             0.355            0.281
Chain 1:   4200       -10117.169             0.317            0.087
Chain 1:   4300       -11967.715             0.327            0.155
Chain 1:   4400       -11139.776             0.239            0.087
Chain 1:   4500       -10250.587             0.220            0.087
Chain 1:   4600       -12560.731             0.200            0.087
Chain 1:   4700       -14674.594             0.085            0.087
Chain 1:   4800        -8718.915             0.150            0.087
Chain 1:   4900        -9698.825             0.157            0.101
Chain 1:   5000       -14222.600             0.186            0.144
Chain 1:   5100        -8402.827             0.252            0.155
Chain 1:   5200       -10364.624             0.263            0.184
Chain 1:   5300       -13393.471             0.270            0.189
Chain 1:   5400        -8489.670             0.320            0.226
Chain 1:   5500       -11924.531             0.340            0.288
Chain 1:   5600       -10738.180             0.333            0.288
Chain 1:   5700        -8620.005             0.343            0.288
Chain 1:   5800        -8443.299             0.277            0.246
Chain 1:   5900        -9884.364             0.281            0.246
Chain 1:   6000        -8773.217             0.262            0.226
Chain 1:   6100       -13202.151             0.227            0.226
Chain 1:   6200        -9163.518             0.252            0.246
Chain 1:   6300        -8334.597             0.239            0.246
Chain 1:   6400       -10513.375             0.202            0.207
Chain 1:   6500        -8564.926             0.196            0.207
Chain 1:   6600        -8931.875             0.189            0.207
Chain 1:   6700       -12555.292             0.193            0.207
Chain 1:   6800        -8385.509             0.241            0.227
Chain 1:   6900        -8176.926             0.229            0.227
Chain 1:   7000        -8616.857             0.221            0.227
Chain 1:   7100        -8239.544             0.192            0.207
Chain 1:   7200        -8543.002             0.152            0.099
Chain 1:   7300        -9551.492             0.153            0.106
Chain 1:   7400        -8582.030             0.143            0.106
Chain 1:   7500       -10786.720             0.141            0.106
Chain 1:   7600       -10263.853             0.142            0.106
Chain 1:   7700        -8583.076             0.132            0.106
Chain 1:   7800       -13401.319             0.119            0.106
Chain 1:   7900        -9200.105             0.162            0.113
Chain 1:   8000        -8162.346             0.169            0.127
Chain 1:   8100       -10502.813             0.187            0.196
Chain 1:   8200        -9278.222             0.197            0.196
Chain 1:   8300        -8512.469             0.195            0.196
Chain 1:   8400        -9191.864             0.191            0.196
Chain 1:   8500       -11040.909             0.188            0.167
Chain 1:   8600        -8024.038             0.220            0.196
Chain 1:   8700        -8454.407             0.206            0.167
Chain 1:   8800        -8538.479             0.171            0.132
Chain 1:   8900        -8705.872             0.127            0.127
Chain 1:   9000        -8838.571             0.116            0.090
Chain 1:   9100        -8197.000             0.101            0.078
Chain 1:   9200        -9780.797             0.104            0.078
Chain 1:   9300        -8211.115             0.114            0.078
Chain 1:   9400        -8150.054             0.108            0.078
Chain 1:   9500        -7972.214             0.093            0.051
Chain 1:   9600        -8269.441             0.059            0.036
Chain 1:   9700        -9299.123             0.065            0.036
Chain 1:   9800        -8658.230             0.072            0.074
Chain 1:   9900        -8116.655             0.076            0.074
Chain 1:   10000        -7970.089             0.077            0.074
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57091.896             1.000            1.000
Chain 1:    200       -17489.926             1.632            2.264
Chain 1:    300        -8774.182             1.419            1.000
Chain 1:    400        -8236.953             1.081            1.000
Chain 1:    500        -8910.360             0.880            0.993
Chain 1:    600        -8051.053             0.751            0.993
Chain 1:    700        -9056.323             0.659            0.111
Chain 1:    800        -8440.086             0.586            0.111
Chain 1:    900        -8130.306             0.525            0.107
Chain 1:   1000        -7748.375             0.478            0.107
Chain 1:   1100        -7704.853             0.378            0.076
Chain 1:   1200        -7831.261             0.153            0.073
Chain 1:   1300        -7766.228             0.055            0.065
Chain 1:   1400        -7857.413             0.050            0.049
Chain 1:   1500        -7638.554             0.045            0.038
Chain 1:   1600        -7804.699             0.036            0.029
Chain 1:   1700        -7556.870             0.028            0.029
Chain 1:   1800        -7620.698             0.022            0.021
Chain 1:   1900        -7624.266             0.018            0.016
Chain 1:   2000        -7626.248             0.013            0.012
Chain 1:   2100        -7638.082             0.013            0.012
Chain 1:   2200        -7752.370             0.013            0.012
Chain 1:   2300        -7642.336             0.013            0.014
Chain 1:   2400        -7696.838             0.013            0.014
Chain 1:   2500        -7643.429             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002845 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85813.242             1.000            1.000
Chain 1:    200       -13521.780             3.173            5.346
Chain 1:    300        -9824.316             2.241            1.000
Chain 1:    400       -10906.950             1.705            1.000
Chain 1:    500        -8765.630             1.413            0.376
Chain 1:    600        -8679.364             1.179            0.376
Chain 1:    700        -8486.369             1.014            0.244
Chain 1:    800        -8188.642             0.892            0.244
Chain 1:    900        -8166.246             0.793            0.099
Chain 1:   1000        -8828.034             0.721            0.099
Chain 1:   1100        -8401.277             0.626            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8758.153             0.096            0.051
Chain 1:   1300        -8329.807             0.063            0.051
Chain 1:   1400        -8371.659             0.054            0.041
Chain 1:   1500        -8283.448             0.031            0.036
Chain 1:   1600        -8293.437             0.030            0.036
Chain 1:   1700        -8183.852             0.029            0.036
Chain 1:   1800        -8240.172             0.026            0.013
Chain 1:   1900        -8119.320             0.027            0.015
Chain 1:   2000        -8178.011             0.020            0.013
Chain 1:   2100        -8316.475             0.017            0.013
Chain 1:   2200        -8119.009             0.015            0.013
Chain 1:   2300        -8266.357             0.012            0.013
Chain 1:   2400        -8111.006             0.013            0.015
Chain 1:   2500        -8180.226             0.013            0.015
Chain 1:   2600        -8094.712             0.014            0.015
Chain 1:   2700        -8126.842             0.013            0.015
Chain 1:   2800        -8088.028             0.013            0.015
Chain 1:   2900        -8179.824             0.012            0.011
Chain 1:   3000        -8004.933             0.014            0.017
Chain 1:   3100        -8170.160             0.014            0.018
Chain 1:   3200        -8043.051             0.013            0.016
Chain 1:   3300        -8052.636             0.012            0.011
Chain 1:   3400        -8203.338             0.012            0.011
Chain 1:   3500        -8190.010             0.011            0.011
Chain 1:   3600        -8000.561             0.012            0.016
Chain 1:   3700        -8143.152             0.014            0.018
Chain 1:   3800        -8007.457             0.015            0.018
Chain 1:   3900        -7942.796             0.015            0.018
Chain 1:   4000        -8017.226             0.013            0.017
Chain 1:   4100        -8007.898             0.011            0.016
Chain 1:   4200        -7996.409             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003455 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372622.389             1.000            1.000
Chain 1:    200     -1580375.379             2.649            4.298
Chain 1:    300      -890362.486             2.024            1.000
Chain 1:    400      -457867.110             1.754            1.000
Chain 1:    500      -358535.209             1.459            0.945
Chain 1:    600      -233553.979             1.305            0.945
Chain 1:    700      -119551.573             1.255            0.945
Chain 1:    800       -86704.373             1.145            0.945
Chain 1:    900       -67001.240             1.051            0.775
Chain 1:   1000       -51756.726             0.975            0.775
Chain 1:   1100       -39190.311             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38364.915             0.479            0.379
Chain 1:   1300       -26272.003             0.448            0.379
Chain 1:   1400       -25988.129             0.355            0.321
Chain 1:   1500       -22562.557             0.342            0.321
Chain 1:   1600       -21775.720             0.292            0.295
Chain 1:   1700       -20643.215             0.202            0.294
Chain 1:   1800       -20586.257             0.165            0.152
Chain 1:   1900       -20912.670             0.137            0.055
Chain 1:   2000       -19419.892             0.115            0.055
Chain 1:   2100       -19658.564             0.084            0.036
Chain 1:   2200       -19885.793             0.083            0.036
Chain 1:   2300       -19502.213             0.039            0.020
Chain 1:   2400       -19274.118             0.039            0.020
Chain 1:   2500       -19076.361             0.025            0.016
Chain 1:   2600       -18706.077             0.024            0.016
Chain 1:   2700       -18662.815             0.018            0.012
Chain 1:   2800       -18379.649             0.020            0.015
Chain 1:   2900       -18661.109             0.019            0.015
Chain 1:   3000       -18647.214             0.012            0.012
Chain 1:   3100       -18732.298             0.011            0.012
Chain 1:   3200       -18422.708             0.012            0.015
Chain 1:   3300       -18627.620             0.011            0.012
Chain 1:   3400       -18102.179             0.013            0.015
Chain 1:   3500       -18714.717             0.015            0.015
Chain 1:   3600       -18020.503             0.017            0.015
Chain 1:   3700       -18408.039             0.018            0.017
Chain 1:   3800       -17366.451             0.023            0.021
Chain 1:   3900       -17362.581             0.021            0.021
Chain 1:   4000       -17479.857             0.022            0.021
Chain 1:   4100       -17393.598             0.022            0.021
Chain 1:   4200       -17209.518             0.021            0.021
Chain 1:   4300       -17348.118             0.021            0.021
Chain 1:   4400       -17304.708             0.019            0.011
Chain 1:   4500       -17207.215             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001274 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49072.574             1.000            1.000
Chain 1:    200       -33990.953             0.722            1.000
Chain 1:    300       -18435.580             0.762            0.844
Chain 1:    400       -21510.659             0.608            0.844
Chain 1:    500       -13210.431             0.612            0.628
Chain 1:    600       -16508.674             0.543            0.628
Chain 1:    700       -15620.374             0.474            0.444
Chain 1:    800       -12550.847             0.445            0.444
Chain 1:    900       -19171.344             0.434            0.345
Chain 1:   1000       -20297.995             0.396            0.345
Chain 1:   1100       -23061.164             0.308            0.245
Chain 1:   1200       -13086.324             0.340            0.245
Chain 1:   1300       -12483.585             0.260            0.200
Chain 1:   1400       -10948.422             0.260            0.200
Chain 1:   1500       -10310.019             0.203            0.140
Chain 1:   1600        -9890.818             0.188            0.120
Chain 1:   1700       -11165.683             0.193            0.120
Chain 1:   1800       -13398.273             0.186            0.120
Chain 1:   1900       -11966.751             0.163            0.120
Chain 1:   2000       -11062.069             0.166            0.120
Chain 1:   2100        -9380.670             0.172            0.120
Chain 1:   2200       -11380.588             0.113            0.120
Chain 1:   2300        -9544.889             0.127            0.140
Chain 1:   2400       -10959.391             0.126            0.129
Chain 1:   2500        -9790.082             0.132            0.129
Chain 1:   2600        -9291.284             0.133            0.129
Chain 1:   2700        -9290.886             0.122            0.129
Chain 1:   2800        -8994.611             0.108            0.120
Chain 1:   2900       -10008.465             0.107            0.119
Chain 1:   3000        -8940.073             0.110            0.120
Chain 1:   3100       -15096.636             0.133            0.120
Chain 1:   3200        -9060.489             0.182            0.120
Chain 1:   3300        -9471.225             0.167            0.119
Chain 1:   3400       -12422.524             0.178            0.119
Chain 1:   3500        -9449.414             0.198            0.120
Chain 1:   3600        -8773.863             0.200            0.120
Chain 1:   3700        -9148.640             0.204            0.120
Chain 1:   3800       -16716.833             0.246            0.238
Chain 1:   3900       -10136.313             0.301            0.315
Chain 1:   4000        -8750.508             0.305            0.315
Chain 1:   4100        -9126.274             0.268            0.238
Chain 1:   4200       -10260.638             0.213            0.158
Chain 1:   4300        -9036.940             0.222            0.158
Chain 1:   4400        -9010.983             0.198            0.135
Chain 1:   4500       -13973.645             0.202            0.135
Chain 1:   4600       -12532.905             0.206            0.135
Chain 1:   4700        -8833.470             0.244            0.158
Chain 1:   4800        -8744.038             0.200            0.135
Chain 1:   4900        -8570.248             0.137            0.115
Chain 1:   5000       -10277.200             0.138            0.115
Chain 1:   5100        -8557.443             0.154            0.135
Chain 1:   5200        -9922.374             0.156            0.138
Chain 1:   5300       -14149.010             0.173            0.166
Chain 1:   5400        -8779.038             0.233            0.201
Chain 1:   5500       -14072.675             0.236            0.201
Chain 1:   5600        -9559.153             0.271            0.299
Chain 1:   5700       -10717.629             0.240            0.201
Chain 1:   5800        -8464.353             0.266            0.266
Chain 1:   5900       -13540.886             0.301            0.299
Chain 1:   6000        -8998.531             0.335            0.375
Chain 1:   6100        -9690.022             0.322            0.375
Chain 1:   6200        -9085.625             0.315            0.375
Chain 1:   6300       -10156.283             0.296            0.375
Chain 1:   6400       -14698.467             0.265            0.309
Chain 1:   6500        -9346.849             0.285            0.309
Chain 1:   6600        -8669.912             0.246            0.266
Chain 1:   6700       -12773.739             0.267            0.309
Chain 1:   6800       -12884.370             0.241            0.309
Chain 1:   6900       -12382.485             0.208            0.105
Chain 1:   7000       -10147.189             0.179            0.105
Chain 1:   7100        -8772.175             0.188            0.157
Chain 1:   7200        -9862.134             0.192            0.157
Chain 1:   7300        -8569.573             0.197            0.157
Chain 1:   7400        -8262.759             0.170            0.151
Chain 1:   7500        -8288.628             0.113            0.111
Chain 1:   7600        -8441.681             0.107            0.111
Chain 1:   7700       -12010.812             0.104            0.111
Chain 1:   7800        -8330.490             0.148            0.151
Chain 1:   7900        -8356.497             0.144            0.151
Chain 1:   8000        -8283.532             0.123            0.111
Chain 1:   8100        -8199.487             0.108            0.037
Chain 1:   8200        -8600.686             0.102            0.037
Chain 1:   8300        -9555.872             0.097            0.037
Chain 1:   8400        -8656.631             0.103            0.047
Chain 1:   8500        -8290.127             0.107            0.047
Chain 1:   8600        -8452.010             0.107            0.047
Chain 1:   8700        -9092.544             0.085            0.047
Chain 1:   8800        -9880.656             0.049            0.047
Chain 1:   8900        -9727.185             0.050            0.047
Chain 1:   9000       -11026.196             0.061            0.070
Chain 1:   9100        -8087.559             0.096            0.080
Chain 1:   9200        -8266.604             0.094            0.080
Chain 1:   9300        -8472.054             0.086            0.070
Chain 1:   9400       -11863.416             0.104            0.070
Chain 1:   9500        -8214.383             0.144            0.080
Chain 1:   9600        -8618.685             0.147            0.080
Chain 1:   9700       -10893.795             0.161            0.118
Chain 1:   9800        -8611.151             0.179            0.209
Chain 1:   9900       -10777.327             0.198            0.209
Chain 1:   10000       -10587.168             0.188            0.209
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58210.242             1.000            1.000
Chain 1:    200       -17725.458             1.642            2.284
Chain 1:    300        -8695.376             1.441            1.038
Chain 1:    400        -8136.318             1.098            1.038
Chain 1:    500        -8347.409             0.883            1.000
Chain 1:    600        -8467.365             0.738            1.000
Chain 1:    700        -8650.158             0.636            0.069
Chain 1:    800        -8648.937             0.556            0.069
Chain 1:    900        -7944.671             0.505            0.069
Chain 1:   1000        -7917.119             0.454            0.069
Chain 1:   1100        -7646.634             0.358            0.035
Chain 1:   1200        -7584.824             0.130            0.025
Chain 1:   1300        -7706.109             0.028            0.021
Chain 1:   1400        -7818.880             0.023            0.016
Chain 1:   1500        -7563.155             0.024            0.016
Chain 1:   1600        -7741.224             0.024            0.021
Chain 1:   1700        -7489.543             0.026            0.023
Chain 1:   1800        -7540.249             0.026            0.023
Chain 1:   1900        -7569.865             0.018            0.016
Chain 1:   2000        -7580.638             0.018            0.016
Chain 1:   2100        -7567.385             0.014            0.014
Chain 1:   2200        -7675.362             0.015            0.014
Chain 1:   2300        -7548.659             0.015            0.014
Chain 1:   2400        -7603.851             0.014            0.014
Chain 1:   2500        -7564.483             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86411.048             1.000            1.000
Chain 1:    200       -13538.646             3.191            5.383
Chain 1:    300        -9876.948             2.251            1.000
Chain 1:    400       -10766.736             1.709            1.000
Chain 1:    500        -8873.030             1.410            0.371
Chain 1:    600        -8861.087             1.175            0.371
Chain 1:    700        -8577.705             1.012            0.213
Chain 1:    800        -8745.294             0.888            0.213
Chain 1:    900        -8711.424             0.790            0.083
Chain 1:   1000        -8609.251             0.712            0.083
Chain 1:   1100        -8664.618             0.613            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8207.620             0.080            0.033
Chain 1:   1300        -8579.497             0.047            0.033
Chain 1:   1400        -8570.056             0.039            0.019
Chain 1:   1500        -8436.676             0.019            0.016
Chain 1:   1600        -8546.355             0.020            0.016
Chain 1:   1700        -8626.036             0.018            0.013
Chain 1:   1800        -8204.931             0.021            0.013
Chain 1:   1900        -8304.215             0.022            0.013
Chain 1:   2000        -8278.547             0.021            0.013
Chain 1:   2100        -8403.456             0.022            0.015
Chain 1:   2200        -8209.939             0.019            0.015
Chain 1:   2300        -8299.010             0.015            0.013
Chain 1:   2400        -8368.117             0.016            0.013
Chain 1:   2500        -8314.262             0.015            0.012
Chain 1:   2600        -8315.176             0.014            0.011
Chain 1:   2700        -8232.116             0.014            0.011
Chain 1:   2800        -8192.649             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003173 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406971.746             1.000            1.000
Chain 1:    200     -1585812.607             2.651            4.301
Chain 1:    300      -890923.297             2.027            1.000
Chain 1:    400      -457318.579             1.757            1.000
Chain 1:    500      -357411.971             1.462            0.948
Chain 1:    600      -232579.380             1.308            0.948
Chain 1:    700      -119061.465             1.257            0.948
Chain 1:    800       -86312.872             1.147            0.948
Chain 1:    900       -66708.966             1.052            0.780
Chain 1:   1000       -51542.098             0.977            0.780
Chain 1:   1100       -39049.134             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38233.360             0.481            0.379
Chain 1:   1300       -26219.335             0.448            0.379
Chain 1:   1400       -25942.779             0.355            0.320
Chain 1:   1500       -22536.072             0.342            0.320
Chain 1:   1600       -21754.748             0.292            0.294
Chain 1:   1700       -20631.778             0.202            0.294
Chain 1:   1800       -20576.928             0.164            0.151
Chain 1:   1900       -20903.145             0.136            0.054
Chain 1:   2000       -19415.683             0.115            0.054
Chain 1:   2100       -19654.242             0.084            0.036
Chain 1:   2200       -19880.286             0.083            0.036
Chain 1:   2300       -19497.791             0.039            0.020
Chain 1:   2400       -19269.849             0.039            0.020
Chain 1:   2500       -19071.646             0.025            0.016
Chain 1:   2600       -18702.031             0.023            0.016
Chain 1:   2700       -18659.082             0.018            0.012
Chain 1:   2800       -18375.737             0.020            0.015
Chain 1:   2900       -18657.020             0.019            0.015
Chain 1:   3000       -18643.327             0.012            0.012
Chain 1:   3100       -18728.276             0.011            0.012
Chain 1:   3200       -18418.980             0.012            0.015
Chain 1:   3300       -18623.704             0.011            0.012
Chain 1:   3400       -18098.540             0.013            0.015
Chain 1:   3500       -18710.462             0.015            0.015
Chain 1:   3600       -18017.099             0.017            0.015
Chain 1:   3700       -18403.864             0.018            0.017
Chain 1:   3800       -17363.450             0.023            0.021
Chain 1:   3900       -17359.545             0.021            0.021
Chain 1:   4000       -17476.902             0.022            0.021
Chain 1:   4100       -17390.585             0.022            0.021
Chain 1:   4200       -17206.845             0.021            0.021
Chain 1:   4300       -17345.285             0.021            0.021
Chain 1:   4400       -17302.100             0.019            0.011
Chain 1:   4500       -17204.572             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49481.999             1.000            1.000
Chain 1:    200       -21402.638             1.156            1.312
Chain 1:    300       -16651.691             0.866            1.000
Chain 1:    400       -16062.146             0.658            1.000
Chain 1:    500       -14799.368             0.544            0.285
Chain 1:    600       -15411.791             0.460            0.285
Chain 1:    700       -19967.813             0.427            0.228
Chain 1:    800       -15604.209             0.408            0.280
Chain 1:    900       -12016.953             0.396            0.280
Chain 1:   1000       -12273.863             0.359            0.280
Chain 1:   1100       -11680.461             0.264            0.228
Chain 1:   1200       -13975.056             0.149            0.164
Chain 1:   1300       -12773.456             0.130            0.094
Chain 1:   1400       -11035.985             0.142            0.157
Chain 1:   1500       -19020.612             0.175            0.164
Chain 1:   1600       -21480.513             0.183            0.164
Chain 1:   1700       -18850.481             0.174            0.157
Chain 1:   1800       -14497.254             0.176            0.157
Chain 1:   1900       -16630.728             0.159            0.140
Chain 1:   2000       -10393.399             0.217            0.157
Chain 1:   2100       -12772.202             0.230            0.164
Chain 1:   2200       -12023.808             0.220            0.157
Chain 1:   2300        -9550.450             0.237            0.186
Chain 1:   2400       -10123.760             0.227            0.186
Chain 1:   2500       -10922.009             0.192            0.140
Chain 1:   2600       -10280.919             0.187            0.140
Chain 1:   2700       -10561.379             0.175            0.128
Chain 1:   2800       -22548.433             0.199            0.128
Chain 1:   2900       -11395.883             0.284            0.186
Chain 1:   3000        -9451.336             0.244            0.186
Chain 1:   3100        -9383.386             0.226            0.073
Chain 1:   3200        -9659.698             0.223            0.073
Chain 1:   3300       -10887.143             0.208            0.073
Chain 1:   3400        -9815.486             0.214            0.109
Chain 1:   3500        -9699.985             0.207            0.109
Chain 1:   3600       -10313.951             0.207            0.109
Chain 1:   3700        -9132.541             0.217            0.113
Chain 1:   3800        -9237.651             0.165            0.109
Chain 1:   3900        -9413.619             0.069            0.060
Chain 1:   4000       -17615.424             0.095            0.060
Chain 1:   4100        -9260.182             0.185            0.109
Chain 1:   4200       -13605.021             0.214            0.113
Chain 1:   4300        -9890.547             0.240            0.129
Chain 1:   4400       -14998.405             0.263            0.319
Chain 1:   4500       -11881.081             0.288            0.319
Chain 1:   4600        -9454.425             0.308            0.319
Chain 1:   4700       -10047.200             0.301            0.319
Chain 1:   4800        -8985.114             0.312            0.319
Chain 1:   4900        -9455.765             0.315            0.319
Chain 1:   5000       -15257.747             0.306            0.319
Chain 1:   5100        -8939.265             0.287            0.319
Chain 1:   5200       -11088.842             0.274            0.262
Chain 1:   5300        -8837.218             0.262            0.257
Chain 1:   5400       -15361.252             0.271            0.257
Chain 1:   5500        -9353.005             0.309            0.257
Chain 1:   5600       -10357.624             0.293            0.255
Chain 1:   5700       -14447.973             0.315            0.283
Chain 1:   5800       -10759.786             0.338            0.343
Chain 1:   5900       -15357.059             0.363            0.343
Chain 1:   6000       -13384.446             0.339            0.299
Chain 1:   6100       -14382.819             0.275            0.283
Chain 1:   6200        -9197.087             0.312            0.299
Chain 1:   6300        -9065.860             0.288            0.299
Chain 1:   6400        -9115.582             0.247            0.283
Chain 1:   6500        -9116.529             0.182            0.147
Chain 1:   6600        -9891.692             0.180            0.147
Chain 1:   6700        -8766.585             0.165            0.128
Chain 1:   6800       -14296.217             0.169            0.128
Chain 1:   6900        -9269.222             0.194            0.128
Chain 1:   7000        -9661.736             0.183            0.078
Chain 1:   7100        -8846.242             0.185            0.092
Chain 1:   7200       -11681.571             0.153            0.092
Chain 1:   7300        -9599.792             0.173            0.128
Chain 1:   7400        -8941.127             0.180            0.128
Chain 1:   7500       -10780.483             0.197            0.171
Chain 1:   7600        -8880.827             0.211            0.214
Chain 1:   7700       -10349.883             0.212            0.214
Chain 1:   7800       -11926.730             0.187            0.171
Chain 1:   7900        -8763.375             0.169            0.171
Chain 1:   8000       -12592.015             0.195            0.214
Chain 1:   8100       -10360.978             0.207            0.215
Chain 1:   8200       -10787.542             0.187            0.214
Chain 1:   8300       -11853.913             0.174            0.171
Chain 1:   8400        -8467.977             0.207            0.214
Chain 1:   8500       -10465.560             0.209            0.214
Chain 1:   8600       -10394.850             0.188            0.191
Chain 1:   8700        -9575.389             0.183            0.191
Chain 1:   8800        -8554.265             0.181            0.191
Chain 1:   8900       -14885.222             0.188            0.191
Chain 1:   9000       -12505.700             0.176            0.190
Chain 1:   9100        -8850.510             0.196            0.190
Chain 1:   9200        -9702.175             0.201            0.190
Chain 1:   9300       -12886.239             0.217            0.191
Chain 1:   9400        -8639.913             0.226            0.191
Chain 1:   9500       -12823.167             0.239            0.247
Chain 1:   9600        -8600.113             0.288            0.326
Chain 1:   9700        -8989.497             0.283            0.326
Chain 1:   9800       -10385.369             0.285            0.326
Chain 1:   9900        -8434.031             0.266            0.247
Chain 1:   10000        -8319.361             0.248            0.247
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001444 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57768.665             1.000            1.000
Chain 1:    200       -18116.608             1.594            2.189
Chain 1:    300        -9015.840             1.399            1.009
Chain 1:    400        -8187.373             1.075            1.009
Chain 1:    500        -8501.853             0.867            1.000
Chain 1:    600        -8506.476             0.723            1.000
Chain 1:    700        -8198.530             0.625            0.101
Chain 1:    800        -8379.321             0.549            0.101
Chain 1:    900        -8017.127             0.493            0.045
Chain 1:   1000        -7919.541             0.445            0.045
Chain 1:   1100        -7712.091             0.348            0.038
Chain 1:   1200        -7783.339             0.130            0.037
Chain 1:   1300        -7923.293             0.031            0.027
Chain 1:   1400        -7731.547             0.023            0.025
Chain 1:   1500        -7537.773             0.022            0.025
Chain 1:   1600        -7692.182             0.024            0.025
Chain 1:   1700        -7640.188             0.021            0.022
Chain 1:   1800        -7630.844             0.019            0.020
Chain 1:   1900        -7632.301             0.014            0.018
Chain 1:   2000        -7721.166             0.014            0.018
Chain 1:   2100        -7579.760             0.014            0.018
Chain 1:   2200        -7869.415             0.016            0.019
Chain 1:   2300        -7588.353             0.018            0.020
Chain 1:   2400        -7549.032             0.016            0.019
Chain 1:   2500        -7580.555             0.014            0.012
Chain 1:   2600        -7526.584             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003184 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86987.364             1.000            1.000
Chain 1:    200       -14107.490             3.083            5.166
Chain 1:    300       -10312.266             2.178            1.000
Chain 1:    400       -12417.035             1.676            1.000
Chain 1:    500        -8729.956             1.425            0.422
Chain 1:    600        -8843.493             1.190            0.422
Chain 1:    700        -9413.226             1.028            0.368
Chain 1:    800        -8516.798             0.913            0.368
Chain 1:    900        -8539.290             0.812            0.170
Chain 1:   1000        -9208.235             0.738            0.170
Chain 1:   1100        -8844.723             0.642            0.105   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8984.001             0.127            0.073
Chain 1:   1300        -8556.150             0.095            0.061
Chain 1:   1400        -8747.068             0.080            0.050
Chain 1:   1500        -8640.350             0.039            0.041
Chain 1:   1600        -8646.427             0.038            0.041
Chain 1:   1700        -8520.067             0.034            0.022
Chain 1:   1800        -8575.933             0.024            0.016
Chain 1:   1900        -8598.313             0.024            0.016
Chain 1:   2000        -8684.809             0.018            0.015
Chain 1:   2100        -8522.693             0.015            0.015
Chain 1:   2200        -8519.869             0.014            0.012
Chain 1:   2300        -8663.347             0.010            0.012
Chain 1:   2400        -8452.751             0.011            0.012
Chain 1:   2500        -8520.784             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002585 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410444.048             1.000            1.000
Chain 1:    200     -1586028.250             2.651            4.303
Chain 1:    300      -892155.056             2.027            1.000
Chain 1:    400      -458645.561             1.756            1.000
Chain 1:    500      -359116.552             1.461            0.945
Chain 1:    600      -233738.075             1.307            0.945
Chain 1:    700      -119892.442             1.256            0.945
Chain 1:    800       -87140.789             1.146            0.945
Chain 1:    900       -67473.391             1.051            0.778
Chain 1:   1000       -52281.513             0.975            0.778
Chain 1:   1100       -39760.886             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38944.888             0.478            0.376
Chain 1:   1300       -26876.707             0.445            0.376
Chain 1:   1400       -26598.146             0.352            0.315
Chain 1:   1500       -23179.650             0.339            0.315
Chain 1:   1600       -22396.629             0.289            0.291
Chain 1:   1700       -21265.697             0.199            0.291
Chain 1:   1800       -21209.526             0.162            0.147
Chain 1:   1900       -21536.693             0.134            0.053
Chain 1:   2000       -20044.174             0.112            0.053
Chain 1:   2100       -20282.479             0.082            0.035
Chain 1:   2200       -20510.242             0.081            0.035
Chain 1:   2300       -20126.099             0.038            0.019
Chain 1:   2400       -19897.829             0.038            0.019
Chain 1:   2500       -19700.169             0.024            0.015
Chain 1:   2600       -19329.060             0.023            0.015
Chain 1:   2700       -19285.696             0.018            0.012
Chain 1:   2800       -19002.306             0.019            0.015
Chain 1:   2900       -19284.015             0.019            0.015
Chain 1:   3000       -19269.978             0.012            0.012
Chain 1:   3100       -19355.166             0.011            0.011
Chain 1:   3200       -19045.163             0.011            0.015
Chain 1:   3300       -19250.465             0.010            0.011
Chain 1:   3400       -18724.272             0.012            0.015
Chain 1:   3500       -19337.920             0.014            0.015
Chain 1:   3600       -18642.298             0.016            0.015
Chain 1:   3700       -19030.839             0.018            0.016
Chain 1:   3800       -17987.076             0.022            0.020
Chain 1:   3900       -17983.196             0.021            0.020
Chain 1:   4000       -18100.437             0.021            0.020
Chain 1:   4100       -18014.088             0.021            0.020
Chain 1:   4200       -17829.561             0.021            0.020
Chain 1:   4300       -17968.444             0.021            0.020
Chain 1:   4400       -17924.629             0.018            0.010
Chain 1:   4500       -17827.108             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001249 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12631.196             1.000            1.000
Chain 1:    200        -9367.664             0.674            1.000
Chain 1:    300        -8239.897             0.495            0.348
Chain 1:    400        -8392.623             0.376            0.348
Chain 1:    500        -8337.330             0.302            0.137
Chain 1:    600        -8148.394             0.256            0.137
Chain 1:    700        -8224.465             0.220            0.023
Chain 1:    800        -8124.464             0.194            0.023
Chain 1:    900        -7963.999             0.175            0.020
Chain 1:   1000        -8170.159             0.160            0.023
Chain 1:   1100        -8113.903             0.061            0.020
Chain 1:   1200        -8072.048             0.026            0.018
Chain 1:   1300        -8037.562             0.013            0.012
Chain 1:   1400        -8044.262             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001374 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55788.381             1.000            1.000
Chain 1:    200       -17454.364             1.598            2.196
Chain 1:    300        -8800.443             1.393            1.000
Chain 1:    400        -8539.376             1.053            1.000
Chain 1:    500        -8131.264             0.852            0.983
Chain 1:    600        -8598.828             0.719            0.983
Chain 1:    700        -8404.565             0.620            0.054
Chain 1:    800        -8200.541             0.545            0.054
Chain 1:    900        -8216.046             0.485            0.050
Chain 1:   1000        -7753.422             0.442            0.054
Chain 1:   1100        -7701.555             0.343            0.050
Chain 1:   1200        -7790.452             0.125            0.031
Chain 1:   1300        -7698.784             0.027            0.025
Chain 1:   1400        -7762.795             0.025            0.023
Chain 1:   1500        -7589.929             0.022            0.023
Chain 1:   1600        -7746.169             0.019            0.020
Chain 1:   1700        -7670.791             0.018            0.012
Chain 1:   1800        -7665.604             0.015            0.011
Chain 1:   1900        -7560.838             0.017            0.012
Chain 1:   2000        -7663.274             0.012            0.012
Chain 1:   2100        -7580.345             0.012            0.012
Chain 1:   2200        -7773.237             0.014            0.013
Chain 1:   2300        -7552.466             0.015            0.014
Chain 1:   2400        -7675.500             0.016            0.016
Chain 1:   2500        -7585.393             0.015            0.014
Chain 1:   2600        -7536.107             0.014            0.013
Chain 1:   2700        -7534.212             0.013            0.013
Chain 1:   2800        -7533.119             0.013            0.013
Chain 1:   2900        -7443.928             0.013            0.012
Chain 1:   3000        -7554.452             0.013            0.012
Chain 1:   3100        -7541.474             0.012            0.012
Chain 1:   3200        -7739.365             0.012            0.012
Chain 1:   3300        -7462.103             0.013            0.012
Chain 1:   3400        -7683.847             0.014            0.012
Chain 1:   3500        -7446.208             0.016            0.015
Chain 1:   3600        -7512.184             0.016            0.015
Chain 1:   3700        -7461.022             0.017            0.015
Chain 1:   3800        -7459.759             0.017            0.015
Chain 1:   3900        -7427.068             0.016            0.015
Chain 1:   4000        -7422.048             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002523 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86409.035             1.000            1.000
Chain 1:    200       -13773.699             3.137            5.273
Chain 1:    300       -10093.186             2.213            1.000
Chain 1:    400       -11178.047             1.684            1.000
Chain 1:    500        -8972.384             1.396            0.365
Chain 1:    600        -9252.058             1.169            0.365
Chain 1:    700        -9364.585             1.003            0.246
Chain 1:    800        -8537.070             0.890            0.246
Chain 1:    900        -8471.534             0.792            0.097
Chain 1:   1000        -8774.022             0.716            0.097
Chain 1:   1100        -8924.237             0.618            0.097   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8452.469             0.096            0.056
Chain 1:   1300        -8728.579             0.063            0.034
Chain 1:   1400        -8726.218             0.053            0.032
Chain 1:   1500        -8633.063             0.030            0.030
Chain 1:   1600        -8731.240             0.028            0.017
Chain 1:   1700        -8810.071             0.027            0.017
Chain 1:   1800        -8377.405             0.023            0.017
Chain 1:   1900        -8481.385             0.023            0.017
Chain 1:   2000        -8456.839             0.020            0.012
Chain 1:   2100        -8592.619             0.020            0.012
Chain 1:   2200        -8386.135             0.017            0.012
Chain 1:   2300        -8482.596             0.015            0.011
Chain 1:   2400        -8545.917             0.016            0.011
Chain 1:   2500        -8490.146             0.015            0.011
Chain 1:   2600        -8494.321             0.014            0.011
Chain 1:   2700        -8409.607             0.014            0.011
Chain 1:   2800        -8366.272             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003183 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426640.312             1.000            1.000
Chain 1:    200     -1590527.788             2.649            4.298
Chain 1:    300      -891964.147             2.027            1.000
Chain 1:    400      -458061.241             1.757            1.000
Chain 1:    500      -357712.666             1.462            0.947
Chain 1:    600      -232566.687             1.308            0.947
Chain 1:    700      -119099.449             1.257            0.947
Chain 1:    800       -86406.627             1.147            0.947
Chain 1:    900       -66830.236             1.052            0.783
Chain 1:   1000       -51702.239             0.976            0.783
Chain 1:   1100       -39245.896             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38433.316             0.480            0.378
Chain 1:   1300       -26449.301             0.447            0.378
Chain 1:   1400       -26176.068             0.354            0.317
Chain 1:   1500       -22778.472             0.341            0.317
Chain 1:   1600       -22000.096             0.290            0.293
Chain 1:   1700       -20880.539             0.200            0.293
Chain 1:   1800       -20826.615             0.163            0.149
Chain 1:   1900       -21152.980             0.135            0.054
Chain 1:   2000       -19667.257             0.113            0.054
Chain 1:   2100       -19905.507             0.083            0.035
Chain 1:   2200       -20131.537             0.082            0.035
Chain 1:   2300       -19749.066             0.038            0.019
Chain 1:   2400       -19521.113             0.039            0.019
Chain 1:   2500       -19322.914             0.025            0.015
Chain 1:   2600       -18953.080             0.023            0.015
Chain 1:   2700       -18910.113             0.018            0.012
Chain 1:   2800       -18626.718             0.019            0.015
Chain 1:   2900       -18908.002             0.019            0.015
Chain 1:   3000       -18894.272             0.012            0.012
Chain 1:   3100       -18979.253             0.011            0.012
Chain 1:   3200       -18669.861             0.011            0.015
Chain 1:   3300       -18874.672             0.011            0.012
Chain 1:   3400       -18349.296             0.012            0.015
Chain 1:   3500       -18961.507             0.015            0.015
Chain 1:   3600       -18267.759             0.016            0.015
Chain 1:   3700       -18654.757             0.018            0.017
Chain 1:   3800       -17613.773             0.023            0.021
Chain 1:   3900       -17609.869             0.021            0.021
Chain 1:   4000       -17727.214             0.022            0.021
Chain 1:   4100       -17640.878             0.022            0.021
Chain 1:   4200       -17457.021             0.021            0.021
Chain 1:   4300       -17595.525             0.021            0.021
Chain 1:   4400       -17552.196             0.018            0.011
Chain 1:   4500       -17454.691             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49208.804             1.000            1.000
Chain 1:    200       -14773.858             1.665            2.331
Chain 1:    300       -19812.281             1.195            1.000
Chain 1:    400       -18307.288             0.917            1.000
Chain 1:    500       -14255.949             0.790            0.284
Chain 1:    600       -14930.102             0.666            0.284
Chain 1:    700       -17983.052             0.595            0.254
Chain 1:    800       -15150.964             0.544            0.254
Chain 1:    900       -12130.398             0.511            0.249
Chain 1:   1000       -30154.586             0.520            0.254
Chain 1:   1100       -18923.314             0.479            0.254
Chain 1:   1200       -10895.104             0.320            0.254
Chain 1:   1300       -11734.495             0.302            0.249
Chain 1:   1400       -17680.849             0.327            0.284
Chain 1:   1500       -12077.641             0.345            0.336
Chain 1:   1600       -12709.919             0.346            0.336
Chain 1:   1700       -19847.813             0.365            0.360
Chain 1:   1800       -13018.544             0.398            0.464
Chain 1:   1900       -11876.180             0.383            0.464
Chain 1:   2000       -19001.813             0.361            0.375
Chain 1:   2100       -12890.732             0.349            0.375
Chain 1:   2200       -11108.776             0.291            0.360
Chain 1:   2300       -11189.397             0.285            0.360
Chain 1:   2400        -9209.996             0.273            0.360
Chain 1:   2500       -10041.677             0.234            0.215
Chain 1:   2600        -9726.200             0.233            0.215
Chain 1:   2700        -9377.687             0.200            0.160
Chain 1:   2800       -10506.338             0.159            0.107
Chain 1:   2900        -9670.948             0.158            0.107
Chain 1:   3000       -11124.330             0.133            0.107
Chain 1:   3100        -9006.715             0.109            0.107
Chain 1:   3200        -9972.724             0.103            0.097
Chain 1:   3300        -9760.425             0.105            0.097
Chain 1:   3400        -9479.051             0.086            0.086
Chain 1:   3500        -9542.523             0.078            0.086
Chain 1:   3600       -14313.506             0.109            0.097
Chain 1:   3700       -19587.918             0.132            0.107
Chain 1:   3800        -9268.363             0.232            0.131
Chain 1:   3900        -9996.947             0.231            0.131
Chain 1:   4000        -9088.630             0.228            0.100
Chain 1:   4100        -8899.958             0.206            0.097
Chain 1:   4200       -11104.974             0.217            0.100
Chain 1:   4300        -9111.894             0.236            0.199
Chain 1:   4400        -9126.006             0.234            0.199
Chain 1:   4500       -10117.825             0.243            0.199
Chain 1:   4600        -8810.448             0.224            0.148
Chain 1:   4700        -9964.194             0.209            0.116
Chain 1:   4800        -8762.117             0.111            0.116
Chain 1:   4900        -8859.013             0.105            0.116
Chain 1:   5000        -9829.982             0.105            0.116
Chain 1:   5100        -8883.110             0.113            0.116
Chain 1:   5200       -14619.217             0.133            0.116
Chain 1:   5300       -13000.750             0.123            0.116
Chain 1:   5400        -9311.527             0.163            0.124
Chain 1:   5500        -9712.298             0.157            0.124
Chain 1:   5600       -14930.833             0.177            0.124
Chain 1:   5700       -10981.862             0.202            0.137
Chain 1:   5800        -8835.520             0.212            0.243
Chain 1:   5900        -9506.415             0.218            0.243
Chain 1:   6000       -11844.977             0.228            0.243
Chain 1:   6100        -9221.971             0.246            0.284
Chain 1:   6200        -9028.408             0.209            0.243
Chain 1:   6300        -9093.071             0.197            0.243
Chain 1:   6400        -9501.057             0.162            0.197
Chain 1:   6500        -9729.323             0.160            0.197
Chain 1:   6600       -10496.141             0.132            0.073
Chain 1:   6700        -9375.004             0.108            0.073
Chain 1:   6800       -10072.071             0.091            0.071
Chain 1:   6900       -12014.326             0.100            0.073
Chain 1:   7000        -8931.978             0.115            0.073
Chain 1:   7100       -10996.549             0.105            0.073
Chain 1:   7200        -8860.621             0.127            0.120
Chain 1:   7300        -9740.313             0.135            0.120
Chain 1:   7400        -8490.622             0.146            0.147
Chain 1:   7500        -9876.465             0.158            0.147
Chain 1:   7600        -8787.050             0.163            0.147
Chain 1:   7700        -8809.551             0.151            0.147
Chain 1:   7800        -8605.729             0.146            0.147
Chain 1:   7900        -9472.729             0.139            0.140
Chain 1:   8000       -10091.640             0.111            0.124
Chain 1:   8100        -8462.831             0.111            0.124
Chain 1:   8200        -9302.940             0.096            0.092
Chain 1:   8300        -8497.284             0.097            0.095
Chain 1:   8400        -9028.020             0.088            0.092
Chain 1:   8500        -9466.140             0.079            0.090
Chain 1:   8600       -10834.362             0.079            0.090
Chain 1:   8700        -8482.055             0.106            0.092
Chain 1:   8800        -9557.873             0.115            0.095
Chain 1:   8900        -8536.665             0.118            0.113
Chain 1:   9000        -8857.598             0.115            0.113
Chain 1:   9100        -8767.296             0.097            0.095
Chain 1:   9200        -9015.148             0.091            0.095
Chain 1:   9300       -11369.228             0.102            0.113
Chain 1:   9400        -9856.922             0.112            0.120
Chain 1:   9500        -9028.395             0.116            0.120
Chain 1:   9600       -10982.424             0.121            0.120
Chain 1:   9700       -10483.247             0.098            0.113
Chain 1:   9800        -9926.092             0.093            0.092
Chain 1:   9900        -8838.187             0.093            0.092
Chain 1:   10000        -9389.485             0.095            0.092
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00143 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58346.508             1.000            1.000
Chain 1:    200       -17761.581             1.642            2.285
Chain 1:    300        -8766.474             1.437            1.026
Chain 1:    400        -8109.428             1.098            1.026
Chain 1:    500        -8786.782             0.894            1.000
Chain 1:    600        -8741.660             0.746            1.000
Chain 1:    700        -7837.359             0.656            0.115
Chain 1:    800        -8106.443             0.578            0.115
Chain 1:    900        -8036.761             0.515            0.081
Chain 1:   1000        -7814.310             0.466            0.081
Chain 1:   1100        -7780.855             0.366            0.077
Chain 1:   1200        -7708.175             0.139            0.033
Chain 1:   1300        -7709.851             0.036            0.028
Chain 1:   1400        -7806.249             0.029            0.012
Chain 1:   1500        -7624.649             0.024            0.012
Chain 1:   1600        -7936.911             0.028            0.024
Chain 1:   1700        -7592.712             0.021            0.024
Chain 1:   1800        -7691.568             0.018            0.013
Chain 1:   1900        -7577.597             0.019            0.015
Chain 1:   2000        -7667.254             0.017            0.013
Chain 1:   2100        -7631.918             0.017            0.013
Chain 1:   2200        -7759.931             0.018            0.015
Chain 1:   2300        -7702.302             0.019            0.015
Chain 1:   2400        -7679.053             0.018            0.015
Chain 1:   2500        -7613.565             0.016            0.013
Chain 1:   2600        -7560.514             0.013            0.012
Chain 1:   2700        -7563.698             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002555 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86682.621             1.000            1.000
Chain 1:    200       -13699.322             3.164            5.328
Chain 1:    300       -10104.125             2.228            1.000
Chain 1:    400       -10785.345             1.687            1.000
Chain 1:    500        -9042.414             1.388            0.356
Chain 1:    600        -8573.713             1.166            0.356
Chain 1:    700        -8772.548             1.002            0.193
Chain 1:    800        -8844.552             0.878            0.193
Chain 1:    900        -8959.625             0.782            0.063
Chain 1:   1000        -8663.516             0.707            0.063
Chain 1:   1100        -8995.407             0.611            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8612.133             0.083            0.045
Chain 1:   1300        -8728.008             0.048            0.037
Chain 1:   1400        -8821.430             0.043            0.034
Chain 1:   1500        -8686.307             0.025            0.023
Chain 1:   1600        -8792.664             0.021            0.016
Chain 1:   1700        -8888.561             0.020            0.013
Chain 1:   1800        -8483.387             0.024            0.016
Chain 1:   1900        -8581.807             0.024            0.016
Chain 1:   2000        -8553.244             0.021            0.013
Chain 1:   2100        -8673.058             0.018            0.013
Chain 1:   2200        -8478.597             0.016            0.013
Chain 1:   2300        -8616.605             0.016            0.014
Chain 1:   2400        -8627.494             0.016            0.014
Chain 1:   2500        -8593.966             0.014            0.012
Chain 1:   2600        -8595.957             0.013            0.011
Chain 1:   2700        -8504.994             0.013            0.011
Chain 1:   2800        -8472.776             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003228 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414082.089             1.000            1.000
Chain 1:    200     -1587117.655             2.651            4.301
Chain 1:    300      -890977.890             2.028            1.000
Chain 1:    400      -457312.780             1.758            1.000
Chain 1:    500      -357275.750             1.462            0.948
Chain 1:    600      -232292.986             1.308            0.948
Chain 1:    700      -118978.251             1.257            0.948
Chain 1:    800       -86278.267             1.148            0.948
Chain 1:    900       -66714.567             1.053            0.781
Chain 1:   1000       -51584.230             0.977            0.781
Chain 1:   1100       -39126.211             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38311.793             0.481            0.379
Chain 1:   1300       -26337.518             0.448            0.379
Chain 1:   1400       -26062.594             0.354            0.318
Chain 1:   1500       -22667.183             0.341            0.318
Chain 1:   1600       -21888.733             0.291            0.293
Chain 1:   1700       -20771.058             0.201            0.293
Chain 1:   1800       -20717.214             0.163            0.150
Chain 1:   1900       -21043.169             0.136            0.054
Chain 1:   2000       -19559.062             0.114            0.054
Chain 1:   2100       -19797.256             0.083            0.036
Chain 1:   2200       -20022.776             0.082            0.036
Chain 1:   2300       -19640.854             0.039            0.019
Chain 1:   2400       -19413.137             0.039            0.019
Chain 1:   2500       -19214.762             0.025            0.015
Chain 1:   2600       -18845.568             0.023            0.015
Chain 1:   2700       -18802.763             0.018            0.012
Chain 1:   2800       -18519.544             0.019            0.015
Chain 1:   2900       -18800.595             0.019            0.015
Chain 1:   3000       -18786.906             0.012            0.012
Chain 1:   3100       -18871.830             0.011            0.012
Chain 1:   3200       -18562.752             0.012            0.015
Chain 1:   3300       -18767.302             0.011            0.012
Chain 1:   3400       -18242.501             0.012            0.015
Chain 1:   3500       -18853.812             0.015            0.015
Chain 1:   3600       -18161.236             0.016            0.015
Chain 1:   3700       -18547.428             0.018            0.017
Chain 1:   3800       -17508.174             0.023            0.021
Chain 1:   3900       -17504.303             0.021            0.021
Chain 1:   4000       -17621.659             0.022            0.021
Chain 1:   4100       -17535.427             0.022            0.021
Chain 1:   4200       -17351.950             0.021            0.021
Chain 1:   4300       -17490.203             0.021            0.021
Chain 1:   4400       -17447.229             0.018            0.011
Chain 1:   4500       -17349.762             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001227 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48365.929             1.000            1.000
Chain 1:    200       -20437.036             1.183            1.367
Chain 1:    300       -12463.922             1.002            1.000
Chain 1:    400       -13296.564             0.767            1.000
Chain 1:    500       -12549.385             0.626            0.640
Chain 1:    600       -20020.653             0.584            0.640
Chain 1:    700       -11416.958             0.608            0.640
Chain 1:    800       -10550.273             0.542            0.640
Chain 1:    900       -12602.857             0.500            0.373
Chain 1:   1000       -11206.259             0.462            0.373
Chain 1:   1100       -10430.115             0.370            0.163
Chain 1:   1200       -11549.623             0.243            0.125
Chain 1:   1300       -12923.096             0.190            0.106
Chain 1:   1400       -10215.538             0.210            0.125
Chain 1:   1500       -17945.337             0.247            0.163
Chain 1:   1600        -9895.979             0.291            0.163
Chain 1:   1700       -25124.443             0.276            0.163
Chain 1:   1800       -15704.449             0.328            0.265
Chain 1:   1900       -10575.382             0.360            0.431
Chain 1:   2000       -21157.139             0.398            0.485
Chain 1:   2100        -9505.440             0.513            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200       -11275.638             0.519            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300       -11734.966             0.512            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400        -9855.807             0.505            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500       -17742.189             0.506            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600        -9596.960             0.510            0.500   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2700       -10782.596             0.460            0.485
Chain 1:   2800       -14615.360             0.426            0.444
Chain 1:   2900        -9264.871             0.436            0.444
Chain 1:   3000        -8499.199             0.395            0.262
Chain 1:   3100        -9109.653             0.279            0.191
Chain 1:   3200        -8696.762             0.268            0.191
Chain 1:   3300        -9719.003             0.274            0.191
Chain 1:   3400        -8443.636             0.270            0.151
Chain 1:   3500       -10196.545             0.243            0.151
Chain 1:   3600       -11963.434             0.173            0.148
Chain 1:   3700        -9184.100             0.192            0.151
Chain 1:   3800        -8525.260             0.174            0.148
Chain 1:   3900        -8565.674             0.117            0.105
Chain 1:   4000       -14317.095             0.148            0.148
Chain 1:   4100       -10917.693             0.172            0.151
Chain 1:   4200       -13977.716             0.189            0.172
Chain 1:   4300        -9649.151             0.224            0.219
Chain 1:   4400        -8891.756             0.217            0.219
Chain 1:   4500        -8686.112             0.202            0.219
Chain 1:   4600       -11350.900             0.211            0.235
Chain 1:   4700       -12200.974             0.188            0.219
Chain 1:   4800        -8214.176             0.228            0.235
Chain 1:   4900        -8354.358             0.230            0.235
Chain 1:   5000        -8353.315             0.189            0.219
Chain 1:   5100        -8670.433             0.162            0.085
Chain 1:   5200        -8554.712             0.141            0.070
Chain 1:   5300        -9739.885             0.109            0.070
Chain 1:   5400        -9709.834             0.101            0.037
Chain 1:   5500        -8253.710             0.116            0.070
Chain 1:   5600       -12361.674             0.126            0.070
Chain 1:   5700       -12157.880             0.120            0.037
Chain 1:   5800        -8296.408             0.118            0.037
Chain 1:   5900       -11800.667             0.146            0.122
Chain 1:   6000        -9161.169             0.175            0.176
Chain 1:   6100        -8514.398             0.179            0.176
Chain 1:   6200        -8361.228             0.180            0.176
Chain 1:   6300       -13034.291             0.203            0.288
Chain 1:   6400        -8601.069             0.254            0.297
Chain 1:   6500        -8213.960             0.241            0.297
Chain 1:   6600        -9674.898             0.223            0.288
Chain 1:   6700        -8718.694             0.233            0.288
Chain 1:   6800       -12183.691             0.215            0.284
Chain 1:   6900        -8872.873             0.222            0.284
Chain 1:   7000        -9321.085             0.198            0.151
Chain 1:   7100        -7974.132             0.207            0.169
Chain 1:   7200       -10753.960             0.231            0.258
Chain 1:   7300        -8316.615             0.225            0.258
Chain 1:   7400       -10285.591             0.193            0.191
Chain 1:   7500        -9999.222             0.191            0.191
Chain 1:   7600        -8297.246             0.196            0.205
Chain 1:   7700        -8147.340             0.187            0.205
Chain 1:   7800        -8556.289             0.163            0.191
Chain 1:   7900       -10464.016             0.144            0.182
Chain 1:   8000       -11624.109             0.149            0.182
Chain 1:   8100        -8044.704             0.177            0.191
Chain 1:   8200        -7719.013             0.155            0.182
Chain 1:   8300       -11979.194             0.162            0.182
Chain 1:   8400       -11239.608             0.149            0.100
Chain 1:   8500        -8439.183             0.179            0.182
Chain 1:   8600        -9646.694             0.171            0.125
Chain 1:   8700        -8488.458             0.183            0.136
Chain 1:   8800        -9103.394             0.185            0.136
Chain 1:   8900        -8966.599             0.168            0.125
Chain 1:   9000       -10360.674             0.172            0.135
Chain 1:   9100        -9479.008             0.137            0.125
Chain 1:   9200        -8737.411             0.141            0.125
Chain 1:   9300        -7928.210             0.116            0.102
Chain 1:   9400        -8599.146             0.117            0.102
Chain 1:   9500        -8368.398             0.086            0.093
Chain 1:   9600        -8245.830             0.075            0.085
Chain 1:   9700        -9116.863             0.071            0.085
Chain 1:   9800        -8575.971             0.071            0.085
Chain 1:   9900        -8465.037             0.071            0.085
Chain 1:   10000        -8444.781             0.057            0.078
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001751 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56421.156             1.000            1.000
Chain 1:    200       -16922.512             1.667            2.334
Chain 1:    300        -8499.302             1.442            1.000
Chain 1:    400        -8630.341             1.085            1.000
Chain 1:    500        -7972.172             0.885            0.991
Chain 1:    600        -8324.617             0.744            0.991
Chain 1:    700        -7815.652             0.647            0.083
Chain 1:    800        -8223.833             0.572            0.083
Chain 1:    900        -7816.165             0.515            0.065
Chain 1:   1000        -7643.091             0.465            0.065
Chain 1:   1100        -7602.833             0.366            0.052
Chain 1:   1200        -7703.118             0.134            0.050
Chain 1:   1300        -7622.927             0.036            0.042
Chain 1:   1400        -7612.324             0.034            0.042
Chain 1:   1500        -7551.914             0.027            0.023
Chain 1:   1600        -7485.312             0.024            0.013
Chain 1:   1700        -7469.113             0.017            0.011
Chain 1:   1800        -7487.663             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003652 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86229.043             1.000            1.000
Chain 1:    200       -12991.409             3.319            5.637
Chain 1:    300        -9450.211             2.337            1.000
Chain 1:    400       -10447.624             1.777            1.000
Chain 1:    500        -8322.376             1.473            0.375
Chain 1:    600        -8032.289             1.233            0.375
Chain 1:    700        -8325.048             1.062            0.255
Chain 1:    800        -8554.631             0.933            0.255
Chain 1:    900        -8352.881             0.832            0.095
Chain 1:   1000        -8074.963             0.752            0.095
Chain 1:   1100        -8336.353             0.655            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.867             0.094            0.035
Chain 1:   1300        -8197.990             0.058            0.034
Chain 1:   1400        -8196.535             0.048            0.031
Chain 1:   1500        -8107.111             0.024            0.028
Chain 1:   1600        -8201.369             0.021            0.027
Chain 1:   1700        -8298.883             0.019            0.024
Chain 1:   1800        -7909.288             0.021            0.024
Chain 1:   1900        -8010.462             0.020            0.013
Chain 1:   2000        -7980.409             0.017            0.012
Chain 1:   2100        -8119.215             0.016            0.012
Chain 1:   2200        -7900.704             0.016            0.012
Chain 1:   2300        -8042.892             0.016            0.013
Chain 1:   2400        -8045.573             0.016            0.013
Chain 1:   2500        -8019.308             0.015            0.013
Chain 1:   2600        -8016.037             0.014            0.013
Chain 1:   2700        -7925.432             0.014            0.013
Chain 1:   2800        -7905.895             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003677 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8432398.599             1.000            1.000
Chain 1:    200     -1590694.014             2.651            4.301
Chain 1:    300      -892092.603             2.028            1.000
Chain 1:    400      -457673.900             1.758            1.000
Chain 1:    500      -357565.115             1.463            0.949
Chain 1:    600      -232203.207             1.309            0.949
Chain 1:    700      -118508.715             1.259            0.949
Chain 1:    800       -85752.246             1.149            0.949
Chain 1:    900       -66119.967             1.055            0.783
Chain 1:   1000       -50939.643             0.979            0.783
Chain 1:   1100       -38445.525             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37618.234             0.484            0.382
Chain 1:   1300       -25617.959             0.452            0.382
Chain 1:   1400       -25337.151             0.358            0.325
Chain 1:   1500       -21936.912             0.346            0.325
Chain 1:   1600       -21155.893             0.295            0.298
Chain 1:   1700       -20035.843             0.205            0.297
Chain 1:   1800       -19980.920             0.167            0.155
Chain 1:   1900       -20306.378             0.139            0.056
Chain 1:   2000       -18822.011             0.117            0.056
Chain 1:   2100       -19060.042             0.086            0.037
Chain 1:   2200       -19285.601             0.085            0.037
Chain 1:   2300       -18903.777             0.040            0.020
Chain 1:   2400       -18676.201             0.040            0.020
Chain 1:   2500       -18478.025             0.026            0.016
Chain 1:   2600       -18109.102             0.024            0.016
Chain 1:   2700       -18066.294             0.019            0.012
Chain 1:   2800       -17783.427             0.020            0.016
Chain 1:   2900       -18064.266             0.020            0.016
Chain 1:   3000       -18050.524             0.012            0.012
Chain 1:   3100       -18135.439             0.011            0.012
Chain 1:   3200       -17826.586             0.012            0.016
Chain 1:   3300       -18030.912             0.011            0.012
Chain 1:   3400       -17506.671             0.013            0.016
Chain 1:   3500       -18117.239             0.015            0.016
Chain 1:   3600       -17425.592             0.017            0.016
Chain 1:   3700       -17811.145             0.019            0.017
Chain 1:   3800       -16773.413             0.024            0.022
Chain 1:   3900       -16769.595             0.022            0.022
Chain 1:   4000       -16886.917             0.023            0.022
Chain 1:   4100       -16800.834             0.023            0.022
Chain 1:   4200       -16617.612             0.022            0.022
Chain 1:   4300       -16755.635             0.022            0.022
Chain 1:   4400       -16712.916             0.019            0.011
Chain 1:   4500       -16615.525             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49033.212             1.000            1.000
Chain 1:    200       -23441.377             1.046            1.092
Chain 1:    300       -15365.220             0.872            1.000
Chain 1:    400       -12542.305             0.711            1.000
Chain 1:    500       -18999.028             0.636            0.526
Chain 1:    600       -14683.905             0.579            0.526
Chain 1:    700       -19491.889             0.532            0.340
Chain 1:    800       -13110.412             0.526            0.487
Chain 1:    900       -18035.859             0.498            0.340
Chain 1:   1000       -30773.747             0.490            0.414
Chain 1:   1100       -10627.532             0.579            0.414   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -13639.569             0.492            0.340
Chain 1:   1300       -11704.973             0.456            0.294
Chain 1:   1400       -10613.285             0.444            0.294
Chain 1:   1500       -10625.821             0.410            0.273
Chain 1:   1600       -10575.931             0.381            0.247
Chain 1:   1700       -11290.535             0.363            0.221
Chain 1:   1800       -10082.473             0.326            0.165
Chain 1:   1900        -9968.307             0.300            0.120
Chain 1:   2000       -12750.431             0.280            0.120
Chain 1:   2100       -10784.479             0.109            0.120
Chain 1:   2200       -14368.979             0.112            0.120
Chain 1:   2300        -9798.215             0.142            0.120
Chain 1:   2400       -14651.214             0.165            0.182
Chain 1:   2500        -9885.096             0.213            0.218
Chain 1:   2600       -10044.817             0.214            0.218
Chain 1:   2700       -10316.217             0.210            0.218
Chain 1:   2800        -9378.162             0.208            0.218
Chain 1:   2900       -14178.699             0.241            0.249
Chain 1:   3000        -9290.283             0.272            0.331
Chain 1:   3100        -9852.438             0.259            0.331
Chain 1:   3200        -9787.109             0.235            0.331
Chain 1:   3300        -9296.731             0.194            0.100
Chain 1:   3400        -9222.013             0.161            0.057
Chain 1:   3500        -9262.237             0.114            0.053
Chain 1:   3600       -17723.264             0.160            0.057
Chain 1:   3700       -10210.436             0.231            0.100
Chain 1:   3800       -11354.466             0.231            0.101
Chain 1:   3900        -9048.575             0.222            0.101
Chain 1:   4000        -9003.052             0.170            0.057
Chain 1:   4100        -8916.113             0.166            0.053
Chain 1:   4200       -10092.870             0.177            0.101
Chain 1:   4300       -16198.889             0.209            0.117
Chain 1:   4400        -8888.962             0.290            0.255
Chain 1:   4500        -9294.583             0.294            0.255
Chain 1:   4600        -8690.218             0.254            0.117
Chain 1:   4700       -10308.201             0.196            0.117
Chain 1:   4800        -8872.767             0.202            0.157
Chain 1:   4900        -9643.519             0.184            0.117
Chain 1:   5000        -8637.778             0.195            0.117
Chain 1:   5100       -11227.994             0.217            0.157
Chain 1:   5200        -9067.917             0.230            0.162
Chain 1:   5300       -14470.925             0.229            0.162
Chain 1:   5400        -8975.555             0.208            0.162
Chain 1:   5500        -9776.217             0.212            0.162
Chain 1:   5600        -9496.143             0.208            0.162
Chain 1:   5700       -16322.069             0.234            0.231
Chain 1:   5800       -12834.599             0.245            0.238
Chain 1:   5900        -8556.670             0.287            0.272
Chain 1:   6000        -9000.815             0.281            0.272
Chain 1:   6100       -11966.037             0.282            0.272
Chain 1:   6200        -9020.897             0.291            0.326
Chain 1:   6300       -11278.158             0.274            0.272
Chain 1:   6400        -9548.553             0.231            0.248
Chain 1:   6500        -9665.639             0.224            0.248
Chain 1:   6600        -8717.374             0.232            0.248
Chain 1:   6700       -14240.770             0.229            0.248
Chain 1:   6800        -8963.707             0.260            0.248
Chain 1:   6900        -8499.634             0.216            0.200
Chain 1:   7000        -8379.786             0.212            0.200
Chain 1:   7100        -9935.524             0.203            0.181
Chain 1:   7200        -8755.793             0.184            0.157
Chain 1:   7300       -11525.439             0.188            0.157
Chain 1:   7400       -10208.679             0.183            0.135
Chain 1:   7500       -10558.957             0.185            0.135
Chain 1:   7600        -8954.493             0.192            0.157
Chain 1:   7700        -9194.875             0.156            0.135
Chain 1:   7800        -8888.887             0.100            0.129
Chain 1:   7900        -8656.057             0.097            0.129
Chain 1:   8000        -8318.639             0.100            0.129
Chain 1:   8100       -11372.572             0.111            0.129
Chain 1:   8200       -12168.478             0.104            0.065
Chain 1:   8300        -8449.244             0.124            0.065
Chain 1:   8400       -11503.413             0.138            0.065
Chain 1:   8500       -10736.195             0.142            0.071
Chain 1:   8600        -8254.297             0.154            0.071
Chain 1:   8700        -9350.694             0.163            0.117
Chain 1:   8800        -8419.068             0.171            0.117
Chain 1:   8900        -8540.356             0.169            0.117
Chain 1:   9000        -8274.415             0.169            0.117
Chain 1:   9100        -9375.628             0.153            0.117
Chain 1:   9200       -10301.016             0.156            0.117
Chain 1:   9300        -8394.834             0.135            0.117
Chain 1:   9400        -9138.129             0.116            0.111
Chain 1:   9500        -8451.346             0.117            0.111
Chain 1:   9600       -10078.486             0.103            0.111
Chain 1:   9700        -8296.956             0.113            0.111
Chain 1:   9800        -8304.834             0.102            0.090
Chain 1:   9900        -9709.328             0.115            0.117
Chain 1:   10000        -8818.712             0.122            0.117
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001506 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57569.800             1.000            1.000
Chain 1:    200       -17595.838             1.636            2.272
Chain 1:    300        -8838.349             1.421            1.000
Chain 1:    400        -8180.489             1.086            1.000
Chain 1:    500        -7676.407             0.882            0.991
Chain 1:    600        -8484.017             0.751            0.991
Chain 1:    700        -7804.377             0.656            0.095
Chain 1:    800        -8257.900             0.581            0.095
Chain 1:    900        -8001.279             0.520            0.087
Chain 1:   1000        -7823.609             0.470            0.087
Chain 1:   1100        -7621.976             0.373            0.080
Chain 1:   1200        -7819.067             0.148            0.066
Chain 1:   1300        -7889.746             0.050            0.055
Chain 1:   1400        -7669.839             0.045            0.032
Chain 1:   1500        -7567.922             0.039            0.029
Chain 1:   1600        -7891.654             0.034            0.029
Chain 1:   1700        -7484.343             0.031            0.029
Chain 1:   1800        -7672.169             0.028            0.026
Chain 1:   1900        -7679.515             0.025            0.025
Chain 1:   2000        -7685.929             0.022            0.025
Chain 1:   2100        -7593.746             0.021            0.024
Chain 1:   2200        -7741.776             0.020            0.019
Chain 1:   2300        -7622.112             0.021            0.019
Chain 1:   2400        -7689.356             0.019            0.016
Chain 1:   2500        -7660.506             0.018            0.016
Chain 1:   2600        -7546.485             0.016            0.015
Chain 1:   2700        -7649.512             0.011            0.013
Chain 1:   2800        -7536.274             0.010            0.013
Chain 1:   2900        -7440.358             0.012            0.013
Chain 1:   3000        -7560.088             0.013            0.015
Chain 1:   3100        -7558.234             0.012            0.015
Chain 1:   3200        -7748.927             0.013            0.015
Chain 1:   3300        -7485.181             0.014            0.015
Chain 1:   3400        -7693.632             0.016            0.015
Chain 1:   3500        -7463.002             0.019            0.016
Chain 1:   3600        -7527.562             0.018            0.016
Chain 1:   3700        -7477.039             0.018            0.016
Chain 1:   3800        -7480.289             0.016            0.016
Chain 1:   3900        -7445.887             0.015            0.016
Chain 1:   4000        -7440.892             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003089 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86170.851             1.000            1.000
Chain 1:    200       -13697.821             3.145            5.291
Chain 1:    300       -10041.530             2.218            1.000
Chain 1:    400       -10987.072             1.685            1.000
Chain 1:    500        -9025.240             1.392            0.364
Chain 1:    600        -8563.809             1.169            0.364
Chain 1:    700        -8751.849             1.005            0.217
Chain 1:    800        -9498.124             0.889            0.217
Chain 1:    900        -8794.805             0.799            0.086
Chain 1:   1000        -8734.944             0.720            0.086
Chain 1:   1100        -8722.010             0.620            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8473.979             0.094            0.079
Chain 1:   1300        -8710.501             0.060            0.054
Chain 1:   1400        -8739.596             0.052            0.029
Chain 1:   1500        -8583.101             0.032            0.027
Chain 1:   1600        -8697.448             0.028            0.021
Chain 1:   1700        -8773.332             0.027            0.018
Chain 1:   1800        -8349.589             0.024            0.018
Chain 1:   1900        -8450.850             0.017            0.013
Chain 1:   2000        -8425.392             0.017            0.013
Chain 1:   2100        -8551.162             0.018            0.015
Chain 1:   2200        -8353.308             0.017            0.015
Chain 1:   2300        -8445.711             0.016            0.013
Chain 1:   2400        -8514.383             0.016            0.013
Chain 1:   2500        -8460.638             0.015            0.012
Chain 1:   2600        -8462.168             0.014            0.011
Chain 1:   2700        -8378.816             0.014            0.011
Chain 1:   2800        -8338.465             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387749.884             1.000            1.000
Chain 1:    200     -1578523.825             2.657            4.314
Chain 1:    300      -889111.467             2.030            1.000
Chain 1:    400      -456667.300             1.759            1.000
Chain 1:    500      -357575.299             1.463            0.947
Chain 1:    600      -232834.283             1.308            0.947
Chain 1:    700      -119311.760             1.257            0.947
Chain 1:    800       -86585.312             1.147            0.947
Chain 1:    900       -66956.310             1.052            0.775
Chain 1:   1000       -51770.528             0.976            0.775
Chain 1:   1100       -39261.505             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38443.509             0.479            0.378
Chain 1:   1300       -26400.447             0.447            0.378
Chain 1:   1400       -26121.818             0.354            0.319
Chain 1:   1500       -22708.585             0.341            0.319
Chain 1:   1600       -21926.082             0.291            0.293
Chain 1:   1700       -20798.866             0.201            0.293
Chain 1:   1800       -20743.347             0.164            0.150
Chain 1:   1900       -21069.662             0.136            0.054
Chain 1:   2000       -19580.301             0.114            0.054
Chain 1:   2100       -19818.685             0.083            0.036
Chain 1:   2200       -20045.329             0.082            0.036
Chain 1:   2300       -19662.328             0.039            0.019
Chain 1:   2400       -19434.342             0.039            0.019
Chain 1:   2500       -19236.467             0.025            0.015
Chain 1:   2600       -18866.411             0.023            0.015
Chain 1:   2700       -18823.384             0.018            0.012
Chain 1:   2800       -18540.207             0.019            0.015
Chain 1:   2900       -18821.496             0.019            0.015
Chain 1:   3000       -18807.665             0.012            0.012
Chain 1:   3100       -18892.697             0.011            0.012
Chain 1:   3200       -18583.272             0.012            0.015
Chain 1:   3300       -18788.111             0.011            0.012
Chain 1:   3400       -18262.899             0.012            0.015
Chain 1:   3500       -18875.033             0.015            0.015
Chain 1:   3600       -18181.336             0.016            0.015
Chain 1:   3700       -18568.400             0.018            0.017
Chain 1:   3800       -17527.643             0.023            0.021
Chain 1:   3900       -17523.803             0.021            0.021
Chain 1:   4000       -17641.063             0.022            0.021
Chain 1:   4100       -17554.800             0.022            0.021
Chain 1:   4200       -17371.005             0.021            0.021
Chain 1:   4300       -17509.444             0.021            0.021
Chain 1:   4400       -17466.164             0.018            0.011
Chain 1:   4500       -17368.703             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001324 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49169.608             1.000            1.000
Chain 1:    200       -20270.127             1.213            1.426
Chain 1:    300       -17189.230             0.868            1.000
Chain 1:    400       -18796.121             0.673            1.000
Chain 1:    500       -13386.262             0.619            0.404
Chain 1:    600       -14722.793             0.531            0.404
Chain 1:    700       -11195.491             0.500            0.315
Chain 1:    800       -10907.457             0.441            0.315
Chain 1:    900       -11217.198             0.395            0.179
Chain 1:   1000       -13126.944             0.370            0.179
Chain 1:   1100       -14818.920             0.281            0.145
Chain 1:   1200       -13438.203             0.149            0.114
Chain 1:   1300       -11287.070             0.150            0.114
Chain 1:   1400       -12667.533             0.153            0.114
Chain 1:   1500       -11163.063             0.126            0.114
Chain 1:   1600       -21709.806             0.165            0.135
Chain 1:   1700       -12289.279             0.210            0.135
Chain 1:   1800       -14242.573             0.221            0.137
Chain 1:   1900       -17434.537             0.237            0.145
Chain 1:   2000       -15850.062             0.232            0.137
Chain 1:   2100       -10418.986             0.273            0.183
Chain 1:   2200       -12329.345             0.278            0.183
Chain 1:   2300       -13046.991             0.265            0.155
Chain 1:   2400       -15280.572             0.268            0.155
Chain 1:   2500        -9241.048             0.320            0.183
Chain 1:   2600        -9212.014             0.272            0.155
Chain 1:   2700       -12041.817             0.219            0.155
Chain 1:   2800       -10202.166             0.223            0.180
Chain 1:   2900        -9326.255             0.214            0.155
Chain 1:   3000        -9669.473             0.208            0.155
Chain 1:   3100        -9568.568             0.157            0.146
Chain 1:   3200        -8937.900             0.148            0.094
Chain 1:   3300        -9573.573             0.150            0.094
Chain 1:   3400       -14070.417             0.167            0.094
Chain 1:   3500        -9394.279             0.151            0.094
Chain 1:   3600       -13453.861             0.181            0.180
Chain 1:   3700       -16857.989             0.178            0.180
Chain 1:   3800       -12770.183             0.192            0.202
Chain 1:   3900        -9483.449             0.217            0.302
Chain 1:   4000        -8739.224             0.222            0.302
Chain 1:   4100        -9001.601             0.224            0.302
Chain 1:   4200       -10499.015             0.231            0.302
Chain 1:   4300       -10215.770             0.227            0.302
Chain 1:   4400        -9088.637             0.208            0.202
Chain 1:   4500        -9159.255             0.159            0.143
Chain 1:   4600       -13989.494             0.163            0.143
Chain 1:   4700        -8618.496             0.205            0.143
Chain 1:   4800        -8900.487             0.176            0.124
Chain 1:   4900        -9474.702             0.148            0.085
Chain 1:   5000        -8753.209             0.147            0.082
Chain 1:   5100        -9714.331             0.154            0.099
Chain 1:   5200       -14773.074             0.174            0.099
Chain 1:   5300        -9009.871             0.236            0.124
Chain 1:   5400        -9738.293             0.231            0.099
Chain 1:   5500        -8639.512             0.243            0.127
Chain 1:   5600       -11325.570             0.232            0.127
Chain 1:   5700        -9405.214             0.190            0.127
Chain 1:   5800       -11217.384             0.203            0.162
Chain 1:   5900        -8862.364             0.223            0.204
Chain 1:   6000        -9560.050             0.222            0.204
Chain 1:   6100        -9606.507             0.213            0.204
Chain 1:   6200        -9133.499             0.184            0.162
Chain 1:   6300       -14334.157             0.156            0.162
Chain 1:   6400        -9788.823             0.195            0.204
Chain 1:   6500       -13290.820             0.209            0.237
Chain 1:   6600       -12237.331             0.194            0.204
Chain 1:   6700       -11174.179             0.183            0.162
Chain 1:   6800        -8489.431             0.198            0.263
Chain 1:   6900       -11475.327             0.198            0.260
Chain 1:   7000        -8462.620             0.226            0.263
Chain 1:   7100        -8342.671             0.227            0.263
Chain 1:   7200        -8457.230             0.223            0.263
Chain 1:   7300        -9852.337             0.201            0.260
Chain 1:   7400       -10192.722             0.158            0.142
Chain 1:   7500        -8760.795             0.148            0.142
Chain 1:   7600        -9043.976             0.143            0.142
Chain 1:   7700        -8760.952             0.136            0.142
Chain 1:   7800        -9561.473             0.113            0.084
Chain 1:   7900        -9138.125             0.092            0.046
Chain 1:   8000        -8470.830             0.064            0.046
Chain 1:   8100        -8624.415             0.064            0.046
Chain 1:   8200        -8271.530             0.067            0.046
Chain 1:   8300       -10102.839             0.071            0.046
Chain 1:   8400        -8722.289             0.084            0.079
Chain 1:   8500        -8587.881             0.069            0.046
Chain 1:   8600        -8981.730             0.070            0.046
Chain 1:   8700        -8882.802             0.068            0.046
Chain 1:   8800        -9541.587             0.066            0.046
Chain 1:   8900        -9137.108             0.066            0.044
Chain 1:   9000        -8699.292             0.063            0.044
Chain 1:   9100        -8514.711             0.064            0.044
Chain 1:   9200        -8625.367             0.061            0.044
Chain 1:   9300        -8702.064             0.044            0.044
Chain 1:   9400        -8948.947             0.031            0.028
Chain 1:   9500        -9184.735             0.032            0.028
Chain 1:   9600        -9484.280             0.030            0.028
Chain 1:   9700        -9233.549             0.032            0.028
Chain 1:   9800        -8963.252             0.028            0.028
Chain 1:   9900       -10478.860             0.038            0.028
Chain 1:   10000        -8293.307             0.059            0.028
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001423 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58626.136             1.000            1.000
Chain 1:    200       -17825.930             1.644            2.289
Chain 1:    300        -8655.509             1.449            1.059
Chain 1:    400        -8118.041             1.104            1.059
Chain 1:    500        -8027.922             0.885            1.000
Chain 1:    600        -8641.609             0.749            1.000
Chain 1:    700        -7738.929             0.659            0.117
Chain 1:    800        -8031.297             0.581            0.117
Chain 1:    900        -7786.388             0.520            0.071
Chain 1:   1000        -7835.980             0.469            0.071
Chain 1:   1100        -7656.726             0.371            0.066
Chain 1:   1200        -7806.471             0.144            0.036
Chain 1:   1300        -7587.515             0.041            0.031
Chain 1:   1400        -7831.195             0.038            0.031
Chain 1:   1500        -7454.936             0.041            0.031
Chain 1:   1600        -7694.304             0.037            0.031
Chain 1:   1700        -7453.908             0.029            0.031
Chain 1:   1800        -7498.572             0.026            0.031
Chain 1:   1900        -7513.518             0.023            0.029
Chain 1:   2000        -7562.829             0.023            0.029
Chain 1:   2100        -7500.747             0.022            0.029
Chain 1:   2200        -7627.912             0.021            0.029
Chain 1:   2300        -7472.031             0.021            0.021
Chain 1:   2400        -7569.825             0.019            0.017
Chain 1:   2500        -7538.209             0.014            0.013
Chain 1:   2600        -7443.256             0.012            0.013
Chain 1:   2700        -7465.026             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003347 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85789.564             1.000            1.000
Chain 1:    200       -13574.284             3.160            5.320
Chain 1:    300        -9958.147             2.228            1.000
Chain 1:    400       -10976.824             1.694            1.000
Chain 1:    500        -8809.497             1.404            0.363
Chain 1:    600        -8549.965             1.175            0.363
Chain 1:    700        -8700.241             1.010            0.246
Chain 1:    800        -9326.088             0.892            0.246
Chain 1:    900        -8798.628             0.800            0.093
Chain 1:   1000        -8564.784             0.722            0.093
Chain 1:   1100        -8847.707             0.626            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8451.013             0.098            0.060
Chain 1:   1300        -8656.366             0.064            0.047
Chain 1:   1400        -8668.361             0.055            0.032
Chain 1:   1500        -8520.546             0.032            0.030
Chain 1:   1600        -8633.518             0.031            0.027
Chain 1:   1700        -8718.266             0.030            0.027
Chain 1:   1800        -8307.957             0.028            0.027
Chain 1:   1900        -8403.652             0.023            0.024
Chain 1:   2000        -8376.583             0.021            0.017
Chain 1:   2100        -8498.424             0.019            0.014
Chain 1:   2200        -8320.502             0.016            0.014
Chain 1:   2300        -8399.909             0.015            0.013
Chain 1:   2400        -8468.412             0.016            0.013
Chain 1:   2500        -8413.922             0.015            0.011
Chain 1:   2600        -8412.624             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003244 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393158.703             1.000            1.000
Chain 1:    200     -1582680.559             2.652            4.303
Chain 1:    300      -890759.304             2.027            1.000
Chain 1:    400      -458107.093             1.756            1.000
Chain 1:    500      -358769.680             1.460            0.944
Chain 1:    600      -233554.133             1.306            0.944
Chain 1:    700      -119521.789             1.256            0.944
Chain 1:    800       -86695.639             1.146            0.944
Chain 1:    900       -66978.432             1.052            0.777
Chain 1:   1000       -51739.911             0.976            0.777
Chain 1:   1100       -39187.678             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38357.580             0.480            0.379
Chain 1:   1300       -26285.773             0.448            0.379
Chain 1:   1400       -26000.950             0.355            0.320
Chain 1:   1500       -22582.153             0.342            0.320
Chain 1:   1600       -21796.733             0.292            0.295
Chain 1:   1700       -20667.109             0.202            0.294
Chain 1:   1800       -20610.599             0.165            0.151
Chain 1:   1900       -20936.593             0.137            0.055
Chain 1:   2000       -19446.425             0.115            0.055
Chain 1:   2100       -19684.694             0.084            0.036
Chain 1:   2200       -19911.490             0.083            0.036
Chain 1:   2300       -19528.462             0.039            0.020
Chain 1:   2400       -19300.557             0.039            0.020
Chain 1:   2500       -19102.790             0.025            0.016
Chain 1:   2600       -18732.835             0.023            0.016
Chain 1:   2700       -18689.746             0.018            0.012
Chain 1:   2800       -18406.742             0.019            0.015
Chain 1:   2900       -18688.021             0.019            0.015
Chain 1:   3000       -18674.081             0.012            0.012
Chain 1:   3100       -18759.110             0.011            0.012
Chain 1:   3200       -18449.789             0.012            0.015
Chain 1:   3300       -18654.514             0.011            0.012
Chain 1:   3400       -18129.542             0.012            0.015
Chain 1:   3500       -18741.337             0.015            0.015
Chain 1:   3600       -18048.119             0.017            0.015
Chain 1:   3700       -18434.878             0.018            0.017
Chain 1:   3800       -17394.820             0.023            0.021
Chain 1:   3900       -17391.011             0.021            0.021
Chain 1:   4000       -17508.271             0.022            0.021
Chain 1:   4100       -17422.076             0.022            0.021
Chain 1:   4200       -17238.363             0.021            0.021
Chain 1:   4300       -17376.696             0.021            0.021
Chain 1:   4400       -17333.543             0.018            0.011
Chain 1:   4500       -17236.131             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003908 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12870.010             1.000            1.000
Chain 1:    200        -9694.827             0.664            1.000
Chain 1:    300        -8311.072             0.498            0.328
Chain 1:    400        -8523.254             0.380            0.328
Chain 1:    500        -8506.551             0.304            0.166
Chain 1:    600        -8231.281             0.259            0.166
Chain 1:    700        -8121.362             0.224            0.033
Chain 1:    800        -8126.399             0.196            0.033
Chain 1:    900        -8182.343             0.175            0.025
Chain 1:   1000        -8206.104             0.158            0.025
Chain 1:   1100        -8223.663             0.058            0.014
Chain 1:   1200        -8149.472             0.026            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001822 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58843.695             1.000            1.000
Chain 1:    200       -18231.672             1.614            2.228
Chain 1:    300        -8935.899             1.423            1.040
Chain 1:    400        -8078.515             1.093            1.040
Chain 1:    500        -8745.052             0.890            1.000
Chain 1:    600        -8889.685             0.744            1.000
Chain 1:    700        -7994.235             0.654            0.112
Chain 1:    800        -8169.649             0.575            0.112
Chain 1:    900        -7907.759             0.515            0.106
Chain 1:   1000        -7935.471             0.464            0.106
Chain 1:   1100        -7805.536             0.365            0.076
Chain 1:   1200        -7944.543             0.144            0.033
Chain 1:   1300        -7732.647             0.043            0.027
Chain 1:   1400        -7634.912             0.034            0.021
Chain 1:   1500        -7535.450             0.027            0.017
Chain 1:   1600        -7748.564             0.029            0.021
Chain 1:   1700        -7590.912             0.019            0.021
Chain 1:   1800        -7588.459             0.017            0.017
Chain 1:   1900        -7586.961             0.014            0.017
Chain 1:   2000        -7709.391             0.015            0.017
Chain 1:   2100        -7536.859             0.016            0.017
Chain 1:   2200        -7775.565             0.017            0.021
Chain 1:   2300        -7531.960             0.018            0.021
Chain 1:   2400        -7555.014             0.017            0.021
Chain 1:   2500        -7590.374             0.016            0.021
Chain 1:   2600        -7518.153             0.014            0.016
Chain 1:   2700        -7491.126             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003654 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86953.357             1.000            1.000
Chain 1:    200       -14035.100             3.098            5.195
Chain 1:    300       -10286.493             2.187            1.000
Chain 1:    400       -11936.105             1.675            1.000
Chain 1:    500        -8828.682             1.410            0.364
Chain 1:    600        -8673.473             1.178            0.364
Chain 1:    700        -9063.397             1.016            0.352
Chain 1:    800        -9229.113             0.891            0.352
Chain 1:    900        -8991.538             0.795            0.138
Chain 1:   1000        -9244.552             0.718            0.138
Chain 1:   1100        -9028.471             0.621            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8585.243             0.106            0.043
Chain 1:   1300        -8901.926             0.073            0.036
Chain 1:   1400        -8772.756             0.061            0.027
Chain 1:   1500        -8761.338             0.026            0.026
Chain 1:   1600        -8861.532             0.025            0.026
Chain 1:   1700        -8916.747             0.022            0.024
Chain 1:   1800        -8462.861             0.025            0.026
Chain 1:   1900        -8573.175             0.024            0.024
Chain 1:   2000        -8573.195             0.021            0.015
Chain 1:   2100        -8729.789             0.021            0.015
Chain 1:   2200        -8470.898             0.018            0.015
Chain 1:   2300        -8649.687             0.017            0.015
Chain 1:   2400        -8470.027             0.018            0.018
Chain 1:   2500        -8546.094             0.018            0.018
Chain 1:   2600        -8457.485             0.018            0.018
Chain 1:   2700        -8490.345             0.018            0.018
Chain 1:   2800        -8442.232             0.013            0.013
Chain 1:   2900        -8553.304             0.013            0.013
Chain 1:   3000        -8493.906             0.014            0.013
Chain 1:   3100        -8434.451             0.013            0.010
Chain 1:   3200        -8407.578             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004427 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 44.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8443108.053             1.000            1.000
Chain 1:    200     -1587104.485             2.660            4.320
Chain 1:    300      -889946.170             2.034            1.000
Chain 1:    400      -457761.715             1.762            1.000
Chain 1:    500      -357703.079             1.465            0.944
Chain 1:    600      -232779.335             1.311            0.944
Chain 1:    700      -119366.303             1.259            0.944
Chain 1:    800       -86718.302             1.149            0.944
Chain 1:    900       -67135.459             1.054            0.783
Chain 1:   1000       -52004.877             0.977            0.783
Chain 1:   1100       -39550.764             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38738.970             0.479            0.376
Chain 1:   1300       -26745.249             0.445            0.376
Chain 1:   1400       -26472.861             0.352            0.315
Chain 1:   1500       -23073.761             0.339            0.315
Chain 1:   1600       -22295.970             0.289            0.292
Chain 1:   1700       -21174.314             0.199            0.291
Chain 1:   1800       -21120.241             0.161            0.147
Chain 1:   1900       -21447.134             0.134            0.053
Chain 1:   2000       -19959.864             0.112            0.053
Chain 1:   2100       -20197.939             0.082            0.035
Chain 1:   2200       -20424.675             0.081            0.035
Chain 1:   2300       -20041.484             0.038            0.019
Chain 1:   2400       -19813.364             0.038            0.019
Chain 1:   2500       -19615.356             0.024            0.015
Chain 1:   2600       -19244.805             0.023            0.015
Chain 1:   2700       -19201.627             0.018            0.012
Chain 1:   2800       -18918.171             0.019            0.015
Chain 1:   2900       -19199.668             0.019            0.015
Chain 1:   3000       -19185.770             0.012            0.012
Chain 1:   3100       -19270.883             0.011            0.012
Chain 1:   3200       -18961.119             0.011            0.015
Chain 1:   3300       -19166.215             0.010            0.012
Chain 1:   3400       -18640.307             0.012            0.015
Chain 1:   3500       -19253.377             0.014            0.015
Chain 1:   3600       -18558.438             0.016            0.015
Chain 1:   3700       -18946.406             0.018            0.016
Chain 1:   3800       -17903.610             0.022            0.020
Chain 1:   3900       -17899.680             0.021            0.020
Chain 1:   4000       -18017.004             0.021            0.020
Chain 1:   4100       -17930.647             0.021            0.020
Chain 1:   4200       -17746.337             0.021            0.020
Chain 1:   4300       -17885.124             0.021            0.020
Chain 1:   4400       -17841.476             0.018            0.010
Chain 1:   4500       -17743.921             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002448 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13033.449             1.000            1.000
Chain 1:    200        -9443.559             0.690            1.000
Chain 1:    300        -8361.799             0.503            0.380
Chain 1:    400        -8545.462             0.383            0.380
Chain 1:    500        -8413.475             0.309            0.129
Chain 1:    600        -8602.184             0.261            0.129
Chain 1:    700        -8162.325             0.232            0.054
Chain 1:    800        -8166.123             0.203            0.054
Chain 1:    900        -8234.259             0.181            0.022
Chain 1:   1000        -8248.778             0.163            0.022
Chain 1:   1100        -8281.558             0.064            0.021
Chain 1:   1200        -8182.549             0.027            0.016
Chain 1:   1300        -8137.728             0.015            0.012
Chain 1:   1400        -8102.296             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001499 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61929.628             1.000            1.000
Chain 1:    200       -18782.202             1.649            2.297
Chain 1:    300        -9143.785             1.450            1.054
Chain 1:    400        -9283.486             1.092            1.054
Chain 1:    500        -8142.273             0.901            1.000
Chain 1:    600        -8848.490             0.764            1.000
Chain 1:    700        -8145.227             0.668            0.140
Chain 1:    800        -8506.045             0.589            0.140
Chain 1:    900        -7929.377             0.532            0.086
Chain 1:   1000        -8344.755             0.484            0.086
Chain 1:   1100        -7595.677             0.394            0.086
Chain 1:   1200        -7971.528             0.169            0.080
Chain 1:   1300        -8056.240             0.064            0.073
Chain 1:   1400        -7724.413             0.067            0.073
Chain 1:   1500        -7604.612             0.055            0.050
Chain 1:   1600        -7799.715             0.049            0.047
Chain 1:   1700        -7741.130             0.041            0.043
Chain 1:   1800        -7692.919             0.038            0.043
Chain 1:   1900        -7628.420             0.031            0.025
Chain 1:   2000        -7756.853             0.028            0.017
Chain 1:   2100        -7647.150             0.019            0.016
Chain 1:   2200        -7909.952             0.018            0.016
Chain 1:   2300        -7691.854             0.020            0.017
Chain 1:   2400        -7644.084             0.016            0.016
Chain 1:   2500        -7693.833             0.015            0.014
Chain 1:   2600        -7612.718             0.014            0.011
Chain 1:   2700        -7515.290             0.014            0.013
Chain 1:   2800        -7749.081             0.017            0.014
Chain 1:   2900        -7455.973             0.020            0.017
Chain 1:   3000        -7606.490             0.020            0.020
Chain 1:   3100        -7604.895             0.019            0.020
Chain 1:   3200        -7803.498             0.018            0.020
Chain 1:   3300        -7489.440             0.019            0.020
Chain 1:   3400        -7727.245             0.022            0.025
Chain 1:   3500        -7522.267             0.024            0.027
Chain 1:   3600        -7590.169             0.024            0.027
Chain 1:   3700        -7547.513             0.023            0.027
Chain 1:   3800        -7517.083             0.020            0.025
Chain 1:   3900        -7486.414             0.017            0.020
Chain 1:   4000        -7480.976             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86863.486             1.000            1.000
Chain 1:    200       -14268.763             3.044            5.088
Chain 1:    300       -10418.522             2.152            1.000
Chain 1:    400       -12809.042             1.661            1.000
Chain 1:    500        -8801.656             1.420            0.455
Chain 1:    600        -8749.998             1.184            0.455
Chain 1:    700        -9017.310             1.019            0.370
Chain 1:    800        -9033.670             0.892            0.370
Chain 1:    900        -9121.506             0.794            0.187
Chain 1:   1000        -9037.777             0.716            0.187
Chain 1:   1100        -9190.522             0.617            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8623.577             0.115            0.030
Chain 1:   1300        -8937.874             0.082            0.030
Chain 1:   1400        -8849.659             0.064            0.017
Chain 1:   1500        -8860.767             0.019            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003717 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8432994.094             1.000            1.000
Chain 1:    200     -1591610.446             2.649            4.298
Chain 1:    300      -893586.636             2.027            1.000
Chain 1:    400      -459512.059             1.756            1.000
Chain 1:    500      -359300.435             1.461            0.945
Chain 1:    600      -233677.168             1.307            0.945
Chain 1:    700      -119891.901             1.256            0.945
Chain 1:    800       -87163.418             1.146            0.945
Chain 1:    900       -67518.036             1.051            0.781
Chain 1:   1000       -52367.586             0.975            0.781
Chain 1:   1100       -39881.208             0.906            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39072.571             0.478            0.375
Chain 1:   1300       -27039.069             0.444            0.375
Chain 1:   1400       -26765.491             0.351            0.313
Chain 1:   1500       -23355.500             0.338            0.313
Chain 1:   1600       -22575.413             0.287            0.291
Chain 1:   1700       -21448.484             0.198            0.289
Chain 1:   1800       -21393.554             0.160            0.146
Chain 1:   1900       -21720.898             0.133            0.053
Chain 1:   2000       -20229.956             0.111            0.053
Chain 1:   2100       -20468.287             0.081            0.035
Chain 1:   2200       -20695.794             0.080            0.035
Chain 1:   2300       -20311.823             0.038            0.019
Chain 1:   2400       -20083.498             0.038            0.019
Chain 1:   2500       -19885.646             0.024            0.015
Chain 1:   2600       -19514.467             0.023            0.015
Chain 1:   2700       -19471.076             0.018            0.012
Chain 1:   2800       -19187.431             0.019            0.015
Chain 1:   2900       -19469.240             0.019            0.014
Chain 1:   3000       -19455.275             0.011            0.012
Chain 1:   3100       -19540.487             0.011            0.011
Chain 1:   3200       -19230.331             0.011            0.014
Chain 1:   3300       -19435.749             0.010            0.011
Chain 1:   3400       -18909.207             0.012            0.014
Chain 1:   3500       -19523.265             0.014            0.015
Chain 1:   3600       -18827.103             0.016            0.015
Chain 1:   3700       -19215.981             0.018            0.016
Chain 1:   3800       -18171.335             0.022            0.020
Chain 1:   3900       -18167.395             0.021            0.020
Chain 1:   4000       -18284.690             0.021            0.020
Chain 1:   4100       -18198.251             0.021            0.020
Chain 1:   4200       -18013.554             0.021            0.020
Chain 1:   4300       -18152.584             0.020            0.020
Chain 1:   4400       -18108.599             0.018            0.010
Chain 1:   4500       -18011.037             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002727 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49095.064             1.000            1.000
Chain 1:    200       -24173.903             1.015            1.031
Chain 1:    300       -21108.994             0.725            1.000
Chain 1:    400       -18540.400             0.579            1.000
Chain 1:    500       -14245.365             0.523            0.302
Chain 1:    600       -12297.580             0.462            0.302
Chain 1:    700       -16802.151             0.435            0.268
Chain 1:    800       -14479.255             0.400            0.268
Chain 1:    900       -15170.074             0.361            0.160
Chain 1:   1000       -11901.688             0.352            0.268
Chain 1:   1100       -10491.565             0.266            0.160
Chain 1:   1200       -11449.003             0.171            0.158
Chain 1:   1300       -15776.170             0.184            0.160
Chain 1:   1400       -16259.229             0.173            0.160
Chain 1:   1500       -10281.205             0.201            0.160
Chain 1:   1600       -11407.677             0.195            0.160
Chain 1:   1700       -10292.964             0.179            0.134
Chain 1:   1800       -10987.516             0.169            0.108
Chain 1:   1900       -10570.812             0.169            0.108
Chain 1:   2000       -10087.277             0.146            0.099
Chain 1:   2100       -10220.429             0.134            0.084
Chain 1:   2200       -10306.727             0.126            0.063
Chain 1:   2300       -11414.857             0.109            0.063
Chain 1:   2400       -19569.664             0.147            0.097
Chain 1:   2500        -9749.436             0.190            0.097
Chain 1:   2600       -15656.425             0.218            0.097
Chain 1:   2700        -9265.987             0.276            0.097
Chain 1:   2800        -9759.378             0.275            0.097
Chain 1:   2900        -9777.412             0.271            0.097
Chain 1:   3000        -9517.181             0.269            0.097
Chain 1:   3100        -8924.860             0.274            0.097
Chain 1:   3200        -9857.884             0.283            0.097
Chain 1:   3300       -15594.673             0.310            0.368
Chain 1:   3400       -17087.028             0.277            0.095
Chain 1:   3500        -9230.277             0.261            0.095
Chain 1:   3600        -9234.299             0.224            0.087
Chain 1:   3700        -9739.552             0.160            0.066
Chain 1:   3800        -9570.973             0.157            0.066
Chain 1:   3900        -9610.272             0.157            0.066
Chain 1:   4000        -8860.668             0.163            0.085
Chain 1:   4100        -9414.574             0.162            0.085
Chain 1:   4200       -10221.954             0.160            0.079
Chain 1:   4300       -10254.166             0.124            0.059
Chain 1:   4400        -9781.075             0.120            0.052
Chain 1:   4500       -16000.603             0.074            0.052
Chain 1:   4600        -8603.217             0.160            0.059
Chain 1:   4700       -10499.067             0.172            0.079
Chain 1:   4800       -13825.367             0.195            0.085
Chain 1:   4900       -13597.741             0.196            0.085
Chain 1:   5000        -9337.037             0.233            0.181
Chain 1:   5100       -13487.206             0.258            0.241
Chain 1:   5200       -13921.043             0.253            0.241
Chain 1:   5300       -10722.694             0.283            0.298
Chain 1:   5400        -8697.316             0.301            0.298
Chain 1:   5500        -8871.337             0.264            0.241
Chain 1:   5600        -8698.991             0.180            0.233
Chain 1:   5700        -9514.299             0.171            0.233
Chain 1:   5800       -12898.459             0.173            0.233
Chain 1:   5900       -13866.114             0.178            0.233
Chain 1:   6000        -8790.031             0.190            0.233
Chain 1:   6100        -9191.237             0.164            0.086
Chain 1:   6200        -8876.282             0.165            0.086
Chain 1:   6300        -8623.364             0.138            0.070
Chain 1:   6400       -12367.198             0.145            0.070
Chain 1:   6500        -8608.888             0.186            0.086
Chain 1:   6600        -9123.991             0.190            0.086
Chain 1:   6700        -8515.534             0.189            0.071
Chain 1:   6800       -10181.281             0.179            0.071
Chain 1:   6900       -12311.220             0.189            0.164
Chain 1:   7000        -9970.260             0.155            0.164
Chain 1:   7100        -8465.654             0.168            0.173
Chain 1:   7200        -8977.340             0.170            0.173
Chain 1:   7300       -10913.571             0.185            0.177
Chain 1:   7400        -9793.520             0.166            0.173
Chain 1:   7500        -9815.618             0.123            0.164
Chain 1:   7600        -9642.343             0.119            0.164
Chain 1:   7700        -9059.825             0.118            0.164
Chain 1:   7800        -9049.661             0.102            0.114
Chain 1:   7900        -8659.644             0.089            0.064
Chain 1:   8000        -8493.112             0.068            0.057
Chain 1:   8100        -9621.912             0.062            0.057
Chain 1:   8200        -8321.773             0.072            0.064
Chain 1:   8300        -9935.985             0.070            0.064
Chain 1:   8400        -8340.328             0.078            0.064
Chain 1:   8500       -10114.609             0.095            0.117
Chain 1:   8600        -8785.519             0.108            0.151
Chain 1:   8700        -8423.193             0.106            0.151
Chain 1:   8800        -8679.564             0.109            0.151
Chain 1:   8900       -10424.743             0.121            0.156
Chain 1:   9000        -9085.925             0.134            0.156
Chain 1:   9100        -9089.197             0.122            0.156
Chain 1:   9200        -8515.137             0.114            0.151
Chain 1:   9300        -9988.700             0.112            0.148
Chain 1:   9400       -11175.463             0.104            0.147
Chain 1:   9500       -10579.717             0.092            0.106
Chain 1:   9600        -8426.442             0.102            0.106
Chain 1:   9700        -8351.188             0.099            0.106
Chain 1:   9800       -10010.349             0.112            0.147
Chain 1:   9900       -11675.185             0.110            0.143
Chain 1:   10000       -10064.039             0.111            0.143
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001488 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58258.090             1.000            1.000
Chain 1:    200       -17880.482             1.629            2.258
Chain 1:    300        -8774.848             1.432            1.038
Chain 1:    400        -8226.214             1.091            1.038
Chain 1:    500        -8196.111             0.873            1.000
Chain 1:    600        -8104.645             0.730            1.000
Chain 1:    700        -7744.566             0.632            0.067
Chain 1:    800        -8257.580             0.561            0.067
Chain 1:    900        -7983.059             0.502            0.062
Chain 1:   1000        -7601.711             0.457            0.062
Chain 1:   1100        -7826.021             0.360            0.050
Chain 1:   1200        -7685.191             0.136            0.046
Chain 1:   1300        -7793.940             0.034            0.034
Chain 1:   1400        -7906.333             0.028            0.029
Chain 1:   1500        -7613.058             0.032            0.034
Chain 1:   1600        -7522.894             0.032            0.034
Chain 1:   1700        -7612.524             0.028            0.029
Chain 1:   1800        -7660.049             0.023            0.018
Chain 1:   1900        -7695.167             0.020            0.014
Chain 1:   2000        -7684.022             0.015            0.014
Chain 1:   2100        -7562.546             0.014            0.014
Chain 1:   2200        -7803.683             0.015            0.014
Chain 1:   2300        -7568.819             0.017            0.014
Chain 1:   2400        -7577.220             0.015            0.012
Chain 1:   2500        -7601.827             0.012            0.012
Chain 1:   2600        -7590.489             0.011            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003895 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87271.191             1.000            1.000
Chain 1:    200       -13672.708             3.191            5.383
Chain 1:    300       -10012.741             2.249            1.000
Chain 1:    400       -10898.047             1.707            1.000
Chain 1:    500        -8998.694             1.408            0.366
Chain 1:    600        -8670.578             1.180            0.366
Chain 1:    700        -8812.814             1.014            0.211
Chain 1:    800        -9298.350             0.893            0.211
Chain 1:    900        -8846.181             0.800            0.081
Chain 1:   1000        -8788.484             0.720            0.081
Chain 1:   1100        -8768.172             0.621            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8520.610             0.085            0.051
Chain 1:   1300        -8731.931             0.051            0.038
Chain 1:   1400        -8706.334             0.043            0.029
Chain 1:   1500        -8560.581             0.024            0.024
Chain 1:   1600        -8672.209             0.021            0.017
Chain 1:   1700        -8753.402             0.021            0.017
Chain 1:   1800        -8330.858             0.021            0.017
Chain 1:   1900        -8431.554             0.017            0.013
Chain 1:   2000        -8405.866             0.016            0.013
Chain 1:   2100        -8530.848             0.018            0.015
Chain 1:   2200        -8336.214             0.017            0.015
Chain 1:   2300        -8426.228             0.016            0.013
Chain 1:   2400        -8495.248             0.016            0.013
Chain 1:   2500        -8441.434             0.015            0.012
Chain 1:   2600        -8442.483             0.014            0.011
Chain 1:   2700        -8359.350             0.014            0.011
Chain 1:   2800        -8319.692             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005543 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 55.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8445623.820             1.000            1.000
Chain 1:    200     -1590713.004             2.655            4.309
Chain 1:    300      -891583.481             2.031            1.000
Chain 1:    400      -457977.318             1.760            1.000
Chain 1:    500      -357990.969             1.464            0.947
Chain 1:    600      -232679.136             1.310            0.947
Chain 1:    700      -119156.736             1.259            0.947
Chain 1:    800       -86407.139             1.149            0.947
Chain 1:    900       -66796.823             1.054            0.784
Chain 1:   1000       -51633.901             0.978            0.784
Chain 1:   1100       -39153.693             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38333.698             0.481            0.379
Chain 1:   1300       -26338.921             0.448            0.379
Chain 1:   1400       -26061.188             0.354            0.319
Chain 1:   1500       -22661.430             0.341            0.319
Chain 1:   1600       -21881.788             0.291            0.294
Chain 1:   1700       -20761.861             0.201            0.294
Chain 1:   1800       -20707.336             0.164            0.150
Chain 1:   1900       -21033.637             0.136            0.054
Chain 1:   2000       -19547.714             0.114            0.054
Chain 1:   2100       -19785.956             0.083            0.036
Chain 1:   2200       -20011.970             0.082            0.036
Chain 1:   2300       -19629.516             0.039            0.019
Chain 1:   2400       -19401.651             0.039            0.019
Chain 1:   2500       -19203.352             0.025            0.016
Chain 1:   2600       -18833.767             0.023            0.016
Chain 1:   2700       -18790.732             0.018            0.012
Chain 1:   2800       -18507.465             0.019            0.015
Chain 1:   2900       -18788.684             0.019            0.015
Chain 1:   3000       -18774.858             0.012            0.012
Chain 1:   3100       -18859.902             0.011            0.012
Chain 1:   3200       -18550.569             0.012            0.015
Chain 1:   3300       -18755.288             0.011            0.012
Chain 1:   3400       -18230.083             0.012            0.015
Chain 1:   3500       -18842.037             0.015            0.015
Chain 1:   3600       -18148.557             0.016            0.015
Chain 1:   3700       -18535.501             0.018            0.017
Chain 1:   3800       -17494.875             0.023            0.021
Chain 1:   3900       -17490.963             0.021            0.021
Chain 1:   4000       -17608.320             0.022            0.021
Chain 1:   4100       -17522.070             0.022            0.021
Chain 1:   4200       -17338.208             0.021            0.021
Chain 1:   4300       -17476.718             0.021            0.021
Chain 1:   4400       -17433.516             0.018            0.011
Chain 1:   4500       -17335.968             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48660.720             1.000            1.000
Chain 1:    200       -12895.900             1.887            2.773
Chain 1:    300       -21606.011             1.392            1.000
Chain 1:    400       -12612.412             1.222            1.000
Chain 1:    500       -13265.813             0.988            0.713
Chain 1:    600       -16308.853             0.854            0.713
Chain 1:    700       -12962.424             0.769            0.403
Chain 1:    800       -12851.503             0.674            0.403
Chain 1:    900       -10988.796             0.618            0.258
Chain 1:   1000       -12678.739             0.569            0.258
Chain 1:   1100       -11380.435             0.481            0.187
Chain 1:   1200       -16897.420             0.236            0.187
Chain 1:   1300       -15011.717             0.208            0.170
Chain 1:   1400       -19867.186             0.162            0.170
Chain 1:   1500       -12057.784             0.221            0.187
Chain 1:   1600       -10534.447             0.217            0.170
Chain 1:   1700       -15188.995             0.222            0.170
Chain 1:   1800        -9262.588             0.285            0.244
Chain 1:   1900       -10306.218             0.278            0.244
Chain 1:   2000       -18896.345             0.310            0.306
Chain 1:   2100        -9736.730             0.393            0.326
Chain 1:   2200        -9331.842             0.365            0.306
Chain 1:   2300        -9770.844             0.357            0.306
Chain 1:   2400        -9452.090             0.336            0.306
Chain 1:   2500       -12068.146             0.293            0.217
Chain 1:   2600       -16738.776             0.306            0.279
Chain 1:   2700        -9489.558             0.352            0.279
Chain 1:   2800        -9555.345             0.289            0.217
Chain 1:   2900        -8727.097             0.288            0.217
Chain 1:   3000        -8727.036             0.242            0.095
Chain 1:   3100        -8553.658             0.150            0.045
Chain 1:   3200       -13988.926             0.185            0.095
Chain 1:   3300       -10000.795             0.220            0.217
Chain 1:   3400        -8852.192             0.230            0.217
Chain 1:   3500        -8996.098             0.210            0.130
Chain 1:   3600       -17206.476             0.230            0.130
Chain 1:   3700        -9310.195             0.238            0.130
Chain 1:   3800        -8711.304             0.244            0.130
Chain 1:   3900        -9903.454             0.247            0.130
Chain 1:   4000        -8658.355             0.261            0.144
Chain 1:   4100        -9332.743             0.266            0.144
Chain 1:   4200        -9100.080             0.230            0.130
Chain 1:   4300        -9667.659             0.196            0.120
Chain 1:   4400        -8764.503             0.193            0.103
Chain 1:   4500        -9097.026             0.195            0.103
Chain 1:   4600        -8528.549             0.154            0.072
Chain 1:   4700        -8743.576             0.072            0.069
Chain 1:   4800        -8283.025             0.071            0.067
Chain 1:   4900        -9407.099             0.071            0.067
Chain 1:   5000       -11718.200             0.076            0.067
Chain 1:   5100        -8806.318             0.102            0.067
Chain 1:   5200       -12757.822             0.130            0.103
Chain 1:   5300        -8422.301             0.176            0.119
Chain 1:   5400        -8392.205             0.166            0.119
Chain 1:   5500       -13097.254             0.198            0.197
Chain 1:   5600       -11655.179             0.204            0.197
Chain 1:   5700        -9383.039             0.226            0.242
Chain 1:   5800       -12716.033             0.246            0.262
Chain 1:   5900       -12085.429             0.240            0.262
Chain 1:   6000       -11482.267             0.225            0.262
Chain 1:   6100       -10797.564             0.198            0.242
Chain 1:   6200        -8480.023             0.195            0.242
Chain 1:   6300        -8335.435             0.145            0.124
Chain 1:   6400        -8404.657             0.145            0.124
Chain 1:   6500       -12481.272             0.142            0.124
Chain 1:   6600       -10774.953             0.146            0.158
Chain 1:   6700        -8107.620             0.154            0.158
Chain 1:   6800        -8871.423             0.137            0.086
Chain 1:   6900       -11252.497             0.153            0.158
Chain 1:   7000        -8908.517             0.174            0.212
Chain 1:   7100        -8053.645             0.178            0.212
Chain 1:   7200       -10848.643             0.176            0.212
Chain 1:   7300       -11060.125             0.177            0.212
Chain 1:   7400        -8065.213             0.213            0.258
Chain 1:   7500        -8085.989             0.180            0.212
Chain 1:   7600        -8176.708             0.166            0.212
Chain 1:   7700        -8557.732             0.137            0.106
Chain 1:   7800       -11136.337             0.152            0.212
Chain 1:   7900        -8211.787             0.166            0.232
Chain 1:   8000        -8724.101             0.146            0.106
Chain 1:   8100        -9048.571             0.139            0.059
Chain 1:   8200       -10752.367             0.129            0.059
Chain 1:   8300        -8153.978             0.159            0.158
Chain 1:   8400        -7867.714             0.125            0.059
Chain 1:   8500        -8450.399             0.132            0.069
Chain 1:   8600       -11406.569             0.157            0.158
Chain 1:   8700        -9591.796             0.171            0.189
Chain 1:   8800        -8232.499             0.165            0.165
Chain 1:   8900       -10698.788             0.152            0.165
Chain 1:   9000        -9362.365             0.161            0.165
Chain 1:   9100        -8354.548             0.169            0.165
Chain 1:   9200        -8502.928             0.155            0.165
Chain 1:   9300        -8225.902             0.126            0.143
Chain 1:   9400        -8295.043             0.124            0.143
Chain 1:   9500        -8237.608             0.117            0.143
Chain 1:   9600        -8275.004             0.092            0.121
Chain 1:   9700        -8116.784             0.075            0.034
Chain 1:   9800        -8023.022             0.060            0.019
Chain 1:   9900        -8038.151             0.037            0.017
Chain 1:   10000        -8140.467             0.024            0.013
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56875.781             1.000            1.000
Chain 1:    200       -17260.643             1.648            2.295
Chain 1:    300        -8615.962             1.433            1.003
Chain 1:    400        -8227.700             1.086            1.003
Chain 1:    500        -7794.907             0.880            1.000
Chain 1:    600        -8300.165             0.744            1.000
Chain 1:    700        -8492.627             0.641            0.061
Chain 1:    800        -8032.069             0.568            0.061
Chain 1:    900        -7893.084             0.507            0.057
Chain 1:   1000        -7780.642             0.457            0.057
Chain 1:   1100        -7675.578             0.359            0.056
Chain 1:   1200        -7718.889             0.130            0.047
Chain 1:   1300        -7704.891             0.030            0.023
Chain 1:   1400        -7748.453             0.026            0.018
Chain 1:   1500        -7557.521             0.022            0.018
Chain 1:   1600        -7627.232             0.017            0.014
Chain 1:   1700        -7479.081             0.017            0.014
Chain 1:   1800        -7513.921             0.012            0.014
Chain 1:   1900        -7527.086             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006057 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 60.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86581.362             1.000            1.000
Chain 1:    200       -13278.028             3.260            5.521
Chain 1:    300        -9638.981             2.299            1.000
Chain 1:    400       -10495.977             1.745            1.000
Chain 1:    500        -8617.247             1.440            0.378
Chain 1:    600        -8381.684             1.204            0.378
Chain 1:    700        -8259.444             1.034            0.218
Chain 1:    800        -8512.874             0.909            0.218
Chain 1:    900        -8456.721             0.809            0.082
Chain 1:   1000        -8260.529             0.730            0.082
Chain 1:   1100        -8408.703             0.632            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7970.625             0.085            0.030
Chain 1:   1300        -8256.987             0.051            0.030
Chain 1:   1400        -8316.249             0.044            0.028
Chain 1:   1500        -8209.458             0.023            0.024
Chain 1:   1600        -8318.698             0.022            0.018
Chain 1:   1700        -8395.683             0.021            0.018
Chain 1:   1800        -7982.515             0.023            0.018
Chain 1:   1900        -8078.736             0.024            0.018
Chain 1:   2000        -8052.039             0.022            0.013
Chain 1:   2100        -8175.017             0.021            0.013
Chain 1:   2200        -7994.792             0.018            0.013
Chain 1:   2300        -8073.515             0.016            0.013
Chain 1:   2400        -8143.202             0.016            0.013
Chain 1:   2500        -8088.714             0.015            0.012
Chain 1:   2600        -8088.323             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003548 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406279.751             1.000            1.000
Chain 1:    200     -1587307.112             2.648            4.296
Chain 1:    300      -890952.907             2.026            1.000
Chain 1:    400      -457347.742             1.756            1.000
Chain 1:    500      -357286.284             1.461            0.948
Chain 1:    600      -232331.414             1.307            0.948
Chain 1:    700      -118786.066             1.257            0.948
Chain 1:    800       -86023.678             1.148            0.948
Chain 1:    900       -66422.012             1.053            0.782
Chain 1:   1000       -51258.141             0.977            0.782
Chain 1:   1100       -38767.647             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37949.212             0.482            0.381
Chain 1:   1300       -25943.445             0.450            0.381
Chain 1:   1400       -25666.264             0.356            0.322
Chain 1:   1500       -22262.236             0.344            0.322
Chain 1:   1600       -21481.167             0.293            0.296
Chain 1:   1700       -20359.695             0.203            0.295
Chain 1:   1800       -20304.935             0.166            0.153
Chain 1:   1900       -20631.043             0.138            0.055
Chain 1:   2000       -19144.319             0.116            0.055
Chain 1:   2100       -19382.856             0.085            0.036
Chain 1:   2200       -19608.752             0.084            0.036
Chain 1:   2300       -19226.418             0.040            0.020
Chain 1:   2400       -18998.533             0.040            0.020
Chain 1:   2500       -18800.264             0.025            0.016
Chain 1:   2600       -18430.873             0.024            0.016
Chain 1:   2700       -18387.902             0.018            0.012
Chain 1:   2800       -18104.638             0.020            0.016
Chain 1:   2900       -18385.807             0.020            0.015
Chain 1:   3000       -18372.132             0.012            0.012
Chain 1:   3100       -18457.102             0.011            0.012
Chain 1:   3200       -18147.895             0.012            0.015
Chain 1:   3300       -18352.503             0.011            0.012
Chain 1:   3400       -17827.504             0.013            0.015
Chain 1:   3500       -18439.209             0.015            0.016
Chain 1:   3600       -17746.071             0.017            0.016
Chain 1:   3700       -18132.702             0.019            0.017
Chain 1:   3800       -17092.673             0.023            0.021
Chain 1:   3900       -17088.758             0.022            0.021
Chain 1:   4000       -17206.125             0.022            0.021
Chain 1:   4100       -17119.878             0.022            0.021
Chain 1:   4200       -16936.159             0.022            0.021
Chain 1:   4300       -17074.581             0.021            0.021
Chain 1:   4400       -17031.457             0.019            0.011
Chain 1:   4500       -16933.934             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12300.409             1.000            1.000
Chain 1:    200        -9143.789             0.673            1.000
Chain 1:    300        -7888.003             0.501            0.345
Chain 1:    400        -8053.997             0.381            0.345
Chain 1:    500        -8005.615             0.306            0.159
Chain 1:    600        -8041.659             0.256            0.159
Chain 1:    700        -7817.417             0.223            0.029
Chain 1:    800        -7809.263             0.196            0.029
Chain 1:    900        -7761.660             0.175            0.021
Chain 1:   1000        -7843.208             0.158            0.021
Chain 1:   1100        -7836.633             0.058            0.010
Chain 1:   1200        -7800.887             0.024            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001425 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56744.865             1.000            1.000
Chain 1:    200       -17321.678             1.638            2.276
Chain 1:    300        -8708.057             1.422            1.000
Chain 1:    400        -8210.039             1.081            1.000
Chain 1:    500        -8097.005             0.868            0.989
Chain 1:    600        -8744.629             0.736            0.989
Chain 1:    700        -7910.673             0.646            0.105
Chain 1:    800        -8126.814             0.568            0.105
Chain 1:    900        -7772.196             0.510            0.074
Chain 1:   1000        -7810.712             0.460            0.074
Chain 1:   1100        -7926.432             0.361            0.061
Chain 1:   1200        -7921.539             0.134            0.046
Chain 1:   1300        -7640.817             0.038            0.037
Chain 1:   1400        -8040.485             0.037            0.037
Chain 1:   1500        -7661.060             0.041            0.046
Chain 1:   1600        -7552.997             0.035            0.037
Chain 1:   1700        -7545.671             0.024            0.027
Chain 1:   1800        -7598.976             0.022            0.015
Chain 1:   1900        -7595.367             0.018            0.014
Chain 1:   2000        -7621.631             0.018            0.014
Chain 1:   2100        -7626.987             0.016            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003873 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86755.233             1.000            1.000
Chain 1:    200       -13370.986             3.244            5.488
Chain 1:    300        -9757.866             2.286            1.000
Chain 1:    400       -10748.340             1.738            1.000
Chain 1:    500        -8583.402             1.441            0.370
Chain 1:    600        -8269.898             1.207            0.370
Chain 1:    700        -8283.249             1.035            0.252
Chain 1:    800        -8461.670             0.908            0.252
Chain 1:    900        -8575.631             0.809            0.092
Chain 1:   1000        -8365.182             0.730            0.092
Chain 1:   1100        -8538.777             0.632            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8153.050             0.088            0.038
Chain 1:   1300        -8360.549             0.054            0.025
Chain 1:   1400        -8479.719             0.046            0.025
Chain 1:   1500        -8316.679             0.023            0.021
Chain 1:   1600        -8434.268             0.020            0.020
Chain 1:   1700        -8514.649             0.021            0.020
Chain 1:   1800        -8104.763             0.024            0.020
Chain 1:   1900        -8201.151             0.024            0.020
Chain 1:   2000        -8173.605             0.022            0.020
Chain 1:   2100        -8295.230             0.021            0.015
Chain 1:   2200        -8137.200             0.018            0.015
Chain 1:   2300        -8199.389             0.016            0.014
Chain 1:   2400        -8265.930             0.016            0.014
Chain 1:   2500        -8211.381             0.015            0.012
Chain 1:   2600        -8209.707             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003714 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405002.404             1.000            1.000
Chain 1:    200     -1583496.240             2.654            4.308
Chain 1:    300      -889917.267             2.029            1.000
Chain 1:    400      -456774.250             1.759            1.000
Chain 1:    500      -357447.982             1.463            0.948
Chain 1:    600      -232392.113             1.309            0.948
Chain 1:    700      -118876.184             1.258            0.948
Chain 1:    800       -86145.925             1.148            0.948
Chain 1:    900       -66528.782             1.053            0.779
Chain 1:   1000       -51359.294             0.978            0.779
Chain 1:   1100       -38867.475             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38046.017             0.481            0.380
Chain 1:   1300       -26034.241             0.449            0.380
Chain 1:   1400       -25755.322             0.356            0.321
Chain 1:   1500       -22350.801             0.343            0.321
Chain 1:   1600       -21569.663             0.293            0.295
Chain 1:   1700       -20447.213             0.203            0.295
Chain 1:   1800       -20392.258             0.165            0.152
Chain 1:   1900       -20718.301             0.137            0.055
Chain 1:   2000       -19231.599             0.115            0.055
Chain 1:   2100       -19469.907             0.085            0.036
Chain 1:   2200       -19695.960             0.084            0.036
Chain 1:   2300       -19313.499             0.039            0.020
Chain 1:   2400       -19085.668             0.039            0.020
Chain 1:   2500       -18887.508             0.025            0.016
Chain 1:   2600       -18518.034             0.024            0.016
Chain 1:   2700       -18475.086             0.018            0.012
Chain 1:   2800       -18191.977             0.020            0.016
Chain 1:   2900       -18473.040             0.020            0.015
Chain 1:   3000       -18459.287             0.012            0.012
Chain 1:   3100       -18544.290             0.011            0.012
Chain 1:   3200       -18235.091             0.012            0.015
Chain 1:   3300       -18439.694             0.011            0.012
Chain 1:   3400       -17914.822             0.013            0.015
Chain 1:   3500       -18526.421             0.015            0.016
Chain 1:   3600       -17833.372             0.017            0.016
Chain 1:   3700       -18219.956             0.019            0.017
Chain 1:   3800       -17180.164             0.023            0.021
Chain 1:   3900       -17176.290             0.022            0.021
Chain 1:   4000       -17293.589             0.022            0.021
Chain 1:   4100       -17207.419             0.022            0.021
Chain 1:   4200       -17023.755             0.022            0.021
Chain 1:   4300       -17162.116             0.021            0.021
Chain 1:   4400       -17119.024             0.019            0.011
Chain 1:   4500       -17021.543             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48616.589             1.000            1.000
Chain 1:    200       -22476.101             1.082            1.163
Chain 1:    300       -29090.876             0.797            1.000
Chain 1:    400       -15929.425             0.804            1.000
Chain 1:    500       -12371.323             0.701            0.826
Chain 1:    600       -14411.321             0.608            0.826
Chain 1:    700       -11212.398             0.562            0.288
Chain 1:    800       -13334.159             0.511            0.288
Chain 1:    900       -10615.157             0.483            0.285
Chain 1:   1000       -10623.933             0.435            0.285
Chain 1:   1100       -19299.810             0.380            0.285
Chain 1:   1200       -18289.820             0.269            0.256
Chain 1:   1300       -12087.837             0.297            0.285
Chain 1:   1400       -11642.060             0.219            0.256
Chain 1:   1500        -9964.244             0.207            0.168
Chain 1:   1600       -12133.182             0.210            0.179
Chain 1:   1700       -20019.440             0.221            0.179
Chain 1:   1800       -12885.199             0.261            0.256
Chain 1:   1900       -10524.580             0.258            0.224
Chain 1:   2000       -10149.952             0.261            0.224
Chain 1:   2100       -19948.000             0.265            0.224
Chain 1:   2200        -9898.332             0.361            0.394
Chain 1:   2300        -9477.264             0.315            0.224
Chain 1:   2400       -11631.062             0.329            0.224
Chain 1:   2500        -9074.326             0.341            0.282
Chain 1:   2600        -9404.544             0.326            0.282
Chain 1:   2700       -15374.423             0.326            0.282
Chain 1:   2800       -12987.399             0.289            0.224
Chain 1:   2900        -9213.374             0.307            0.282
Chain 1:   3000        -8767.691             0.309            0.282
Chain 1:   3100        -9753.009             0.270            0.185
Chain 1:   3200       -13162.251             0.194            0.185
Chain 1:   3300        -8985.966             0.236            0.259
Chain 1:   3400       -14280.564             0.254            0.282
Chain 1:   3500        -8722.156             0.290            0.371
Chain 1:   3600        -9543.338             0.295            0.371
Chain 1:   3700       -14427.488             0.290            0.339
Chain 1:   3800       -14934.758             0.275            0.339
Chain 1:   3900       -13307.066             0.246            0.259
Chain 1:   4000        -9007.440             0.289            0.339
Chain 1:   4100        -8635.237             0.283            0.339
Chain 1:   4200        -9453.188             0.266            0.339
Chain 1:   4300        -9792.781             0.223            0.122
Chain 1:   4400        -9500.624             0.189            0.087
Chain 1:   4500        -8605.855             0.136            0.087
Chain 1:   4600        -8257.812             0.131            0.087
Chain 1:   4700        -8274.379             0.098            0.043
Chain 1:   4800       -12642.217             0.129            0.087
Chain 1:   4900        -9812.371             0.145            0.087
Chain 1:   5000        -9295.100             0.103            0.056
Chain 1:   5100        -8520.374             0.108            0.087
Chain 1:   5200       -13849.563             0.138            0.091
Chain 1:   5300        -8538.684             0.197            0.104
Chain 1:   5400       -14305.396             0.234            0.288
Chain 1:   5500       -14161.068             0.224            0.288
Chain 1:   5600        -8634.847             0.284            0.345
Chain 1:   5700       -11468.530             0.309            0.345
Chain 1:   5800        -8743.911             0.305            0.312
Chain 1:   5900        -8227.276             0.283            0.312
Chain 1:   6000        -9729.484             0.293            0.312
Chain 1:   6100       -12211.565             0.304            0.312
Chain 1:   6200        -8180.393             0.315            0.312
Chain 1:   6300       -12075.181             0.285            0.312
Chain 1:   6400       -12142.526             0.245            0.247
Chain 1:   6500       -11509.755             0.249            0.247
Chain 1:   6600        -8827.383             0.216            0.247
Chain 1:   6700        -8585.734             0.194            0.203
Chain 1:   6800        -8188.381             0.168            0.154
Chain 1:   6900       -10927.635             0.186            0.203
Chain 1:   7000        -8313.204             0.202            0.251
Chain 1:   7100        -8626.930             0.186            0.251
Chain 1:   7200        -8133.625             0.143            0.061
Chain 1:   7300       -10425.231             0.132            0.061
Chain 1:   7400       -12306.189             0.147            0.153
Chain 1:   7500        -9652.135             0.169            0.220
Chain 1:   7600        -9011.980             0.146            0.153
Chain 1:   7700        -8196.761             0.153            0.153
Chain 1:   7800        -9059.368             0.158            0.153
Chain 1:   7900       -12306.063             0.159            0.153
Chain 1:   8000        -8709.606             0.169            0.153
Chain 1:   8100        -8529.421             0.167            0.153
Chain 1:   8200       -11322.928             0.186            0.220
Chain 1:   8300        -9641.401             0.181            0.174
Chain 1:   8400        -8148.936             0.184            0.183
Chain 1:   8500        -8206.930             0.157            0.174
Chain 1:   8600        -7943.940             0.154            0.174
Chain 1:   8700       -11632.451             0.175            0.183
Chain 1:   8800        -8079.570             0.210            0.247
Chain 1:   8900        -8641.933             0.190            0.183
Chain 1:   9000        -8323.798             0.153            0.174
Chain 1:   9100        -8358.042             0.151            0.174
Chain 1:   9200        -9663.367             0.140            0.135
Chain 1:   9300       -11149.846             0.136            0.133
Chain 1:   9400       -13344.182             0.134            0.133
Chain 1:   9500        -8084.777             0.198            0.135
Chain 1:   9600        -8694.188             0.202            0.135
Chain 1:   9700       -10117.587             0.184            0.135
Chain 1:   9800        -8846.524             0.155            0.135
Chain 1:   9900        -8876.840             0.148            0.135
Chain 1:   10000        -9277.780             0.149            0.135
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57288.830             1.000            1.000
Chain 1:    200       -17365.340             1.650            2.299
Chain 1:    300        -8629.892             1.437            1.012
Chain 1:    400        -8222.044             1.090            1.012
Chain 1:    500        -8045.306             0.877            1.000
Chain 1:    600        -8357.975             0.737            1.000
Chain 1:    700        -7741.942             0.643            0.080
Chain 1:    800        -8002.306             0.567            0.080
Chain 1:    900        -7866.261             0.506            0.050
Chain 1:   1000        -8127.062             0.458            0.050
Chain 1:   1100        -7524.498             0.366            0.050
Chain 1:   1200        -7559.390             0.137            0.037
Chain 1:   1300        -7540.342             0.036            0.033
Chain 1:   1400        -7801.754             0.034            0.033
Chain 1:   1500        -7516.371             0.036            0.034
Chain 1:   1600        -7672.170             0.034            0.033
Chain 1:   1700        -7471.150             0.029            0.032
Chain 1:   1800        -7528.522             0.026            0.027
Chain 1:   1900        -7538.240             0.025            0.027
Chain 1:   2000        -7478.187             0.022            0.020
Chain 1:   2100        -7431.024             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003257 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85864.314             1.000            1.000
Chain 1:    200       -13362.351             3.213            5.426
Chain 1:    300        -9739.671             2.266            1.000
Chain 1:    400       -10707.742             1.722            1.000
Chain 1:    500        -8718.495             1.423            0.372
Chain 1:    600        -8222.850             1.196            0.372
Chain 1:    700        -8283.074             1.026            0.228
Chain 1:    800        -9064.913             0.909            0.228
Chain 1:    900        -8571.242             0.814            0.090
Chain 1:   1000        -8384.191             0.735            0.090
Chain 1:   1100        -8526.381             0.637            0.086   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8259.280             0.097            0.060
Chain 1:   1300        -8388.488             0.062            0.058
Chain 1:   1400        -8428.334             0.053            0.032
Chain 1:   1500        -8321.210             0.032            0.022
Chain 1:   1600        -8425.689             0.027            0.017
Chain 1:   1700        -8517.952             0.027            0.017
Chain 1:   1800        -8107.279             0.024            0.017
Chain 1:   1900        -8203.236             0.019            0.015
Chain 1:   2000        -8175.911             0.017            0.013
Chain 1:   2100        -8297.587             0.017            0.013
Chain 1:   2200        -8134.839             0.016            0.013
Chain 1:   2300        -8200.357             0.015            0.012
Chain 1:   2400        -8267.744             0.015            0.012
Chain 1:   2500        -8213.525             0.015            0.012
Chain 1:   2600        -8211.899             0.013            0.011
Chain 1:   2700        -8129.133             0.013            0.010
Chain 1:   2800        -8094.214             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003599 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391224.325             1.000            1.000
Chain 1:    200     -1585082.056             2.647            4.294
Chain 1:    300      -892029.343             2.024            1.000
Chain 1:    400      -458429.287             1.754            1.000
Chain 1:    500      -358579.689             1.459            0.946
Chain 1:    600      -233386.945             1.305            0.946
Chain 1:    700      -119343.215             1.255            0.946
Chain 1:    800       -86488.726             1.146            0.946
Chain 1:    900       -66786.090             1.051            0.777
Chain 1:   1000       -51543.784             0.976            0.777
Chain 1:   1100       -38985.867             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38159.433             0.481            0.380
Chain 1:   1300       -26082.623             0.449            0.380
Chain 1:   1400       -25798.344             0.356            0.322
Chain 1:   1500       -22377.232             0.343            0.322
Chain 1:   1600       -21591.436             0.293            0.296
Chain 1:   1700       -20461.236             0.203            0.295
Chain 1:   1800       -20404.564             0.166            0.153
Chain 1:   1900       -20730.590             0.138            0.055
Chain 1:   2000       -19240.110             0.116            0.055
Chain 1:   2100       -19478.451             0.085            0.036
Chain 1:   2200       -19705.196             0.084            0.036
Chain 1:   2300       -19322.224             0.040            0.020
Chain 1:   2400       -19094.331             0.040            0.020
Chain 1:   2500       -18896.546             0.025            0.016
Chain 1:   2600       -18526.578             0.024            0.016
Chain 1:   2700       -18483.568             0.018            0.012
Chain 1:   2800       -18200.479             0.020            0.016
Chain 1:   2900       -18481.844             0.020            0.015
Chain 1:   3000       -18467.928             0.012            0.012
Chain 1:   3100       -18552.892             0.011            0.012
Chain 1:   3200       -18243.600             0.012            0.015
Chain 1:   3300       -18448.374             0.011            0.012
Chain 1:   3400       -17923.347             0.013            0.015
Chain 1:   3500       -18535.127             0.015            0.016
Chain 1:   3600       -17842.057             0.017            0.016
Chain 1:   3700       -18228.667             0.019            0.017
Chain 1:   3800       -17188.720             0.023            0.021
Chain 1:   3900       -17184.939             0.022            0.021
Chain 1:   4000       -17302.208             0.022            0.021
Chain 1:   4100       -17215.928             0.022            0.021
Chain 1:   4200       -17032.326             0.022            0.021
Chain 1:   4300       -17170.592             0.021            0.021
Chain 1:   4400       -17127.480             0.019            0.011
Chain 1:   4500       -17030.084             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002929 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12527.956             1.000            1.000
Chain 1:    200        -9420.400             0.665            1.000
Chain 1:    300        -8184.594             0.494            0.330
Chain 1:    400        -8290.689             0.373            0.330
Chain 1:    500        -8325.279             0.300            0.151
Chain 1:    600        -8079.306             0.255            0.151
Chain 1:    700        -8001.769             0.220            0.030
Chain 1:    800        -8004.032             0.192            0.030
Chain 1:    900        -8033.239             0.171            0.013
Chain 1:   1000        -8029.864             0.154            0.013
Chain 1:   1100        -8075.587             0.055            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003005 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56762.019             1.000            1.000
Chain 1:    200       -17531.264             1.619            2.238
Chain 1:    300        -8804.005             1.410            1.000
Chain 1:    400        -8223.265             1.075            1.000
Chain 1:    500        -8626.008             0.869            0.991
Chain 1:    600        -8490.093             0.727            0.991
Chain 1:    700        -8135.994             0.629            0.071
Chain 1:    800        -8238.722             0.552            0.071
Chain 1:    900        -7953.313             0.495            0.047
Chain 1:   1000        -7776.148             0.448            0.047
Chain 1:   1100        -7705.334             0.349            0.044
Chain 1:   1200        -7643.195             0.126            0.036
Chain 1:   1300        -7825.003             0.029            0.023
Chain 1:   1400        -7933.688             0.023            0.023
Chain 1:   1500        -7625.484             0.023            0.023
Chain 1:   1600        -7807.218             0.023            0.023
Chain 1:   1700        -7542.275             0.022            0.023
Chain 1:   1800        -7644.664             0.023            0.023
Chain 1:   1900        -7542.278             0.020            0.023
Chain 1:   2000        -7653.833             0.019            0.015
Chain 1:   2100        -7628.323             0.019            0.015
Chain 1:   2200        -7761.833             0.020            0.017
Chain 1:   2300        -7629.945             0.019            0.017
Chain 1:   2400        -7694.719             0.019            0.017
Chain 1:   2500        -7608.120             0.016            0.015
Chain 1:   2600        -7587.865             0.014            0.014
Chain 1:   2700        -7583.831             0.010            0.013
Chain 1:   2800        -7526.363             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006863 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 68.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86931.392             1.000            1.000
Chain 1:    200       -13634.289             3.188            5.376
Chain 1:    300        -9981.858             2.247            1.000
Chain 1:    400       -10839.494             1.705            1.000
Chain 1:    500        -8959.513             1.406            0.366
Chain 1:    600        -8710.431             1.177            0.366
Chain 1:    700        -8651.434             1.009            0.210
Chain 1:    800        -9309.984             0.892            0.210
Chain 1:    900        -8745.783             0.800            0.079
Chain 1:   1000        -8602.432             0.722            0.079
Chain 1:   1100        -8833.945             0.624            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8340.480             0.093            0.065
Chain 1:   1300        -8671.033             0.060            0.059
Chain 1:   1400        -8673.930             0.052            0.038
Chain 1:   1500        -8546.370             0.033            0.029
Chain 1:   1600        -8654.120             0.031            0.026
Chain 1:   1700        -8737.013             0.031            0.026
Chain 1:   1800        -8321.968             0.029            0.026
Chain 1:   1900        -8418.614             0.024            0.017
Chain 1:   2000        -8392.069             0.023            0.015
Chain 1:   2100        -8515.109             0.021            0.014
Chain 1:   2200        -8334.486             0.018            0.014
Chain 1:   2300        -8413.282             0.015            0.012
Chain 1:   2400        -8483.019             0.016            0.012
Chain 1:   2500        -8428.651             0.015            0.011
Chain 1:   2600        -8428.363             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003845 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398150.617             1.000            1.000
Chain 1:    200     -1586271.051             2.647            4.294
Chain 1:    300      -892184.259             2.024            1.000
Chain 1:    400      -458616.303             1.754            1.000
Chain 1:    500      -358728.411             1.459            0.945
Chain 1:    600      -233537.983             1.305            0.945
Chain 1:    700      -119520.856             1.255            0.945
Chain 1:    800       -86666.870             1.146            0.945
Chain 1:    900       -66978.288             1.051            0.778
Chain 1:   1000       -51752.842             0.975            0.778
Chain 1:   1100       -39208.844             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38382.612             0.480            0.379
Chain 1:   1300       -26322.455             0.448            0.379
Chain 1:   1400       -26040.186             0.355            0.320
Chain 1:   1500       -22622.777             0.342            0.320
Chain 1:   1600       -21837.428             0.292            0.294
Chain 1:   1700       -20709.489             0.202            0.294
Chain 1:   1800       -20653.130             0.164            0.151
Chain 1:   1900       -20979.204             0.136            0.054
Chain 1:   2000       -19489.266             0.115            0.054
Chain 1:   2100       -19727.891             0.084            0.036
Chain 1:   2200       -19954.382             0.083            0.036
Chain 1:   2300       -19571.516             0.039            0.020
Chain 1:   2400       -19343.573             0.039            0.020
Chain 1:   2500       -19145.574             0.025            0.016
Chain 1:   2600       -18775.886             0.023            0.016
Chain 1:   2700       -18732.822             0.018            0.012
Chain 1:   2800       -18449.699             0.019            0.015
Chain 1:   2900       -18730.933             0.019            0.015
Chain 1:   3000       -18717.181             0.012            0.012
Chain 1:   3100       -18802.167             0.011            0.012
Chain 1:   3200       -18492.871             0.012            0.015
Chain 1:   3300       -18697.528             0.011            0.012
Chain 1:   3400       -18172.527             0.012            0.015
Chain 1:   3500       -18784.337             0.015            0.015
Chain 1:   3600       -18091.075             0.017            0.015
Chain 1:   3700       -18477.852             0.018            0.017
Chain 1:   3800       -17437.667             0.023            0.021
Chain 1:   3900       -17433.776             0.021            0.021
Chain 1:   4000       -17551.100             0.022            0.021
Chain 1:   4100       -17464.887             0.022            0.021
Chain 1:   4200       -17281.103             0.021            0.021
Chain 1:   4300       -17419.515             0.021            0.021
Chain 1:   4400       -17376.365             0.018            0.011
Chain 1:   4500       -17278.875             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002775 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49249.937             1.000            1.000
Chain 1:    200       -18447.912             1.335            1.670
Chain 1:    300       -18637.940             0.893            1.000
Chain 1:    400       -14290.803             0.746            1.000
Chain 1:    500       -16999.292             0.629            0.304
Chain 1:    600       -14274.506             0.556            0.304
Chain 1:    700       -12169.554             0.501            0.191
Chain 1:    800       -13850.578             0.454            0.191
Chain 1:    900       -14968.340             0.411            0.173
Chain 1:   1000       -10516.603             0.413            0.191
Chain 1:   1100       -17521.037             0.353            0.191
Chain 1:   1200       -10489.952             0.253            0.191
Chain 1:   1300       -13183.909             0.272            0.204
Chain 1:   1400       -21583.663             0.281            0.204
Chain 1:   1500       -22319.245             0.268            0.204
Chain 1:   1600       -11342.892             0.346            0.389
Chain 1:   1700       -11836.954             0.333            0.389
Chain 1:   1800       -10392.053             0.334            0.389
Chain 1:   1900       -18599.336             0.371            0.400
Chain 1:   2000       -16197.245             0.343            0.389
Chain 1:   2100       -19366.123             0.320            0.204
Chain 1:   2200       -10978.166             0.329            0.204
Chain 1:   2300        -9426.367             0.325            0.165
Chain 1:   2400        -9955.554             0.292            0.164
Chain 1:   2500       -13331.998             0.314            0.165
Chain 1:   2600        -9641.446             0.255            0.165
Chain 1:   2700        -9434.458             0.253            0.165
Chain 1:   2800        -9620.116             0.241            0.165
Chain 1:   2900        -9953.594             0.200            0.164
Chain 1:   3000       -11457.942             0.199            0.164
Chain 1:   3100        -8766.034             0.213            0.165
Chain 1:   3200        -9334.555             0.143            0.131
Chain 1:   3300       -10844.859             0.140            0.131
Chain 1:   3400       -17870.596             0.174            0.139
Chain 1:   3500        -9669.828             0.234            0.139
Chain 1:   3600        -8779.067             0.206            0.131
Chain 1:   3700        -9352.516             0.210            0.131
Chain 1:   3800        -9339.881             0.208            0.131
Chain 1:   3900        -8903.667             0.209            0.131
Chain 1:   4000        -8990.229             0.197            0.101
Chain 1:   4100        -9434.877             0.171            0.061
Chain 1:   4200       -13544.919             0.195            0.101
Chain 1:   4300        -9112.710             0.230            0.101
Chain 1:   4400        -9640.329             0.196            0.061
Chain 1:   4500        -9626.985             0.112            0.055
Chain 1:   4600        -8990.669             0.109            0.055
Chain 1:   4700        -9912.139             0.112            0.055
Chain 1:   4800        -8816.969             0.124            0.071
Chain 1:   4900        -8947.626             0.121            0.071
Chain 1:   5000       -16640.265             0.166            0.093
Chain 1:   5100        -9489.228             0.236            0.124
Chain 1:   5200       -13515.927             0.236            0.124
Chain 1:   5300       -12270.940             0.197            0.101
Chain 1:   5400        -8993.834             0.228            0.124
Chain 1:   5500       -11320.051             0.249            0.205
Chain 1:   5600       -14716.924             0.265            0.231
Chain 1:   5700        -9138.430             0.317            0.298
Chain 1:   5800        -8509.346             0.311            0.298
Chain 1:   5900        -8663.082             0.312            0.298
Chain 1:   6000        -9353.463             0.273            0.231
Chain 1:   6100        -8921.837             0.202            0.205
Chain 1:   6200        -9345.739             0.177            0.101
Chain 1:   6300        -8861.968             0.172            0.074
Chain 1:   6400        -8787.761             0.137            0.074
Chain 1:   6500        -8951.606             0.118            0.055
Chain 1:   6600        -9938.760             0.105            0.055
Chain 1:   6700        -9214.944             0.052            0.055
Chain 1:   6800        -9580.907             0.048            0.048
Chain 1:   6900        -8814.746             0.055            0.055
Chain 1:   7000        -9738.564             0.057            0.055
Chain 1:   7100        -8312.171             0.070            0.079
Chain 1:   7200        -8875.926             0.071            0.079
Chain 1:   7300       -10438.626             0.081            0.087
Chain 1:   7400        -8708.947             0.100            0.095
Chain 1:   7500       -10572.343             0.116            0.099
Chain 1:   7600        -8501.617             0.130            0.150
Chain 1:   7700        -8656.522             0.124            0.150
Chain 1:   7800        -9900.892             0.133            0.150
Chain 1:   7900        -8547.950             0.140            0.158
Chain 1:   8000        -9027.074             0.136            0.158
Chain 1:   8100        -8614.993             0.123            0.150
Chain 1:   8200       -12426.233             0.148            0.158
Chain 1:   8300        -8365.906             0.181            0.176
Chain 1:   8400        -9802.602             0.176            0.158
Chain 1:   8500       -12266.114             0.179            0.158
Chain 1:   8600        -8463.764             0.199            0.158
Chain 1:   8700        -8382.436             0.198            0.158
Chain 1:   8800        -8689.421             0.189            0.158
Chain 1:   8900        -9325.827             0.180            0.147
Chain 1:   9000       -10933.847             0.190            0.147
Chain 1:   9100        -8449.977             0.214            0.201
Chain 1:   9200       -14585.725             0.226            0.201
Chain 1:   9300        -8725.162             0.244            0.201
Chain 1:   9400        -8548.045             0.232            0.201
Chain 1:   9500        -8339.254             0.214            0.147
Chain 1:   9600        -8484.146             0.171            0.068
Chain 1:   9700        -8851.422             0.174            0.068
Chain 1:   9800        -8552.459             0.174            0.068
Chain 1:   9900        -8631.690             0.168            0.041
Chain 1:   10000       -10961.749             0.175            0.041
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003884 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46905.446             1.000            1.000
Chain 1:    200       -15880.501             1.477            1.954
Chain 1:    300        -8824.602             1.251            1.000
Chain 1:    400        -8676.994             0.943            1.000
Chain 1:    500        -8560.202             0.757            0.800
Chain 1:    600        -8715.460             0.634            0.800
Chain 1:    700        -7833.307             0.559            0.113
Chain 1:    800        -8223.472             0.495            0.113
Chain 1:    900        -7876.285             0.445            0.047
Chain 1:   1000        -7882.630             0.401            0.047
Chain 1:   1100        -7824.611             0.301            0.044
Chain 1:   1200        -7815.929             0.106            0.018
Chain 1:   1300        -7784.507             0.027            0.017
Chain 1:   1400        -8018.960             0.028            0.018
Chain 1:   1500        -7568.795             0.032            0.029
Chain 1:   1600        -7747.734             0.033            0.029
Chain 1:   1700        -7582.525             0.024            0.023
Chain 1:   1800        -7684.173             0.020            0.022
Chain 1:   1900        -7566.175             0.018            0.016
Chain 1:   2000        -7663.280             0.019            0.016
Chain 1:   2100        -7574.422             0.019            0.016
Chain 1:   2200        -7726.141             0.021            0.020
Chain 1:   2300        -7589.681             0.022            0.020
Chain 1:   2400        -7589.338             0.020            0.018
Chain 1:   2500        -7606.586             0.014            0.016
Chain 1:   2600        -7533.612             0.012            0.013
Chain 1:   2700        -7637.268             0.012            0.013
Chain 1:   2800        -7623.997             0.010            0.013
Chain 1:   2900        -7392.318             0.012            0.013
Chain 1:   3000        -7530.380             0.013            0.014
Chain 1:   3100        -7528.261             0.011            0.014
Chain 1:   3200        -7733.353             0.012            0.014
Chain 1:   3300        -7456.717             0.014            0.014
Chain 1:   3400        -7677.013             0.017            0.018
Chain 1:   3500        -7440.640             0.020            0.027
Chain 1:   3600        -7507.200             0.020            0.027
Chain 1:   3700        -7456.365             0.019            0.027
Chain 1:   3800        -7453.944             0.019            0.027
Chain 1:   3900        -7422.081             0.016            0.018
Chain 1:   4000        -7415.358             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006565 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 65.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86137.351             1.000            1.000
Chain 1:    200       -13748.686             3.133            5.265
Chain 1:    300       -10022.880             2.212            1.000
Chain 1:    400       -11311.093             1.688            1.000
Chain 1:    500        -9045.974             1.400            0.372
Chain 1:    600        -8507.248             1.177            0.372
Chain 1:    700        -9221.765             1.020            0.250
Chain 1:    800        -8336.772             0.906            0.250
Chain 1:    900        -8351.241             0.806            0.114
Chain 1:   1000        -8716.551             0.729            0.114
Chain 1:   1100        -8825.615             0.630            0.106   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8378.416             0.109            0.077
Chain 1:   1300        -8659.014             0.075            0.063
Chain 1:   1400        -8651.594             0.064            0.053
Chain 1:   1500        -8538.664             0.040            0.042
Chain 1:   1600        -8641.435             0.035            0.032
Chain 1:   1700        -8702.241             0.028            0.013
Chain 1:   1800        -8262.827             0.023            0.013
Chain 1:   1900        -8368.516             0.024            0.013
Chain 1:   2000        -8348.756             0.020            0.013
Chain 1:   2100        -8480.213             0.020            0.013
Chain 1:   2200        -8269.744             0.017            0.013
Chain 1:   2300        -8365.425             0.015            0.013
Chain 1:   2400        -8429.573             0.016            0.013
Chain 1:   2500        -8376.911             0.015            0.012
Chain 1:   2600        -8387.333             0.014            0.011
Chain 1:   2700        -8297.464             0.015            0.011
Chain 1:   2800        -8247.272             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004032 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378497.040             1.000            1.000
Chain 1:    200     -1582261.202             2.648            4.295
Chain 1:    300      -891686.715             2.023            1.000
Chain 1:    400      -458473.349             1.754            1.000
Chain 1:    500      -359210.050             1.458            0.945
Chain 1:    600      -234049.275             1.304            0.945
Chain 1:    700      -119918.990             1.254            0.945
Chain 1:    800       -87007.184             1.144            0.945
Chain 1:    900       -67285.272             1.050            0.774
Chain 1:   1000       -52028.407             0.974            0.774
Chain 1:   1100       -39447.051             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38620.518             0.479            0.378
Chain 1:   1300       -26515.152             0.447            0.378
Chain 1:   1400       -26229.752             0.354            0.319
Chain 1:   1500       -22800.092             0.341            0.319
Chain 1:   1600       -22011.715             0.291            0.293
Chain 1:   1700       -20878.117             0.201            0.293
Chain 1:   1800       -20820.786             0.164            0.150
Chain 1:   1900       -21147.341             0.136            0.054
Chain 1:   2000       -19653.477             0.114            0.054
Chain 1:   2100       -19892.275             0.084            0.036
Chain 1:   2200       -20119.575             0.083            0.036
Chain 1:   2300       -19735.914             0.039            0.019
Chain 1:   2400       -19507.768             0.039            0.019
Chain 1:   2500       -19309.888             0.025            0.015
Chain 1:   2600       -18939.546             0.023            0.015
Chain 1:   2700       -18896.302             0.018            0.012
Chain 1:   2800       -18612.993             0.019            0.015
Chain 1:   2900       -18894.561             0.019            0.015
Chain 1:   3000       -18880.694             0.012            0.012
Chain 1:   3100       -18965.753             0.011            0.012
Chain 1:   3200       -18656.116             0.012            0.015
Chain 1:   3300       -18861.071             0.011            0.012
Chain 1:   3400       -18335.434             0.012            0.015
Chain 1:   3500       -18948.239             0.015            0.015
Chain 1:   3600       -18253.762             0.016            0.015
Chain 1:   3700       -18641.463             0.018            0.017
Chain 1:   3800       -17599.379             0.023            0.021
Chain 1:   3900       -17595.499             0.021            0.021
Chain 1:   4000       -17712.788             0.022            0.021
Chain 1:   4100       -17626.465             0.022            0.021
Chain 1:   4200       -17442.311             0.021            0.021
Chain 1:   4300       -17580.985             0.021            0.021
Chain 1:   4400       -17537.494             0.018            0.011
Chain 1:   4500       -17439.986             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13124.173             1.000            1.000
Chain 1:    200        -9836.675             0.667            1.000
Chain 1:    300        -8490.296             0.498            0.334
Chain 1:    400        -8687.279             0.379            0.334
Chain 1:    500        -8567.203             0.306            0.159
Chain 1:    600        -8424.144             0.258            0.159
Chain 1:    700        -8345.941             0.222            0.023
Chain 1:    800        -8435.991             0.196            0.023
Chain 1:    900        -8385.762             0.175            0.017
Chain 1:   1000        -8369.388             0.157            0.017
Chain 1:   1100        -8400.641             0.058            0.014
Chain 1:   1200        -8326.365             0.025            0.011
Chain 1:   1300        -8275.315             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62766.584             1.000            1.000
Chain 1:    200       -18749.254             1.674            2.348
Chain 1:    300        -9400.399             1.447            1.000
Chain 1:    400        -8770.091             1.104            1.000
Chain 1:    500        -9477.368             0.898            0.995
Chain 1:    600        -9423.225             0.749            0.995
Chain 1:    700        -7907.670             0.669            0.192
Chain 1:    800        -8860.591             0.599            0.192
Chain 1:    900        -8214.112             0.541            0.108
Chain 1:   1000        -7944.868             0.491            0.108
Chain 1:   1100        -8137.325             0.393            0.079
Chain 1:   1200        -7926.673             0.161            0.075
Chain 1:   1300        -7715.850             0.064            0.072
Chain 1:   1400        -7924.505             0.060            0.034
Chain 1:   1500        -7769.793             0.054            0.027
Chain 1:   1600        -7935.760             0.056            0.027
Chain 1:   1700        -7556.722             0.042            0.027
Chain 1:   1800        -7739.566             0.033            0.027
Chain 1:   1900        -7674.129             0.026            0.026
Chain 1:   2000        -7808.786             0.024            0.024
Chain 1:   2100        -7718.634             0.023            0.024
Chain 1:   2200        -7903.090             0.023            0.023
Chain 1:   2300        -7712.621             0.023            0.023
Chain 1:   2400        -7656.999             0.021            0.021
Chain 1:   2500        -7654.801             0.019            0.021
Chain 1:   2600        -7664.437             0.017            0.017
Chain 1:   2700        -7657.392             0.012            0.012
Chain 1:   2800        -7762.945             0.011            0.012
Chain 1:   2900        -7490.446             0.014            0.014
Chain 1:   3000        -7632.268             0.014            0.014
Chain 1:   3100        -7636.435             0.013            0.014
Chain 1:   3200        -7878.296             0.013            0.014
Chain 1:   3300        -7515.959             0.016            0.014
Chain 1:   3400        -7693.235             0.017            0.019
Chain 1:   3500        -7570.479             0.019            0.019
Chain 1:   3600        -7592.736             0.019            0.019
Chain 1:   3700        -7511.776             0.020            0.019
Chain 1:   3800        -7505.865             0.019            0.019
Chain 1:   3900        -7517.865             0.015            0.016
Chain 1:   4000        -7516.895             0.013            0.011
Chain 1:   4100        -7515.692             0.013            0.011
Chain 1:   4200        -7611.942             0.012            0.011
Chain 1:   4300        -7499.769             0.008            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003297 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86988.622             1.000            1.000
Chain 1:    200       -14354.348             3.030            5.060
Chain 1:    300       -10526.454             2.141            1.000
Chain 1:    400       -12638.159             1.648            1.000
Chain 1:    500        -9068.193             1.397            0.394
Chain 1:    600        -9332.101             1.169            0.394
Chain 1:    700        -9090.669             1.006            0.364
Chain 1:    800        -8682.209             0.886            0.364
Chain 1:    900        -8838.017             0.789            0.167
Chain 1:   1000        -9352.203             0.716            0.167
Chain 1:   1100        -9246.067             0.617            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8745.444             0.117            0.055
Chain 1:   1300        -9164.454             0.085            0.047
Chain 1:   1400        -8988.791             0.070            0.046
Chain 1:   1500        -8941.367             0.031            0.028
Chain 1:   1600        -9065.020             0.030            0.027
Chain 1:   1700        -9107.568             0.028            0.020
Chain 1:   1800        -8640.509             0.028            0.020
Chain 1:   1900        -8756.774             0.028            0.020
Chain 1:   2000        -8775.963             0.023            0.014
Chain 1:   2100        -8863.697             0.023            0.014
Chain 1:   2200        -8636.170             0.019            0.014
Chain 1:   2300        -8821.074             0.017            0.014
Chain 1:   2400        -8661.692             0.017            0.014
Chain 1:   2500        -8725.665             0.017            0.014
Chain 1:   2600        -8633.276             0.017            0.013
Chain 1:   2700        -8668.167             0.017            0.013
Chain 1:   2800        -8623.779             0.012            0.011
Chain 1:   2900        -8734.554             0.012            0.011
Chain 1:   3000        -8642.767             0.013            0.011
Chain 1:   3100        -8610.481             0.012            0.011
Chain 1:   3200        -8579.960             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003788 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8433402.467             1.000            1.000
Chain 1:    200     -1586128.229             2.658            4.317
Chain 1:    300      -891909.386             2.032            1.000
Chain 1:    400      -459156.318             1.759            1.000
Chain 1:    500      -359311.104             1.463            0.942
Chain 1:    600      -234067.777             1.308            0.942
Chain 1:    700      -120152.609             1.257            0.942
Chain 1:    800       -87392.292             1.147            0.942
Chain 1:    900       -67716.423             1.052            0.778
Chain 1:   1000       -52519.662             0.975            0.778
Chain 1:   1100       -39999.990             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39183.326             0.477            0.375
Chain 1:   1300       -27118.022             0.444            0.375
Chain 1:   1400       -26840.609             0.350            0.313
Chain 1:   1500       -23422.697             0.337            0.313
Chain 1:   1600       -22639.900             0.287            0.291
Chain 1:   1700       -21509.365             0.198            0.289
Chain 1:   1800       -21453.384             0.160            0.146
Chain 1:   1900       -21780.516             0.133            0.053
Chain 1:   2000       -20288.200             0.111            0.053
Chain 1:   2100       -20526.515             0.081            0.035
Chain 1:   2200       -20754.202             0.080            0.035
Chain 1:   2300       -20370.123             0.038            0.019
Chain 1:   2400       -20141.845             0.038            0.019
Chain 1:   2500       -19944.146             0.024            0.015
Chain 1:   2600       -19572.982             0.023            0.015
Chain 1:   2700       -19529.589             0.018            0.012
Chain 1:   2800       -19246.154             0.019            0.015
Chain 1:   2900       -19527.869             0.019            0.014
Chain 1:   3000       -19513.901             0.011            0.012
Chain 1:   3100       -19599.084             0.011            0.011
Chain 1:   3200       -19289.004             0.011            0.014
Chain 1:   3300       -19494.342             0.010            0.011
Chain 1:   3400       -18968.054             0.012            0.014
Chain 1:   3500       -19581.782             0.014            0.015
Chain 1:   3600       -18886.023             0.016            0.015
Chain 1:   3700       -19274.662             0.018            0.016
Chain 1:   3800       -18230.657             0.022            0.020
Chain 1:   3900       -18226.743             0.021            0.020
Chain 1:   4000       -18344.013             0.021            0.020
Chain 1:   4100       -18257.635             0.021            0.020
Chain 1:   4200       -18073.056             0.021            0.020
Chain 1:   4300       -18211.984             0.020            0.020
Chain 1:   4400       -18168.115             0.018            0.010
Chain 1:   4500       -18070.588             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48694.544             1.000            1.000
Chain 1:    200       -14471.590             1.682            2.365
Chain 1:    300       -16516.120             1.163            1.000
Chain 1:    400       -13074.528             0.938            1.000
Chain 1:    500       -14692.130             0.772            0.263
Chain 1:    600       -17642.722             0.672            0.263
Chain 1:    700       -12427.876             0.636            0.263
Chain 1:    800       -14644.614             0.575            0.263
Chain 1:    900       -14366.734             0.513            0.167
Chain 1:   1000       -44993.016             0.530            0.263
Chain 1:   1100       -11012.101             0.739            0.263   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -25101.293             0.558            0.263   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -16229.953             0.601            0.420   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -10897.691             0.623            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500        -9384.955             0.628            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1600       -10039.436             0.618            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700        -9879.237             0.578            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800       -12232.706             0.582            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -14004.408             0.593            0.489   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -10020.247             0.564            0.398   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100       -10291.333             0.258            0.192
Chain 1:   2200       -11328.282             0.211            0.161
Chain 1:   2300       -10194.156             0.168            0.127
Chain 1:   2400        -9951.486             0.121            0.111
Chain 1:   2500       -10120.159             0.107            0.092
Chain 1:   2600        -9477.744             0.107            0.092
Chain 1:   2700        -9051.863             0.110            0.092
Chain 1:   2800       -15505.259             0.133            0.092
Chain 1:   2900        -9464.293             0.184            0.092
Chain 1:   3000       -10775.917             0.156            0.092
Chain 1:   3100        -8737.219             0.177            0.111
Chain 1:   3200       -10251.577             0.182            0.122
Chain 1:   3300        -9726.155             0.177            0.122
Chain 1:   3400       -10429.736             0.181            0.122
Chain 1:   3500       -11715.000             0.190            0.122
Chain 1:   3600        -9589.484             0.206            0.148
Chain 1:   3700       -11193.878             0.215            0.148
Chain 1:   3800        -9065.590             0.197            0.148
Chain 1:   3900        -9506.764             0.138            0.143
Chain 1:   4000        -9421.828             0.127            0.143
Chain 1:   4100        -8659.465             0.112            0.110
Chain 1:   4200        -9406.466             0.105            0.088
Chain 1:   4300       -14152.402             0.134            0.110
Chain 1:   4400        -8719.723             0.189            0.143
Chain 1:   4500        -8661.901             0.179            0.143
Chain 1:   4600       -13466.568             0.192            0.143
Chain 1:   4700        -8308.644             0.240            0.235
Chain 1:   4800        -8295.257             0.217            0.088
Chain 1:   4900        -9100.381             0.221            0.088
Chain 1:   5000       -11660.096             0.242            0.220
Chain 1:   5100        -9017.091             0.262            0.293
Chain 1:   5200        -8767.243             0.257            0.293
Chain 1:   5300       -10169.921             0.238            0.220
Chain 1:   5400       -16934.676             0.215            0.220
Chain 1:   5500        -8303.074             0.319            0.293
Chain 1:   5600        -9560.120             0.296            0.220
Chain 1:   5700       -13469.085             0.263            0.220
Chain 1:   5800        -8343.396             0.324            0.290
Chain 1:   5900       -12063.673             0.346            0.293
Chain 1:   6000        -9980.555             0.345            0.293
Chain 1:   6100        -9105.178             0.325            0.290
Chain 1:   6200        -8585.652             0.329            0.290
Chain 1:   6300        -8746.128             0.317            0.290
Chain 1:   6400        -9676.827             0.286            0.209
Chain 1:   6500       -11552.463             0.199            0.162
Chain 1:   6600       -13235.466             0.198            0.162
Chain 1:   6700       -10682.955             0.193            0.162
Chain 1:   6800       -10108.043             0.137            0.127
Chain 1:   6900        -8474.620             0.126            0.127
Chain 1:   7000        -9560.140             0.116            0.114
Chain 1:   7100       -15664.495             0.146            0.127
Chain 1:   7200        -8493.437             0.224            0.162
Chain 1:   7300       -12239.634             0.253            0.193
Chain 1:   7400        -8612.347             0.285            0.239
Chain 1:   7500        -8655.660             0.270            0.239
Chain 1:   7600       -12597.968             0.288            0.306
Chain 1:   7700       -11099.241             0.278            0.306
Chain 1:   7800        -9266.014             0.292            0.306
Chain 1:   7900       -11968.168             0.295            0.306
Chain 1:   8000        -8354.026             0.327            0.313
Chain 1:   8100        -8663.886             0.292            0.306
Chain 1:   8200        -8709.828             0.208            0.226
Chain 1:   8300       -11961.093             0.204            0.226
Chain 1:   8400        -8016.240             0.211            0.226
Chain 1:   8500       -11513.654             0.241            0.272
Chain 1:   8600        -8031.671             0.253            0.272
Chain 1:   8700        -8648.564             0.247            0.272
Chain 1:   8800        -8092.760             0.234            0.272
Chain 1:   8900        -8658.511             0.218            0.272
Chain 1:   9000        -9635.067             0.185            0.101
Chain 1:   9100        -9291.484             0.185            0.101
Chain 1:   9200        -8111.060             0.199            0.146
Chain 1:   9300        -8994.103             0.182            0.101
Chain 1:   9400        -8267.353             0.141            0.098
Chain 1:   9500        -7817.535             0.117            0.088
Chain 1:   9600        -7978.385             0.075            0.071
Chain 1:   9700        -8670.633             0.076            0.080
Chain 1:   9800       -10134.370             0.084            0.088
Chain 1:   9900       -11255.195             0.087            0.098
Chain 1:   10000        -7954.694             0.119            0.098
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00168 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57760.397             1.000            1.000
Chain 1:    200       -17622.486             1.639            2.278
Chain 1:    300        -8626.810             1.440            1.043
Chain 1:    400        -8177.822             1.094            1.043
Chain 1:    500        -7896.357             0.882            1.000
Chain 1:    600        -8040.779             0.738            1.000
Chain 1:    700        -7709.240             0.639            0.055
Chain 1:    800        -7990.791             0.563            0.055
Chain 1:    900        -7796.216             0.504            0.043
Chain 1:   1000        -7618.279             0.456            0.043
Chain 1:   1100        -7780.735             0.358            0.036
Chain 1:   1200        -7536.607             0.133            0.035
Chain 1:   1300        -7726.501             0.031            0.032
Chain 1:   1400        -7823.435             0.027            0.025
Chain 1:   1500        -7570.589             0.027            0.025
Chain 1:   1600        -7482.632             0.026            0.025
Chain 1:   1700        -7443.938             0.022            0.025
Chain 1:   1800        -7613.162             0.021            0.023
Chain 1:   1900        -7580.264             0.019            0.022
Chain 1:   2000        -7558.629             0.017            0.021
Chain 1:   2100        -7487.476             0.016            0.012
Chain 1:   2200        -7658.940             0.015            0.012
Chain 1:   2300        -7519.737             0.014            0.012
Chain 1:   2400        -7607.496             0.014            0.012
Chain 1:   2500        -7445.666             0.013            0.012
Chain 1:   2600        -7482.888             0.012            0.012
Chain 1:   2700        -7501.448             0.012            0.012
Chain 1:   2800        -7502.340             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006234 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 62.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86187.992             1.000            1.000
Chain 1:    200       -13410.563             3.213            5.427
Chain 1:    300        -9741.192             2.268            1.000
Chain 1:    400       -10594.205             1.721            1.000
Chain 1:    500        -8553.124             1.425            0.377
Chain 1:    600        -8170.903             1.195            0.377
Chain 1:    700        -8190.816             1.025            0.239
Chain 1:    800        -8593.902             0.902            0.239
Chain 1:    900        -8497.199             0.803            0.081
Chain 1:   1000        -8296.236             0.725            0.081
Chain 1:   1100        -8510.237             0.628            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8033.035             0.091            0.047
Chain 1:   1300        -8312.783             0.057            0.047
Chain 1:   1400        -8354.500             0.049            0.034
Chain 1:   1500        -8283.617             0.026            0.025
Chain 1:   1600        -8385.981             0.023            0.024
Chain 1:   1700        -8454.994             0.023            0.024
Chain 1:   1800        -8025.728             0.024            0.024
Chain 1:   1900        -8129.427             0.024            0.024
Chain 1:   2000        -8104.414             0.022            0.013
Chain 1:   2100        -8233.582             0.021            0.013
Chain 1:   2200        -8030.845             0.018            0.013
Chain 1:   2300        -8126.025             0.016            0.012
Chain 1:   2400        -8192.530             0.016            0.012
Chain 1:   2500        -8138.410             0.016            0.012
Chain 1:   2600        -8141.708             0.015            0.012
Chain 1:   2700        -8057.507             0.015            0.012
Chain 1:   2800        -8015.142             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392256.273             1.000            1.000
Chain 1:    200     -1582607.105             2.651            4.303
Chain 1:    300      -889163.790             2.028            1.000
Chain 1:    400      -456645.548             1.757            1.000
Chain 1:    500      -357263.261             1.462            0.947
Chain 1:    600      -232608.478             1.307            0.947
Chain 1:    700      -119059.293             1.257            0.947
Chain 1:    800       -86274.414             1.147            0.947
Chain 1:    900       -66655.599             1.052            0.780
Chain 1:   1000       -51471.751             0.977            0.780
Chain 1:   1100       -38959.514             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38141.279             0.481            0.380
Chain 1:   1300       -26109.735             0.449            0.380
Chain 1:   1400       -25830.530             0.355            0.321
Chain 1:   1500       -22419.940             0.343            0.321
Chain 1:   1600       -21637.067             0.293            0.295
Chain 1:   1700       -20512.311             0.203            0.294
Chain 1:   1800       -20456.937             0.165            0.152
Chain 1:   1900       -20783.338             0.137            0.055
Chain 1:   2000       -19294.565             0.115            0.055
Chain 1:   2100       -19533.136             0.084            0.036
Chain 1:   2200       -19759.485             0.083            0.036
Chain 1:   2300       -19376.703             0.039            0.020
Chain 1:   2400       -19148.727             0.039            0.020
Chain 1:   2500       -18950.537             0.025            0.016
Chain 1:   2600       -18580.762             0.024            0.016
Chain 1:   2700       -18537.715             0.018            0.012
Chain 1:   2800       -18254.361             0.020            0.016
Chain 1:   2900       -18535.736             0.020            0.015
Chain 1:   3000       -18521.971             0.012            0.012
Chain 1:   3100       -18606.957             0.011            0.012
Chain 1:   3200       -18297.570             0.012            0.015
Chain 1:   3300       -18502.350             0.011            0.012
Chain 1:   3400       -17977.021             0.013            0.015
Chain 1:   3500       -18589.215             0.015            0.016
Chain 1:   3600       -17895.508             0.017            0.016
Chain 1:   3700       -18282.589             0.019            0.017
Chain 1:   3800       -17241.602             0.023            0.021
Chain 1:   3900       -17237.697             0.022            0.021
Chain 1:   4000       -17355.046             0.022            0.021
Chain 1:   4100       -17268.728             0.022            0.021
Chain 1:   4200       -17084.833             0.022            0.021
Chain 1:   4300       -17223.375             0.021            0.021
Chain 1:   4400       -17180.096             0.019            0.011
Chain 1:   4500       -17082.564             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002008 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 20.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12578.896             1.000            1.000
Chain 1:    200        -9415.986             0.668            1.000
Chain 1:    300        -8048.325             0.502            0.336
Chain 1:    400        -8177.182             0.380            0.336
Chain 1:    500        -8140.012             0.305            0.170
Chain 1:    600        -7952.056             0.258            0.170
Chain 1:    700        -7905.293             0.222            0.024
Chain 1:    800        -7914.649             0.195            0.024
Chain 1:    900        -7890.272             0.173            0.016
Chain 1:   1000        -7971.578             0.157            0.016
Chain 1:   1100        -8047.486             0.058            0.010
Chain 1:   1200        -7923.621             0.026            0.010
Chain 1:   1300        -7868.908             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001646 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55871.262             1.000            1.000
Chain 1:    200       -17265.864             1.618            2.236
Chain 1:    300        -8727.459             1.405            1.000
Chain 1:    400        -8321.787             1.066            1.000
Chain 1:    500        -8436.883             0.855            0.978
Chain 1:    600        -8348.790             0.715            0.978
Chain 1:    700        -7773.416             0.623            0.074
Chain 1:    800        -8141.341             0.551            0.074
Chain 1:    900        -8125.594             0.490            0.049
Chain 1:   1000        -7815.938             0.445            0.049
Chain 1:   1100        -7630.569             0.347            0.045
Chain 1:   1200        -7826.392             0.126            0.040
Chain 1:   1300        -7585.445             0.031            0.032
Chain 1:   1400        -7679.327             0.028            0.025
Chain 1:   1500        -7560.372             0.028            0.025
Chain 1:   1600        -7854.753             0.031            0.032
Chain 1:   1700        -7565.006             0.027            0.032
Chain 1:   1800        -7655.283             0.024            0.025
Chain 1:   1900        -7640.697             0.024            0.025
Chain 1:   2000        -7632.650             0.020            0.024
Chain 1:   2100        -7640.293             0.018            0.016
Chain 1:   2200        -7741.258             0.016            0.013
Chain 1:   2300        -7603.775             0.015            0.013
Chain 1:   2400        -7665.425             0.015            0.013
Chain 1:   2500        -7605.994             0.014            0.012
Chain 1:   2600        -7529.226             0.011            0.010
Chain 1:   2700        -7568.995             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005603 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 56.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87006.390             1.000            1.000
Chain 1:    200       -13570.089             3.206            5.412
Chain 1:    300        -9907.832             2.260            1.000
Chain 1:    400       -10744.529             1.715            1.000
Chain 1:    500        -8892.567             1.413            0.370
Chain 1:    600        -8559.137             1.184            0.370
Chain 1:    700        -8433.949             1.017            0.208
Chain 1:    800        -8842.085             0.896            0.208
Chain 1:    900        -8657.843             0.799            0.078
Chain 1:   1000        -8612.584             0.719            0.078
Chain 1:   1100        -8743.124             0.621            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8256.289             0.086            0.046
Chain 1:   1300        -8590.520             0.053            0.039
Chain 1:   1400        -8597.857             0.045            0.039
Chain 1:   1500        -8467.684             0.026            0.021
Chain 1:   1600        -8576.099             0.023            0.015
Chain 1:   1700        -8654.839             0.022            0.015
Chain 1:   1800        -8234.404             0.023            0.015
Chain 1:   1900        -8333.424             0.022            0.015
Chain 1:   2000        -8307.494             0.022            0.015
Chain 1:   2100        -8432.020             0.022            0.015
Chain 1:   2200        -8241.283             0.018            0.015
Chain 1:   2300        -8328.045             0.015            0.013
Chain 1:   2400        -8397.345             0.016            0.013
Chain 1:   2500        -8343.405             0.015            0.012
Chain 1:   2600        -8344.061             0.014            0.010
Chain 1:   2700        -8261.102             0.014            0.010
Chain 1:   2800        -8222.071             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 55.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422845.923             1.000            1.000
Chain 1:    200     -1587356.343             2.653            4.306
Chain 1:    300      -891660.811             2.029            1.000
Chain 1:    400      -457721.908             1.759            1.000
Chain 1:    500      -357854.979             1.463            0.948
Chain 1:    600      -232648.375             1.309            0.948
Chain 1:    700      -119097.298             1.258            0.948
Chain 1:    800       -86348.646             1.148            0.948
Chain 1:    900       -66723.468             1.053            0.780
Chain 1:   1000       -51552.775             0.977            0.780
Chain 1:   1100       -39063.366             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38245.632             0.481            0.379
Chain 1:   1300       -26237.217             0.449            0.379
Chain 1:   1400       -25959.883             0.355            0.320
Chain 1:   1500       -22555.744             0.342            0.320
Chain 1:   1600       -21775.216             0.292            0.294
Chain 1:   1700       -20653.187             0.202            0.294
Chain 1:   1800       -20598.495             0.164            0.151
Chain 1:   1900       -20924.672             0.136            0.054
Chain 1:   2000       -19437.914             0.115            0.054
Chain 1:   2100       -19676.320             0.084            0.036
Chain 1:   2200       -19902.326             0.083            0.036
Chain 1:   2300       -19519.892             0.039            0.020
Chain 1:   2400       -19291.981             0.039            0.020
Chain 1:   2500       -19093.781             0.025            0.016
Chain 1:   2600       -18724.115             0.023            0.016
Chain 1:   2700       -18681.192             0.018            0.012
Chain 1:   2800       -18397.869             0.019            0.015
Chain 1:   2900       -18679.149             0.019            0.015
Chain 1:   3000       -18665.325             0.012            0.012
Chain 1:   3100       -18750.320             0.011            0.012
Chain 1:   3200       -18441.015             0.012            0.015
Chain 1:   3300       -18645.773             0.011            0.012
Chain 1:   3400       -18120.606             0.012            0.015
Chain 1:   3500       -18732.491             0.015            0.015
Chain 1:   3600       -18039.157             0.017            0.015
Chain 1:   3700       -18425.927             0.018            0.017
Chain 1:   3800       -17385.538             0.023            0.021
Chain 1:   3900       -17381.647             0.021            0.021
Chain 1:   4000       -17498.985             0.022            0.021
Chain 1:   4100       -17412.666             0.022            0.021
Chain 1:   4200       -17228.946             0.021            0.021
Chain 1:   4300       -17367.358             0.021            0.021
Chain 1:   4400       -17324.186             0.018            0.011
Chain 1:   4500       -17226.654             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001322 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48506.426             1.000            1.000
Chain 1:    200       -16491.963             1.471            1.941
Chain 1:    300       -14292.018             1.032            1.000
Chain 1:    400       -19269.436             0.838            1.000
Chain 1:    500       -12411.772             0.781            0.553
Chain 1:    600       -17203.105             0.697            0.553
Chain 1:    700       -16474.365             0.604            0.279
Chain 1:    800       -10737.363             0.595            0.534
Chain 1:    900       -15379.276             0.563            0.302
Chain 1:   1000       -10883.068             0.548            0.413
Chain 1:   1100       -10008.524             0.457            0.302
Chain 1:   1200       -20671.610             0.314            0.302
Chain 1:   1300       -16781.676             0.322            0.302
Chain 1:   1400       -11312.774             0.344            0.413
Chain 1:   1500        -9804.496             0.304            0.302
Chain 1:   1600       -10755.336             0.285            0.302
Chain 1:   1700       -11835.220             0.290            0.302
Chain 1:   1800       -13569.615             0.249            0.232
Chain 1:   1900        -9886.340             0.257            0.232
Chain 1:   2000       -12344.836             0.235            0.199
Chain 1:   2100        -9120.070             0.262            0.232
Chain 1:   2200        -9874.702             0.218            0.199
Chain 1:   2300       -11201.466             0.206            0.154
Chain 1:   2400       -15948.437             0.188            0.154
Chain 1:   2500       -14670.625             0.181            0.128
Chain 1:   2600        -9346.912             0.229            0.199
Chain 1:   2700       -13152.771             0.249            0.289
Chain 1:   2800        -8775.062             0.286            0.298
Chain 1:   2900       -12061.458             0.276            0.289
Chain 1:   3000        -9347.910             0.285            0.290
Chain 1:   3100        -9364.466             0.250            0.289
Chain 1:   3200        -9085.992             0.246            0.289
Chain 1:   3300       -10380.066             0.246            0.289
Chain 1:   3400       -14774.304             0.246            0.289
Chain 1:   3500        -9980.882             0.286            0.290
Chain 1:   3600       -14024.667             0.257            0.289
Chain 1:   3700        -9302.357             0.279            0.290
Chain 1:   3800        -9983.960             0.236            0.288
Chain 1:   3900        -9340.531             0.216            0.288
Chain 1:   4000        -9385.133             0.187            0.125
Chain 1:   4100        -8843.744             0.193            0.125
Chain 1:   4200       -11504.218             0.213            0.231
Chain 1:   4300       -12899.225             0.212            0.231
Chain 1:   4400       -10450.293             0.205            0.231
Chain 1:   4500        -9482.212             0.167            0.108
Chain 1:   4600        -8354.975             0.152            0.108
Chain 1:   4700        -8356.741             0.101            0.102
Chain 1:   4800        -8436.389             0.096            0.102
Chain 1:   4900        -9886.819             0.103            0.108
Chain 1:   5000        -8867.431             0.114            0.115
Chain 1:   5100        -8639.662             0.111            0.115
Chain 1:   5200        -8688.484             0.088            0.108
Chain 1:   5300        -9419.292             0.085            0.102
Chain 1:   5400        -8663.604             0.071            0.087
Chain 1:   5500       -11844.045             0.087            0.087
Chain 1:   5600        -9952.958             0.093            0.087
Chain 1:   5700        -8521.828             0.109            0.115
Chain 1:   5800        -8425.442             0.110            0.115
Chain 1:   5900        -8838.240             0.100            0.087
Chain 1:   6000        -9142.335             0.091            0.078
Chain 1:   6100        -8329.171             0.099            0.087
Chain 1:   6200        -8845.870             0.104            0.087
Chain 1:   6300        -8476.930             0.100            0.087
Chain 1:   6400       -15081.606             0.136            0.098
Chain 1:   6500       -11064.660             0.145            0.098
Chain 1:   6600        -8319.986             0.159            0.098
Chain 1:   6700        -9374.491             0.153            0.098
Chain 1:   6800       -12808.237             0.179            0.112
Chain 1:   6900        -8172.980             0.231            0.268
Chain 1:   7000       -12183.491             0.261            0.329
Chain 1:   7100       -10974.940             0.262            0.329
Chain 1:   7200       -10132.600             0.264            0.329
Chain 1:   7300        -8925.488             0.274            0.329
Chain 1:   7400       -11220.463             0.250            0.268
Chain 1:   7500        -8219.386             0.250            0.268
Chain 1:   7600        -9102.131             0.227            0.205
Chain 1:   7700        -8465.018             0.223            0.205
Chain 1:   7800        -9006.972             0.203            0.135
Chain 1:   7900        -8276.627             0.155            0.110
Chain 1:   8000       -10909.681             0.146            0.110
Chain 1:   8100       -12933.102             0.151            0.135
Chain 1:   8200       -11698.793             0.153            0.135
Chain 1:   8300       -10860.065             0.147            0.106
Chain 1:   8400       -11746.877             0.134            0.097
Chain 1:   8500        -8213.198             0.141            0.097
Chain 1:   8600        -8526.190             0.135            0.088
Chain 1:   8700        -8650.886             0.129            0.088
Chain 1:   8800        -8896.730             0.125            0.088
Chain 1:   8900        -9155.960             0.119            0.077
Chain 1:   9000        -8293.400             0.106            0.077
Chain 1:   9100       -11062.700             0.115            0.077
Chain 1:   9200        -8678.008             0.132            0.077
Chain 1:   9300        -8119.948             0.131            0.075
Chain 1:   9400        -8439.292             0.127            0.069
Chain 1:   9500       -10509.060             0.104            0.069
Chain 1:   9600        -8921.115             0.118            0.104
Chain 1:   9700       -10723.624             0.133            0.168
Chain 1:   9800        -8472.735             0.157            0.178
Chain 1:   9900        -9601.530             0.166            0.178
Chain 1:   10000        -8713.064             0.166            0.178
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001579 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56765.542             1.000            1.000
Chain 1:    200       -17242.511             1.646            2.292
Chain 1:    300        -8642.422             1.429            1.000
Chain 1:    400        -8071.439             1.090            1.000
Chain 1:    500        -8286.320             0.877            0.995
Chain 1:    600        -7942.584             0.738            0.995
Chain 1:    700        -7949.532             0.633            0.071
Chain 1:    800        -8017.538             0.555            0.071
Chain 1:    900        -8047.895             0.493            0.043
Chain 1:   1000        -7844.306             0.447            0.043
Chain 1:   1100        -7662.718             0.349            0.026
Chain 1:   1200        -7738.644             0.121            0.026
Chain 1:   1300        -7757.206             0.021            0.024
Chain 1:   1400        -7660.537             0.016            0.013
Chain 1:   1500        -7612.089             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003778 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85913.938             1.000            1.000
Chain 1:    200       -13298.303             3.230            5.461
Chain 1:    300        -9771.560             2.274            1.000
Chain 1:    400       -10641.155             1.726            1.000
Chain 1:    500        -8701.308             1.425            0.361
Chain 1:    600        -8535.652             1.191            0.361
Chain 1:    700        -8560.229             1.021            0.223
Chain 1:    800        -9032.412             0.900            0.223
Chain 1:    900        -8585.904             0.806            0.082
Chain 1:   1000        -8356.245             0.728            0.082
Chain 1:   1100        -8531.091             0.630            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8292.867             0.087            0.052
Chain 1:   1300        -8350.425             0.051            0.029
Chain 1:   1400        -8441.875             0.044            0.027
Chain 1:   1500        -8375.590             0.023            0.020
Chain 1:   1600        -8383.097             0.021            0.020
Chain 1:   1700        -8319.062             0.022            0.020
Chain 1:   1800        -8199.117             0.018            0.015
Chain 1:   1900        -8315.616             0.014            0.014
Chain 1:   2000        -8275.579             0.012            0.011
Chain 1:   2100        -8410.863             0.011            0.011
Chain 1:   2200        -8198.892             0.011            0.011
Chain 1:   2300        -8339.651             0.012            0.014
Chain 1:   2400        -8350.424             0.011            0.014
Chain 1:   2500        -8318.992             0.011            0.014
Chain 1:   2600        -8315.005             0.011            0.014
Chain 1:   2700        -8224.887             0.011            0.014
Chain 1:   2800        -8204.134             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005684 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 56.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388904.246             1.000            1.000
Chain 1:    200     -1582089.557             2.651            4.302
Chain 1:    300      -890124.580             2.027            1.000
Chain 1:    400      -456754.393             1.757            1.000
Chain 1:    500      -357416.527             1.461            0.949
Chain 1:    600      -232626.028             1.307            0.949
Chain 1:    700      -118964.755             1.257            0.949
Chain 1:    800       -86181.584             1.147            0.949
Chain 1:    900       -66539.233             1.053            0.777
Chain 1:   1000       -51336.540             0.977            0.777
Chain 1:   1100       -38813.662             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37986.768             0.481            0.380
Chain 1:   1300       -25953.304             0.450            0.380
Chain 1:   1400       -25670.334             0.356            0.323
Chain 1:   1500       -22260.402             0.344            0.323
Chain 1:   1600       -21476.694             0.294            0.296
Chain 1:   1700       -20352.375             0.204            0.295
Chain 1:   1800       -20296.618             0.166            0.153
Chain 1:   1900       -20622.174             0.138            0.055
Chain 1:   2000       -19135.393             0.116            0.055
Chain 1:   2100       -19373.653             0.085            0.036
Chain 1:   2200       -19599.501             0.084            0.036
Chain 1:   2300       -19217.401             0.040            0.020
Chain 1:   2400       -18989.752             0.040            0.020
Chain 1:   2500       -18791.682             0.025            0.016
Chain 1:   2600       -18422.586             0.024            0.016
Chain 1:   2700       -18379.786             0.018            0.012
Chain 1:   2800       -18096.876             0.020            0.016
Chain 1:   2900       -18377.845             0.020            0.015
Chain 1:   3000       -18364.105             0.012            0.012
Chain 1:   3100       -18448.969             0.011            0.012
Chain 1:   3200       -18140.084             0.012            0.015
Chain 1:   3300       -18344.487             0.011            0.012
Chain 1:   3400       -17820.145             0.013            0.015
Chain 1:   3500       -18430.873             0.015            0.016
Chain 1:   3600       -17739.104             0.017            0.016
Chain 1:   3700       -18124.743             0.019            0.017
Chain 1:   3800       -17086.791             0.023            0.021
Chain 1:   3900       -17083.004             0.022            0.021
Chain 1:   4000       -17200.303             0.022            0.021
Chain 1:   4100       -17114.148             0.022            0.021
Chain 1:   4200       -16930.952             0.022            0.021
Chain 1:   4300       -17068.974             0.021            0.021
Chain 1:   4400       -17026.225             0.019            0.011
Chain 1:   4500       -16928.838             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48952.678             1.000            1.000
Chain 1:    200       -20094.494             1.218            1.436
Chain 1:    300       -15008.878             0.925            1.000
Chain 1:    400       -13611.974             0.719            1.000
Chain 1:    500       -15964.642             0.605            0.339
Chain 1:    600       -16757.434             0.512            0.339
Chain 1:    700       -10606.008             0.522            0.339
Chain 1:    800       -13142.049             0.481            0.339
Chain 1:    900       -11117.107             0.447            0.193
Chain 1:   1000       -34724.983             0.471            0.339
Chain 1:   1100       -13805.541             0.522            0.339   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -14006.485             0.380            0.193
Chain 1:   1300       -17514.753             0.366            0.193
Chain 1:   1400       -11343.425             0.410            0.200
Chain 1:   1500        -9612.394             0.414            0.200
Chain 1:   1600       -10070.553             0.413            0.200
Chain 1:   1700       -15050.358             0.389            0.200
Chain 1:   1800       -20399.541             0.395            0.262
Chain 1:   1900       -10454.533             0.472            0.331
Chain 1:   2000       -14828.326             0.434            0.295
Chain 1:   2100        -9561.277             0.337            0.295
Chain 1:   2200       -10518.421             0.345            0.295
Chain 1:   2300        -9281.261             0.338            0.295
Chain 1:   2400       -10084.570             0.292            0.262
Chain 1:   2500       -10252.369             0.276            0.262
Chain 1:   2600        -9177.840             0.283            0.262
Chain 1:   2700        -9003.616             0.252            0.133
Chain 1:   2800       -13499.832             0.259            0.133
Chain 1:   2900        -9404.112             0.207            0.133
Chain 1:   3000       -10797.453             0.191            0.129
Chain 1:   3100       -10112.135             0.142            0.117
Chain 1:   3200        -9146.194             0.144            0.117
Chain 1:   3300       -13333.318             0.162            0.117
Chain 1:   3400       -18015.625             0.180            0.129
Chain 1:   3500       -15069.792             0.198            0.195
Chain 1:   3600       -13953.602             0.194            0.195
Chain 1:   3700       -14772.499             0.198            0.195
Chain 1:   3800        -9875.993             0.214            0.195
Chain 1:   3900       -10415.426             0.175            0.129
Chain 1:   4000        -8607.965             0.184            0.195
Chain 1:   4100        -9907.777             0.190            0.195
Chain 1:   4200        -9354.797             0.185            0.195
Chain 1:   4300        -9009.894             0.158            0.131
Chain 1:   4400        -8691.031             0.135            0.080
Chain 1:   4500        -9655.574             0.126            0.080
Chain 1:   4600        -9212.562             0.123            0.059
Chain 1:   4700        -9066.228             0.119            0.059
Chain 1:   4800        -8883.778             0.071            0.052
Chain 1:   4900        -8498.487             0.071            0.048
Chain 1:   5000        -9301.486             0.058            0.048
Chain 1:   5100        -8688.056             0.052            0.048
Chain 1:   5200        -8691.531             0.046            0.045
Chain 1:   5300        -9505.327             0.051            0.048
Chain 1:   5400        -9175.160             0.051            0.048
Chain 1:   5500        -9142.410             0.041            0.045
Chain 1:   5600        -8559.577             0.043            0.045
Chain 1:   5700        -9234.784             0.049            0.068
Chain 1:   5800        -9799.978             0.053            0.068
Chain 1:   5900        -8684.646             0.061            0.071
Chain 1:   6000       -12078.891             0.080            0.071
Chain 1:   6100       -11632.183             0.077            0.068
Chain 1:   6200        -8762.599             0.110            0.073
Chain 1:   6300        -8531.391             0.104            0.068
Chain 1:   6400        -9855.888             0.114            0.073
Chain 1:   6500        -8666.024             0.127            0.128
Chain 1:   6600        -8366.248             0.124            0.128
Chain 1:   6700       -10603.095             0.138            0.134
Chain 1:   6800        -8964.326             0.150            0.137
Chain 1:   6900        -8856.826             0.139            0.137
Chain 1:   7000        -8847.415             0.111            0.134
Chain 1:   7100        -8958.862             0.108            0.134
Chain 1:   7200       -12228.321             0.102            0.134
Chain 1:   7300       -13867.071             0.111            0.134
Chain 1:   7400        -8374.165             0.163            0.137
Chain 1:   7500        -9663.390             0.163            0.133
Chain 1:   7600        -8607.632             0.172            0.133
Chain 1:   7700        -8727.410             0.152            0.123
Chain 1:   7800       -11429.598             0.157            0.123
Chain 1:   7900        -8105.893             0.197            0.133
Chain 1:   8000        -8183.652             0.198            0.133
Chain 1:   8100        -8365.695             0.199            0.133
Chain 1:   8200        -8773.969             0.177            0.123
Chain 1:   8300        -8253.627             0.171            0.123
Chain 1:   8400        -8876.474             0.113            0.070
Chain 1:   8500        -8310.600             0.106            0.068
Chain 1:   8600        -9788.927             0.109            0.068
Chain 1:   8700        -9358.563             0.112            0.068
Chain 1:   8800        -8679.681             0.096            0.068
Chain 1:   8900        -8572.045             0.057            0.063
Chain 1:   9000        -9809.782             0.068            0.068
Chain 1:   9100        -8711.475             0.079            0.070
Chain 1:   9200        -9069.776             0.078            0.070
Chain 1:   9300        -8689.100             0.076            0.070
Chain 1:   9400       -10956.270             0.090            0.078
Chain 1:   9500        -8165.715             0.117            0.126
Chain 1:   9600        -9550.176             0.117            0.126
Chain 1:   9700        -8864.386             0.120            0.126
Chain 1:   9800        -9006.633             0.113            0.126
Chain 1:   9900        -9222.654             0.115            0.126
Chain 1:   10000        -8068.659             0.116            0.126
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004171 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58119.805             1.000            1.000
Chain 1:    200       -17767.279             1.636            2.271
Chain 1:    300        -8715.376             1.437            1.039
Chain 1:    400        -8137.646             1.095            1.039
Chain 1:    500        -8401.469             0.882            1.000
Chain 1:    600        -8393.060             0.736            1.000
Chain 1:    700        -8022.691             0.637            0.071
Chain 1:    800        -8257.214             0.561            0.071
Chain 1:    900        -8051.683             0.501            0.046
Chain 1:   1000        -7678.388             0.456            0.049
Chain 1:   1100        -7710.598             0.357            0.046
Chain 1:   1200        -7610.881             0.131            0.031
Chain 1:   1300        -7640.207             0.027            0.028
Chain 1:   1400        -7677.884             0.021            0.026
Chain 1:   1500        -7604.139             0.019            0.013
Chain 1:   1600        -7648.551             0.019            0.013
Chain 1:   1700        -7518.741             0.016            0.013
Chain 1:   1800        -7541.845             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00446 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 44.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85695.961             1.000            1.000
Chain 1:    200       -13585.179             3.154            5.308
Chain 1:    300        -9893.604             2.227            1.000
Chain 1:    400       -11026.699             1.696            1.000
Chain 1:    500        -8880.519             1.405            0.373
Chain 1:    600        -8724.566             1.174            0.373
Chain 1:    700        -8247.281             1.014            0.242
Chain 1:    800        -8633.167             0.893            0.242
Chain 1:    900        -8633.633             0.794            0.103
Chain 1:   1000        -8483.735             0.716            0.103
Chain 1:   1100        -8679.400             0.619            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8178.607             0.094            0.058
Chain 1:   1300        -8525.526             0.061            0.045
Chain 1:   1400        -8512.318             0.051            0.041
Chain 1:   1500        -8433.577             0.027            0.023
Chain 1:   1600        -8536.959             0.027            0.023
Chain 1:   1700        -8598.351             0.022            0.018
Chain 1:   1800        -8166.138             0.023            0.018
Chain 1:   1900        -8270.240             0.024            0.018
Chain 1:   2000        -8245.331             0.022            0.013
Chain 1:   2100        -8208.020             0.021            0.012
Chain 1:   2200        -8187.196             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003223 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8383424.705             1.000            1.000
Chain 1:    200     -1580362.166             2.652            4.305
Chain 1:    300      -890739.565             2.026            1.000
Chain 1:    400      -458126.831             1.756            1.000
Chain 1:    500      -358977.664             1.460            0.944
Chain 1:    600      -233944.429             1.306            0.944
Chain 1:    700      -119801.252             1.255            0.944
Chain 1:    800       -86891.766             1.146            0.944
Chain 1:    900       -67150.080             1.051            0.774
Chain 1:   1000       -51879.533             0.975            0.774
Chain 1:   1100       -39289.267             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38460.880             0.479            0.379
Chain 1:   1300       -26344.916             0.448            0.379
Chain 1:   1400       -26058.171             0.354            0.320
Chain 1:   1500       -22626.357             0.342            0.320
Chain 1:   1600       -21837.763             0.292            0.294
Chain 1:   1700       -20702.590             0.202            0.294
Chain 1:   1800       -20644.849             0.165            0.152
Chain 1:   1900       -20971.332             0.137            0.055
Chain 1:   2000       -19477.303             0.115            0.055
Chain 1:   2100       -19715.922             0.084            0.036
Chain 1:   2200       -19943.338             0.083            0.036
Chain 1:   2300       -19559.620             0.039            0.020
Chain 1:   2400       -19331.535             0.039            0.020
Chain 1:   2500       -19133.841             0.025            0.016
Chain 1:   2600       -18763.425             0.023            0.016
Chain 1:   2700       -18720.213             0.018            0.012
Chain 1:   2800       -18437.043             0.019            0.015
Chain 1:   2900       -18718.569             0.019            0.015
Chain 1:   3000       -18704.636             0.012            0.012
Chain 1:   3100       -18789.700             0.011            0.012
Chain 1:   3200       -18480.102             0.012            0.015
Chain 1:   3300       -18685.056             0.011            0.012
Chain 1:   3400       -18159.585             0.012            0.015
Chain 1:   3500       -18772.133             0.015            0.015
Chain 1:   3600       -18077.993             0.017            0.015
Chain 1:   3700       -18465.495             0.018            0.017
Chain 1:   3800       -17423.933             0.023            0.021
Chain 1:   3900       -17420.105             0.021            0.021
Chain 1:   4000       -17537.363             0.022            0.021
Chain 1:   4100       -17451.066             0.022            0.021
Chain 1:   4200       -17267.057             0.021            0.021
Chain 1:   4300       -17405.604             0.021            0.021
Chain 1:   4400       -17362.225             0.018            0.011
Chain 1:   4500       -17264.757             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005859 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 58.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12102.686             1.000            1.000
Chain 1:    200        -9089.782             0.666            1.000
Chain 1:    300        -7981.854             0.490            0.331
Chain 1:    400        -8119.290             0.372            0.331
Chain 1:    500        -8040.910             0.299            0.139
Chain 1:    600        -7837.846             0.254            0.139
Chain 1:    700        -7776.435             0.219            0.026
Chain 1:    800        -7787.394             0.192            0.026
Chain 1:    900        -7826.740             0.171            0.017
Chain 1:   1000        -7834.734             0.154            0.017
Chain 1:   1100        -7895.624             0.055            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001488 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55390.638             1.000            1.000
Chain 1:    200       -16840.099             1.645            2.289
Chain 1:    300        -8562.248             1.419            1.000
Chain 1:    400        -8797.979             1.071            1.000
Chain 1:    500        -8433.310             0.865            0.967
Chain 1:    600        -8855.094             0.729            0.967
Chain 1:    700        -7770.147             0.645            0.140
Chain 1:    800        -8201.980             0.571            0.140
Chain 1:    900        -7918.153             0.511            0.053
Chain 1:   1000        -7594.493             0.464            0.053
Chain 1:   1100        -7622.517             0.365            0.048
Chain 1:   1200        -7648.229             0.136            0.043
Chain 1:   1300        -7637.087             0.040            0.043
Chain 1:   1400        -7829.873             0.039            0.043
Chain 1:   1500        -7624.583             0.038            0.036
Chain 1:   1600        -7586.159             0.034            0.027
Chain 1:   1700        -7507.489             0.021            0.025
Chain 1:   1800        -7558.847             0.016            0.010
Chain 1:   1900        -7569.267             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003491 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86649.293             1.000            1.000
Chain 1:    200       -13177.531             3.288            5.576
Chain 1:    300        -9639.410             2.314            1.000
Chain 1:    400       -10372.122             1.753            1.000
Chain 1:    500        -8561.317             1.445            0.367
Chain 1:    600        -8299.005             1.209            0.367
Chain 1:    700        -8607.702             1.042            0.212
Chain 1:    800        -8549.952             0.912            0.212
Chain 1:    900        -8502.142             0.812            0.071
Chain 1:   1000        -8234.960             0.734            0.071
Chain 1:   1100        -8556.874             0.637            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.427             0.085            0.038
Chain 1:   1300        -8351.473             0.052            0.036
Chain 1:   1400        -8376.435             0.045            0.032
Chain 1:   1500        -8285.850             0.025            0.032
Chain 1:   1600        -8381.449             0.023            0.029
Chain 1:   1700        -8478.628             0.020            0.011
Chain 1:   1800        -8085.073             0.025            0.029
Chain 1:   1900        -8186.585             0.025            0.029
Chain 1:   2000        -8156.789             0.022            0.012
Chain 1:   2100        -8294.639             0.020            0.012
Chain 1:   2200        -8076.622             0.017            0.012
Chain 1:   2300        -8218.537             0.016            0.012
Chain 1:   2400        -8225.991             0.016            0.012
Chain 1:   2500        -8195.608             0.015            0.012
Chain 1:   2600        -8191.901             0.014            0.012
Chain 1:   2700        -8102.554             0.014            0.012
Chain 1:   2800        -8082.120             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003788 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8421669.413             1.000            1.000
Chain 1:    200     -1586063.779             2.655            4.310
Chain 1:    300      -890777.643             2.030            1.000
Chain 1:    400      -457302.148             1.760            1.000
Chain 1:    500      -357239.173             1.464            0.948
Chain 1:    600      -232140.796             1.310            0.948
Chain 1:    700      -118614.785             1.259            0.948
Chain 1:    800       -85880.286             1.149            0.948
Chain 1:    900       -66266.528             1.055            0.781
Chain 1:   1000       -51094.288             0.979            0.781
Chain 1:   1100       -38610.620             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37789.474             0.482            0.381
Chain 1:   1300       -25796.478             0.451            0.381
Chain 1:   1400       -25518.753             0.357            0.323
Chain 1:   1500       -22118.901             0.344            0.323
Chain 1:   1600       -21338.712             0.294            0.297
Chain 1:   1700       -20218.988             0.204            0.296
Chain 1:   1800       -20164.487             0.166            0.154
Chain 1:   1900       -20490.017             0.138            0.055
Chain 1:   2000       -19005.809             0.116            0.055
Chain 1:   2100       -19243.998             0.085            0.037
Chain 1:   2200       -19469.362             0.084            0.037
Chain 1:   2300       -19087.699             0.040            0.020
Chain 1:   2400       -18860.045             0.040            0.020
Chain 1:   2500       -18661.866             0.026            0.016
Chain 1:   2600       -18292.869             0.024            0.016
Chain 1:   2700       -18250.191             0.019            0.012
Chain 1:   2800       -17967.139             0.020            0.016
Chain 1:   2900       -18248.116             0.020            0.015
Chain 1:   3000       -18234.380             0.012            0.012
Chain 1:   3100       -18319.223             0.011            0.012
Chain 1:   3200       -18010.409             0.012            0.015
Chain 1:   3300       -18214.797             0.011            0.012
Chain 1:   3400       -17690.472             0.013            0.015
Chain 1:   3500       -18301.087             0.015            0.016
Chain 1:   3600       -17609.447             0.017            0.016
Chain 1:   3700       -17994.920             0.019            0.017
Chain 1:   3800       -16957.139             0.023            0.021
Chain 1:   3900       -16953.323             0.022            0.021
Chain 1:   4000       -17070.658             0.022            0.021
Chain 1:   4100       -16984.451             0.023            0.021
Chain 1:   4200       -16801.320             0.022            0.021
Chain 1:   4300       -16939.311             0.022            0.021
Chain 1:   4400       -16896.587             0.019            0.011
Chain 1:   4500       -16799.168             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001458 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12299.717             1.000            1.000
Chain 1:    200        -9218.587             0.667            1.000
Chain 1:    300        -8046.688             0.493            0.334
Chain 1:    400        -8171.629             0.374            0.334
Chain 1:    500        -8113.136             0.300            0.146
Chain 1:    600        -7983.247             0.253            0.146
Chain 1:    700        -7913.543             0.218            0.016
Chain 1:    800        -7896.759             0.191            0.016
Chain 1:    900        -7843.900             0.171            0.015
Chain 1:   1000        -7948.386             0.155            0.015
Chain 1:   1100        -7916.612             0.055            0.013
Chain 1:   1200        -7956.253             0.022            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001609 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61614.584             1.000            1.000
Chain 1:    200       -17786.783             1.732            2.464
Chain 1:    300        -8790.983             1.496            1.023
Chain 1:    400        -9141.556             1.131            1.023
Chain 1:    500        -7948.462             0.935            1.000
Chain 1:    600        -8277.232             0.786            1.000
Chain 1:    700        -7847.464             0.681            0.150
Chain 1:    800        -8125.731             0.601            0.150
Chain 1:    900        -7971.868             0.536            0.055
Chain 1:   1000        -7744.914             0.485            0.055
Chain 1:   1100        -7729.040             0.386            0.040
Chain 1:   1200        -7534.533             0.142            0.038
Chain 1:   1300        -7817.275             0.043            0.036
Chain 1:   1400        -7785.132             0.040            0.034
Chain 1:   1500        -7595.366             0.027            0.029
Chain 1:   1600        -7495.032             0.024            0.026
Chain 1:   1700        -7495.747             0.019            0.025
Chain 1:   1800        -7572.211             0.017            0.019
Chain 1:   1900        -7626.455             0.015            0.013
Chain 1:   2000        -7555.093             0.013            0.010
Chain 1:   2100        -7568.769             0.013            0.010
Chain 1:   2200        -7653.748             0.012            0.010
Chain 1:   2300        -7558.865             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005486 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 54.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86867.533             1.000            1.000
Chain 1:    200       -13409.172             3.239            5.478
Chain 1:    300        -9829.749             2.281            1.000
Chain 1:    400       -10689.580             1.731            1.000
Chain 1:    500        -8753.856             1.429            0.364
Chain 1:    600        -8601.051             1.194            0.364
Chain 1:    700        -8456.146             1.026            0.221
Chain 1:    800        -8892.674             0.903            0.221
Chain 1:    900        -8674.767             0.806            0.080
Chain 1:   1000        -8478.581             0.728            0.080
Chain 1:   1100        -8709.775             0.630            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8489.369             0.085            0.027
Chain 1:   1300        -8585.263             0.050            0.026
Chain 1:   1400        -8583.096             0.042            0.025
Chain 1:   1500        -8434.273             0.021            0.023
Chain 1:   1600        -8549.522             0.021            0.023
Chain 1:   1700        -8631.390             0.020            0.023
Chain 1:   1800        -8239.848             0.020            0.023
Chain 1:   1900        -8341.856             0.019            0.018
Chain 1:   2000        -8312.239             0.017            0.013
Chain 1:   2100        -8438.338             0.016            0.013
Chain 1:   2200        -8224.203             0.016            0.013
Chain 1:   2300        -8370.703             0.016            0.015
Chain 1:   2400        -8386.209             0.016            0.015
Chain 1:   2500        -8352.726             0.015            0.013
Chain 1:   2600        -8354.634             0.014            0.012
Chain 1:   2700        -8261.535             0.014            0.012
Chain 1:   2800        -8234.612             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003083 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8427405.189             1.000            1.000
Chain 1:    200     -1590521.866             2.649            4.299
Chain 1:    300      -892611.329             2.027            1.000
Chain 1:    400      -458307.432             1.757            1.000
Chain 1:    500      -358008.132             1.462            0.948
Chain 1:    600      -232778.234             1.308            0.948
Chain 1:    700      -119009.062             1.257            0.948
Chain 1:    800       -86224.824             1.148            0.948
Chain 1:    900       -66580.342             1.053            0.782
Chain 1:   1000       -51389.859             0.977            0.782
Chain 1:   1100       -38885.803             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38060.965             0.482            0.380
Chain 1:   1300       -26046.934             0.450            0.380
Chain 1:   1400       -25767.099             0.356            0.322
Chain 1:   1500       -22362.437             0.343            0.322
Chain 1:   1600       -21580.668             0.293            0.296
Chain 1:   1700       -20458.500             0.203            0.295
Chain 1:   1800       -20403.319             0.165            0.152
Chain 1:   1900       -20729.015             0.137            0.055
Chain 1:   2000       -19243.112             0.115            0.055
Chain 1:   2100       -19481.341             0.084            0.036
Chain 1:   2200       -19707.135             0.083            0.036
Chain 1:   2300       -19325.035             0.039            0.020
Chain 1:   2400       -19097.310             0.039            0.020
Chain 1:   2500       -18899.191             0.025            0.016
Chain 1:   2600       -18529.962             0.024            0.016
Chain 1:   2700       -18487.095             0.018            0.012
Chain 1:   2800       -18204.070             0.020            0.016
Chain 1:   2900       -18485.098             0.020            0.015
Chain 1:   3000       -18471.366             0.012            0.012
Chain 1:   3100       -18556.270             0.011            0.012
Chain 1:   3200       -18247.262             0.012            0.015
Chain 1:   3300       -18451.745             0.011            0.012
Chain 1:   3400       -17927.175             0.013            0.015
Chain 1:   3500       -18538.212             0.015            0.016
Chain 1:   3600       -17845.991             0.017            0.016
Chain 1:   3700       -18231.938             0.019            0.017
Chain 1:   3800       -17193.308             0.023            0.021
Chain 1:   3900       -17189.463             0.022            0.021
Chain 1:   4000       -17306.798             0.022            0.021
Chain 1:   4100       -17220.616             0.022            0.021
Chain 1:   4200       -17037.225             0.022            0.021
Chain 1:   4300       -17175.373             0.021            0.021
Chain 1:   4400       -17132.495             0.019            0.011
Chain 1:   4500       -17035.066             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48595.680             1.000            1.000
Chain 1:    200       -15887.236             1.529            2.059
Chain 1:    300       -18745.450             1.070            1.000
Chain 1:    400       -14723.208             0.871            1.000
Chain 1:    500       -22670.374             0.767            0.351
Chain 1:    600       -11947.175             0.789            0.898
Chain 1:    700       -16583.187             0.716            0.351
Chain 1:    800       -12531.395             0.667            0.351
Chain 1:    900       -14446.098             0.608            0.323
Chain 1:   1000       -11949.836             0.568            0.323
Chain 1:   1100       -15022.065             0.488            0.280
Chain 1:   1200       -12485.531             0.303            0.273
Chain 1:   1300       -10776.171             0.303            0.273
Chain 1:   1400       -18744.788             0.318            0.280
Chain 1:   1500       -10259.529             0.366            0.280
Chain 1:   1600       -10524.343             0.279            0.209
Chain 1:   1700        -9619.261             0.260            0.205
Chain 1:   1800       -17170.403             0.272            0.205
Chain 1:   1900        -9523.842             0.339            0.209
Chain 1:   2000        -9371.236             0.320            0.205
Chain 1:   2100       -11433.837             0.317            0.203
Chain 1:   2200       -10004.905             0.311            0.180
Chain 1:   2300       -11454.587             0.308            0.180
Chain 1:   2400       -17160.781             0.299            0.180
Chain 1:   2500        -9991.499             0.288            0.180
Chain 1:   2600        -8804.020             0.299            0.180
Chain 1:   2700        -9291.558             0.295            0.180
Chain 1:   2800        -9235.969             0.251            0.143
Chain 1:   2900        -9530.293             0.174            0.135
Chain 1:   3000       -10173.823             0.179            0.135
Chain 1:   3100        -9451.043             0.168            0.127
Chain 1:   3200        -9014.681             0.159            0.076
Chain 1:   3300        -9536.567             0.152            0.063
Chain 1:   3400        -9180.627             0.122            0.055
Chain 1:   3500        -9386.097             0.053            0.052
Chain 1:   3600        -8864.150             0.045            0.052
Chain 1:   3700        -9412.033             0.046            0.055
Chain 1:   3800       -10581.193             0.056            0.058
Chain 1:   3900       -10083.204             0.058            0.058
Chain 1:   4000        -9317.613             0.060            0.058
Chain 1:   4100        -9041.636             0.055            0.055
Chain 1:   4200       -13845.252             0.085            0.058
Chain 1:   4300        -9215.267             0.130            0.059
Chain 1:   4400        -8761.111             0.131            0.059
Chain 1:   4500       -14125.918             0.167            0.082
Chain 1:   4600       -13224.937             0.168            0.082
Chain 1:   4700        -9156.972             0.207            0.110
Chain 1:   4800       -11925.882             0.219            0.232
Chain 1:   4900       -11181.385             0.220            0.232
Chain 1:   5000       -10134.733             0.223            0.232
Chain 1:   5100        -8778.013             0.235            0.232
Chain 1:   5200        -8691.644             0.201            0.155
Chain 1:   5300        -8824.201             0.153            0.103
Chain 1:   5400       -10770.508             0.165            0.155
Chain 1:   5500       -13077.134             0.145            0.155
Chain 1:   5600        -8492.909             0.192            0.176
Chain 1:   5700        -9010.066             0.154            0.155
Chain 1:   5800        -9740.214             0.138            0.103
Chain 1:   5900       -11216.039             0.144            0.132
Chain 1:   6000        -9010.360             0.159            0.155
Chain 1:   6100       -11380.367             0.164            0.176
Chain 1:   6200        -8391.266             0.199            0.181
Chain 1:   6300        -8499.508             0.198            0.181
Chain 1:   6400       -11747.163             0.208            0.208
Chain 1:   6500        -9311.975             0.216            0.245
Chain 1:   6600       -12600.240             0.188            0.245
Chain 1:   6700        -9078.937             0.222            0.261
Chain 1:   6800        -9521.589             0.219            0.261
Chain 1:   6900       -10237.781             0.213            0.261
Chain 1:   7000       -12205.269             0.204            0.261
Chain 1:   7100        -8136.349             0.233            0.262
Chain 1:   7200       -11824.840             0.229            0.262
Chain 1:   7300        -8445.937             0.268            0.276
Chain 1:   7400        -8086.218             0.244            0.262
Chain 1:   7500        -8944.605             0.228            0.261
Chain 1:   7600        -9031.984             0.203            0.161
Chain 1:   7700       -11191.091             0.183            0.161
Chain 1:   7800        -8291.357             0.214            0.193
Chain 1:   7900       -11301.278             0.233            0.266
Chain 1:   8000        -8402.299             0.252            0.312
Chain 1:   8100        -8354.322             0.202            0.266
Chain 1:   8200        -8869.498             0.177            0.193
Chain 1:   8300       -10308.424             0.151            0.140
Chain 1:   8400       -10143.451             0.148            0.140
Chain 1:   8500        -9033.158             0.151            0.140
Chain 1:   8600        -8701.910             0.153            0.140
Chain 1:   8700        -8063.042             0.142            0.123
Chain 1:   8800        -8693.153             0.114            0.079
Chain 1:   8900        -8515.403             0.090            0.072
Chain 1:   9000        -9454.455             0.065            0.072
Chain 1:   9100        -8112.701             0.081            0.079
Chain 1:   9200        -9812.025             0.093            0.099
Chain 1:   9300        -8104.388             0.100            0.099
Chain 1:   9400        -8199.047             0.099            0.099
Chain 1:   9500        -8331.689             0.089            0.079
Chain 1:   9600        -8488.132             0.087            0.079
Chain 1:   9700        -8633.890             0.080            0.072
Chain 1:   9800        -8716.282             0.074            0.021
Chain 1:   9900        -8371.261             0.076            0.041
Chain 1:   10000        -8256.514             0.068            0.018
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001609 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61633.621             1.000            1.000
Chain 1:    200       -17595.580             1.751            2.503
Chain 1:    300        -8723.508             1.507            1.017
Chain 1:    400        -8965.646             1.137            1.017
Chain 1:    500        -7925.265             0.936            1.000
Chain 1:    600        -8667.353             0.794            1.000
Chain 1:    700        -8172.732             0.689            0.131
Chain 1:    800        -8255.104             0.604            0.131
Chain 1:    900        -7867.400             0.543            0.086
Chain 1:   1000        -7852.932             0.489            0.086
Chain 1:   1100        -7669.308             0.391            0.061
Chain 1:   1200        -7573.113             0.142            0.049
Chain 1:   1300        -7745.476             0.042            0.027
Chain 1:   1400        -7659.316             0.041            0.024
Chain 1:   1500        -7593.246             0.029            0.022
Chain 1:   1600        -7503.530             0.021            0.013
Chain 1:   1700        -7498.438             0.015            0.012
Chain 1:   1800        -7577.683             0.015            0.012
Chain 1:   1900        -7462.726             0.012            0.012
Chain 1:   2000        -7558.497             0.013            0.013
Chain 1:   2100        -7595.924             0.011            0.012
Chain 1:   2200        -7683.975             0.011            0.011
Chain 1:   2300        -7567.361             0.010            0.011
Chain 1:   2400        -7582.972             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003597 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86322.613             1.000            1.000
Chain 1:    200       -13249.300             3.258            5.515
Chain 1:    300        -9706.912             2.293            1.000
Chain 1:    400       -10621.147             1.742            1.000
Chain 1:    500        -8613.265             1.440            0.365
Chain 1:    600        -8290.675             1.206            0.365
Chain 1:    700        -8369.188             1.035            0.233
Chain 1:    800        -8949.586             0.914            0.233
Chain 1:    900        -8600.894             0.817            0.086
Chain 1:   1000        -8285.336             0.739            0.086
Chain 1:   1100        -8409.278             0.641            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8235.890             0.091            0.041
Chain 1:   1300        -8461.459             0.057            0.039
Chain 1:   1400        -8439.843             0.049            0.038
Chain 1:   1500        -8348.325             0.027            0.027
Chain 1:   1600        -8442.495             0.024            0.021
Chain 1:   1700        -8536.770             0.024            0.021
Chain 1:   1800        -8149.410             0.022            0.021
Chain 1:   1900        -8251.429             0.020            0.015
Chain 1:   2000        -8221.456             0.016            0.012
Chain 1:   2100        -8357.881             0.016            0.012
Chain 1:   2200        -8140.879             0.017            0.012
Chain 1:   2300        -8282.416             0.016            0.012
Chain 1:   2400        -8291.896             0.016            0.012
Chain 1:   2500        -8260.306             0.015            0.012
Chain 1:   2600        -8257.723             0.014            0.012
Chain 1:   2700        -8167.668             0.014            0.012
Chain 1:   2800        -8146.627             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00403 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416341.150             1.000            1.000
Chain 1:    200     -1589107.381             2.648            4.296
Chain 1:    300      -891520.074             2.026            1.000
Chain 1:    400      -457569.398             1.757            1.000
Chain 1:    500      -357599.799             1.461            0.948
Chain 1:    600      -232360.748             1.308            0.948
Chain 1:    700      -118718.004             1.258            0.948
Chain 1:    800       -85968.241             1.148            0.948
Chain 1:    900       -66354.057             1.053            0.782
Chain 1:   1000       -51186.816             0.978            0.782
Chain 1:   1100       -38698.396             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37872.277             0.482            0.381
Chain 1:   1300       -25875.938             0.451            0.381
Chain 1:   1400       -25595.590             0.357            0.323
Chain 1:   1500       -22196.333             0.344            0.323
Chain 1:   1600       -21415.555             0.294            0.296
Chain 1:   1700       -20295.928             0.204            0.296
Chain 1:   1800       -20241.088             0.166            0.153
Chain 1:   1900       -20566.636             0.138            0.055
Chain 1:   2000       -19082.171             0.116            0.055
Chain 1:   2100       -19320.241             0.085            0.036
Chain 1:   2200       -19545.867             0.084            0.036
Chain 1:   2300       -19163.962             0.040            0.020
Chain 1:   2400       -18936.360             0.040            0.020
Chain 1:   2500       -18738.175             0.025            0.016
Chain 1:   2600       -18369.264             0.024            0.016
Chain 1:   2700       -18326.387             0.019            0.012
Chain 1:   2800       -18043.523             0.020            0.016
Chain 1:   2900       -18324.343             0.020            0.015
Chain 1:   3000       -18310.623             0.012            0.012
Chain 1:   3100       -18395.569             0.011            0.012
Chain 1:   3200       -18086.683             0.012            0.015
Chain 1:   3300       -18291.004             0.011            0.012
Chain 1:   3400       -17766.717             0.013            0.015
Chain 1:   3500       -18377.398             0.015            0.016
Chain 1:   3600       -17685.557             0.017            0.016
Chain 1:   3700       -18071.280             0.019            0.017
Chain 1:   3800       -17033.300             0.023            0.021
Chain 1:   3900       -17029.469             0.022            0.021
Chain 1:   4000       -17146.785             0.022            0.021
Chain 1:   4100       -17060.735             0.022            0.021
Chain 1:   4200       -16877.418             0.022            0.021
Chain 1:   4300       -17015.511             0.022            0.021
Chain 1:   4400       -16972.744             0.019            0.011
Chain 1:   4500       -16875.336             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001347 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48373.338             1.000            1.000
Chain 1:    200       -19909.739             1.215            1.430
Chain 1:    300       -18783.035             0.830            1.000
Chain 1:    400      -112043.581             0.830            1.000
Chain 1:    500       -14031.398             2.061            1.000
Chain 1:    600       -16968.297             1.747            1.000
Chain 1:    700       -11734.501             1.561            0.832
Chain 1:    800       -13395.751             1.381            0.832
Chain 1:    900       -10574.395             1.257            0.446
Chain 1:   1000       -11633.362             1.141            0.446
Chain 1:   1100       -11225.473             1.044            0.267   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -15518.022             0.929            0.267   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -12096.200             0.951            0.277   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -10934.656             0.879            0.267   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -11621.910             0.186            0.173
Chain 1:   1600        -9506.100             0.191            0.223
Chain 1:   1700       -16844.972             0.190            0.223
Chain 1:   1800       -17528.882             0.182            0.223
Chain 1:   1900       -22406.228             0.177            0.218
Chain 1:   2000       -10256.942             0.286            0.223
Chain 1:   2100       -11110.370             0.290            0.223
Chain 1:   2200       -10738.054             0.266            0.218
Chain 1:   2300        -9254.715             0.254            0.160
Chain 1:   2400        -9179.201             0.244            0.160
Chain 1:   2500       -17441.413             0.285            0.218
Chain 1:   2600       -17829.005             0.265            0.160
Chain 1:   2700        -8935.084             0.321            0.160
Chain 1:   2800       -13274.664             0.350            0.218
Chain 1:   2900        -9037.268             0.375            0.327
Chain 1:   3000       -11529.920             0.278            0.216
Chain 1:   3100        -9095.773             0.297            0.268
Chain 1:   3200       -12226.085             0.319            0.268
Chain 1:   3300        -9933.231             0.327            0.268
Chain 1:   3400       -15804.514             0.363            0.327
Chain 1:   3500        -8702.638             0.397            0.327
Chain 1:   3600        -9039.101             0.399            0.327
Chain 1:   3700       -15114.875             0.339            0.327
Chain 1:   3800        -9883.976             0.360            0.371
Chain 1:   3900        -8722.198             0.326            0.268
Chain 1:   4000        -8434.013             0.308            0.268
Chain 1:   4100        -8650.927             0.284            0.256
Chain 1:   4200        -9597.740             0.268            0.231
Chain 1:   4300       -10211.186             0.251            0.133
Chain 1:   4400        -8774.553             0.230            0.133
Chain 1:   4500        -8848.280             0.149            0.099
Chain 1:   4600       -12041.599             0.172            0.133
Chain 1:   4700        -9846.415             0.154            0.133
Chain 1:   4800        -8363.050             0.119            0.133
Chain 1:   4900        -8765.612             0.110            0.099
Chain 1:   5000       -10181.027             0.121            0.139
Chain 1:   5100        -8686.205             0.135            0.164
Chain 1:   5200       -15302.420             0.169            0.172
Chain 1:   5300       -11669.803             0.194            0.177
Chain 1:   5400       -14926.650             0.199            0.218
Chain 1:   5500        -9111.562             0.262            0.223
Chain 1:   5600        -8126.657             0.248            0.218
Chain 1:   5700        -8819.617             0.233            0.177
Chain 1:   5800        -8385.949             0.221            0.172
Chain 1:   5900       -12852.804             0.251            0.218
Chain 1:   6000       -10522.292             0.259            0.221
Chain 1:   6100        -8353.859             0.268            0.260
Chain 1:   6200        -8448.366             0.226            0.221
Chain 1:   6300        -8136.559             0.199            0.218
Chain 1:   6400       -13747.060             0.218            0.221
Chain 1:   6500        -9671.678             0.196            0.221
Chain 1:   6600        -8547.953             0.197            0.221
Chain 1:   6700        -8072.221             0.195            0.221
Chain 1:   6800       -10245.776             0.211            0.221
Chain 1:   6900        -9814.953             0.181            0.212
Chain 1:   7000        -9838.939             0.159            0.131
Chain 1:   7100        -8139.743             0.154            0.131
Chain 1:   7200        -9396.228             0.166            0.134
Chain 1:   7300        -8448.465             0.173            0.134
Chain 1:   7400       -10443.955             0.152            0.134
Chain 1:   7500        -8467.702             0.133            0.134
Chain 1:   7600        -8372.775             0.121            0.134
Chain 1:   7700        -8248.615             0.116            0.134
Chain 1:   7800       -10887.050             0.119            0.134
Chain 1:   7900        -7966.425             0.152            0.191
Chain 1:   8000        -8100.457             0.153            0.191
Chain 1:   8100        -9404.399             0.146            0.139
Chain 1:   8200        -9957.667             0.138            0.139
Chain 1:   8300        -7943.425             0.152            0.191
Chain 1:   8400        -7950.376             0.133            0.139
Chain 1:   8500        -8016.335             0.111            0.056
Chain 1:   8600       -11220.868             0.138            0.139
Chain 1:   8700       -10090.372             0.148            0.139
Chain 1:   8800        -9146.080             0.134            0.112
Chain 1:   8900       -11084.622             0.115            0.112
Chain 1:   9000       -10034.072             0.124            0.112
Chain 1:   9100        -9660.408             0.114            0.105
Chain 1:   9200        -8427.540             0.123            0.112
Chain 1:   9300        -8046.952             0.102            0.105
Chain 1:   9400        -8196.863             0.104            0.105
Chain 1:   9500       -10980.093             0.128            0.112
Chain 1:   9600        -8055.810             0.136            0.112
Chain 1:   9700        -8101.199             0.126            0.105
Chain 1:   9800        -8665.404             0.122            0.105
Chain 1:   9900        -9154.114             0.110            0.065
Chain 1:   10000        -7818.997             0.116            0.065
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56662.921             1.000            1.000
Chain 1:    200       -17072.676             1.659            2.319
Chain 1:    300        -8487.897             1.443            1.011
Chain 1:    400        -8911.992             1.094            1.011
Chain 1:    500        -8474.681             0.886            1.000
Chain 1:    600        -8603.328             0.741            1.000
Chain 1:    700        -7658.375             0.653            0.123
Chain 1:    800        -7927.658             0.575            0.123
Chain 1:    900        -7787.679             0.513            0.052
Chain 1:   1000        -7630.018             0.464            0.052
Chain 1:   1100        -7511.110             0.366            0.048
Chain 1:   1200        -7472.696             0.134            0.034
Chain 1:   1300        -7590.871             0.035            0.021
Chain 1:   1400        -7786.733             0.032            0.021
Chain 1:   1500        -7488.838             0.031            0.021
Chain 1:   1600        -7467.633             0.030            0.021
Chain 1:   1700        -7390.739             0.019            0.018
Chain 1:   1800        -7455.698             0.016            0.016
Chain 1:   1900        -7508.497             0.015            0.016
Chain 1:   2000        -7503.066             0.013            0.010
Chain 1:   2100        -7525.261             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003963 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85195.125             1.000            1.000
Chain 1:    200       -13069.778             3.259            5.518
Chain 1:    300        -9541.381             2.296            1.000
Chain 1:    400       -10384.067             1.742            1.000
Chain 1:    500        -8445.869             1.440            0.370
Chain 1:    600        -8108.951             1.207            0.370
Chain 1:    700        -8332.054             1.038            0.229
Chain 1:    800        -8488.854             0.911            0.229
Chain 1:    900        -8414.814             0.811            0.081
Chain 1:   1000        -8168.628             0.732            0.081
Chain 1:   1100        -8442.188             0.636            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8183.752             0.087            0.032
Chain 1:   1300        -8292.785             0.051            0.032
Chain 1:   1400        -8309.378             0.043            0.030
Chain 1:   1500        -8191.038             0.022            0.027
Chain 1:   1600        -8282.397             0.019            0.018
Chain 1:   1700        -8377.433             0.017            0.014
Chain 1:   1800        -7993.855             0.020            0.014
Chain 1:   1900        -8095.236             0.021            0.014
Chain 1:   2000        -8064.964             0.018            0.013
Chain 1:   2100        -8204.375             0.016            0.013
Chain 1:   2200        -7985.519             0.016            0.013
Chain 1:   2300        -8127.823             0.016            0.014
Chain 1:   2400        -8013.779             0.018            0.014
Chain 1:   2500        -8071.658             0.017            0.014
Chain 1:   2600        -8085.637             0.016            0.014
Chain 1:   2700        -8007.821             0.016            0.014
Chain 1:   2800        -7989.655             0.011            0.013
Chain 1:   2900        -8001.612             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00625 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 62.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8384213.760             1.000            1.000
Chain 1:    200     -1583857.606             2.647            4.294
Chain 1:    300      -892003.513             2.023            1.000
Chain 1:    400      -458065.318             1.754            1.000
Chain 1:    500      -358569.637             1.459            0.947
Chain 1:    600      -233328.206             1.305            0.947
Chain 1:    700      -119166.022             1.256            0.947
Chain 1:    800       -86243.005             1.146            0.947
Chain 1:    900       -66514.033             1.052            0.776
Chain 1:   1000       -51247.863             0.976            0.776
Chain 1:   1100       -38670.099             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37831.033             0.482            0.382
Chain 1:   1300       -25750.588             0.451            0.382
Chain 1:   1400       -25461.210             0.358            0.325
Chain 1:   1500       -22039.573             0.345            0.325
Chain 1:   1600       -21251.638             0.295            0.298
Chain 1:   1700       -20122.520             0.205            0.297
Chain 1:   1800       -20065.193             0.167            0.155
Chain 1:   1900       -20390.673             0.139            0.056
Chain 1:   2000       -18901.130             0.117            0.056
Chain 1:   2100       -19139.496             0.086            0.037
Chain 1:   2200       -19365.817             0.085            0.037
Chain 1:   2300       -18983.307             0.040            0.020
Chain 1:   2400       -18755.643             0.040            0.020
Chain 1:   2500       -18557.691             0.026            0.016
Chain 1:   2600       -18188.579             0.024            0.016
Chain 1:   2700       -18145.634             0.019            0.012
Chain 1:   2800       -17862.895             0.020            0.016
Chain 1:   2900       -18143.813             0.020            0.015
Chain 1:   3000       -18130.074             0.012            0.012
Chain 1:   3100       -18214.979             0.011            0.012
Chain 1:   3200       -17906.084             0.012            0.015
Chain 1:   3300       -18110.405             0.011            0.012
Chain 1:   3400       -17586.142             0.013            0.015
Chain 1:   3500       -18196.885             0.015            0.016
Chain 1:   3600       -17505.054             0.017            0.016
Chain 1:   3700       -17890.824             0.019            0.017
Chain 1:   3800       -16852.855             0.024            0.022
Chain 1:   3900       -16849.070             0.022            0.022
Chain 1:   4000       -16966.352             0.023            0.022
Chain 1:   4100       -16880.300             0.023            0.022
Chain 1:   4200       -16696.988             0.022            0.022
Chain 1:   4300       -16835.054             0.022            0.022
Chain 1:   4400       -16792.312             0.019            0.011
Chain 1:   4500       -16694.938             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001493 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12449.999             1.000            1.000
Chain 1:    200        -9325.446             0.668            1.000
Chain 1:    300        -8203.795             0.491            0.335
Chain 1:    400        -8384.191             0.373            0.335
Chain 1:    500        -8209.204             0.303            0.137
Chain 1:    600        -8137.607             0.254            0.137
Chain 1:    700        -8053.843             0.219            0.022
Chain 1:    800        -8130.006             0.193            0.022
Chain 1:    900        -7961.503             0.174            0.021
Chain 1:   1000        -8081.681             0.158            0.021
Chain 1:   1100        -8088.701             0.058            0.021
Chain 1:   1200        -8067.328             0.025            0.015
Chain 1:   1300        -8149.947             0.012            0.010
Chain 1:   1400        -8048.027             0.011            0.010
Chain 1:   1500        -8135.073             0.010            0.010
Chain 1:   1600        -8087.300             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58375.629             1.000            1.000
Chain 1:    200       -17738.828             1.645            2.291
Chain 1:    300        -8714.197             1.442            1.036
Chain 1:    400        -8216.951             1.097            1.036
Chain 1:    500        -8537.291             0.885            1.000
Chain 1:    600        -8403.181             0.740            1.000
Chain 1:    700        -8004.641             0.641            0.061
Chain 1:    800        -8147.697             0.563            0.061
Chain 1:    900        -7930.553             0.504            0.050
Chain 1:   1000        -7761.096             0.456            0.050
Chain 1:   1100        -7830.658             0.357            0.038
Chain 1:   1200        -7632.902             0.130            0.027
Chain 1:   1300        -7824.028             0.029            0.026
Chain 1:   1400        -7889.593             0.024            0.024
Chain 1:   1500        -7607.859             0.024            0.024
Chain 1:   1600        -7731.285             0.024            0.024
Chain 1:   1700        -7533.595             0.021            0.024
Chain 1:   1800        -7652.918             0.021            0.024
Chain 1:   1900        -7651.767             0.018            0.022
Chain 1:   2000        -7668.597             0.016            0.016
Chain 1:   2100        -7629.011             0.016            0.016
Chain 1:   2200        -7716.693             0.015            0.016
Chain 1:   2300        -7611.825             0.014            0.014
Chain 1:   2400        -7664.832             0.013            0.014
Chain 1:   2500        -7569.136             0.011            0.013
Chain 1:   2600        -7554.342             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86213.188             1.000            1.000
Chain 1:    200       -13548.308             3.182            5.363
Chain 1:    300        -9973.945             2.241            1.000
Chain 1:    400       -10772.101             1.699            1.000
Chain 1:    500        -8920.539             1.401            0.358
Chain 1:    600        -8463.691             1.176            0.358
Chain 1:    700        -8836.419             1.014            0.208
Chain 1:    800        -9267.535             0.893            0.208
Chain 1:    900        -8839.216             0.799            0.074
Chain 1:   1000        -8584.764             0.722            0.074
Chain 1:   1100        -8793.180             0.625            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8467.187             0.092            0.048
Chain 1:   1300        -8687.455             0.059            0.047
Chain 1:   1400        -8682.916             0.052            0.042
Chain 1:   1500        -8580.424             0.032            0.039
Chain 1:   1600        -8681.236             0.028            0.030
Chain 1:   1700        -8770.441             0.025            0.025
Chain 1:   1800        -8370.225             0.025            0.025
Chain 1:   1900        -8470.888             0.021            0.024
Chain 1:   2000        -8441.820             0.018            0.012
Chain 1:   2100        -8562.271             0.018            0.012
Chain 1:   2200        -8338.797             0.016            0.012
Chain 1:   2300        -8500.069             0.016            0.012
Chain 1:   2400        -8512.174             0.016            0.012
Chain 1:   2500        -8483.900             0.015            0.012
Chain 1:   2600        -8487.258             0.014            0.012
Chain 1:   2700        -8392.391             0.014            0.012
Chain 1:   2800        -8362.528             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003982 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431779.964             1.000            1.000
Chain 1:    200     -1588000.385             2.655            4.310
Chain 1:    300      -890658.482             2.031            1.000
Chain 1:    400      -457848.478             1.759            1.000
Chain 1:    500      -357755.423             1.464            0.945
Chain 1:    600      -232639.985             1.309            0.945
Chain 1:    700      -119038.556             1.259            0.945
Chain 1:    800       -86309.334             1.149            0.945
Chain 1:    900       -66690.467             1.054            0.783
Chain 1:   1000       -51518.506             0.978            0.783
Chain 1:   1100       -39034.532             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38210.228             0.481            0.379
Chain 1:   1300       -26210.563             0.448            0.379
Chain 1:   1400       -25932.074             0.355            0.320
Chain 1:   1500       -22531.764             0.342            0.320
Chain 1:   1600       -21751.723             0.292            0.294
Chain 1:   1700       -20630.874             0.202            0.294
Chain 1:   1800       -20576.154             0.164            0.151
Chain 1:   1900       -20902.017             0.136            0.054
Chain 1:   2000       -19416.734             0.115            0.054
Chain 1:   2100       -19654.747             0.084            0.036
Chain 1:   2200       -19880.697             0.083            0.036
Chain 1:   2300       -19498.439             0.039            0.020
Chain 1:   2400       -19270.688             0.039            0.020
Chain 1:   2500       -19072.649             0.025            0.016
Chain 1:   2600       -18703.246             0.023            0.016
Chain 1:   2700       -18660.306             0.018            0.012
Chain 1:   2800       -18377.308             0.019            0.015
Chain 1:   2900       -18658.318             0.019            0.015
Chain 1:   3000       -18644.537             0.012            0.012
Chain 1:   3100       -18729.505             0.011            0.012
Chain 1:   3200       -18420.427             0.012            0.015
Chain 1:   3300       -18624.942             0.011            0.012
Chain 1:   3400       -18100.274             0.012            0.015
Chain 1:   3500       -18711.523             0.015            0.015
Chain 1:   3600       -18018.954             0.017            0.015
Chain 1:   3700       -18405.185             0.018            0.017
Chain 1:   3800       -17366.100             0.023            0.021
Chain 1:   3900       -17362.263             0.021            0.021
Chain 1:   4000       -17479.575             0.022            0.021
Chain 1:   4100       -17393.428             0.022            0.021
Chain 1:   4200       -17209.903             0.021            0.021
Chain 1:   4300       -17348.139             0.021            0.021
Chain 1:   4400       -17305.167             0.018            0.011
Chain 1:   4500       -17207.728             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48691.715             1.000            1.000
Chain 1:    200       -15121.793             1.610            2.220
Chain 1:    300       -17872.282             1.125            1.000
Chain 1:    400       -22045.821             0.891            1.000
Chain 1:    500       -12131.016             0.876            0.817
Chain 1:    600       -23180.301             0.810            0.817
Chain 1:    700       -14828.051             0.774            0.563
Chain 1:    800       -13369.279             0.691            0.563
Chain 1:    900       -18676.936             0.646            0.477
Chain 1:   1000       -13461.163             0.620            0.477
Chain 1:   1100       -11884.379             0.533            0.387   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11103.735             0.318            0.284
Chain 1:   1300       -12457.911             0.314            0.284
Chain 1:   1400       -10921.346             0.309            0.284
Chain 1:   1500       -16121.600             0.260            0.284
Chain 1:   1600       -12064.587             0.246            0.284
Chain 1:   1700        -9447.373             0.217            0.277
Chain 1:   1800       -10890.024             0.219            0.277
Chain 1:   1900       -10320.471             0.196            0.141
Chain 1:   2000       -11937.772             0.171            0.135
Chain 1:   2100       -12987.016             0.166            0.135
Chain 1:   2200        -9737.248             0.192            0.141
Chain 1:   2300       -14182.610             0.213            0.277
Chain 1:   2400       -10697.318             0.231            0.313
Chain 1:   2500        -9865.735             0.207            0.277
Chain 1:   2600        -9188.709             0.181            0.135
Chain 1:   2700        -8858.270             0.157            0.132
Chain 1:   2800       -10581.407             0.160            0.135
Chain 1:   2900        -8653.497             0.177            0.163
Chain 1:   3000       -10568.969             0.182            0.181
Chain 1:   3100        -8749.533             0.194            0.208
Chain 1:   3200        -9033.054             0.164            0.181
Chain 1:   3300        -8878.290             0.134            0.163
Chain 1:   3400       -10050.217             0.114            0.117
Chain 1:   3500       -12295.687             0.123            0.163
Chain 1:   3600        -9096.235             0.151            0.181
Chain 1:   3700        -9302.111             0.150            0.181
Chain 1:   3800       -12971.784             0.162            0.183
Chain 1:   3900        -9247.485             0.180            0.183
Chain 1:   4000        -9749.073             0.167            0.183
Chain 1:   4100        -9056.227             0.154            0.117
Chain 1:   4200       -12751.977             0.179            0.183
Chain 1:   4300       -12763.091             0.178            0.183
Chain 1:   4400       -12356.696             0.169            0.183
Chain 1:   4500        -8920.397             0.190            0.283
Chain 1:   4600        -8423.591             0.160            0.077
Chain 1:   4700       -10265.208             0.176            0.179
Chain 1:   4800        -8651.078             0.166            0.179
Chain 1:   4900       -12654.915             0.158            0.179
Chain 1:   5000       -15007.453             0.168            0.179
Chain 1:   5100       -13265.154             0.174            0.179
Chain 1:   5200        -8702.257             0.197            0.179
Chain 1:   5300       -12874.237             0.230            0.187
Chain 1:   5400       -14503.212             0.238            0.187
Chain 1:   5500        -9302.603             0.255            0.187
Chain 1:   5600        -8922.340             0.253            0.187
Chain 1:   5700       -10853.309             0.253            0.187
Chain 1:   5800       -11383.627             0.239            0.178
Chain 1:   5900        -9138.099             0.232            0.178
Chain 1:   6000        -8721.594             0.221            0.178
Chain 1:   6100        -9230.817             0.214            0.178
Chain 1:   6200       -10765.524             0.175            0.143
Chain 1:   6300        -8589.587             0.168            0.143
Chain 1:   6400        -8948.127             0.161            0.143
Chain 1:   6500        -8603.122             0.109            0.055
Chain 1:   6600       -10280.578             0.121            0.143
Chain 1:   6700        -9222.629             0.115            0.115
Chain 1:   6800       -11891.442             0.133            0.143
Chain 1:   6900       -12221.886             0.111            0.115
Chain 1:   7000       -10109.614             0.127            0.143
Chain 1:   7100        -8096.841             0.146            0.163
Chain 1:   7200       -11867.610             0.164            0.209
Chain 1:   7300        -7986.766             0.187            0.209
Chain 1:   7400        -8304.663             0.187            0.209
Chain 1:   7500        -8288.697             0.183            0.209
Chain 1:   7600        -8082.089             0.169            0.209
Chain 1:   7700        -8118.975             0.158            0.209
Chain 1:   7800        -8208.578             0.137            0.038
Chain 1:   7900        -8668.619             0.140            0.053
Chain 1:   8000        -8687.483             0.119            0.038
Chain 1:   8100        -8420.767             0.097            0.032
Chain 1:   8200       -10690.250             0.087            0.032
Chain 1:   8300        -8001.087             0.072            0.032
Chain 1:   8400       -10783.088             0.094            0.032
Chain 1:   8500        -9634.721             0.105            0.053
Chain 1:   8600        -8550.202             0.115            0.119
Chain 1:   8700        -7981.789             0.122            0.119
Chain 1:   8800        -8213.728             0.124            0.119
Chain 1:   8900        -9631.031             0.133            0.127
Chain 1:   9000        -8143.337             0.151            0.147
Chain 1:   9100        -8188.053             0.149            0.147
Chain 1:   9200        -8070.904             0.129            0.127
Chain 1:   9300        -8488.434             0.100            0.119
Chain 1:   9400        -8286.242             0.077            0.071
Chain 1:   9500        -8409.655             0.066            0.049
Chain 1:   9600        -8097.197             0.058            0.039
Chain 1:   9700        -9353.768             0.064            0.039
Chain 1:   9800        -8205.136             0.075            0.049
Chain 1:   9900        -8986.497             0.069            0.049
Chain 1:   10000        -8188.765             0.061            0.049
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001891 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58488.585             1.000            1.000
Chain 1:    200       -17269.934             1.693            2.387
Chain 1:    300        -8625.677             1.463            1.002
Chain 1:    400        -8191.002             1.110            1.002
Chain 1:    500        -8036.026             0.892            1.000
Chain 1:    600        -7899.430             0.746            1.000
Chain 1:    700        -7689.154             0.644            0.053
Chain 1:    800        -7949.095             0.567            0.053
Chain 1:    900        -7888.930             0.505            0.033
Chain 1:   1000        -7957.874             0.455            0.033
Chain 1:   1100        -7597.917             0.360            0.033
Chain 1:   1200        -7590.532             0.122            0.027
Chain 1:   1300        -7588.212             0.021            0.019
Chain 1:   1400        -7846.703             0.019            0.019
Chain 1:   1500        -7582.773             0.021            0.027
Chain 1:   1600        -7493.217             0.020            0.027
Chain 1:   1700        -7468.888             0.018            0.012
Chain 1:   1800        -7506.271             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003288 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85922.987             1.000            1.000
Chain 1:    200       -13128.167             3.272            5.545
Chain 1:    300        -9613.436             2.304            1.000
Chain 1:    400       -10389.203             1.746            1.000
Chain 1:    500        -8521.268             1.441            0.366
Chain 1:    600        -8195.906             1.207            0.366
Chain 1:    700        -8419.530             1.039            0.219
Chain 1:    800        -8447.558             0.909            0.219
Chain 1:    900        -8500.356             0.809            0.075
Chain 1:   1000        -8349.614             0.730            0.075
Chain 1:   1100        -8519.226             0.632            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8266.781             0.080            0.031
Chain 1:   1300        -8206.110             0.045            0.027
Chain 1:   1400        -8184.978             0.037            0.020
Chain 1:   1500        -8242.142             0.016            0.018
Chain 1:   1600        -8244.838             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004185 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409188.620             1.000            1.000
Chain 1:    200     -1582050.582             2.658            4.315
Chain 1:    300      -888825.112             2.032            1.000
Chain 1:    400      -456375.618             1.761            1.000
Chain 1:    500      -356772.547             1.464            0.948
Chain 1:    600      -231886.298             1.310            0.948
Chain 1:    700      -118466.058             1.260            0.948
Chain 1:    800       -85796.049             1.150            0.948
Chain 1:    900       -66196.593             1.055            0.780
Chain 1:   1000       -51037.068             0.979            0.780
Chain 1:   1100       -38563.798             0.912            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37739.316             0.482            0.381
Chain 1:   1300       -25749.797             0.451            0.381
Chain 1:   1400       -25471.342             0.357            0.323
Chain 1:   1500       -22073.867             0.345            0.323
Chain 1:   1600       -21294.336             0.294            0.297
Chain 1:   1700       -20174.819             0.204            0.296
Chain 1:   1800       -20120.291             0.166            0.154
Chain 1:   1900       -20445.815             0.138            0.055
Chain 1:   2000       -18961.989             0.116            0.055
Chain 1:   2100       -19199.888             0.085            0.037
Chain 1:   2200       -19425.468             0.084            0.037
Chain 1:   2300       -19043.647             0.040            0.020
Chain 1:   2400       -18816.057             0.040            0.020
Chain 1:   2500       -18618.016             0.026            0.016
Chain 1:   2600       -18248.974             0.024            0.016
Chain 1:   2700       -18206.229             0.019            0.012
Chain 1:   2800       -17923.409             0.020            0.016
Chain 1:   2900       -18204.215             0.020            0.015
Chain 1:   3000       -18190.448             0.012            0.012
Chain 1:   3100       -18275.341             0.011            0.012
Chain 1:   3200       -17966.532             0.012            0.015
Chain 1:   3300       -18170.868             0.011            0.012
Chain 1:   3400       -17646.690             0.013            0.015
Chain 1:   3500       -18257.196             0.015            0.016
Chain 1:   3600       -17565.609             0.017            0.016
Chain 1:   3700       -17951.105             0.019            0.017
Chain 1:   3800       -16913.523             0.023            0.021
Chain 1:   3900       -16909.735             0.022            0.021
Chain 1:   4000       -17027.025             0.023            0.021
Chain 1:   4100       -16940.946             0.023            0.021
Chain 1:   4200       -16757.788             0.022            0.021
Chain 1:   4300       -16895.763             0.022            0.021
Chain 1:   4400       -16853.053             0.019            0.011
Chain 1:   4500       -16755.677             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12833.353             1.000            1.000
Chain 1:    200        -9718.545             0.660            1.000
Chain 1:    300        -8274.044             0.498            0.321
Chain 1:    400        -8470.354             0.380            0.321
Chain 1:    500        -8386.537             0.306            0.175
Chain 1:    600        -8219.085             0.258            0.175
Chain 1:    700        -8105.772             0.223            0.023
Chain 1:    800        -8103.848             0.195            0.023
Chain 1:    900        -8168.489             0.175            0.020
Chain 1:   1000        -8126.046             0.158            0.020
Chain 1:   1100        -8217.047             0.059            0.014
Chain 1:   1200        -8141.636             0.028            0.011
Chain 1:   1300        -8072.639             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001804 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58252.800             1.000            1.000
Chain 1:    200       -18056.845             1.613            2.226
Chain 1:    300        -8882.978             1.420            1.033
Chain 1:    400        -8118.008             1.088            1.033
Chain 1:    500        -8337.247             0.876            1.000
Chain 1:    600        -8710.721             0.737            1.000
Chain 1:    700        -8610.128             0.633            0.094
Chain 1:    800        -8260.933             0.560            0.094
Chain 1:    900        -8047.724             0.500            0.043
Chain 1:   1000        -7908.591             0.452            0.043
Chain 1:   1100        -7740.249             0.354            0.042
Chain 1:   1200        -7936.276             0.134            0.026
Chain 1:   1300        -7842.454             0.032            0.026
Chain 1:   1400        -7829.955             0.023            0.025
Chain 1:   1500        -7617.616             0.023            0.025
Chain 1:   1600        -7730.431             0.020            0.022
Chain 1:   1700        -7571.976             0.021            0.022
Chain 1:   1800        -7671.731             0.018            0.021
Chain 1:   1900        -7630.194             0.016            0.018
Chain 1:   2000        -7705.435             0.015            0.015
Chain 1:   2100        -7604.618             0.014            0.013
Chain 1:   2200        -7817.560             0.015            0.013
Chain 1:   2300        -7579.834             0.017            0.015
Chain 1:   2400        -7653.494             0.017            0.015
Chain 1:   2500        -7597.334             0.015            0.013
Chain 1:   2600        -7566.254             0.014            0.013
Chain 1:   2700        -7496.515             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003304 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86702.865             1.000            1.000
Chain 1:    200       -13912.062             3.116            5.232
Chain 1:    300       -10178.711             2.200            1.000
Chain 1:    400       -11580.293             1.680            1.000
Chain 1:    500        -9163.969             1.397            0.367
Chain 1:    600        -9232.929             1.165            0.367
Chain 1:    700        -8621.437             1.009            0.264
Chain 1:    800        -8959.311             0.887            0.264
Chain 1:    900        -8970.942             0.789            0.121
Chain 1:   1000        -8736.342             0.713            0.121
Chain 1:   1100        -9016.155             0.616            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8554.704             0.098            0.054
Chain 1:   1300        -8763.778             0.064            0.038
Chain 1:   1400        -8804.607             0.052            0.031
Chain 1:   1500        -8703.254             0.027            0.027
Chain 1:   1600        -8811.006             0.027            0.027
Chain 1:   1700        -8878.468             0.021            0.024
Chain 1:   1800        -8442.120             0.022            0.024
Chain 1:   1900        -8547.200             0.024            0.024
Chain 1:   2000        -8523.401             0.021            0.012
Chain 1:   2100        -8665.908             0.020            0.012
Chain 1:   2200        -8453.759             0.017            0.012
Chain 1:   2300        -8614.137             0.016            0.012
Chain 1:   2400        -8449.592             0.018            0.016
Chain 1:   2500        -8521.095             0.017            0.016
Chain 1:   2600        -8433.161             0.017            0.016
Chain 1:   2700        -8467.369             0.017            0.016
Chain 1:   2800        -8427.158             0.012            0.012
Chain 1:   2900        -8520.764             0.012            0.011
Chain 1:   3000        -8354.636             0.014            0.016
Chain 1:   3100        -8509.846             0.014            0.018
Chain 1:   3200        -8381.682             0.013            0.015
Chain 1:   3300        -8389.574             0.011            0.011
Chain 1:   3400        -8550.888             0.011            0.011
Chain 1:   3500        -8561.167             0.010            0.011
Chain 1:   3600        -8338.104             0.012            0.015
Chain 1:   3700        -8484.547             0.013            0.017
Chain 1:   3800        -8344.535             0.015            0.017
Chain 1:   3900        -8278.939             0.014            0.017
Chain 1:   4000        -8355.327             0.013            0.017
Chain 1:   4100        -8350.285             0.011            0.015
Chain 1:   4200        -8334.262             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003438 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389769.237             1.000            1.000
Chain 1:    200     -1582347.058             2.651            4.302
Chain 1:    300      -891977.159             2.025            1.000
Chain 1:    400      -458946.196             1.755            1.000
Chain 1:    500      -359378.284             1.459            0.944
Chain 1:    600      -234105.419             1.305            0.944
Chain 1:    700      -119978.004             1.255            0.944
Chain 1:    800       -87095.657             1.145            0.944
Chain 1:    900       -67381.231             1.050            0.774
Chain 1:   1000       -52134.944             0.975            0.774
Chain 1:   1100       -39569.893             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38742.867             0.478            0.378
Chain 1:   1300       -26652.724             0.446            0.378
Chain 1:   1400       -26368.872             0.353            0.318
Chain 1:   1500       -22943.326             0.340            0.318
Chain 1:   1600       -22156.469             0.290            0.293
Chain 1:   1700       -21024.451             0.200            0.292
Chain 1:   1800       -20967.345             0.163            0.149
Chain 1:   1900       -21293.888             0.135            0.054
Chain 1:   2000       -19801.027             0.114            0.054
Chain 1:   2100       -20039.753             0.083            0.036
Chain 1:   2200       -20266.953             0.082            0.036
Chain 1:   2300       -19883.337             0.039            0.019
Chain 1:   2400       -19655.221             0.039            0.019
Chain 1:   2500       -19457.343             0.025            0.015
Chain 1:   2600       -19087.090             0.023            0.015
Chain 1:   2700       -19043.820             0.018            0.012
Chain 1:   2800       -18760.613             0.019            0.015
Chain 1:   2900       -19042.048             0.019            0.015
Chain 1:   3000       -19028.225             0.012            0.012
Chain 1:   3100       -19113.306             0.011            0.012
Chain 1:   3200       -18803.676             0.011            0.015
Chain 1:   3300       -19008.583             0.011            0.012
Chain 1:   3400       -18483.061             0.012            0.015
Chain 1:   3500       -19095.707             0.014            0.015
Chain 1:   3600       -18401.325             0.016            0.015
Chain 1:   3700       -18789.005             0.018            0.016
Chain 1:   3800       -17747.117             0.022            0.021
Chain 1:   3900       -17743.196             0.021            0.021
Chain 1:   4000       -17860.508             0.022            0.021
Chain 1:   4100       -17774.264             0.022            0.021
Chain 1:   4200       -17590.065             0.021            0.021
Chain 1:   4300       -17728.759             0.021            0.021
Chain 1:   4400       -17685.316             0.018            0.010
Chain 1:   4500       -17587.770             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001336 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49627.718             1.000            1.000
Chain 1:    200       -22965.729             1.080            1.161
Chain 1:    300       -21320.036             0.746            1.000
Chain 1:    400       -13340.050             0.709            1.000
Chain 1:    500       -16432.051             0.605            0.598
Chain 1:    600       -12091.197             0.564            0.598
Chain 1:    700       -17627.954             0.528            0.359
Chain 1:    800       -12987.257             0.507            0.359
Chain 1:    900       -14405.807             0.461            0.357
Chain 1:   1000       -15744.814             0.424            0.357
Chain 1:   1100       -10939.414             0.368            0.357
Chain 1:   1200       -14291.506             0.275            0.314
Chain 1:   1300       -12661.920             0.280            0.314
Chain 1:   1400       -11659.619             0.229            0.235
Chain 1:   1500       -10742.766             0.219            0.235
Chain 1:   1600       -12701.371             0.198            0.154
Chain 1:   1700       -10182.171             0.192            0.154
Chain 1:   1800       -12523.052             0.175            0.154
Chain 1:   1900       -10664.690             0.182            0.174
Chain 1:   2000       -12336.656             0.187            0.174
Chain 1:   2100       -18262.973             0.176            0.174
Chain 1:   2200       -10794.597             0.221            0.174
Chain 1:   2300       -10113.988             0.215            0.174
Chain 1:   2400       -10223.212             0.208            0.174
Chain 1:   2500       -11524.695             0.211            0.174
Chain 1:   2600       -13203.836             0.208            0.174
Chain 1:   2700        -9845.628             0.217            0.174
Chain 1:   2800       -10560.300             0.205            0.136
Chain 1:   2900       -10280.167             0.191            0.127
Chain 1:   3000       -15843.221             0.212            0.127
Chain 1:   3100        -9495.993             0.247            0.127
Chain 1:   3200        -9453.442             0.178            0.113
Chain 1:   3300       -10521.089             0.181            0.113
Chain 1:   3400       -10027.628             0.185            0.113
Chain 1:   3500        -9758.494             0.177            0.101
Chain 1:   3600       -11565.938             0.179            0.101
Chain 1:   3700       -10014.970             0.161            0.101
Chain 1:   3800       -13526.689             0.180            0.155
Chain 1:   3900        -9536.867             0.219            0.156
Chain 1:   4000       -10402.468             0.192            0.155
Chain 1:   4100       -10080.335             0.129            0.101
Chain 1:   4200       -12823.051             0.150            0.155
Chain 1:   4300       -10340.952             0.163            0.156
Chain 1:   4400       -14309.261             0.186            0.214
Chain 1:   4500       -10981.298             0.214            0.240
Chain 1:   4600       -14743.993             0.224            0.255
Chain 1:   4700        -9340.575             0.266            0.260
Chain 1:   4800       -12951.016             0.268            0.277
Chain 1:   4900        -9532.144             0.262            0.277
Chain 1:   5000       -12914.200             0.280            0.277
Chain 1:   5100        -9896.074             0.307            0.279
Chain 1:   5200       -15352.607             0.321            0.303
Chain 1:   5300       -14197.539             0.306            0.303
Chain 1:   5400        -9066.647             0.334            0.305
Chain 1:   5500        -9813.522             0.312            0.305
Chain 1:   5600       -10395.782             0.292            0.305
Chain 1:   5700       -10722.913             0.237            0.279
Chain 1:   5800        -9506.678             0.222            0.262
Chain 1:   5900       -12759.073             0.212            0.255
Chain 1:   6000        -9732.580             0.216            0.255
Chain 1:   6100        -9142.183             0.192            0.128
Chain 1:   6200        -8743.864             0.161            0.081
Chain 1:   6300       -11205.815             0.175            0.128
Chain 1:   6400       -11544.084             0.122            0.076
Chain 1:   6500        -9870.842             0.131            0.128
Chain 1:   6600        -9152.660             0.133            0.128
Chain 1:   6700        -8984.362             0.132            0.128
Chain 1:   6800        -9845.986             0.128            0.088
Chain 1:   6900        -9151.418             0.110            0.078
Chain 1:   7000        -9172.905             0.079            0.076
Chain 1:   7100        -8945.525             0.075            0.076
Chain 1:   7200        -9047.918             0.072            0.076
Chain 1:   7300        -8852.033             0.052            0.029
Chain 1:   7400        -8855.498             0.049            0.025
Chain 1:   7500       -12063.816             0.059            0.025
Chain 1:   7600        -9926.429             0.073            0.025
Chain 1:   7700        -8827.135             0.083            0.076
Chain 1:   7800        -9331.479             0.080            0.054
Chain 1:   7900        -9001.108             0.076            0.037
Chain 1:   8000        -9017.879             0.076            0.037
Chain 1:   8100        -8823.267             0.075            0.037
Chain 1:   8200        -9055.724             0.077            0.037
Chain 1:   8300        -9468.502             0.079            0.044
Chain 1:   8400       -10045.175             0.085            0.054
Chain 1:   8500        -9877.507             0.060            0.044
Chain 1:   8600        -9937.348             0.039            0.037
Chain 1:   8700        -9139.352             0.035            0.037
Chain 1:   8800        -8747.243             0.034            0.037
Chain 1:   8900        -9457.906             0.038            0.044
Chain 1:   9000        -8936.971             0.044            0.045
Chain 1:   9100        -8898.061             0.042            0.045
Chain 1:   9200       -11853.852             0.064            0.057
Chain 1:   9300       -10224.095             0.076            0.058
Chain 1:   9400        -9113.695             0.082            0.075
Chain 1:   9500        -9904.191             0.089            0.080
Chain 1:   9600       -10514.698             0.094            0.080
Chain 1:   9700       -11477.659             0.093            0.080
Chain 1:   9800        -9260.351             0.113            0.084
Chain 1:   9900       -11156.898             0.122            0.122
Chain 1:   10000        -8779.398             0.144            0.159
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -64012.744             1.000            1.000
Chain 1:    200       -18895.584             1.694            2.388
Chain 1:    300        -9095.655             1.488            1.077
Chain 1:    400        -8192.190             1.144            1.077
Chain 1:    500        -8915.347             0.931            1.000
Chain 1:    600        -9434.416             0.785            1.000
Chain 1:    700        -7866.001             0.702            0.199
Chain 1:    800        -8573.982             0.624            0.199
Chain 1:    900        -8016.197             0.563            0.110
Chain 1:   1000        -7640.339             0.511            0.110
Chain 1:   1100        -7662.923             0.412            0.083
Chain 1:   1200        -7914.794             0.176            0.081
Chain 1:   1300        -7890.124             0.069            0.070
Chain 1:   1400        -7778.164             0.059            0.055
Chain 1:   1500        -7567.879             0.054            0.049
Chain 1:   1600        -7739.538             0.050            0.032
Chain 1:   1700        -7454.284             0.034            0.032
Chain 1:   1800        -7506.944             0.027            0.028
Chain 1:   1900        -7562.291             0.020            0.022
Chain 1:   2000        -7600.615             0.016            0.014
Chain 1:   2100        -7541.454             0.016            0.014
Chain 1:   2200        -7757.840             0.016            0.014
Chain 1:   2300        -7549.510             0.019            0.022
Chain 1:   2400        -7578.780             0.017            0.022
Chain 1:   2500        -7579.341             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002905 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86237.457             1.000            1.000
Chain 1:    200       -14219.758             3.032            5.065
Chain 1:    300       -10527.668             2.138            1.000
Chain 1:    400       -11713.352             1.629            1.000
Chain 1:    500        -9536.905             1.349            0.351
Chain 1:    600        -9150.956             1.131            0.351
Chain 1:    700        -8914.515             0.973            0.228
Chain 1:    800        -9209.797             0.856            0.228
Chain 1:    900        -9325.057             0.762            0.101
Chain 1:   1000        -9216.279             0.687            0.101
Chain 1:   1100        -9284.068             0.588            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8832.600             0.086            0.042
Chain 1:   1300        -9182.646             0.055            0.038
Chain 1:   1400        -9177.169             0.045            0.032
Chain 1:   1500        -9040.011             0.024            0.027
Chain 1:   1600        -9159.133             0.021            0.015
Chain 1:   1700        -9225.156             0.019            0.013
Chain 1:   1800        -8791.378             0.021            0.013
Chain 1:   1900        -8894.700             0.021            0.013
Chain 1:   2000        -8870.212             0.020            0.013
Chain 1:   2100        -8835.315             0.019            0.013
Chain 1:   2200        -8812.859             0.014            0.012
Chain 1:   2300        -8948.566             0.012            0.012
Chain 1:   2400        -8795.597             0.014            0.013
Chain 1:   2500        -8864.852             0.013            0.012
Chain 1:   2600        -8783.044             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003445 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372150.760             1.000            1.000
Chain 1:    200     -1577392.285             2.654            4.308
Chain 1:    300      -890074.590             2.027            1.000
Chain 1:    400      -457984.853             1.756            1.000
Chain 1:    500      -358945.862             1.460            0.943
Chain 1:    600      -234076.900             1.305            0.943
Chain 1:    700      -120181.683             1.254            0.943
Chain 1:    800       -87370.633             1.144            0.943
Chain 1:    900       -67673.493             1.050            0.772
Chain 1:   1000       -52444.992             0.974            0.772
Chain 1:   1100       -39890.307             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39067.739             0.477            0.376
Chain 1:   1300       -26977.353             0.444            0.376
Chain 1:   1400       -26694.340             0.351            0.315
Chain 1:   1500       -23269.230             0.338            0.315
Chain 1:   1600       -22483.246             0.288            0.291
Chain 1:   1700       -21350.269             0.199            0.290
Chain 1:   1800       -21293.437             0.161            0.147
Chain 1:   1900       -21619.959             0.134            0.053
Chain 1:   2000       -20127.273             0.112            0.053
Chain 1:   2100       -20365.699             0.082            0.035
Chain 1:   2200       -20593.051             0.081            0.035
Chain 1:   2300       -20209.400             0.038            0.019
Chain 1:   2400       -19981.326             0.038            0.019
Chain 1:   2500       -19783.618             0.024            0.015
Chain 1:   2600       -19413.105             0.023            0.015
Chain 1:   2700       -19369.925             0.018            0.012
Chain 1:   2800       -19086.736             0.019            0.015
Chain 1:   2900       -19368.215             0.019            0.015
Chain 1:   3000       -19354.309             0.011            0.012
Chain 1:   3100       -19439.374             0.011            0.011
Chain 1:   3200       -19129.722             0.011            0.015
Chain 1:   3300       -19334.745             0.010            0.011
Chain 1:   3400       -18809.211             0.012            0.015
Chain 1:   3500       -19421.865             0.014            0.015
Chain 1:   3600       -18727.570             0.016            0.015
Chain 1:   3700       -19115.123             0.018            0.016
Chain 1:   3800       -18073.406             0.022            0.020
Chain 1:   3900       -18069.588             0.021            0.020
Chain 1:   4000       -18186.818             0.021            0.020
Chain 1:   4100       -18100.527             0.021            0.020
Chain 1:   4200       -17916.517             0.021            0.020
Chain 1:   4300       -18055.072             0.020            0.020
Chain 1:   4400       -18011.634             0.018            0.010
Chain 1:   4500       -17914.187             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12187.608             1.000            1.000
Chain 1:    200        -8941.786             0.681            1.000
Chain 1:    300        -7850.089             0.501            0.363
Chain 1:    400        -7997.765             0.380            0.363
Chain 1:    500        -7950.640             0.305            0.139
Chain 1:    600        -7802.631             0.258            0.139
Chain 1:    700        -7722.917             0.222            0.019
Chain 1:    800        -7731.512             0.195            0.019
Chain 1:    900        -7646.891             0.174            0.018
Chain 1:   1000        -7827.507             0.159            0.019
Chain 1:   1100        -7857.450             0.059            0.018
Chain 1:   1200        -7753.263             0.025            0.013
Chain 1:   1300        -7699.544             0.011            0.011
Chain 1:   1400        -7715.560             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56901.533             1.000            1.000
Chain 1:    200       -17201.740             1.654            2.308
Chain 1:    300        -8664.647             1.431            1.000
Chain 1:    400        -8357.532             1.082            1.000
Chain 1:    500        -8105.755             0.872            0.985
Chain 1:    600        -8268.231             0.730            0.985
Chain 1:    700        -8160.924             0.628            0.037
Chain 1:    800        -7959.883             0.552            0.037
Chain 1:    900        -7959.887             0.491            0.031
Chain 1:   1000        -7847.988             0.443            0.031
Chain 1:   1100        -7893.794             0.344            0.025
Chain 1:   1200        -7738.004             0.115            0.020
Chain 1:   1300        -7697.159             0.017            0.020
Chain 1:   1400        -7685.489             0.014            0.014
Chain 1:   1500        -7644.248             0.011            0.013
Chain 1:   1600        -7611.458             0.010            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004086 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85613.444             1.000            1.000
Chain 1:    200       -13278.327             3.224            5.448
Chain 1:    300        -9680.481             2.273            1.000
Chain 1:    400       -10670.554             1.728            1.000
Chain 1:    500        -8574.069             1.431            0.372
Chain 1:    600        -8161.821             1.201            0.372
Chain 1:    700        -8268.502             1.031            0.245
Chain 1:    800        -8704.054             0.909            0.245
Chain 1:    900        -8557.377             0.810            0.093
Chain 1:   1000        -8246.917             0.732            0.093
Chain 1:   1100        -8527.545             0.636            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8191.687             0.095            0.050
Chain 1:   1300        -8392.301             0.060            0.041
Chain 1:   1400        -8398.334             0.051            0.038
Chain 1:   1500        -8257.718             0.028            0.033
Chain 1:   1600        -8370.039             0.025            0.024
Chain 1:   1700        -8455.628             0.024            0.024
Chain 1:   1800        -8049.472             0.024            0.024
Chain 1:   1900        -8145.881             0.024            0.024
Chain 1:   2000        -8118.045             0.020            0.017
Chain 1:   2100        -8238.685             0.019            0.015
Chain 1:   2200        -8051.726             0.017            0.015
Chain 1:   2300        -8185.910             0.016            0.015
Chain 1:   2400        -8194.081             0.016            0.015
Chain 1:   2500        -8158.935             0.015            0.013
Chain 1:   2600        -8156.954             0.014            0.012
Chain 1:   2700        -8071.341             0.014            0.012
Chain 1:   2800        -8036.322             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003157 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413721.727             1.000            1.000
Chain 1:    200     -1587392.141             2.650            4.300
Chain 1:    300      -890709.731             2.028            1.000
Chain 1:    400      -457070.968             1.758            1.000
Chain 1:    500      -357149.649             1.462            0.949
Chain 1:    600      -232121.012             1.308            0.949
Chain 1:    700      -118665.655             1.258            0.949
Chain 1:    800       -85967.920             1.148            0.949
Chain 1:    900       -66374.483             1.053            0.782
Chain 1:   1000       -51227.728             0.978            0.782
Chain 1:   1100       -38756.060             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37937.337             0.482            0.380
Chain 1:   1300       -25944.431             0.450            0.380
Chain 1:   1400       -25668.182             0.356            0.322
Chain 1:   1500       -22268.523             0.344            0.322
Chain 1:   1600       -21489.019             0.293            0.296
Chain 1:   1700       -20368.837             0.203            0.295
Chain 1:   1800       -20314.487             0.165            0.153
Chain 1:   1900       -20640.512             0.137            0.055
Chain 1:   2000       -19155.072             0.116            0.055
Chain 1:   2100       -19393.262             0.085            0.036
Chain 1:   2200       -19619.133             0.084            0.036
Chain 1:   2300       -19236.900             0.039            0.020
Chain 1:   2400       -19009.085             0.040            0.020
Chain 1:   2500       -18810.933             0.025            0.016
Chain 1:   2600       -18441.472             0.024            0.016
Chain 1:   2700       -18398.589             0.018            0.012
Chain 1:   2800       -18115.411             0.020            0.016
Chain 1:   2900       -18396.524             0.020            0.015
Chain 1:   3000       -18382.770             0.012            0.012
Chain 1:   3100       -18467.715             0.011            0.012
Chain 1:   3200       -18158.587             0.012            0.015
Chain 1:   3300       -18363.177             0.011            0.012
Chain 1:   3400       -17838.299             0.013            0.015
Chain 1:   3500       -18449.832             0.015            0.016
Chain 1:   3600       -17756.952             0.017            0.016
Chain 1:   3700       -18143.352             0.019            0.017
Chain 1:   3800       -17103.752             0.023            0.021
Chain 1:   3900       -17099.888             0.022            0.021
Chain 1:   4000       -17217.213             0.022            0.021
Chain 1:   4100       -17130.989             0.022            0.021
Chain 1:   4200       -16947.403             0.022            0.021
Chain 1:   4300       -17085.700             0.021            0.021
Chain 1:   4400       -17042.634             0.019            0.011
Chain 1:   4500       -16945.176             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49833.498             1.000            1.000
Chain 1:    200       -24012.357             1.038            1.075
Chain 1:    300       -14649.706             0.905            1.000
Chain 1:    400       -17826.857             0.723            1.000
Chain 1:    500       -16372.288             0.596            0.639
Chain 1:    600       -24798.371             0.554            0.639
Chain 1:    700       -16430.010             0.547            0.509
Chain 1:    800       -15635.876             0.485            0.509
Chain 1:    900       -25030.450             0.473            0.375
Chain 1:   1000       -13813.606             0.507            0.509
Chain 1:   1100       -14570.022             0.412            0.375
Chain 1:   1200       -21051.582             0.335            0.340
Chain 1:   1300       -12591.233             0.339            0.340
Chain 1:   1400       -12080.033             0.325            0.340
Chain 1:   1500       -10991.295             0.326            0.340
Chain 1:   1600       -18261.933             0.332            0.375
Chain 1:   1700       -16966.838             0.289            0.308
Chain 1:   1800       -11687.803             0.329            0.375
Chain 1:   1900       -11020.314             0.297            0.308
Chain 1:   2000       -15191.950             0.243            0.275
Chain 1:   2100       -18990.909             0.258            0.275
Chain 1:   2200       -13381.225             0.269            0.275
Chain 1:   2300       -10204.932             0.233            0.275
Chain 1:   2400       -15481.592             0.263            0.311
Chain 1:   2500       -10859.266             0.296            0.341
Chain 1:   2600        -9955.068             0.265            0.311
Chain 1:   2700       -12929.819             0.280            0.311
Chain 1:   2800       -12046.145             0.243            0.275
Chain 1:   2900       -10414.699             0.252            0.275
Chain 1:   3000       -12971.690             0.245            0.230
Chain 1:   3100       -10225.882             0.251            0.269
Chain 1:   3200       -10192.278             0.210            0.230
Chain 1:   3300       -16905.804             0.218            0.230
Chain 1:   3400        -9549.587             0.261            0.230
Chain 1:   3500       -10177.583             0.225            0.197
Chain 1:   3600        -9365.817             0.224            0.197
Chain 1:   3700       -15762.302             0.242            0.197
Chain 1:   3800       -15931.445             0.236            0.197
Chain 1:   3900        -9752.003             0.283            0.269
Chain 1:   4000        -9623.517             0.265            0.269
Chain 1:   4100       -10413.643             0.246            0.087
Chain 1:   4200       -10100.766             0.249            0.087
Chain 1:   4300       -15013.371             0.242            0.087
Chain 1:   4400       -10393.714             0.209            0.087
Chain 1:   4500       -11562.911             0.213            0.101
Chain 1:   4600        -9387.802             0.227            0.232
Chain 1:   4700       -13822.609             0.219            0.232
Chain 1:   4800        -9455.487             0.264            0.321
Chain 1:   4900       -14280.362             0.235            0.321
Chain 1:   5000       -15224.565             0.239            0.321
Chain 1:   5100       -10978.482             0.270            0.327
Chain 1:   5200        -9638.162             0.281            0.327
Chain 1:   5300        -9344.751             0.252            0.321
Chain 1:   5400        -9145.044             0.209            0.232
Chain 1:   5500        -8993.289             0.201            0.232
Chain 1:   5600       -15546.675             0.220            0.321
Chain 1:   5700       -14813.990             0.193            0.139
Chain 1:   5800        -9732.330             0.199            0.139
Chain 1:   5900        -9601.747             0.166            0.062
Chain 1:   6000        -9371.061             0.163            0.049
Chain 1:   6100        -9344.011             0.124            0.031
Chain 1:   6200        -8940.011             0.115            0.031
Chain 1:   6300       -14156.186             0.149            0.045
Chain 1:   6400       -15331.196             0.154            0.049
Chain 1:   6500        -9344.753             0.217            0.077
Chain 1:   6600        -9110.635             0.177            0.049
Chain 1:   6700        -8774.952             0.176            0.045
Chain 1:   6800       -11393.405             0.147            0.045
Chain 1:   6900       -11397.854             0.145            0.045
Chain 1:   7000        -9805.048             0.159            0.077
Chain 1:   7100       -13173.693             0.184            0.162
Chain 1:   7200        -9150.844             0.224            0.230
Chain 1:   7300       -10485.374             0.200            0.162
Chain 1:   7400        -8668.831             0.213            0.210
Chain 1:   7500       -12203.429             0.178            0.210
Chain 1:   7600        -9446.172             0.204            0.230
Chain 1:   7700        -9318.000             0.202            0.230
Chain 1:   7800        -8870.798             0.184            0.210
Chain 1:   7900        -8737.467             0.186            0.210
Chain 1:   8000       -14146.779             0.208            0.256
Chain 1:   8100       -11826.516             0.202            0.210
Chain 1:   8200       -12286.545             0.161            0.196
Chain 1:   8300        -8961.656             0.186            0.210
Chain 1:   8400       -13200.947             0.197            0.290
Chain 1:   8500        -9013.123             0.214            0.292
Chain 1:   8600       -13792.836             0.220            0.321
Chain 1:   8700       -12525.555             0.229            0.321
Chain 1:   8800        -8920.763             0.264            0.347
Chain 1:   8900        -9220.321             0.266            0.347
Chain 1:   9000       -13368.023             0.258            0.321
Chain 1:   9100        -8741.888             0.292            0.347
Chain 1:   9200        -9219.288             0.293            0.347
Chain 1:   9300       -10214.094             0.266            0.321
Chain 1:   9400        -8806.894             0.250            0.310
Chain 1:   9500       -10279.122             0.218            0.160
Chain 1:   9600        -9498.023             0.191            0.143
Chain 1:   9700        -8575.890             0.192            0.143
Chain 1:   9800        -8664.636             0.152            0.108
Chain 1:   9900        -8983.887             0.153            0.108
Chain 1:   10000        -8748.022             0.124            0.097
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001565 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62810.111             1.000            1.000
Chain 1:    200       -18385.743             1.708            2.416
Chain 1:    300        -9128.961             1.477            1.014
Chain 1:    400        -8420.808             1.129            1.014
Chain 1:    500        -8083.620             0.911            1.000
Chain 1:    600        -8206.952             0.762            1.000
Chain 1:    700        -8680.982             0.661            0.084
Chain 1:    800        -8294.564             0.584            0.084
Chain 1:    900        -8123.960             0.521            0.055
Chain 1:   1000        -7632.920             0.476            0.064
Chain 1:   1100        -7991.072             0.380            0.055
Chain 1:   1200        -7771.319             0.141            0.047
Chain 1:   1300        -7650.286             0.042            0.045
Chain 1:   1400        -7531.209             0.035            0.042
Chain 1:   1500        -7472.687             0.031            0.028
Chain 1:   1600        -7705.499             0.033            0.030
Chain 1:   1700        -7670.516             0.028            0.028
Chain 1:   1800        -7728.649             0.024            0.021
Chain 1:   1900        -7571.511             0.024            0.021
Chain 1:   2000        -7580.292             0.018            0.016
Chain 1:   2100        -7537.815             0.014            0.016
Chain 1:   2200        -7736.165             0.013            0.016
Chain 1:   2300        -7575.929             0.014            0.016
Chain 1:   2400        -7485.913             0.014            0.012
Chain 1:   2500        -7561.554             0.014            0.012
Chain 1:   2600        -7470.450             0.012            0.012
Chain 1:   2700        -7362.946             0.013            0.012
Chain 1:   2800        -7653.276             0.016            0.015
Chain 1:   2900        -7304.162             0.019            0.015
Chain 1:   3000        -7483.057             0.021            0.021
Chain 1:   3100        -7452.615             0.021            0.021
Chain 1:   3200        -7691.053             0.021            0.021
Chain 1:   3300        -7346.829             0.024            0.024
Chain 1:   3400        -7590.662             0.026            0.031
Chain 1:   3500        -7374.611             0.028            0.031
Chain 1:   3600        -7438.796             0.028            0.031
Chain 1:   3700        -7396.640             0.027            0.031
Chain 1:   3800        -7366.529             0.023            0.029
Chain 1:   3900        -7339.214             0.019            0.024
Chain 1:   4000        -7335.237             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006106 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 61.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86540.182             1.000            1.000
Chain 1:    200       -14139.626             3.060            5.120
Chain 1:    300       -10461.185             2.157            1.000
Chain 1:    400       -11506.181             1.641            1.000
Chain 1:    500        -9441.987             1.356            0.352
Chain 1:    600        -9209.417             1.134            0.352
Chain 1:    700        -8844.789             0.978            0.219
Chain 1:    800        -9219.074             0.861            0.219
Chain 1:    900        -9342.867             0.767            0.091
Chain 1:   1000        -8979.131             0.694            0.091
Chain 1:   1100        -9075.658             0.595            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8876.376             0.085            0.041
Chain 1:   1300        -9089.168             0.053            0.041
Chain 1:   1400        -9094.456             0.044            0.025
Chain 1:   1500        -8990.709             0.023            0.023
Chain 1:   1600        -9096.016             0.022            0.022
Chain 1:   1700        -9171.168             0.018            0.013
Chain 1:   1800        -8741.318             0.019            0.013
Chain 1:   1900        -8845.027             0.019            0.012
Chain 1:   2000        -8820.288             0.015            0.012
Chain 1:   2100        -8952.111             0.016            0.012
Chain 1:   2200        -8748.188             0.016            0.012
Chain 1:   2300        -8843.246             0.014            0.012
Chain 1:   2400        -8908.577             0.015            0.012
Chain 1:   2500        -8853.901             0.015            0.012
Chain 1:   2600        -8857.792             0.013            0.011
Chain 1:   2700        -8773.173             0.014            0.011
Chain 1:   2800        -8730.331             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003965 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8375450.733             1.000            1.000
Chain 1:    200     -1577238.271             2.655            4.310
Chain 1:    300      -889634.039             2.028            1.000
Chain 1:    400      -457787.383             1.757            1.000
Chain 1:    500      -358653.102             1.461            0.943
Chain 1:    600      -233779.418             1.306            0.943
Chain 1:    700      -119979.615             1.255            0.943
Chain 1:    800       -87182.006             1.145            0.943
Chain 1:    900       -67510.080             1.050            0.773
Chain 1:   1000       -52291.787             0.974            0.773
Chain 1:   1100       -39752.399             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38930.959             0.477            0.376
Chain 1:   1300       -26859.711             0.445            0.376
Chain 1:   1400       -26579.052             0.351            0.315
Chain 1:   1500       -23158.278             0.339            0.315
Chain 1:   1600       -22373.296             0.289            0.291
Chain 1:   1700       -21242.880             0.199            0.291
Chain 1:   1800       -21186.661             0.162            0.148
Chain 1:   1900       -21513.010             0.134            0.053
Chain 1:   2000       -20021.674             0.112            0.053
Chain 1:   2100       -20260.252             0.082            0.035
Chain 1:   2200       -20487.151             0.081            0.035
Chain 1:   2300       -20103.873             0.038            0.019
Chain 1:   2400       -19875.798             0.038            0.019
Chain 1:   2500       -19677.987             0.024            0.015
Chain 1:   2600       -19307.760             0.023            0.015
Chain 1:   2700       -19264.673             0.018            0.012
Chain 1:   2800       -18981.448             0.019            0.015
Chain 1:   2900       -19262.841             0.019            0.015
Chain 1:   3000       -19249.016             0.012            0.012
Chain 1:   3100       -19334.048             0.011            0.011
Chain 1:   3200       -19024.540             0.011            0.015
Chain 1:   3300       -19229.426             0.010            0.011
Chain 1:   3400       -18704.075             0.012            0.015
Chain 1:   3500       -19316.448             0.014            0.015
Chain 1:   3600       -18622.458             0.016            0.015
Chain 1:   3700       -19009.744             0.018            0.016
Chain 1:   3800       -17968.519             0.022            0.020
Chain 1:   3900       -17964.664             0.021            0.020
Chain 1:   4000       -18081.925             0.021            0.020
Chain 1:   4100       -17995.632             0.021            0.020
Chain 1:   4200       -17811.719             0.021            0.020
Chain 1:   4300       -17950.230             0.020            0.020
Chain 1:   4400       -17906.872             0.018            0.010
Chain 1:   4500       -17809.393             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49015.484             1.000            1.000
Chain 1:    200       -20747.407             1.181            1.362
Chain 1:    300       -13350.821             0.972            1.000
Chain 1:    400       -19716.523             0.810            1.000
Chain 1:    500       -13537.284             0.739            0.554
Chain 1:    600       -29610.264             0.706            0.554
Chain 1:    700       -15942.359             0.728            0.554
Chain 1:    800       -13836.083             0.656            0.554
Chain 1:    900       -10652.512             0.616            0.543
Chain 1:   1000       -16851.228             0.591            0.543
Chain 1:   1100       -11043.073             0.544            0.526   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -24493.891             0.463            0.526   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -10960.203             0.531            0.526   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -22500.634             0.550            0.526   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -12649.945             0.582            0.543   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1600       -29090.759             0.584            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700       -10169.527             0.685            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800        -9777.376             0.673            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -11479.704             0.658            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -11524.176             0.622            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100       -19677.031             0.611            0.549   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200        -9496.399             0.663            0.565   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300        -9872.385             0.543            0.513   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400        -9994.836             0.493            0.414
Chain 1:   2500       -12796.161             0.437            0.219
Chain 1:   2600        -9851.506             0.411            0.219
Chain 1:   2700        -9088.986             0.233            0.148
Chain 1:   2800       -15522.545             0.271            0.219
Chain 1:   2900        -9596.734             0.317            0.299
Chain 1:   3000        -9674.490             0.318            0.299
Chain 1:   3100        -8843.204             0.286            0.219
Chain 1:   3200        -9707.934             0.188            0.094
Chain 1:   3300        -9130.687             0.190            0.094
Chain 1:   3400       -13894.351             0.223            0.219
Chain 1:   3500        -8972.655             0.256            0.299
Chain 1:   3600        -9231.218             0.229            0.094
Chain 1:   3700       -12150.703             0.245            0.240
Chain 1:   3800        -8773.396             0.242            0.240
Chain 1:   3900        -9092.822             0.183            0.094
Chain 1:   4000       -14655.710             0.221            0.240
Chain 1:   4100       -10171.125             0.255            0.343
Chain 1:   4200        -8987.566             0.260            0.343
Chain 1:   4300       -10646.501             0.269            0.343
Chain 1:   4400        -8752.876             0.256            0.240
Chain 1:   4500       -11468.535             0.225            0.237
Chain 1:   4600       -10297.310             0.234            0.237
Chain 1:   4700       -10433.472             0.211            0.216
Chain 1:   4800        -9016.398             0.188            0.157
Chain 1:   4900        -9403.966             0.189            0.157
Chain 1:   5000       -14572.484             0.186            0.157
Chain 1:   5100       -10349.626             0.183            0.157
Chain 1:   5200       -11654.308             0.181            0.157
Chain 1:   5300       -12447.238             0.172            0.157
Chain 1:   5400        -8560.277             0.195            0.157
Chain 1:   5500        -8823.941             0.175            0.114
Chain 1:   5600       -13763.394             0.199            0.157
Chain 1:   5700       -12655.858             0.207            0.157
Chain 1:   5800        -9327.312             0.227            0.355
Chain 1:   5900        -9160.605             0.224            0.355
Chain 1:   6000        -8481.276             0.197            0.112
Chain 1:   6100       -10015.412             0.171            0.112
Chain 1:   6200        -9035.023             0.171            0.109
Chain 1:   6300        -9017.330             0.165            0.109
Chain 1:   6400        -9751.665             0.127            0.088
Chain 1:   6500        -9493.993             0.127            0.088
Chain 1:   6600        -8901.026             0.098            0.080
Chain 1:   6700        -9464.524             0.095            0.075
Chain 1:   6800        -8729.050             0.067            0.075
Chain 1:   6900       -11419.278             0.089            0.080
Chain 1:   7000       -12967.955             0.093            0.084
Chain 1:   7100        -8450.310             0.131            0.084
Chain 1:   7200       -11178.534             0.145            0.084
Chain 1:   7300        -8711.506             0.173            0.119
Chain 1:   7400        -8347.906             0.170            0.119
Chain 1:   7500       -10437.393             0.187            0.200
Chain 1:   7600        -9715.376             0.188            0.200
Chain 1:   7700        -8327.422             0.199            0.200
Chain 1:   7800       -11948.649             0.220            0.236
Chain 1:   7900        -8325.980             0.240            0.244
Chain 1:   8000        -8430.818             0.230            0.244
Chain 1:   8100        -8763.745             0.180            0.200
Chain 1:   8200        -9492.337             0.163            0.167
Chain 1:   8300       -11203.555             0.150            0.153
Chain 1:   8400        -8300.951             0.181            0.167
Chain 1:   8500        -9167.419             0.170            0.153
Chain 1:   8600        -9360.908             0.165            0.153
Chain 1:   8700        -8286.350             0.161            0.130
Chain 1:   8800        -8385.019             0.132            0.095
Chain 1:   8900       -11015.642             0.113            0.095
Chain 1:   9000       -11576.703             0.116            0.095
Chain 1:   9100        -8255.695             0.153            0.130
Chain 1:   9200       -10862.655             0.169            0.153
Chain 1:   9300       -11215.682             0.157            0.130
Chain 1:   9400        -8747.329             0.150            0.130
Chain 1:   9500        -8788.860             0.141            0.130
Chain 1:   9600        -9852.310             0.150            0.130
Chain 1:   9700        -9936.492             0.138            0.108
Chain 1:   9800        -8447.307             0.154            0.176
Chain 1:   9900        -8979.741             0.136            0.108
Chain 1:   10000        -8341.318             0.139            0.108
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001578 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58093.006             1.000            1.000
Chain 1:    200       -17750.081             1.636            2.273
Chain 1:    300        -8693.353             1.438            1.042
Chain 1:    400        -8204.065             1.094            1.042
Chain 1:    500        -8396.293             0.879            1.000
Chain 1:    600        -8895.580             0.742            1.000
Chain 1:    700        -8102.206             0.650            0.098
Chain 1:    800        -8171.140             0.570            0.098
Chain 1:    900        -7955.718             0.510            0.060
Chain 1:   1000        -7845.470             0.460            0.060
Chain 1:   1100        -7744.721             0.361            0.056
Chain 1:   1200        -7685.964             0.135            0.027
Chain 1:   1300        -7610.757             0.032            0.023
Chain 1:   1400        -7637.388             0.026            0.014
Chain 1:   1500        -7580.495             0.025            0.013
Chain 1:   1600        -7768.518             0.021            0.013
Chain 1:   1700        -7529.074             0.015            0.013
Chain 1:   1800        -7625.336             0.015            0.013
Chain 1:   1900        -7649.500             0.013            0.013
Chain 1:   2000        -7630.519             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003265 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86819.380             1.000            1.000
Chain 1:    200       -13536.193             3.207            5.414
Chain 1:    300        -9965.785             2.257            1.000
Chain 1:    400       -10914.398             1.715            1.000
Chain 1:    500        -8883.323             1.418            0.358
Chain 1:    600        -8513.751             1.189            0.358
Chain 1:    700        -8654.918             1.021            0.229
Chain 1:    800        -9317.401             0.902            0.229
Chain 1:    900        -8825.603             0.808            0.087
Chain 1:   1000        -8546.380             0.731            0.087
Chain 1:   1100        -8716.557             0.633            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8513.010             0.094            0.056
Chain 1:   1300        -8707.340             0.060            0.043
Chain 1:   1400        -8697.492             0.051            0.033
Chain 1:   1500        -8600.404             0.030            0.024
Chain 1:   1600        -8696.907             0.027            0.022
Chain 1:   1700        -8786.404             0.026            0.022
Chain 1:   1800        -8397.342             0.023            0.022
Chain 1:   1900        -8499.725             0.019            0.020
Chain 1:   2000        -8469.785             0.016            0.012
Chain 1:   2100        -8600.499             0.016            0.012
Chain 1:   2200        -8386.695             0.016            0.012
Chain 1:   2300        -8528.873             0.015            0.012
Chain 1:   2400        -8541.733             0.015            0.012
Chain 1:   2500        -8509.603             0.015            0.012
Chain 1:   2600        -8509.873             0.013            0.012
Chain 1:   2700        -8417.801             0.014            0.012
Chain 1:   2800        -8393.343             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003271 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410042.501             1.000            1.000
Chain 1:    200     -1589592.072             2.645            4.291
Chain 1:    300      -892679.857             2.024            1.000
Chain 1:    400      -458388.808             1.755            1.000
Chain 1:    500      -358278.927             1.460            0.947
Chain 1:    600      -232964.217             1.306            0.947
Chain 1:    700      -119191.315             1.256            0.947
Chain 1:    800       -86384.810             1.146            0.947
Chain 1:    900       -66736.939             1.052            0.781
Chain 1:   1000       -51539.061             0.976            0.781
Chain 1:   1100       -39025.840             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38199.432             0.481            0.380
Chain 1:   1300       -26179.835             0.449            0.380
Chain 1:   1400       -25898.540             0.355            0.321
Chain 1:   1500       -22491.895             0.343            0.321
Chain 1:   1600       -21709.200             0.292            0.295
Chain 1:   1700       -20586.710             0.202            0.294
Chain 1:   1800       -20531.343             0.165            0.151
Chain 1:   1900       -20856.965             0.137            0.055
Chain 1:   2000       -19370.817             0.115            0.055
Chain 1:   2100       -19609.152             0.084            0.036
Chain 1:   2200       -19834.865             0.083            0.036
Chain 1:   2300       -19452.837             0.039            0.020
Chain 1:   2400       -19225.131             0.039            0.020
Chain 1:   2500       -19026.966             0.025            0.016
Chain 1:   2600       -18657.888             0.023            0.016
Chain 1:   2700       -18615.062             0.018            0.012
Chain 1:   2800       -18332.034             0.020            0.015
Chain 1:   2900       -18613.039             0.019            0.015
Chain 1:   3000       -18599.330             0.012            0.012
Chain 1:   3100       -18684.218             0.011            0.012
Chain 1:   3200       -18375.293             0.012            0.015
Chain 1:   3300       -18579.705             0.011            0.012
Chain 1:   3400       -18055.235             0.013            0.015
Chain 1:   3500       -18666.135             0.015            0.015
Chain 1:   3600       -17974.117             0.017            0.015
Chain 1:   3700       -18359.908             0.018            0.017
Chain 1:   3800       -17321.578             0.023            0.021
Chain 1:   3900       -17317.739             0.021            0.021
Chain 1:   4000       -17435.077             0.022            0.021
Chain 1:   4100       -17348.902             0.022            0.021
Chain 1:   4200       -17165.579             0.021            0.021
Chain 1:   4300       -17303.695             0.021            0.021
Chain 1:   4400       -17260.882             0.019            0.011
Chain 1:   4500       -17163.446             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001538 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49326.460             1.000            1.000
Chain 1:    200       -14828.186             1.663            2.327
Chain 1:    300       -13666.936             1.137            1.000
Chain 1:    400       -18585.097             0.919            1.000
Chain 1:    500       -12895.344             0.823            0.441
Chain 1:    600       -17272.973             0.728            0.441
Chain 1:    700       -16356.962             0.632            0.265
Chain 1:    800       -10741.034             0.619            0.441
Chain 1:    900       -19649.465             0.600            0.441
Chain 1:   1000       -15677.883             0.566            0.441
Chain 1:   1100       -10134.845             0.520            0.441   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11836.257             0.302            0.265
Chain 1:   1300       -11236.432             0.299            0.265
Chain 1:   1400       -10185.913             0.283            0.253
Chain 1:   1500       -11692.689             0.252            0.253
Chain 1:   1600       -23044.105             0.275            0.253
Chain 1:   1700       -15401.516             0.319            0.453
Chain 1:   1800       -12560.054             0.290            0.253
Chain 1:   1900       -10466.425             0.264            0.226
Chain 1:   2000       -15310.843             0.271            0.226
Chain 1:   2100       -10307.725             0.265            0.226
Chain 1:   2200       -10206.558             0.251            0.226
Chain 1:   2300        -9557.791             0.253            0.226
Chain 1:   2400        -9333.683             0.245            0.226
Chain 1:   2500       -16624.368             0.276            0.316
Chain 1:   2600        -9521.541             0.301            0.316
Chain 1:   2700        -9551.987             0.252            0.226
Chain 1:   2800       -12407.963             0.252            0.230
Chain 1:   2900       -14794.025             0.248            0.230
Chain 1:   3000        -9071.436             0.280            0.230
Chain 1:   3100        -9542.850             0.236            0.161
Chain 1:   3200        -9056.263             0.241            0.161
Chain 1:   3300        -9149.292             0.235            0.161
Chain 1:   3400        -8840.213             0.236            0.161
Chain 1:   3500        -8886.522             0.192            0.054
Chain 1:   3600        -9013.300             0.119            0.049
Chain 1:   3700        -9529.742             0.124            0.054
Chain 1:   3800        -8649.371             0.112            0.054
Chain 1:   3900       -10052.399             0.109            0.054
Chain 1:   4000        -8888.298             0.059            0.054
Chain 1:   4100       -14605.099             0.094            0.054
Chain 1:   4200       -12634.562             0.104            0.102
Chain 1:   4300       -11501.977             0.113            0.102
Chain 1:   4400        -9123.478             0.135            0.131
Chain 1:   4500       -10095.033             0.144            0.131
Chain 1:   4600        -9839.931             0.146            0.131
Chain 1:   4700       -13793.393             0.169            0.140
Chain 1:   4800        -8690.591             0.217            0.156
Chain 1:   4900        -8875.329             0.205            0.156
Chain 1:   5000        -9884.736             0.203            0.156
Chain 1:   5100        -9125.641             0.172            0.102
Chain 1:   5200        -8827.805             0.159            0.098
Chain 1:   5300       -11703.420             0.174            0.102
Chain 1:   5400        -9416.366             0.172            0.102
Chain 1:   5500        -8565.885             0.173            0.102
Chain 1:   5600        -8536.964             0.170            0.102
Chain 1:   5700        -8459.732             0.143            0.099
Chain 1:   5800        -8911.189             0.089            0.083
Chain 1:   5900       -11300.904             0.108            0.099
Chain 1:   6000        -8326.116             0.134            0.099
Chain 1:   6100        -8757.101             0.130            0.099
Chain 1:   6200       -11188.959             0.149            0.211
Chain 1:   6300        -8686.644             0.153            0.211
Chain 1:   6400       -13165.534             0.163            0.211
Chain 1:   6500        -8874.003             0.201            0.217
Chain 1:   6600        -9456.844             0.207            0.217
Chain 1:   6700        -8449.172             0.218            0.217
Chain 1:   6800        -8348.412             0.214            0.217
Chain 1:   6900        -8190.365             0.195            0.217
Chain 1:   7000       -14225.925             0.201            0.217
Chain 1:   7100        -8202.444             0.270            0.288
Chain 1:   7200        -9495.967             0.262            0.288
Chain 1:   7300        -8285.324             0.248            0.146
Chain 1:   7400        -8606.950             0.217            0.136
Chain 1:   7500        -8272.048             0.173            0.119
Chain 1:   7600        -9293.622             0.178            0.119
Chain 1:   7700        -8134.079             0.180            0.136
Chain 1:   7800        -9909.310             0.197            0.143
Chain 1:   7900        -8776.141             0.208            0.143
Chain 1:   8000        -8411.599             0.170            0.136
Chain 1:   8100        -8381.151             0.097            0.129
Chain 1:   8200        -8268.255             0.085            0.110
Chain 1:   8300        -8273.608             0.070            0.043
Chain 1:   8400        -9329.161             0.078            0.110
Chain 1:   8500        -8681.486             0.081            0.110
Chain 1:   8600        -8013.189             0.078            0.083
Chain 1:   8700        -8348.271             0.068            0.075
Chain 1:   8800       -10586.602             0.071            0.075
Chain 1:   8900        -8411.387             0.084            0.075
Chain 1:   9000        -8492.219             0.081            0.075
Chain 1:   9100        -8880.993             0.085            0.075
Chain 1:   9200        -8216.487             0.092            0.081
Chain 1:   9300        -8102.487             0.093            0.081
Chain 1:   9400        -8061.629             0.082            0.075
Chain 1:   9500        -8239.133             0.077            0.044
Chain 1:   9600        -8360.141             0.070            0.040
Chain 1:   9700       -11269.755             0.092            0.044
Chain 1:   9800        -9139.703             0.094            0.044
Chain 1:   9900        -9204.170             0.069            0.022
Chain 1:   10000        -8035.880             0.082            0.044
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001564 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63313.030             1.000            1.000
Chain 1:    200       -18378.963             1.722            2.445
Chain 1:    300        -8828.006             1.509            1.082
Chain 1:    400        -7926.140             1.160            1.082
Chain 1:    500        -8656.722             0.945            1.000
Chain 1:    600        -9706.700             0.806            1.000
Chain 1:    700        -7941.142             0.722            0.222
Chain 1:    800        -8307.338             0.637            0.222
Chain 1:    900        -7907.920             0.572            0.114
Chain 1:   1000        -7793.967             0.516            0.114
Chain 1:   1100        -7820.015             0.417            0.108
Chain 1:   1200        -7506.574             0.176            0.084
Chain 1:   1300        -7534.494             0.069            0.051
Chain 1:   1400        -7837.173             0.061            0.044
Chain 1:   1500        -7594.422             0.056            0.042
Chain 1:   1600        -7794.489             0.048            0.039
Chain 1:   1700        -7514.333             0.029            0.037
Chain 1:   1800        -7557.824             0.025            0.032
Chain 1:   1900        -7574.743             0.020            0.026
Chain 1:   2000        -7552.385             0.019            0.026
Chain 1:   2100        -7491.631             0.020            0.026
Chain 1:   2200        -7750.007             0.019            0.026
Chain 1:   2300        -7534.564             0.021            0.029
Chain 1:   2400        -7546.908             0.018            0.026
Chain 1:   2500        -7577.775             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003318 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86283.790             1.000            1.000
Chain 1:    200       -13620.454             3.167            5.335
Chain 1:    300        -9888.125             2.237            1.000
Chain 1:    400       -11249.960             1.708            1.000
Chain 1:    500        -8850.716             1.421            0.377
Chain 1:    600        -8590.541             1.189            0.377
Chain 1:    700        -8476.669             1.021            0.271
Chain 1:    800        -8139.660             0.899            0.271
Chain 1:    900        -8239.146             0.800            0.121
Chain 1:   1000        -8542.050             0.724            0.121
Chain 1:   1100        -8644.869             0.625            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8221.845             0.097            0.041
Chain 1:   1300        -8550.374             0.063            0.038
Chain 1:   1400        -8306.357             0.053            0.035
Chain 1:   1500        -8387.186             0.027            0.030
Chain 1:   1600        -8491.792             0.026            0.029
Chain 1:   1700        -8552.344             0.025            0.029
Chain 1:   1800        -8107.646             0.026            0.029
Chain 1:   1900        -8215.149             0.026            0.029
Chain 1:   2000        -8201.778             0.023            0.013
Chain 1:   2100        -8318.418             0.023            0.014
Chain 1:   2200        -8112.865             0.021            0.014
Chain 1:   2300        -8208.383             0.018            0.013
Chain 1:   2400        -8275.274             0.016            0.012
Chain 1:   2500        -8223.650             0.015            0.012
Chain 1:   2600        -8237.737             0.014            0.012
Chain 1:   2700        -8145.185             0.015            0.012
Chain 1:   2800        -8092.550             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003236 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405508.110             1.000            1.000
Chain 1:    200     -1584176.697             2.653            4.306
Chain 1:    300      -890278.847             2.028            1.000
Chain 1:    400      -456794.506             1.759            1.000
Chain 1:    500      -357002.721             1.463            0.949
Chain 1:    600      -232174.023             1.309            0.949
Chain 1:    700      -118938.137             1.258            0.949
Chain 1:    800       -86240.209             1.148            0.949
Chain 1:    900       -66687.741             1.053            0.779
Chain 1:   1000       -51569.252             0.977            0.779
Chain 1:   1100       -39111.731             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38305.352             0.480            0.379
Chain 1:   1300       -26315.247             0.448            0.379
Chain 1:   1400       -26042.857             0.354            0.319
Chain 1:   1500       -22642.127             0.341            0.319
Chain 1:   1600       -21863.428             0.291            0.293
Chain 1:   1700       -20742.806             0.201            0.293
Chain 1:   1800       -20688.961             0.163            0.150
Chain 1:   1900       -21015.645             0.136            0.054
Chain 1:   2000       -19528.661             0.114            0.054
Chain 1:   2100       -19767.076             0.083            0.036
Chain 1:   2200       -19993.285             0.082            0.036
Chain 1:   2300       -19610.586             0.039            0.020
Chain 1:   2400       -19382.556             0.039            0.020
Chain 1:   2500       -19184.201             0.025            0.016
Chain 1:   2600       -18814.162             0.023            0.016
Chain 1:   2700       -18771.180             0.018            0.012
Chain 1:   2800       -18487.587             0.019            0.015
Chain 1:   2900       -18769.018             0.019            0.015
Chain 1:   3000       -18755.304             0.012            0.012
Chain 1:   3100       -18840.313             0.011            0.012
Chain 1:   3200       -18530.730             0.012            0.015
Chain 1:   3300       -18735.711             0.011            0.012
Chain 1:   3400       -18209.964             0.012            0.015
Chain 1:   3500       -18822.704             0.015            0.015
Chain 1:   3600       -18128.299             0.017            0.015
Chain 1:   3700       -18515.799             0.018            0.017
Chain 1:   3800       -17473.735             0.023            0.021
Chain 1:   3900       -17469.811             0.021            0.021
Chain 1:   4000       -17587.157             0.022            0.021
Chain 1:   4100       -17500.743             0.022            0.021
Chain 1:   4200       -17316.719             0.021            0.021
Chain 1:   4300       -17455.382             0.021            0.021
Chain 1:   4400       -17411.883             0.018            0.011
Chain 1:   4500       -17314.347             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11922.649             1.000            1.000
Chain 1:    200        -8876.852             0.672            1.000
Chain 1:    300        -7856.745             0.491            0.343
Chain 1:    400        -7950.762             0.371            0.343
Chain 1:    500        -7783.058             0.301            0.130
Chain 1:    600        -7667.194             0.254            0.130
Chain 1:    700        -7756.094             0.219            0.022
Chain 1:    800        -7627.040             0.194            0.022
Chain 1:    900        -7678.897             0.173            0.017
Chain 1:   1000        -7665.513             0.156            0.017
Chain 1:   1100        -7725.210             0.057            0.015
Chain 1:   1200        -7623.392             0.024            0.013
Chain 1:   1300        -7603.117             0.011            0.012
Chain 1:   1400        -7609.231             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001439 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56416.657             1.000            1.000
Chain 1:    200       -16895.680             1.670            2.339
Chain 1:    300        -8449.230             1.446            1.000
Chain 1:    400        -8591.599             1.089            1.000
Chain 1:    500        -8198.822             0.881            1.000
Chain 1:    600        -8788.391             0.745            1.000
Chain 1:    700        -7640.451             0.660            0.150
Chain 1:    800        -7884.489             0.581            0.150
Chain 1:    900        -7583.213             0.521            0.067
Chain 1:   1000        -7613.060             0.470            0.067
Chain 1:   1100        -7591.763             0.370            0.048
Chain 1:   1200        -7695.953             0.137            0.040
Chain 1:   1300        -7536.141             0.039            0.031
Chain 1:   1400        -7754.993             0.041            0.031
Chain 1:   1500        -7525.717             0.039            0.030
Chain 1:   1600        -7488.211             0.033            0.028
Chain 1:   1700        -7413.239             0.019            0.021
Chain 1:   1800        -7446.792             0.016            0.014
Chain 1:   1900        -7442.212             0.012            0.010
Chain 1:   2000        -7500.786             0.012            0.010
Chain 1:   2100        -7528.819             0.013            0.010
Chain 1:   2200        -7567.794             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86094.605             1.000            1.000
Chain 1:    200       -12969.049             3.319            5.638
Chain 1:    300        -9445.685             2.337            1.000
Chain 1:    400       -10260.913             1.773            1.000
Chain 1:    500        -8294.977             1.466            0.373
Chain 1:    600        -8048.999             1.226            0.373
Chain 1:    700        -8347.159             1.056            0.237
Chain 1:    800        -8522.770             0.927            0.237
Chain 1:    900        -8366.087             0.826            0.079
Chain 1:   1000        -8059.023             0.747            0.079
Chain 1:   1100        -8346.417             0.651            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8113.259             0.090            0.036
Chain 1:   1300        -8219.273             0.054            0.034
Chain 1:   1400        -8215.748             0.046            0.031
Chain 1:   1500        -8120.639             0.023            0.029
Chain 1:   1600        -8204.923             0.021            0.021
Chain 1:   1700        -8310.027             0.019            0.019
Chain 1:   1800        -7925.062             0.022            0.019
Chain 1:   1900        -8023.795             0.021            0.013
Chain 1:   2000        -7993.799             0.018            0.013
Chain 1:   2100        -8137.844             0.016            0.013
Chain 1:   2200        -7916.273             0.016            0.013
Chain 1:   2300        -8055.788             0.016            0.013
Chain 1:   2400        -7940.202             0.018            0.015
Chain 1:   2500        -8000.680             0.017            0.015
Chain 1:   2600        -8014.158             0.016            0.015
Chain 1:   2700        -7935.191             0.016            0.015
Chain 1:   2800        -7919.549             0.011            0.012
Chain 1:   2900        -7916.510             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410200.220             1.000            1.000
Chain 1:    200     -1586599.506             2.650            4.301
Chain 1:    300      -891287.569             2.027            1.000
Chain 1:    400      -457493.297             1.757            1.000
Chain 1:    500      -357426.934             1.462            0.948
Chain 1:    600      -232389.504             1.308            0.948
Chain 1:    700      -118617.266             1.258            0.948
Chain 1:    800       -85807.983             1.149            0.948
Chain 1:    900       -66159.084             1.054            0.780
Chain 1:   1000       -50956.642             0.978            0.780
Chain 1:   1100       -38440.732             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37612.776             0.483            0.382
Chain 1:   1300       -25593.493             0.452            0.382
Chain 1:   1400       -25311.619             0.358            0.326
Chain 1:   1500       -21905.225             0.346            0.326
Chain 1:   1600       -21122.347             0.296            0.298
Chain 1:   1700       -19999.982             0.205            0.297
Chain 1:   1800       -19944.478             0.168            0.156
Chain 1:   1900       -20269.845             0.139            0.056
Chain 1:   2000       -18784.329             0.117            0.056
Chain 1:   2100       -19022.579             0.086            0.037
Chain 1:   2200       -19248.118             0.085            0.037
Chain 1:   2300       -18866.322             0.040            0.020
Chain 1:   2400       -18638.725             0.040            0.020
Chain 1:   2500       -18440.597             0.026            0.016
Chain 1:   2600       -18071.763             0.024            0.016
Chain 1:   2700       -18029.013             0.019            0.013
Chain 1:   2800       -17746.151             0.020            0.016
Chain 1:   2900       -18027.016             0.020            0.016
Chain 1:   3000       -18013.331             0.012            0.013
Chain 1:   3100       -18098.169             0.011            0.012
Chain 1:   3200       -17789.413             0.012            0.016
Chain 1:   3300       -17993.689             0.011            0.012
Chain 1:   3400       -17469.572             0.013            0.016
Chain 1:   3500       -18079.945             0.015            0.016
Chain 1:   3600       -17388.611             0.017            0.016
Chain 1:   3700       -17773.922             0.019            0.017
Chain 1:   3800       -16736.634             0.024            0.022
Chain 1:   3900       -16732.829             0.022            0.022
Chain 1:   4000       -16850.160             0.023            0.022
Chain 1:   4100       -16764.041             0.023            0.022
Chain 1:   4200       -16580.953             0.022            0.022
Chain 1:   4300       -16718.894             0.022            0.022
Chain 1:   4400       -16676.268             0.019            0.011
Chain 1:   4500       -16578.880             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001349 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12227.653             1.000            1.000
Chain 1:    200        -9201.240             0.664            1.000
Chain 1:    300        -7923.048             0.497            0.329
Chain 1:    400        -8036.657             0.376            0.329
Chain 1:    500        -8006.523             0.302            0.161
Chain 1:    600        -7828.755             0.255            0.161
Chain 1:    700        -7734.605             0.220            0.023
Chain 1:    800        -7745.963             0.193            0.023
Chain 1:    900        -7708.177             0.172            0.014
Chain 1:   1000        -7774.439             0.156            0.014
Chain 1:   1100        -7812.505             0.056            0.012
Chain 1:   1200        -7772.703             0.024            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61547.691             1.000            1.000
Chain 1:    200       -17790.620             1.730            2.460
Chain 1:    300        -8832.748             1.491            1.014
Chain 1:    400        -9362.063             1.133            1.014
Chain 1:    500        -8079.586             0.938            1.000
Chain 1:    600        -8354.398             0.787            1.000
Chain 1:    700        -7751.556             0.686            0.159
Chain 1:    800        -8201.076             0.607            0.159
Chain 1:    900        -7927.377             0.543            0.078
Chain 1:   1000        -7832.578             0.490            0.078
Chain 1:   1100        -7685.211             0.392            0.057
Chain 1:   1200        -7730.804             0.147            0.055
Chain 1:   1300        -7673.509             0.046            0.035
Chain 1:   1400        -7830.979             0.042            0.033
Chain 1:   1500        -7607.169             0.029            0.029
Chain 1:   1600        -7757.381             0.028            0.020
Chain 1:   1700        -7501.711             0.024            0.020
Chain 1:   1800        -7586.835             0.019            0.019
Chain 1:   1900        -7557.400             0.016            0.019
Chain 1:   2000        -7601.512             0.016            0.019
Chain 1:   2100        -7584.383             0.014            0.011
Chain 1:   2200        -7695.159             0.015            0.014
Chain 1:   2300        -7602.582             0.015            0.014
Chain 1:   2400        -7580.707             0.014            0.012
Chain 1:   2500        -7582.971             0.011            0.011
Chain 1:   2600        -7551.885             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003226 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85903.473             1.000            1.000
Chain 1:    200       -13411.972             3.202            5.405
Chain 1:    300        -9754.642             2.260            1.000
Chain 1:    400       -10758.257             1.718            1.000
Chain 1:    500        -8723.115             1.421            0.375
Chain 1:    600        -8210.021             1.195            0.375
Chain 1:    700        -8220.746             1.024            0.233
Chain 1:    800        -8444.474             0.900            0.233
Chain 1:    900        -8511.368             0.801            0.093
Chain 1:   1000        -8497.715             0.721            0.093
Chain 1:   1100        -8636.512             0.622            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8195.029             0.087            0.054
Chain 1:   1300        -8433.256             0.052            0.028
Chain 1:   1400        -8453.733             0.043            0.026
Chain 1:   1500        -8295.472             0.022            0.019
Chain 1:   1600        -8409.576             0.017            0.016
Chain 1:   1700        -8486.274             0.018            0.016
Chain 1:   1800        -8063.261             0.020            0.016
Chain 1:   1900        -8164.213             0.021            0.016
Chain 1:   2000        -8138.543             0.021            0.016
Chain 1:   2100        -8263.980             0.021            0.015
Chain 1:   2200        -8067.677             0.018            0.015
Chain 1:   2300        -8158.958             0.016            0.014
Chain 1:   2400        -8227.797             0.017            0.014
Chain 1:   2500        -8173.978             0.016            0.012
Chain 1:   2600        -8175.242             0.014            0.011
Chain 1:   2700        -8092.052             0.014            0.011
Chain 1:   2800        -8052.044             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002783 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396149.700             1.000            1.000
Chain 1:    200     -1579125.737             2.658            4.317
Chain 1:    300      -891148.353             2.030            1.000
Chain 1:    400      -458362.091             1.758            1.000
Chain 1:    500      -359323.636             1.462            0.944
Chain 1:    600      -234087.803             1.307            0.944
Chain 1:    700      -119759.021             1.257            0.944
Chain 1:    800       -86835.401             1.147            0.944
Chain 1:    900       -67050.663             1.053            0.772
Chain 1:   1000       -51749.463             0.977            0.772
Chain 1:   1100       -39139.955             0.909            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38302.712             0.480            0.379
Chain 1:   1300       -26167.775             0.449            0.379
Chain 1:   1400       -25877.520             0.355            0.322
Chain 1:   1500       -22442.076             0.343            0.322
Chain 1:   1600       -21652.019             0.293            0.296
Chain 1:   1700       -20514.794             0.203            0.295
Chain 1:   1800       -20456.283             0.166            0.153
Chain 1:   1900       -20782.529             0.138            0.055
Chain 1:   2000       -19287.932             0.116            0.055
Chain 1:   2100       -19526.417             0.085            0.036
Chain 1:   2200       -19754.034             0.084            0.036
Chain 1:   2300       -19370.196             0.040            0.020
Chain 1:   2400       -19142.156             0.040            0.020
Chain 1:   2500       -18944.633             0.025            0.016
Chain 1:   2600       -18574.267             0.024            0.016
Chain 1:   2700       -18531.002             0.018            0.012
Chain 1:   2800       -18248.098             0.020            0.016
Chain 1:   2900       -18529.461             0.020            0.015
Chain 1:   3000       -18515.482             0.012            0.012
Chain 1:   3100       -18600.569             0.011            0.012
Chain 1:   3200       -18291.034             0.012            0.015
Chain 1:   3300       -18495.896             0.011            0.012
Chain 1:   3400       -17970.694             0.013            0.015
Chain 1:   3500       -18582.942             0.015            0.016
Chain 1:   3600       -17889.121             0.017            0.016
Chain 1:   3700       -18276.429             0.019            0.017
Chain 1:   3800       -17235.484             0.023            0.021
Chain 1:   3900       -17231.681             0.022            0.021
Chain 1:   4000       -17348.894             0.022            0.021
Chain 1:   4100       -17262.718             0.022            0.021
Chain 1:   4200       -17078.773             0.022            0.021
Chain 1:   4300       -17217.236             0.021            0.021
Chain 1:   4400       -17173.955             0.019            0.011
Chain 1:   4500       -17076.515             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003853 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13259.571             1.000            1.000
Chain 1:    200       -10034.271             0.661            1.000
Chain 1:    300        -8560.096             0.498            0.321
Chain 1:    400        -8823.946             0.381            0.321
Chain 1:    500        -8683.704             0.308            0.172
Chain 1:    600        -8544.917             0.259            0.172
Chain 1:    700        -8412.630             0.225            0.030
Chain 1:    800        -8355.055             0.197            0.030
Chain 1:    900        -8526.602             0.178            0.020
Chain 1:   1000        -8496.552             0.160            0.020
Chain 1:   1100        -8502.475             0.060            0.016
Chain 1:   1200        -8428.294             0.029            0.016
Chain 1:   1300        -8369.134             0.013            0.016
Chain 1:   1400        -8385.147             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58925.513             1.000            1.000
Chain 1:    200       -18499.041             1.593            2.185
Chain 1:    300        -9158.269             1.402            1.020
Chain 1:    400        -8294.146             1.077            1.020
Chain 1:    500        -9036.013             0.878            1.000
Chain 1:    600        -8369.444             0.745            1.000
Chain 1:    700        -7869.429             0.648            0.104
Chain 1:    800        -8666.361             0.578            0.104
Chain 1:    900        -8079.276             0.522            0.092
Chain 1:   1000        -7779.070             0.474            0.092
Chain 1:   1100        -7691.788             0.375            0.082
Chain 1:   1200        -8093.970             0.161            0.080
Chain 1:   1300        -8032.104             0.060            0.073
Chain 1:   1400        -8114.095             0.051            0.064
Chain 1:   1500        -7641.578             0.049            0.062
Chain 1:   1600        -7823.160             0.043            0.050
Chain 1:   1700        -8029.262             0.039            0.039
Chain 1:   1800        -7825.528             0.033            0.026
Chain 1:   1900        -7612.424             0.028            0.026
Chain 1:   2000        -7859.481             0.028            0.026
Chain 1:   2100        -7739.930             0.028            0.026
Chain 1:   2200        -7957.513             0.026            0.026
Chain 1:   2300        -7714.836             0.028            0.027
Chain 1:   2400        -7898.900             0.029            0.027
Chain 1:   2500        -7723.046             0.025            0.026
Chain 1:   2600        -7661.495             0.024            0.026
Chain 1:   2700        -7600.948             0.022            0.026
Chain 1:   2800        -7775.090             0.022            0.023
Chain 1:   2900        -7507.387             0.023            0.023
Chain 1:   3000        -7655.973             0.021            0.023
Chain 1:   3100        -7649.806             0.020            0.023
Chain 1:   3200        -7818.836             0.019            0.022
Chain 1:   3300        -7537.010             0.020            0.022
Chain 1:   3400        -7771.793             0.021            0.022
Chain 1:   3500        -7572.435             0.021            0.022
Chain 1:   3600        -7640.228             0.021            0.022
Chain 1:   3700        -7593.783             0.021            0.022
Chain 1:   3800        -7564.894             0.019            0.022
Chain 1:   3900        -7536.273             0.016            0.019
Chain 1:   4000        -7528.814             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87484.110             1.000            1.000
Chain 1:    200       -14375.440             3.043            5.086
Chain 1:    300       -10599.256             2.147            1.000
Chain 1:    400       -12114.447             1.642            1.000
Chain 1:    500        -9404.943             1.371            0.356
Chain 1:    600        -9118.883             1.148            0.356
Chain 1:    700        -8985.454             0.986            0.288
Chain 1:    800        -9196.799             0.866            0.288
Chain 1:    900        -9332.915             0.771            0.125
Chain 1:   1000        -8991.511             0.698            0.125
Chain 1:   1100        -9310.761             0.601            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8913.117             0.097            0.038
Chain 1:   1300        -9211.227             0.065            0.034
Chain 1:   1400        -9045.823             0.054            0.032
Chain 1:   1500        -9063.889             0.025            0.031
Chain 1:   1600        -9150.490             0.023            0.023
Chain 1:   1700        -9199.102             0.022            0.023
Chain 1:   1800        -8742.592             0.025            0.032
Chain 1:   1900        -8854.022             0.025            0.032
Chain 1:   2000        -8872.123             0.021            0.018
Chain 1:   2100        -8980.003             0.019            0.013
Chain 1:   2200        -8747.724             0.017            0.013
Chain 1:   2300        -8935.817             0.016            0.013
Chain 1:   2400        -8750.139             0.016            0.013
Chain 1:   2500        -8826.449             0.017            0.013
Chain 1:   2600        -8735.372             0.017            0.013
Chain 1:   2700        -8769.153             0.017            0.013
Chain 1:   2800        -8720.216             0.012            0.012
Chain 1:   2900        -8834.813             0.012            0.012
Chain 1:   3000        -8749.520             0.013            0.012
Chain 1:   3100        -8712.346             0.012            0.010
Chain 1:   3200        -8684.545             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003575 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418581.311             1.000            1.000
Chain 1:    200     -1586192.213             2.654            4.307
Chain 1:    300      -890249.003             2.030            1.000
Chain 1:    400      -457390.956             1.759            1.000
Chain 1:    500      -357458.432             1.463            0.946
Chain 1:    600      -232676.135             1.309            0.946
Chain 1:    700      -119542.533             1.257            0.946
Chain 1:    800       -86915.570             1.147            0.946
Chain 1:    900       -67385.532             1.051            0.782
Chain 1:   1000       -52289.823             0.975            0.782
Chain 1:   1100       -39855.309             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39053.146             0.478            0.375
Chain 1:   1300       -27077.380             0.444            0.375
Chain 1:   1400       -26808.034             0.350            0.312
Chain 1:   1500       -23411.458             0.337            0.312
Chain 1:   1600       -22634.782             0.286            0.290
Chain 1:   1700       -21515.215             0.197            0.289
Chain 1:   1800       -21461.921             0.160            0.145
Chain 1:   1900       -21788.900             0.132            0.052
Chain 1:   2000       -20302.241             0.111            0.052
Chain 1:   2100       -20540.568             0.081            0.034
Chain 1:   2200       -20766.914             0.080            0.034
Chain 1:   2300       -20384.009             0.037            0.019
Chain 1:   2400       -20155.883             0.037            0.019
Chain 1:   2500       -19957.573             0.024            0.015
Chain 1:   2600       -19587.154             0.022            0.015
Chain 1:   2700       -19544.107             0.017            0.012
Chain 1:   2800       -19260.394             0.019            0.015
Chain 1:   2900       -19541.960             0.019            0.014
Chain 1:   3000       -19528.177             0.011            0.012
Chain 1:   3100       -19613.237             0.011            0.011
Chain 1:   3200       -19303.442             0.011            0.014
Chain 1:   3300       -19508.619             0.010            0.011
Chain 1:   3400       -18982.510             0.012            0.014
Chain 1:   3500       -19595.748             0.014            0.015
Chain 1:   3600       -18900.672             0.016            0.015
Chain 1:   3700       -19288.656             0.018            0.016
Chain 1:   3800       -18245.546             0.022            0.020
Chain 1:   3900       -18241.592             0.020            0.020
Chain 1:   4000       -18358.942             0.021            0.020
Chain 1:   4100       -18272.459             0.021            0.020
Chain 1:   4200       -18088.210             0.021            0.020
Chain 1:   4300       -18227.026             0.020            0.020
Chain 1:   4400       -18183.335             0.018            0.010
Chain 1:   4500       -18085.752             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12444.471             1.000            1.000
Chain 1:    200        -9393.230             0.662            1.000
Chain 1:    300        -7925.358             0.503            0.325
Chain 1:    400        -8150.916             0.384            0.325
Chain 1:    500        -7901.306             0.314            0.185
Chain 1:    600        -7866.724             0.262            0.185
Chain 1:    700        -7805.229             0.226            0.032
Chain 1:    800        -7741.860             0.199            0.032
Chain 1:    900        -7731.057             0.177            0.028
Chain 1:   1000        -7977.258             0.162            0.031
Chain 1:   1100        -7899.593             0.063            0.028
Chain 1:   1200        -7817.162             0.032            0.011
Chain 1:   1300        -7738.887             0.014            0.010
Chain 1:   1400        -7767.670             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58127.855             1.000            1.000
Chain 1:    200       -17736.451             1.639            2.277
Chain 1:    300        -8711.263             1.438            1.036
Chain 1:    400        -8106.204             1.097            1.036
Chain 1:    500        -8777.580             0.893            1.000
Chain 1:    600        -8630.671             0.747            1.000
Chain 1:    700        -7775.951             0.656            0.110
Chain 1:    800        -8236.777             0.581            0.110
Chain 1:    900        -7790.131             0.523            0.076
Chain 1:   1000        -7764.780             0.471            0.076
Chain 1:   1100        -7857.671             0.372            0.075
Chain 1:   1200        -7742.408             0.146            0.057
Chain 1:   1300        -7819.654             0.043            0.056
Chain 1:   1400        -7688.237             0.037            0.017
Chain 1:   1500        -7541.875             0.032            0.017
Chain 1:   1600        -7770.728             0.033            0.019
Chain 1:   1700        -7538.067             0.025            0.019
Chain 1:   1800        -7649.010             0.021            0.017
Chain 1:   1900        -7525.804             0.017            0.016
Chain 1:   2000        -7600.529             0.017            0.016
Chain 1:   2100        -7628.488             0.017            0.016
Chain 1:   2200        -7687.459             0.016            0.016
Chain 1:   2300        -7550.635             0.017            0.017
Chain 1:   2400        -7607.138             0.016            0.016
Chain 1:   2500        -7607.391             0.014            0.015
Chain 1:   2600        -7509.349             0.012            0.013
Chain 1:   2700        -7548.960             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003253 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86446.964             1.000            1.000
Chain 1:    200       -13571.952             3.185            5.370
Chain 1:    300        -9862.288             2.249            1.000
Chain 1:    400       -11233.387             1.717            1.000
Chain 1:    500        -8838.037             1.428            0.376
Chain 1:    600        -8388.476             1.199            0.376
Chain 1:    700        -8437.641             1.028            0.271
Chain 1:    800        -8169.900             0.904            0.271
Chain 1:    900        -8215.081             0.804            0.122
Chain 1:   1000        -8631.149             0.728            0.122
Chain 1:   1100        -8648.418             0.629            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8242.098             0.097            0.049
Chain 1:   1300        -8540.471             0.063            0.048
Chain 1:   1400        -8501.551             0.051            0.035
Chain 1:   1500        -8381.457             0.025            0.033
Chain 1:   1600        -8485.281             0.021            0.014
Chain 1:   1700        -8555.805             0.021            0.014
Chain 1:   1800        -8120.630             0.023            0.014
Chain 1:   1900        -8225.128             0.024            0.014
Chain 1:   2000        -8201.146             0.019            0.013
Chain 1:   2100        -8347.967             0.021            0.014
Chain 1:   2200        -8132.988             0.019            0.014
Chain 1:   2300        -8288.599             0.017            0.014
Chain 1:   2400        -8128.069             0.019            0.018
Chain 1:   2500        -8199.087             0.018            0.018
Chain 1:   2600        -8111.364             0.018            0.018
Chain 1:   2700        -8145.431             0.018            0.018
Chain 1:   2800        -8105.559             0.013            0.013
Chain 1:   2900        -8198.730             0.013            0.011
Chain 1:   3000        -8030.741             0.014            0.018
Chain 1:   3100        -8188.231             0.015            0.019
Chain 1:   3200        -8060.242             0.013            0.016
Chain 1:   3300        -8067.938             0.012            0.011
Chain 1:   3400        -8226.528             0.012            0.011
Chain 1:   3500        -8232.364             0.011            0.011
Chain 1:   3600        -8016.844             0.012            0.016
Chain 1:   3700        -8162.398             0.014            0.018
Chain 1:   3800        -8023.423             0.015            0.018
Chain 1:   3900        -7958.073             0.015            0.018
Chain 1:   4000        -8033.216             0.014            0.017
Chain 1:   4100        -8023.798             0.012            0.016
Chain 1:   4200        -8013.753             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003058 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395358.338             1.000            1.000
Chain 1:    200     -1583887.303             2.650            4.300
Chain 1:    300      -891195.132             2.026            1.000
Chain 1:    400      -457996.043             1.756            1.000
Chain 1:    500      -358375.485             1.460            0.946
Chain 1:    600      -233385.320             1.306            0.946
Chain 1:    700      -119477.827             1.256            0.946
Chain 1:    800       -86644.951             1.146            0.946
Chain 1:    900       -66964.464             1.051            0.777
Chain 1:   1000       -51743.716             0.976            0.777
Chain 1:   1100       -39198.982             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38377.239             0.480            0.379
Chain 1:   1300       -26306.902             0.448            0.379
Chain 1:   1400       -26026.120             0.354            0.320
Chain 1:   1500       -22605.304             0.342            0.320
Chain 1:   1600       -21820.145             0.292            0.294
Chain 1:   1700       -20690.193             0.202            0.294
Chain 1:   1800       -20633.811             0.164            0.151
Chain 1:   1900       -20960.325             0.137            0.055
Chain 1:   2000       -19468.687             0.115            0.055
Chain 1:   2100       -19707.421             0.084            0.036
Chain 1:   2200       -19934.333             0.083            0.036
Chain 1:   2300       -19550.987             0.039            0.020
Chain 1:   2400       -19322.877             0.039            0.020
Chain 1:   2500       -19124.940             0.025            0.016
Chain 1:   2600       -18754.771             0.023            0.016
Chain 1:   2700       -18711.573             0.018            0.012
Chain 1:   2800       -18428.247             0.019            0.015
Chain 1:   2900       -18709.724             0.019            0.015
Chain 1:   3000       -18695.932             0.012            0.012
Chain 1:   3100       -18780.974             0.011            0.012
Chain 1:   3200       -18471.386             0.012            0.015
Chain 1:   3300       -18676.305             0.011            0.012
Chain 1:   3400       -18150.777             0.012            0.015
Chain 1:   3500       -18763.371             0.015            0.015
Chain 1:   3600       -18069.091             0.017            0.015
Chain 1:   3700       -18456.623             0.018            0.017
Chain 1:   3800       -17414.882             0.023            0.021
Chain 1:   3900       -17410.964             0.021            0.021
Chain 1:   4000       -17528.278             0.022            0.021
Chain 1:   4100       -17441.976             0.022            0.021
Chain 1:   4200       -17257.874             0.021            0.021
Chain 1:   4300       -17396.520             0.021            0.021
Chain 1:   4400       -17353.095             0.018            0.011
Chain 1:   4500       -17255.558             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48831.976             1.000            1.000
Chain 1:    200       -18233.351             1.339            1.678
Chain 1:    300       -20320.299             0.927            1.000
Chain 1:    400       -15228.515             0.779            1.000
Chain 1:    500       -22604.704             0.688            0.334
Chain 1:    600       -12796.551             0.701            0.766
Chain 1:    700       -12661.988             0.603            0.334
Chain 1:    800       -15172.347             0.548            0.334
Chain 1:    900       -14940.217             0.489            0.326
Chain 1:   1000       -11839.535             0.466            0.326
Chain 1:   1100       -10829.856             0.375            0.262
Chain 1:   1200       -10563.662             0.210            0.165
Chain 1:   1300       -17370.812             0.239            0.262
Chain 1:   1400       -10931.520             0.265            0.262
Chain 1:   1500       -11567.954             0.237            0.165
Chain 1:   1600       -13092.627             0.172            0.116
Chain 1:   1700       -11898.880             0.181            0.116
Chain 1:   1800       -11040.641             0.173            0.100
Chain 1:   1900       -11122.061             0.172            0.100
Chain 1:   2000        -9299.969             0.165            0.100
Chain 1:   2100       -10379.060             0.166            0.104
Chain 1:   2200       -10584.068             0.166            0.104
Chain 1:   2300        -9344.767             0.140            0.104
Chain 1:   2400        -9287.089             0.081            0.100
Chain 1:   2500        -9869.794             0.082            0.100
Chain 1:   2600       -10025.006             0.072            0.078
Chain 1:   2700        -9104.615             0.072            0.078
Chain 1:   2800       -13644.370             0.097            0.101
Chain 1:   2900        -9607.307             0.139            0.104
Chain 1:   3000       -21064.268             0.173            0.104
Chain 1:   3100        -9488.858             0.285            0.133
Chain 1:   3200       -12619.706             0.308            0.248
Chain 1:   3300       -10465.982             0.315            0.248
Chain 1:   3400        -9709.163             0.322            0.248
Chain 1:   3500        -8988.827             0.325            0.248
Chain 1:   3600        -8841.518             0.325            0.248
Chain 1:   3700        -9243.245             0.319            0.248
Chain 1:   3800        -8702.582             0.292            0.206
Chain 1:   3900       -13525.012             0.285            0.206
Chain 1:   4000       -10007.262             0.266            0.206
Chain 1:   4100        -8712.561             0.159            0.149
Chain 1:   4200        -8879.709             0.136            0.080
Chain 1:   4300        -9760.700             0.125            0.080
Chain 1:   4400        -8957.795             0.126            0.090
Chain 1:   4500        -8532.758             0.123            0.090
Chain 1:   4600       -12207.942             0.151            0.090
Chain 1:   4700       -10167.184             0.167            0.149
Chain 1:   4800        -8941.288             0.174            0.149
Chain 1:   4900       -10130.491             0.150            0.137
Chain 1:   5000        -8767.010             0.131            0.137
Chain 1:   5100        -8357.592             0.121            0.117
Chain 1:   5200       -12109.474             0.150            0.137
Chain 1:   5300       -11835.519             0.143            0.137
Chain 1:   5400        -8956.458             0.167            0.156
Chain 1:   5500        -8379.175             0.168            0.156
Chain 1:   5600       -11600.121             0.166            0.156
Chain 1:   5700        -8339.200             0.185            0.156
Chain 1:   5800        -9222.280             0.181            0.156
Chain 1:   5900       -14164.018             0.204            0.278
Chain 1:   6000        -9481.654             0.238            0.310
Chain 1:   6100       -13920.831             0.265            0.319
Chain 1:   6200        -8328.315             0.301            0.321
Chain 1:   6300       -11554.515             0.327            0.321
Chain 1:   6400       -13095.787             0.306            0.319
Chain 1:   6500       -11740.644             0.311            0.319
Chain 1:   6600        -8433.685             0.322            0.349
Chain 1:   6700        -8292.248             0.285            0.319
Chain 1:   6800        -8426.442             0.277            0.319
Chain 1:   6900        -8265.911             0.244            0.279
Chain 1:   7000        -8827.324             0.201            0.118
Chain 1:   7100       -10150.382             0.182            0.118
Chain 1:   7200       -10271.887             0.116            0.115
Chain 1:   7300        -8538.876             0.109            0.115
Chain 1:   7400        -8892.548             0.101            0.064
Chain 1:   7500        -8122.458             0.099            0.064
Chain 1:   7600       -11198.878             0.087            0.064
Chain 1:   7700        -8634.769             0.115            0.095
Chain 1:   7800       -11111.593             0.136            0.130
Chain 1:   7900        -8231.533             0.169            0.203
Chain 1:   8000       -11944.817             0.194            0.223
Chain 1:   8100        -8400.429             0.223            0.275
Chain 1:   8200        -8643.119             0.224            0.275
Chain 1:   8300        -8254.503             0.209            0.275
Chain 1:   8400        -8675.531             0.210            0.275
Chain 1:   8500       -11542.848             0.225            0.275
Chain 1:   8600        -8709.018             0.230            0.297
Chain 1:   8700        -8909.366             0.203            0.248
Chain 1:   8800        -9954.471             0.191            0.248
Chain 1:   8900        -9969.792             0.156            0.105
Chain 1:   9000       -10214.259             0.127            0.049
Chain 1:   9100        -8424.868             0.106            0.049
Chain 1:   9200        -8217.189             0.106            0.049
Chain 1:   9300        -8982.399             0.110            0.085
Chain 1:   9400        -8377.707             0.112            0.085
Chain 1:   9500        -8224.569             0.089            0.072
Chain 1:   9600        -9190.266             0.067            0.072
Chain 1:   9700        -8146.658             0.078            0.085
Chain 1:   9800        -9068.766             0.077            0.085
Chain 1:   9900        -8168.631             0.088            0.102
Chain 1:   10000        -9074.738             0.096            0.102
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61546.934             1.000            1.000
Chain 1:    200       -17773.551             1.731            2.463
Chain 1:    300        -8826.227             1.492            1.014
Chain 1:    400        -9211.806             1.130            1.014
Chain 1:    500        -8079.313             0.932            1.000
Chain 1:    600        -8479.489             0.784            1.000
Chain 1:    700        -8108.949             0.679            0.140
Chain 1:    800        -8128.908             0.594            0.140
Chain 1:    900        -7960.241             0.531            0.047
Chain 1:   1000        -7728.764             0.481            0.047
Chain 1:   1100        -7769.903             0.381            0.046
Chain 1:   1200        -7741.501             0.135            0.042
Chain 1:   1300        -7549.080             0.036            0.030
Chain 1:   1400        -7846.812             0.036            0.030
Chain 1:   1500        -7590.254             0.025            0.030
Chain 1:   1600        -7509.772             0.022            0.025
Chain 1:   1700        -7643.696             0.019            0.021
Chain 1:   1800        -7616.343             0.019            0.021
Chain 1:   1900        -7471.592             0.019            0.019
Chain 1:   2000        -7563.629             0.017            0.018
Chain 1:   2100        -7582.246             0.017            0.018
Chain 1:   2200        -7684.730             0.018            0.018
Chain 1:   2300        -7584.412             0.016            0.013
Chain 1:   2400        -7586.537             0.013            0.013
Chain 1:   2500        -7612.972             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004512 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 45.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86261.120             1.000            1.000
Chain 1:    200       -13427.950             3.212            5.424
Chain 1:    300        -9787.316             2.265            1.000
Chain 1:    400       -10811.558             1.723            1.000
Chain 1:    500        -8667.085             1.428            0.372
Chain 1:    600        -8641.288             1.190            0.372
Chain 1:    700        -8282.515             1.026            0.247
Chain 1:    800        -8683.486             0.904            0.247
Chain 1:    900        -8589.996             0.805            0.095
Chain 1:   1000        -8450.028             0.726            0.095
Chain 1:   1100        -8610.336             0.628            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8432.824             0.087            0.043
Chain 1:   1300        -8545.471             0.051            0.021
Chain 1:   1400        -8308.127             0.045            0.021
Chain 1:   1500        -8314.234             0.020            0.019
Chain 1:   1600        -8300.185             0.020            0.019
Chain 1:   1700        -8200.370             0.017            0.017
Chain 1:   1800        -8101.703             0.014            0.013
Chain 1:   1900        -8225.452             0.014            0.015
Chain 1:   2000        -8189.346             0.013            0.013
Chain 1:   2100        -8321.005             0.012            0.013
Chain 1:   2200        -8134.568             0.013            0.013
Chain 1:   2300        -8214.181             0.012            0.012
Chain 1:   2400        -8283.652             0.010            0.012
Chain 1:   2500        -8229.145             0.011            0.012
Chain 1:   2600        -8228.783             0.011            0.012
Chain 1:   2700        -8146.151             0.011            0.010
Chain 1:   2800        -8107.973             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003249 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426830.443             1.000            1.000
Chain 1:    200     -1587903.179             2.653            4.307
Chain 1:    300      -891485.160             2.029            1.000
Chain 1:    400      -457857.754             1.759            1.000
Chain 1:    500      -358069.471             1.463            0.947
Chain 1:    600      -232816.165             1.309            0.947
Chain 1:    700      -119059.454             1.258            0.947
Chain 1:    800       -86317.002             1.148            0.947
Chain 1:    900       -66664.828             1.053            0.781
Chain 1:   1000       -51477.333             0.978            0.781
Chain 1:   1100       -38972.539             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38147.591             0.481            0.379
Chain 1:   1300       -26119.929             0.449            0.379
Chain 1:   1400       -25839.548             0.356            0.321
Chain 1:   1500       -22432.425             0.343            0.321
Chain 1:   1600       -21650.707             0.293            0.295
Chain 1:   1700       -20526.292             0.203            0.295
Chain 1:   1800       -20470.829             0.165            0.152
Chain 1:   1900       -20796.982             0.137            0.055
Chain 1:   2000       -19309.379             0.115            0.055
Chain 1:   2100       -19547.463             0.084            0.036
Chain 1:   2200       -19773.975             0.083            0.036
Chain 1:   2300       -19391.157             0.039            0.020
Chain 1:   2400       -19163.283             0.039            0.020
Chain 1:   2500       -18965.334             0.025            0.016
Chain 1:   2600       -18595.453             0.024            0.016
Chain 1:   2700       -18552.385             0.018            0.012
Chain 1:   2800       -18269.318             0.020            0.015
Chain 1:   2900       -18550.504             0.020            0.015
Chain 1:   3000       -18536.635             0.012            0.012
Chain 1:   3100       -18621.671             0.011            0.012
Chain 1:   3200       -18312.307             0.012            0.015
Chain 1:   3300       -18517.054             0.011            0.012
Chain 1:   3400       -17991.968             0.013            0.015
Chain 1:   3500       -18603.868             0.015            0.015
Chain 1:   3600       -17910.473             0.017            0.015
Chain 1:   3700       -18297.332             0.019            0.017
Chain 1:   3800       -17256.973             0.023            0.021
Chain 1:   3900       -17253.119             0.022            0.021
Chain 1:   4000       -17370.404             0.022            0.021
Chain 1:   4100       -17284.217             0.022            0.021
Chain 1:   4200       -17100.409             0.022            0.021
Chain 1:   4300       -17238.821             0.021            0.021
Chain 1:   4400       -17195.616             0.019            0.011
Chain 1:   4500       -17098.162             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12695.621             1.000            1.000
Chain 1:    200        -9584.486             0.662            1.000
Chain 1:    300        -8280.518             0.494            0.325
Chain 1:    400        -8558.158             0.379            0.325
Chain 1:    500        -8388.997             0.307            0.157
Chain 1:    600        -8267.901             0.258            0.157
Chain 1:    700        -8164.310             0.223            0.032
Chain 1:    800        -8171.887             0.195            0.032
Chain 1:    900        -8110.670             0.174            0.020
Chain 1:   1000        -8288.645             0.159            0.021
Chain 1:   1100        -8304.025             0.059            0.020
Chain 1:   1200        -8186.442             0.028            0.015
Chain 1:   1300        -8162.628             0.013            0.014
Chain 1:   1400        -8168.730             0.010            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001537 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46706.470             1.000            1.000
Chain 1:    200       -15916.035             1.467            1.935
Chain 1:    300        -8866.661             1.243            1.000
Chain 1:    400        -8271.073             0.950            1.000
Chain 1:    500        -8811.044             0.773            0.795
Chain 1:    600        -9491.662             0.656            0.795
Chain 1:    700        -8554.450             0.578            0.110
Chain 1:    800        -8080.387             0.513            0.110
Chain 1:    900        -7853.199             0.459            0.072
Chain 1:   1000        -7972.309             0.415            0.072
Chain 1:   1100        -7689.346             0.318            0.072
Chain 1:   1200        -7677.683             0.125            0.061
Chain 1:   1300        -7847.235             0.048            0.059
Chain 1:   1400        -8019.968             0.043            0.037
Chain 1:   1500        -7595.915             0.042            0.037
Chain 1:   1600        -7771.613             0.037            0.029
Chain 1:   1700        -7626.128             0.028            0.023
Chain 1:   1800        -7713.915             0.023            0.022
Chain 1:   1900        -7636.618             0.022            0.022
Chain 1:   2000        -7680.857             0.021            0.022
Chain 1:   2100        -7615.038             0.018            0.019
Chain 1:   2200        -7767.014             0.020            0.020
Chain 1:   2300        -7600.421             0.020            0.020
Chain 1:   2400        -7644.758             0.018            0.019
Chain 1:   2500        -7659.443             0.013            0.011
Chain 1:   2600        -7555.501             0.012            0.011
Chain 1:   2700        -7556.329             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003698 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86944.443             1.000            1.000
Chain 1:    200       -13843.906             3.140            5.280
Chain 1:    300       -10190.570             2.213            1.000
Chain 1:    400       -11048.955             1.679            1.000
Chain 1:    500        -9129.981             1.385            0.359
Chain 1:    600        -8684.293             1.163            0.359
Chain 1:    700        -8681.261             0.997            0.210
Chain 1:    800        -9244.240             0.880            0.210
Chain 1:    900        -8969.644             0.786            0.078
Chain 1:   1000        -8640.256             0.711            0.078
Chain 1:   1100        -8800.907             0.613            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8613.700             0.087            0.051
Chain 1:   1300        -8872.022             0.054            0.038
Chain 1:   1400        -8886.580             0.046            0.031
Chain 1:   1500        -8733.104             0.027            0.029
Chain 1:   1600        -8846.865             0.023            0.022
Chain 1:   1700        -8924.467             0.024            0.022
Chain 1:   1800        -8502.342             0.023            0.022
Chain 1:   1900        -8602.692             0.021            0.018
Chain 1:   2000        -8577.067             0.017            0.018
Chain 1:   2100        -8702.114             0.017            0.014
Chain 1:   2200        -8507.370             0.017            0.014
Chain 1:   2300        -8597.418             0.015            0.013
Chain 1:   2400        -8666.454             0.016            0.013
Chain 1:   2500        -8612.662             0.015            0.012
Chain 1:   2600        -8613.713             0.014            0.010
Chain 1:   2700        -8530.559             0.014            0.010
Chain 1:   2800        -8490.908             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002625 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8394990.166             1.000            1.000
Chain 1:    200     -1580408.983             2.656            4.312
Chain 1:    300      -890109.037             2.029            1.000
Chain 1:    400      -457546.746             1.758            1.000
Chain 1:    500      -358439.212             1.462            0.945
Chain 1:    600      -233372.099             1.308            0.945
Chain 1:    700      -119603.434             1.257            0.945
Chain 1:    800       -86836.157             1.147            0.945
Chain 1:    900       -67164.899             1.052            0.776
Chain 1:   1000       -51956.979             0.976            0.776
Chain 1:   1100       -39430.152             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38605.301             0.479            0.377
Chain 1:   1300       -26549.400             0.447            0.377
Chain 1:   1400       -26267.407             0.353            0.318
Chain 1:   1500       -22852.135             0.340            0.318
Chain 1:   1600       -22068.304             0.290            0.293
Chain 1:   1700       -20940.030             0.201            0.293
Chain 1:   1800       -20883.890             0.163            0.149
Chain 1:   1900       -21210.198             0.135            0.054
Chain 1:   2000       -19720.273             0.114            0.054
Chain 1:   2100       -19958.558             0.083            0.036
Chain 1:   2200       -20185.426             0.082            0.036
Chain 1:   2300       -19802.233             0.039            0.019
Chain 1:   2400       -19574.262             0.039            0.019
Chain 1:   2500       -19376.413             0.025            0.015
Chain 1:   2600       -19006.356             0.023            0.015
Chain 1:   2700       -18963.238             0.018            0.012
Chain 1:   2800       -18680.183             0.019            0.015
Chain 1:   2900       -18961.436             0.019            0.015
Chain 1:   3000       -18947.526             0.012            0.012
Chain 1:   3100       -19032.595             0.011            0.012
Chain 1:   3200       -18723.167             0.011            0.015
Chain 1:   3300       -18927.960             0.011            0.012
Chain 1:   3400       -18402.795             0.012            0.015
Chain 1:   3500       -19014.934             0.015            0.015
Chain 1:   3600       -18321.206             0.016            0.015
Chain 1:   3700       -18708.350             0.018            0.017
Chain 1:   3800       -17667.564             0.023            0.021
Chain 1:   3900       -17663.715             0.021            0.021
Chain 1:   4000       -17780.965             0.022            0.021
Chain 1:   4100       -17694.786             0.022            0.021
Chain 1:   4200       -17510.879             0.021            0.021
Chain 1:   4300       -17649.361             0.021            0.021
Chain 1:   4400       -17606.087             0.018            0.011
Chain 1:   4500       -17508.616             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12428.839             1.000            1.000
Chain 1:    200        -9372.486             0.663            1.000
Chain 1:    300        -8157.642             0.492            0.326
Chain 1:    400        -8271.470             0.372            0.326
Chain 1:    500        -8130.792             0.301            0.149
Chain 1:    600        -8045.811             0.253            0.149
Chain 1:    700        -7949.983             0.218            0.017
Chain 1:    800        -7995.077             0.192            0.017
Chain 1:    900        -8119.865             0.172            0.015
Chain 1:   1000        -8053.841             0.156            0.015
Chain 1:   1100        -8050.277             0.056            0.014
Chain 1:   1200        -7971.676             0.024            0.012
Chain 1:   1300        -7923.239             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001436 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56743.784             1.000            1.000
Chain 1:    200       -17439.359             1.627            2.254
Chain 1:    300        -8773.194             1.414            1.000
Chain 1:    400        -8425.546             1.071            1.000
Chain 1:    500        -8794.337             0.865            0.988
Chain 1:    600        -8990.011             0.724            0.988
Chain 1:    700        -8109.301             0.636            0.109
Chain 1:    800        -8227.293             0.559            0.109
Chain 1:    900        -7953.750             0.500            0.042
Chain 1:   1000        -7828.659             0.452            0.042
Chain 1:   1100        -7617.235             0.355            0.041
Chain 1:   1200        -7900.878             0.133            0.036
Chain 1:   1300        -7746.400             0.036            0.034
Chain 1:   1400        -7728.038             0.032            0.028
Chain 1:   1500        -7609.229             0.030            0.022
Chain 1:   1600        -7762.328             0.029            0.020
Chain 1:   1700        -7547.529             0.021            0.020
Chain 1:   1800        -7745.834             0.023            0.026
Chain 1:   1900        -7639.066             0.021            0.020
Chain 1:   2000        -7628.125             0.019            0.020
Chain 1:   2100        -7610.369             0.017            0.020
Chain 1:   2200        -7735.149             0.015            0.016
Chain 1:   2300        -7633.751             0.014            0.016
Chain 1:   2400        -7677.065             0.014            0.016
Chain 1:   2500        -7593.003             0.014            0.014
Chain 1:   2600        -7574.543             0.012            0.013
Chain 1:   2700        -7575.312             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003849 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87001.298             1.000            1.000
Chain 1:    200       -13549.677             3.210            5.421
Chain 1:    300        -9915.299             2.262            1.000
Chain 1:    400       -10787.581             1.717            1.000
Chain 1:    500        -8859.371             1.417            0.367
Chain 1:    600        -8411.705             1.190            0.367
Chain 1:    700        -8490.815             1.021            0.218
Chain 1:    800        -8727.387             0.897            0.218
Chain 1:    900        -8704.408             0.798            0.081
Chain 1:   1000        -8543.144             0.720            0.081
Chain 1:   1100        -8774.701             0.622            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8292.547             0.086            0.053
Chain 1:   1300        -8582.109             0.053            0.034
Chain 1:   1400        -8608.518             0.045            0.027
Chain 1:   1500        -8500.377             0.025            0.026
Chain 1:   1600        -8609.762             0.020            0.019
Chain 1:   1700        -8688.914             0.020            0.019
Chain 1:   1800        -8278.188             0.023            0.019
Chain 1:   1900        -8374.178             0.024            0.019
Chain 1:   2000        -8347.021             0.022            0.013
Chain 1:   2100        -8469.075             0.021            0.013
Chain 1:   2200        -8288.702             0.017            0.013
Chain 1:   2300        -8370.047             0.015            0.013
Chain 1:   2400        -8438.902             0.015            0.013
Chain 1:   2500        -8384.308             0.015            0.011
Chain 1:   2600        -8383.138             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003493 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409694.373             1.000            1.000
Chain 1:    200     -1590375.768             2.644            4.288
Chain 1:    300      -892862.480             2.023            1.000
Chain 1:    400      -458055.988             1.755            1.000
Chain 1:    500      -357636.216             1.460            0.949
Chain 1:    600      -232354.902             1.306            0.949
Chain 1:    700      -118908.452             1.256            0.949
Chain 1:    800       -86169.897             1.147            0.949
Chain 1:    900       -66590.262             1.052            0.781
Chain 1:   1000       -51456.703             0.976            0.781
Chain 1:   1100       -38993.163             0.908            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38181.835             0.481            0.380
Chain 1:   1300       -26199.646             0.449            0.380
Chain 1:   1400       -25925.585             0.355            0.320
Chain 1:   1500       -22527.534             0.342            0.320
Chain 1:   1600       -21748.807             0.292            0.294
Chain 1:   1700       -20629.779             0.202            0.294
Chain 1:   1800       -20575.780             0.164            0.151
Chain 1:   1900       -20901.861             0.136            0.054
Chain 1:   2000       -19416.804             0.114            0.054
Chain 1:   2100       -19655.170             0.084            0.036
Chain 1:   2200       -19880.831             0.083            0.036
Chain 1:   2300       -19498.737             0.039            0.020
Chain 1:   2400       -19270.890             0.039            0.020
Chain 1:   2500       -19072.597             0.025            0.016
Chain 1:   2600       -18703.177             0.023            0.016
Chain 1:   2700       -18660.345             0.018            0.012
Chain 1:   2800       -18377.002             0.019            0.015
Chain 1:   2900       -18658.193             0.019            0.015
Chain 1:   3000       -18644.494             0.012            0.012
Chain 1:   3100       -18729.431             0.011            0.012
Chain 1:   3200       -18420.240             0.012            0.015
Chain 1:   3300       -18624.918             0.011            0.012
Chain 1:   3400       -18099.895             0.012            0.015
Chain 1:   3500       -18711.524             0.015            0.015
Chain 1:   3600       -18018.569             0.017            0.015
Chain 1:   3700       -18404.991             0.018            0.017
Chain 1:   3800       -17365.177             0.023            0.021
Chain 1:   3900       -17361.299             0.021            0.021
Chain 1:   4000       -17478.657             0.022            0.021
Chain 1:   4100       -17392.337             0.022            0.021
Chain 1:   4200       -17208.778             0.021            0.021
Chain 1:   4300       -17347.094             0.021            0.021
Chain 1:   4400       -17304.018             0.019            0.011
Chain 1:   4500       -17206.514             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001353 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13070.517             1.000            1.000
Chain 1:    200        -9877.112             0.662            1.000
Chain 1:    300        -8914.300             0.477            0.323
Chain 1:    400        -8389.113             0.373            0.323
Chain 1:    500        -8321.795             0.300            0.108
Chain 1:    600        -8240.780             0.252            0.108
Chain 1:    700        -8108.857             0.218            0.063
Chain 1:    800        -8137.803             0.191            0.063
Chain 1:    900        -8014.732             0.172            0.016
Chain 1:   1000        -8152.078             0.156            0.017
Chain 1:   1100        -8290.288             0.058            0.017
Chain 1:   1200        -8161.457             0.027            0.016
Chain 1:   1300        -8088.378             0.017            0.016
Chain 1:   1400        -8125.061             0.012            0.015
Chain 1:   1500        -8215.753             0.012            0.015
Chain 1:   1600        -8126.514             0.012            0.015
Chain 1:   1700        -8092.993             0.011            0.011
Chain 1:   1800        -8064.393             0.011            0.011
Chain 1:   1900        -8092.311             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58559.532             1.000            1.000
Chain 1:    200       -18321.126             1.598            2.196
Chain 1:    300        -8982.538             1.412            1.040
Chain 1:    400        -8164.612             1.084            1.040
Chain 1:    500        -8358.202             0.872            1.000
Chain 1:    600        -9291.118             0.743            1.000
Chain 1:    700        -8366.854             0.653            0.110
Chain 1:    800        -8471.074             0.573            0.110
Chain 1:    900        -8073.060             0.515            0.100
Chain 1:   1000        -7833.823             0.466            0.100
Chain 1:   1100        -7991.376             0.368            0.100
Chain 1:   1200        -8061.559             0.149            0.049
Chain 1:   1300        -7839.240             0.048            0.031
Chain 1:   1400        -7783.472             0.039            0.028
Chain 1:   1500        -7575.023             0.039            0.028
Chain 1:   1600        -7842.336             0.033            0.028
Chain 1:   1700        -7627.959             0.025            0.028
Chain 1:   1800        -7765.669             0.025            0.028
Chain 1:   1900        -7658.593             0.022            0.028
Chain 1:   2000        -7725.191             0.019            0.020
Chain 1:   2100        -7643.357             0.018            0.018
Chain 1:   2200        -7878.284             0.021            0.028
Chain 1:   2300        -7597.030             0.021            0.028
Chain 1:   2400        -7605.155             0.021            0.028
Chain 1:   2500        -7616.253             0.018            0.018
Chain 1:   2600        -7592.022             0.015            0.014
Chain 1:   2700        -7501.964             0.014            0.012
Chain 1:   2800        -7578.432             0.013            0.011
Chain 1:   2900        -7431.779             0.013            0.011
Chain 1:   3000        -7590.130             0.015            0.012
Chain 1:   3100        -7585.387             0.014            0.012
Chain 1:   3200        -7807.239             0.013            0.012
Chain 1:   3300        -7525.527             0.013            0.012
Chain 1:   3400        -7772.460             0.017            0.020
Chain 1:   3500        -7498.221             0.020            0.021
Chain 1:   3600        -7561.205             0.021            0.021
Chain 1:   3700        -7513.899             0.020            0.021
Chain 1:   3800        -7520.763             0.019            0.021
Chain 1:   3900        -7474.560             0.018            0.021
Chain 1:   4000        -7461.091             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002532 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86450.043             1.000            1.000
Chain 1:    200       -14066.970             3.073            5.146
Chain 1:    300       -10291.814             2.171            1.000
Chain 1:    400       -12035.327             1.664            1.000
Chain 1:    500        -8774.832             1.406            0.372
Chain 1:    600        -8590.592             1.175            0.372
Chain 1:    700        -8842.709             1.011            0.367
Chain 1:    800        -9096.148             0.888            0.367
Chain 1:    900        -9021.388             0.791            0.145
Chain 1:   1000        -9299.173             0.714            0.145
Chain 1:   1100        -8967.082             0.618            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8600.661             0.108            0.037
Chain 1:   1300        -8912.809             0.075            0.035
Chain 1:   1400        -8820.987             0.061            0.030
Chain 1:   1500        -8772.227             0.025            0.029
Chain 1:   1600        -8869.184             0.024            0.029
Chain 1:   1700        -8924.283             0.021            0.028
Chain 1:   1800        -8466.459             0.024            0.030
Chain 1:   1900        -8578.318             0.024            0.030
Chain 1:   2000        -8583.949             0.022            0.013
Chain 1:   2100        -8528.011             0.019            0.011
Chain 1:   2200        -8500.233             0.015            0.010
Chain 1:   2300        -8684.266             0.013            0.010
Chain 1:   2400        -8474.736             0.015            0.011
Chain 1:   2500        -8546.687             0.015            0.011
Chain 1:   2600        -8459.638             0.015            0.010
Chain 1:   2700        -8495.223             0.015            0.010
Chain 1:   2800        -8446.487             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378269.387             1.000            1.000
Chain 1:    200     -1582019.341             2.648            4.296
Chain 1:    300      -890810.439             2.024            1.000
Chain 1:    400      -457713.648             1.755            1.000
Chain 1:    500      -358479.751             1.459            0.946
Chain 1:    600      -233594.656             1.305            0.946
Chain 1:    700      -119897.419             1.254            0.946
Chain 1:    800       -87081.589             1.144            0.946
Chain 1:    900       -67435.596             1.050            0.776
Chain 1:   1000       -52239.221             0.974            0.776
Chain 1:   1100       -39704.187             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38889.933             0.478            0.377
Chain 1:   1300       -26822.975             0.445            0.377
Chain 1:   1400       -26543.933             0.352            0.316
Chain 1:   1500       -23123.329             0.339            0.316
Chain 1:   1600       -22338.521             0.289            0.291
Chain 1:   1700       -21208.840             0.199            0.291
Chain 1:   1800       -21152.763             0.162            0.148
Chain 1:   1900       -21479.698             0.134            0.053
Chain 1:   2000       -19987.368             0.113            0.053
Chain 1:   2100       -20226.179             0.082            0.035
Chain 1:   2200       -20453.286             0.081            0.035
Chain 1:   2300       -20069.706             0.038            0.019
Chain 1:   2400       -19841.478             0.038            0.019
Chain 1:   2500       -19643.436             0.024            0.015
Chain 1:   2600       -19272.989             0.023            0.015
Chain 1:   2700       -19229.744             0.018            0.012
Chain 1:   2800       -18946.179             0.019            0.015
Chain 1:   2900       -19227.886             0.019            0.015
Chain 1:   3000       -19214.048             0.012            0.012
Chain 1:   3100       -19299.112             0.011            0.012
Chain 1:   3200       -18989.332             0.011            0.015
Chain 1:   3300       -19194.438             0.010            0.012
Chain 1:   3400       -18668.443             0.012            0.015
Chain 1:   3500       -19281.678             0.014            0.015
Chain 1:   3600       -18586.649             0.016            0.015
Chain 1:   3700       -18974.732             0.018            0.016
Chain 1:   3800       -17931.710             0.022            0.020
Chain 1:   3900       -17927.774             0.021            0.020
Chain 1:   4000       -18045.110             0.021            0.020
Chain 1:   4100       -17958.690             0.021            0.020
Chain 1:   4200       -17774.361             0.021            0.020
Chain 1:   4300       -17913.192             0.021            0.020
Chain 1:   4400       -17869.548             0.018            0.010
Chain 1:   4500       -17771.972             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001341 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48729.057             1.000            1.000
Chain 1:    200       -18921.600             1.288            1.575
Chain 1:    300       -20862.794             0.889            1.000
Chain 1:    400       -19142.779             0.690            1.000
Chain 1:    500       -23699.189             0.590            0.192
Chain 1:    600       -18724.850             0.536            0.266
Chain 1:    700       -14361.051             0.503            0.266
Chain 1:    800       -14260.169             0.441            0.266
Chain 1:    900       -13260.765             0.400            0.192
Chain 1:   1000       -12425.674             0.367            0.192
Chain 1:   1100       -12651.096             0.269            0.093
Chain 1:   1200       -10634.504             0.130            0.093
Chain 1:   1300       -11219.377             0.126            0.090
Chain 1:   1400       -13817.320             0.136            0.188
Chain 1:   1500       -10115.686             0.153            0.188
Chain 1:   1600       -13344.559             0.151            0.188
Chain 1:   1700       -12172.638             0.130            0.096
Chain 1:   1800       -10583.595             0.144            0.150
Chain 1:   1900       -19978.481             0.184            0.188
Chain 1:   2000       -16058.810             0.202            0.190
Chain 1:   2100        -9440.308             0.270            0.242
Chain 1:   2200        -9370.839             0.252            0.242
Chain 1:   2300        -9509.711             0.248            0.242
Chain 1:   2400       -18279.763             0.277            0.244
Chain 1:   2500       -10831.715             0.309            0.244
Chain 1:   2600        -9060.998             0.305            0.244
Chain 1:   2700        -9174.309             0.296            0.244
Chain 1:   2800        -9368.444             0.283            0.244
Chain 1:   2900        -9570.695             0.238            0.195
Chain 1:   3000        -9095.542             0.219            0.052
Chain 1:   3100        -9951.312             0.158            0.052
Chain 1:   3200        -9177.878             0.165            0.084
Chain 1:   3300        -9170.028             0.164            0.084
Chain 1:   3400        -9775.251             0.122            0.062
Chain 1:   3500       -10324.462             0.059            0.053
Chain 1:   3600        -9325.235             0.050            0.053
Chain 1:   3700        -9319.122             0.049            0.053
Chain 1:   3800       -13480.454             0.078            0.062
Chain 1:   3900        -9839.124             0.113            0.084
Chain 1:   4000       -10408.985             0.113            0.084
Chain 1:   4100        -8865.701             0.122            0.084
Chain 1:   4200       -12574.822             0.143            0.107
Chain 1:   4300       -13721.495             0.151            0.107
Chain 1:   4400        -9122.039             0.195            0.174
Chain 1:   4500       -12572.028             0.217            0.274
Chain 1:   4600       -12562.504             0.207            0.274
Chain 1:   4700       -11952.576             0.212            0.274
Chain 1:   4800        -8547.911             0.221            0.274
Chain 1:   4900       -10130.214             0.199            0.174
Chain 1:   5000        -9264.219             0.203            0.174
Chain 1:   5100       -13135.512             0.215            0.274
Chain 1:   5200        -9568.600             0.223            0.274
Chain 1:   5300       -14321.003             0.248            0.295
Chain 1:   5400        -9557.165             0.247            0.295
Chain 1:   5500       -13280.725             0.248            0.295
Chain 1:   5600       -11797.838             0.260            0.295
Chain 1:   5700        -8713.160             0.291            0.332
Chain 1:   5800        -8508.090             0.253            0.295
Chain 1:   5900        -8901.964             0.242            0.295
Chain 1:   6000       -11263.329             0.254            0.295
Chain 1:   6100        -8473.342             0.257            0.329
Chain 1:   6200        -8431.426             0.220            0.280
Chain 1:   6300        -9546.968             0.199            0.210
Chain 1:   6400        -8308.932             0.164            0.149
Chain 1:   6500        -9365.689             0.147            0.126
Chain 1:   6600        -8564.997             0.144            0.117
Chain 1:   6700        -8804.997             0.111            0.113
Chain 1:   6800       -12998.257             0.141            0.117
Chain 1:   6900        -9987.708             0.167            0.149
Chain 1:   7000        -8741.119             0.160            0.143
Chain 1:   7100        -9341.572             0.134            0.117
Chain 1:   7200        -8559.642             0.142            0.117
Chain 1:   7300       -11633.678             0.157            0.143
Chain 1:   7400        -8857.169             0.173            0.143
Chain 1:   7500        -8248.088             0.169            0.143
Chain 1:   7600        -8512.697             0.163            0.143
Chain 1:   7700        -8837.325             0.164            0.143
Chain 1:   7800       -11741.415             0.157            0.143
Chain 1:   7900        -8462.907             0.165            0.143
Chain 1:   8000        -9958.934             0.166            0.150
Chain 1:   8100        -8318.359             0.179            0.197
Chain 1:   8200       -11886.968             0.200            0.247
Chain 1:   8300       -12251.524             0.177            0.197
Chain 1:   8400       -12983.952             0.151            0.150
Chain 1:   8500        -8409.768             0.198            0.197
Chain 1:   8600        -8234.528             0.197            0.197
Chain 1:   8700        -8333.403             0.195            0.197
Chain 1:   8800       -10261.799             0.189            0.188
Chain 1:   8900       -10218.504             0.150            0.150
Chain 1:   9000       -10854.853             0.141            0.059
Chain 1:   9100        -8875.639             0.144            0.059
Chain 1:   9200       -10168.487             0.126            0.059
Chain 1:   9300       -10247.255             0.124            0.059
Chain 1:   9400       -10997.877             0.125            0.068
Chain 1:   9500       -10211.395             0.079            0.068
Chain 1:   9600        -8348.782             0.099            0.077
Chain 1:   9700        -8414.972             0.098            0.077
Chain 1:   9800        -9685.477             0.093            0.077
Chain 1:   9900       -10189.170             0.097            0.077
Chain 1:   10000        -8608.593             0.110            0.127
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001506 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57084.050             1.000            1.000
Chain 1:    200       -17416.317             1.639            2.278
Chain 1:    300        -8733.392             1.424            1.000
Chain 1:    400        -8400.804             1.078            1.000
Chain 1:    500        -8411.654             0.863            0.994
Chain 1:    600        -8578.139             0.722            0.994
Chain 1:    700        -8014.232             0.629            0.070
Chain 1:    800        -8968.559             0.564            0.106
Chain 1:    900        -7975.121             0.515            0.106
Chain 1:   1000        -7934.346             0.464            0.106
Chain 1:   1100        -8174.990             0.367            0.070
Chain 1:   1200        -7605.888             0.147            0.070
Chain 1:   1300        -7929.576             0.051            0.041
Chain 1:   1400        -7937.908             0.047            0.041
Chain 1:   1500        -7636.515             0.051            0.041
Chain 1:   1600        -7713.793             0.050            0.041
Chain 1:   1700        -7555.274             0.045            0.039
Chain 1:   1800        -7635.892             0.036            0.029
Chain 1:   1900        -7596.729             0.024            0.021
Chain 1:   2000        -7672.713             0.024            0.021
Chain 1:   2100        -7625.110             0.022            0.011
Chain 1:   2200        -7733.063             0.016            0.011
Chain 1:   2300        -7641.667             0.013            0.011
Chain 1:   2400        -7682.107             0.013            0.011
Chain 1:   2500        -7616.982             0.010            0.010
Chain 1:   2600        -7575.502             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005211 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 52.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86139.230             1.000            1.000
Chain 1:    200       -13445.312             3.203            5.407
Chain 1:    300        -9850.619             2.257            1.000
Chain 1:    400       -10780.708             1.714            1.000
Chain 1:    500        -8796.762             1.417            0.365
Chain 1:    600        -8360.471             1.189            0.365
Chain 1:    700        -8469.255             1.021            0.226
Chain 1:    800        -9053.172             0.902            0.226
Chain 1:    900        -8638.737             0.807            0.086
Chain 1:   1000        -8472.409             0.728            0.086
Chain 1:   1100        -8696.278             0.631            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8325.336             0.094            0.052
Chain 1:   1300        -8556.765             0.061            0.048
Chain 1:   1400        -8557.253             0.052            0.045
Chain 1:   1500        -8452.255             0.031            0.027
Chain 1:   1600        -8553.800             0.027            0.026
Chain 1:   1700        -8643.230             0.026            0.026
Chain 1:   1800        -8238.776             0.025            0.026
Chain 1:   1900        -8337.953             0.021            0.020
Chain 1:   2000        -8309.321             0.020            0.012
Chain 1:   2100        -8429.118             0.018            0.012
Chain 1:   2200        -8223.457             0.017            0.012
Chain 1:   2300        -8371.366             0.016            0.012
Chain 1:   2400        -8248.816             0.017            0.014
Chain 1:   2500        -8313.045             0.017            0.014
Chain 1:   2600        -8336.177             0.016            0.014
Chain 1:   2700        -8254.801             0.016            0.014
Chain 1:   2800        -8227.771             0.011            0.012
Chain 1:   2900        -8283.168             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003729 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408858.037             1.000            1.000
Chain 1:    200     -1587116.580             2.649            4.298
Chain 1:    300      -891753.894             2.026            1.000
Chain 1:    400      -457774.180             1.756            1.000
Chain 1:    500      -357882.144             1.461            0.948
Chain 1:    600      -232728.761             1.307            0.948
Chain 1:    700      -119043.326             1.257            0.948
Chain 1:    800       -86287.851             1.147            0.948
Chain 1:    900       -66654.266             1.052            0.780
Chain 1:   1000       -51472.379             0.977            0.780
Chain 1:   1100       -38967.462             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38144.973             0.481            0.380
Chain 1:   1300       -26120.976             0.449            0.380
Chain 1:   1400       -25840.747             0.355            0.321
Chain 1:   1500       -22433.522             0.343            0.321
Chain 1:   1600       -21651.490             0.293            0.295
Chain 1:   1700       -20527.744             0.203            0.295
Chain 1:   1800       -20472.378             0.165            0.152
Chain 1:   1900       -20798.325             0.137            0.055
Chain 1:   2000       -19311.271             0.115            0.055
Chain 1:   2100       -19549.446             0.084            0.036
Chain 1:   2200       -19775.641             0.083            0.036
Chain 1:   2300       -19393.173             0.039            0.020
Chain 1:   2400       -19165.385             0.039            0.020
Chain 1:   2500       -18967.360             0.025            0.016
Chain 1:   2600       -18597.803             0.024            0.016
Chain 1:   2700       -18554.870             0.018            0.012
Chain 1:   2800       -18271.814             0.020            0.015
Chain 1:   2900       -18552.936             0.020            0.015
Chain 1:   3000       -18539.148             0.012            0.012
Chain 1:   3100       -18624.104             0.011            0.012
Chain 1:   3200       -18314.937             0.012            0.015
Chain 1:   3300       -18519.557             0.011            0.012
Chain 1:   3400       -17994.737             0.013            0.015
Chain 1:   3500       -18606.187             0.015            0.015
Chain 1:   3600       -17913.467             0.017            0.015
Chain 1:   3700       -18299.800             0.019            0.017
Chain 1:   3800       -17260.404             0.023            0.021
Chain 1:   3900       -17256.586             0.022            0.021
Chain 1:   4000       -17373.892             0.022            0.021
Chain 1:   4100       -17287.684             0.022            0.021
Chain 1:   4200       -17104.154             0.022            0.021
Chain 1:   4300       -17242.394             0.021            0.021
Chain 1:   4400       -17199.373             0.019            0.011
Chain 1:   4500       -17101.950             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001455 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49269.116             1.000            1.000
Chain 1:    200       -21151.120             1.165            1.329
Chain 1:    300       -13609.653             0.961            1.000
Chain 1:    400       -12697.689             0.739            1.000
Chain 1:    500       -17507.053             0.646            0.554
Chain 1:    600       -16002.842             0.554            0.554
Chain 1:    700       -12428.375             0.516            0.288
Chain 1:    800       -13296.064             0.460            0.288
Chain 1:    900       -11756.986             0.423            0.275
Chain 1:   1000       -13316.780             0.392            0.275
Chain 1:   1100       -19000.413             0.322            0.275
Chain 1:   1200       -13816.172             0.227            0.275
Chain 1:   1300       -13430.246             0.174            0.131
Chain 1:   1400       -12938.778             0.171            0.131
Chain 1:   1500       -23348.977             0.188            0.131
Chain 1:   1600       -21332.858             0.188            0.131
Chain 1:   1700       -10335.409             0.266            0.131
Chain 1:   1800       -11744.879             0.271            0.131
Chain 1:   1900       -11396.573             0.261            0.120
Chain 1:   2000       -10519.616             0.258            0.120
Chain 1:   2100       -10694.428             0.230            0.095
Chain 1:   2200       -18500.315             0.234            0.095
Chain 1:   2300       -11466.459             0.293            0.120
Chain 1:   2400        -9314.941             0.312            0.231
Chain 1:   2500       -10948.418             0.282            0.149
Chain 1:   2600        -9407.145             0.289            0.164
Chain 1:   2700        -9639.492             0.185            0.149
Chain 1:   2800       -12969.550             0.199            0.164
Chain 1:   2900       -10118.259             0.224            0.231
Chain 1:   3000       -13437.879             0.241            0.247
Chain 1:   3100       -13987.269             0.243            0.247
Chain 1:   3200       -11580.329             0.221            0.231
Chain 1:   3300       -11881.409             0.163            0.208
Chain 1:   3400       -17365.038             0.171            0.208
Chain 1:   3500       -10289.193             0.225            0.247
Chain 1:   3600        -9479.562             0.217            0.247
Chain 1:   3700       -12453.686             0.239            0.247
Chain 1:   3800        -9009.317             0.251            0.247
Chain 1:   3900       -13381.206             0.256            0.247
Chain 1:   4000        -9401.338             0.273            0.316
Chain 1:   4100        -9966.682             0.275            0.316
Chain 1:   4200       -11378.344             0.267            0.316
Chain 1:   4300       -13739.335             0.281            0.316
Chain 1:   4400       -12577.741             0.259            0.239
Chain 1:   4500        -9356.519             0.225            0.239
Chain 1:   4600       -12523.259             0.241            0.253
Chain 1:   4700       -15364.439             0.236            0.253
Chain 1:   4800        -9018.209             0.268            0.253
Chain 1:   4900        -8970.025             0.236            0.185
Chain 1:   5000       -10008.918             0.204            0.172
Chain 1:   5100        -9140.514             0.208            0.172
Chain 1:   5200        -9742.396             0.202            0.172
Chain 1:   5300       -12908.695             0.209            0.185
Chain 1:   5400        -9477.444             0.236            0.245
Chain 1:   5500       -13904.785             0.233            0.245
Chain 1:   5600       -12359.491             0.221            0.185
Chain 1:   5700       -13398.860             0.210            0.125
Chain 1:   5800        -9028.752             0.188            0.125
Chain 1:   5900        -9002.536             0.188            0.125
Chain 1:   6000        -9832.619             0.186            0.125
Chain 1:   6100        -9369.358             0.181            0.125
Chain 1:   6200       -14324.132             0.210            0.245
Chain 1:   6300        -9339.637             0.238            0.318
Chain 1:   6400       -15053.334             0.240            0.318
Chain 1:   6500        -9010.985             0.275            0.346
Chain 1:   6600        -9577.948             0.269            0.346
Chain 1:   6700        -9055.963             0.267            0.346
Chain 1:   6800        -9297.978             0.221            0.084
Chain 1:   6900       -11750.139             0.242            0.209
Chain 1:   7000        -8788.866             0.267            0.337
Chain 1:   7100        -8433.709             0.266            0.337
Chain 1:   7200        -9019.558             0.238            0.209
Chain 1:   7300        -9582.077             0.190            0.065
Chain 1:   7400        -9819.085             0.155            0.059
Chain 1:   7500       -10129.935             0.091            0.059
Chain 1:   7600        -8669.249             0.102            0.059
Chain 1:   7700        -8925.581             0.099            0.059
Chain 1:   7800        -8772.030             0.098            0.059
Chain 1:   7900        -8652.194             0.079            0.042
Chain 1:   8000        -8744.005             0.046            0.031
Chain 1:   8100       -11773.575             0.067            0.031
Chain 1:   8200        -8704.228             0.096            0.031
Chain 1:   8300        -8482.123             0.093            0.029
Chain 1:   8400       -11387.703             0.116            0.031
Chain 1:   8500        -8576.528             0.146            0.168
Chain 1:   8600       -12363.273             0.160            0.255
Chain 1:   8700        -8769.666             0.198            0.257
Chain 1:   8800       -10851.495             0.215            0.257
Chain 1:   8900       -11102.559             0.216            0.257
Chain 1:   9000        -8929.022             0.239            0.257
Chain 1:   9100        -9848.114             0.223            0.255
Chain 1:   9200        -8978.199             0.197            0.243
Chain 1:   9300        -9501.053             0.200            0.243
Chain 1:   9400        -9183.971             0.178            0.192
Chain 1:   9500        -9440.448             0.148            0.097
Chain 1:   9600        -9160.131             0.121            0.093
Chain 1:   9700        -8484.987             0.087            0.080
Chain 1:   9800        -9958.458             0.083            0.080
Chain 1:   9900       -11824.906             0.097            0.093
Chain 1:   10000       -10678.650             0.083            0.093
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51454.577             1.000            1.000
Chain 1:    200       -16641.185             1.546            2.092
Chain 1:    300        -8942.605             1.318            1.000
Chain 1:    400        -9247.784             0.996            1.000
Chain 1:    500        -8590.992             0.812            0.861
Chain 1:    600        -9358.265             0.691            0.861
Chain 1:    700        -8302.925             0.610            0.127
Chain 1:    800        -8386.393             0.535            0.127
Chain 1:    900        -7941.647             0.482            0.082
Chain 1:   1000        -7964.073             0.434            0.082
Chain 1:   1100        -7753.131             0.337            0.076
Chain 1:   1200        -7970.024             0.130            0.056
Chain 1:   1300        -7823.330             0.046            0.033
Chain 1:   1400        -7731.005             0.044            0.027
Chain 1:   1500        -7622.422             0.038            0.027
Chain 1:   1600        -7964.084             0.034            0.027
Chain 1:   1700        -7572.073             0.026            0.027
Chain 1:   1800        -7694.204             0.027            0.027
Chain 1:   1900        -7661.543             0.022            0.019
Chain 1:   2000        -7780.153             0.023            0.019
Chain 1:   2100        -7655.647             0.022            0.016
Chain 1:   2200        -7836.984             0.021            0.016
Chain 1:   2300        -7616.488             0.022            0.016
Chain 1:   2400        -7745.854             0.023            0.017
Chain 1:   2500        -7703.552             0.022            0.017
Chain 1:   2600        -7635.147             0.019            0.016
Chain 1:   2700        -7640.197             0.014            0.016
Chain 1:   2800        -7592.043             0.013            0.015
Chain 1:   2900        -7493.898             0.013            0.015
Chain 1:   3000        -7617.282             0.014            0.016
Chain 1:   3100        -7603.146             0.012            0.013
Chain 1:   3200        -7802.113             0.012            0.013
Chain 1:   3300        -7526.481             0.013            0.013
Chain 1:   3400        -7746.867             0.014            0.013
Chain 1:   3500        -7510.229             0.017            0.016
Chain 1:   3600        -7576.761             0.017            0.016
Chain 1:   3700        -7525.207             0.018            0.016
Chain 1:   3800        -7524.033             0.017            0.016
Chain 1:   3900        -7491.509             0.016            0.016
Chain 1:   4000        -7486.394             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003593 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86281.564             1.000            1.000
Chain 1:    200       -13846.555             3.116            5.231
Chain 1:    300       -10172.022             2.198            1.000
Chain 1:    400       -11205.340             1.671            1.000
Chain 1:    500        -9155.551             1.382            0.361
Chain 1:    600        -8639.129             1.161            0.361
Chain 1:    700        -8974.011             1.001            0.224
Chain 1:    800        -9446.377             0.882            0.224
Chain 1:    900        -8890.480             0.791            0.092
Chain 1:   1000        -8759.802             0.713            0.092
Chain 1:   1100        -8812.920             0.614            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8603.738             0.093            0.060
Chain 1:   1300        -8843.934             0.060            0.050
Chain 1:   1400        -8870.988             0.051            0.037
Chain 1:   1500        -8713.045             0.030            0.027
Chain 1:   1600        -8828.290             0.026            0.024
Chain 1:   1700        -8901.598             0.023            0.018
Chain 1:   1800        -8475.055             0.023            0.018
Chain 1:   1900        -8577.684             0.018            0.015
Chain 1:   2000        -8552.551             0.017            0.013
Chain 1:   2100        -8679.672             0.017            0.015
Chain 1:   2200        -8478.624             0.017            0.015
Chain 1:   2300        -8572.987             0.016            0.013
Chain 1:   2400        -8640.923             0.016            0.013
Chain 1:   2500        -8587.136             0.015            0.012
Chain 1:   2600        -8589.572             0.014            0.011
Chain 1:   2700        -8505.762             0.014            0.011
Chain 1:   2800        -8464.327             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003837 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390926.955             1.000            1.000
Chain 1:    200     -1585438.787             2.646            4.292
Chain 1:    300      -892089.251             2.023            1.000
Chain 1:    400      -457956.603             1.754            1.000
Chain 1:    500      -358236.588             1.459            0.948
Chain 1:    600      -233303.327             1.305            0.948
Chain 1:    700      -119592.573             1.255            0.948
Chain 1:    800       -86767.217             1.145            0.948
Chain 1:    900       -67129.384             1.050            0.777
Chain 1:   1000       -51936.402             0.975            0.777
Chain 1:   1100       -39411.205             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38592.669             0.479            0.378
Chain 1:   1300       -26548.716             0.447            0.378
Chain 1:   1400       -26269.136             0.353            0.318
Chain 1:   1500       -22854.534             0.340            0.318
Chain 1:   1600       -22070.399             0.290            0.293
Chain 1:   1700       -20944.283             0.201            0.293
Chain 1:   1800       -20888.567             0.163            0.149
Chain 1:   1900       -21214.856             0.135            0.054
Chain 1:   2000       -19725.353             0.114            0.054
Chain 1:   2100       -19964.045             0.083            0.036
Chain 1:   2200       -20190.396             0.082            0.036
Chain 1:   2300       -19807.644             0.039            0.019
Chain 1:   2400       -19579.662             0.039            0.019
Chain 1:   2500       -19381.468             0.025            0.015
Chain 1:   2600       -19011.745             0.023            0.015
Chain 1:   2700       -18968.727             0.018            0.012
Chain 1:   2800       -18685.364             0.019            0.015
Chain 1:   2900       -18966.723             0.019            0.015
Chain 1:   3000       -18953.054             0.012            0.012
Chain 1:   3100       -19037.994             0.011            0.012
Chain 1:   3200       -18728.615             0.011            0.015
Chain 1:   3300       -18933.385             0.011            0.012
Chain 1:   3400       -18408.081             0.012            0.015
Chain 1:   3500       -19020.211             0.014            0.015
Chain 1:   3600       -18326.639             0.016            0.015
Chain 1:   3700       -18713.589             0.018            0.017
Chain 1:   3800       -17672.786             0.023            0.021
Chain 1:   3900       -17668.884             0.021            0.021
Chain 1:   4000       -17786.247             0.022            0.021
Chain 1:   4100       -17699.908             0.022            0.021
Chain 1:   4200       -17516.086             0.021            0.021
Chain 1:   4300       -17654.578             0.021            0.021
Chain 1:   4400       -17611.325             0.018            0.010
Chain 1:   4500       -17513.820             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001304 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12593.846             1.000            1.000
Chain 1:    200        -9477.453             0.664            1.000
Chain 1:    300        -7997.924             0.505            0.329
Chain 1:    400        -8261.341             0.386            0.329
Chain 1:    500        -8149.732             0.312            0.185
Chain 1:    600        -7988.270             0.263            0.185
Chain 1:    700        -7875.707             0.228            0.032
Chain 1:    800        -7880.187             0.199            0.032
Chain 1:    900        -7857.831             0.177            0.020
Chain 1:   1000        -7941.608             0.161            0.020
Chain 1:   1100        -7975.593             0.061            0.014
Chain 1:   1200        -7927.001             0.029            0.014
Chain 1:   1300        -7851.534             0.011            0.011
Chain 1:   1400        -7873.007             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58478.297             1.000            1.000
Chain 1:    200       -17956.358             1.628            2.257
Chain 1:    300        -8788.448             1.433            1.043
Chain 1:    400        -8173.201             1.094            1.043
Chain 1:    500        -8866.831             0.891            1.000
Chain 1:    600        -8565.007             0.748            1.000
Chain 1:    700        -7777.135             0.656            0.101
Chain 1:    800        -8227.482             0.581            0.101
Chain 1:    900        -7993.348             0.519            0.078
Chain 1:   1000        -7889.206             0.469            0.078
Chain 1:   1100        -7827.695             0.370            0.075
Chain 1:   1200        -7876.372             0.144            0.055
Chain 1:   1300        -7804.246             0.041            0.035
Chain 1:   1400        -7841.396             0.034            0.029
Chain 1:   1500        -7572.457             0.030            0.029
Chain 1:   1600        -7737.298             0.028            0.021
Chain 1:   1700        -7553.995             0.021            0.021
Chain 1:   1800        -7588.033             0.016            0.013
Chain 1:   1900        -7604.048             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002969 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87393.964             1.000            1.000
Chain 1:    200       -13704.386             3.189            5.377
Chain 1:    300        -9975.693             2.250            1.000
Chain 1:    400       -11295.091             1.717            1.000
Chain 1:    500        -8899.933             1.427            0.374
Chain 1:    600        -8765.300             1.192            0.374
Chain 1:    700        -8547.563             1.025            0.269
Chain 1:    800        -8294.126             0.901            0.269
Chain 1:    900        -8297.709             0.801            0.117
Chain 1:   1000        -8598.453             0.724            0.117
Chain 1:   1100        -8750.720             0.626            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8339.777             0.093            0.035
Chain 1:   1300        -8654.941             0.060            0.035
Chain 1:   1400        -8617.682             0.048            0.031
Chain 1:   1500        -8492.019             0.023            0.025
Chain 1:   1600        -8595.345             0.023            0.025
Chain 1:   1700        -8660.357             0.021            0.017
Chain 1:   1800        -8222.515             0.023            0.017
Chain 1:   1900        -8327.886             0.024            0.017
Chain 1:   2000        -8305.464             0.021            0.015
Chain 1:   2100        -8448.236             0.021            0.015
Chain 1:   2200        -8234.455             0.019            0.015
Chain 1:   2300        -8393.018             0.017            0.015
Chain 1:   2400        -8229.873             0.018            0.017
Chain 1:   2500        -8302.380             0.018            0.017
Chain 1:   2600        -8214.053             0.018            0.017
Chain 1:   2700        -8247.904             0.017            0.017
Chain 1:   2800        -8207.322             0.013            0.013
Chain 1:   2900        -8301.664             0.012            0.011
Chain 1:   3000        -8137.842             0.014            0.017
Chain 1:   3100        -8290.417             0.014            0.018
Chain 1:   3200        -8161.746             0.013            0.016
Chain 1:   3300        -8172.449             0.012            0.011
Chain 1:   3400        -8338.743             0.012            0.011
Chain 1:   3500        -8349.043             0.011            0.011
Chain 1:   3600        -8118.072             0.013            0.016
Chain 1:   3700        -8265.229             0.014            0.018
Chain 1:   3800        -8124.088             0.015            0.018
Chain 1:   3900        -8058.170             0.015            0.018
Chain 1:   4000        -8137.111             0.014            0.017
Chain 1:   4100        -8129.670             0.012            0.016
Chain 1:   4200        -8114.534             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003504 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8437331.031             1.000            1.000
Chain 1:    200     -1589953.629             2.653            4.307
Chain 1:    300      -891061.827             2.030            1.000
Chain 1:    400      -457568.989             1.760            1.000
Chain 1:    500      -357326.601             1.464            0.947
Chain 1:    600      -232253.025             1.310            0.947
Chain 1:    700      -118927.811             1.259            0.947
Chain 1:    800       -86248.679             1.149            0.947
Chain 1:    900       -66696.400             1.054            0.784
Chain 1:   1000       -51585.972             0.978            0.784
Chain 1:   1100       -39142.350             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38332.483             0.481            0.379
Chain 1:   1300       -26366.349             0.448            0.379
Chain 1:   1400       -26094.134             0.354            0.318
Chain 1:   1500       -22701.295             0.341            0.318
Chain 1:   1600       -21924.277             0.291            0.293
Chain 1:   1700       -20807.027             0.201            0.293
Chain 1:   1800       -20753.548             0.163            0.149
Chain 1:   1900       -21080.099             0.135            0.054
Chain 1:   2000       -19595.041             0.114            0.054
Chain 1:   2100       -19833.360             0.083            0.035
Chain 1:   2200       -20059.301             0.082            0.035
Chain 1:   2300       -19676.838             0.039            0.019
Chain 1:   2400       -19448.858             0.039            0.019
Chain 1:   2500       -19250.432             0.025            0.015
Chain 1:   2600       -18880.637             0.023            0.015
Chain 1:   2700       -18837.587             0.018            0.012
Chain 1:   2800       -18554.088             0.019            0.015
Chain 1:   2900       -18835.405             0.019            0.015
Chain 1:   3000       -18821.649             0.012            0.012
Chain 1:   3100       -18906.708             0.011            0.012
Chain 1:   3200       -18597.181             0.012            0.015
Chain 1:   3300       -18802.052             0.011            0.012
Chain 1:   3400       -18276.484             0.012            0.015
Chain 1:   3500       -18888.965             0.015            0.015
Chain 1:   3600       -18194.781             0.016            0.015
Chain 1:   3700       -18582.160             0.018            0.017
Chain 1:   3800       -17540.458             0.023            0.021
Chain 1:   3900       -17536.485             0.021            0.021
Chain 1:   4000       -17653.866             0.022            0.021
Chain 1:   4100       -17567.549             0.022            0.021
Chain 1:   4200       -17383.455             0.021            0.021
Chain 1:   4300       -17522.140             0.021            0.021
Chain 1:   4400       -17478.718             0.018            0.011
Chain 1:   4500       -17381.126             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001333 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49182.866             1.000            1.000
Chain 1:    200       -18594.363             1.323            1.645
Chain 1:    300       -14914.771             0.964            1.000
Chain 1:    400       -20740.377             0.793            1.000
Chain 1:    500       -16320.783             0.689            0.281
Chain 1:    600       -14199.635             0.599            0.281
Chain 1:    700       -18286.517             0.545            0.271
Chain 1:    800       -15811.838             0.497            0.271
Chain 1:    900       -14284.921             0.453            0.247
Chain 1:   1000       -13268.663             0.416            0.247
Chain 1:   1100       -13695.198             0.319            0.223
Chain 1:   1200       -11842.391             0.170            0.157
Chain 1:   1300       -14081.876             0.161            0.157
Chain 1:   1400       -10656.493             0.165            0.157
Chain 1:   1500       -12691.906             0.154            0.157
Chain 1:   1600       -12201.432             0.143            0.157
Chain 1:   1700       -19332.772             0.158            0.157
Chain 1:   1800       -13223.678             0.188            0.159
Chain 1:   1900       -10936.038             0.199            0.160
Chain 1:   2000       -17813.522             0.229            0.209
Chain 1:   2100       -10005.179             0.304            0.321
Chain 1:   2200       -10471.939             0.293            0.321
Chain 1:   2300        -9492.851             0.288            0.321
Chain 1:   2400        -9532.978             0.256            0.209
Chain 1:   2500        -9842.067             0.243            0.209
Chain 1:   2600       -10353.598             0.244            0.209
Chain 1:   2700       -11857.174             0.220            0.127
Chain 1:   2800       -10501.503             0.186            0.127
Chain 1:   2900        -9458.330             0.177            0.110
Chain 1:   3000        -9338.415             0.139            0.103
Chain 1:   3100       -11667.982             0.081            0.103
Chain 1:   3200       -11667.261             0.077            0.103
Chain 1:   3300       -11545.586             0.067            0.049
Chain 1:   3400       -17042.180             0.099            0.110
Chain 1:   3500        -9147.216             0.182            0.127
Chain 1:   3600       -10798.045             0.193            0.129
Chain 1:   3700       -10513.424             0.183            0.129
Chain 1:   3800       -10123.311             0.174            0.110
Chain 1:   3900       -13912.743             0.190            0.153
Chain 1:   4000       -14871.596             0.195            0.153
Chain 1:   4100        -9191.200             0.237            0.153
Chain 1:   4200        -8918.626             0.240            0.153
Chain 1:   4300       -10918.597             0.257            0.183
Chain 1:   4400        -9155.682             0.244            0.183
Chain 1:   4500        -9020.818             0.159            0.153
Chain 1:   4600       -11564.363             0.166            0.183
Chain 1:   4700        -9324.784             0.187            0.193
Chain 1:   4800        -8713.504             0.191            0.193
Chain 1:   4900        -9873.056             0.175            0.183
Chain 1:   5000       -12258.587             0.188            0.193
Chain 1:   5100       -12516.109             0.128            0.183
Chain 1:   5200       -16257.920             0.148            0.193
Chain 1:   5300       -10849.990             0.180            0.195
Chain 1:   5400        -9223.945             0.178            0.195
Chain 1:   5500       -14569.186             0.213            0.220
Chain 1:   5600        -8922.698             0.255            0.230
Chain 1:   5700       -14691.949             0.270            0.230
Chain 1:   5800       -11085.560             0.296            0.325
Chain 1:   5900        -8624.803             0.312            0.325
Chain 1:   6000        -9103.641             0.298            0.325
Chain 1:   6100        -9834.845             0.303            0.325
Chain 1:   6200       -13257.166             0.306            0.325
Chain 1:   6300        -9811.765             0.292            0.325
Chain 1:   6400       -14681.478             0.307            0.332
Chain 1:   6500        -9641.010             0.323            0.332
Chain 1:   6600        -9031.399             0.266            0.325
Chain 1:   6700       -10869.910             0.244            0.285
Chain 1:   6800        -8595.798             0.238            0.265
Chain 1:   6900       -12700.050             0.242            0.265
Chain 1:   7000       -11596.181             0.246            0.265
Chain 1:   7100       -11735.676             0.240            0.265
Chain 1:   7200        -8880.480             0.246            0.322
Chain 1:   7300        -9166.760             0.214            0.265
Chain 1:   7400        -8558.622             0.188            0.169
Chain 1:   7500       -10858.761             0.157            0.169
Chain 1:   7600        -8975.694             0.171            0.210
Chain 1:   7700       -10645.661             0.170            0.210
Chain 1:   7800       -10730.001             0.144            0.157
Chain 1:   7900        -9467.128             0.125            0.133
Chain 1:   8000       -11693.838             0.135            0.157
Chain 1:   8100       -11067.413             0.139            0.157
Chain 1:   8200        -8836.256             0.132            0.157
Chain 1:   8300       -11627.638             0.153            0.190
Chain 1:   8400        -8739.896             0.179            0.210
Chain 1:   8500        -9803.877             0.169            0.190
Chain 1:   8600       -12481.131             0.169            0.190
Chain 1:   8700        -8359.386             0.203            0.215
Chain 1:   8800        -8788.564             0.207            0.215
Chain 1:   8900        -9780.069             0.204            0.215
Chain 1:   9000       -10110.695             0.188            0.215
Chain 1:   9100        -9380.265             0.190            0.215
Chain 1:   9200        -9136.515             0.167            0.109
Chain 1:   9300        -8745.820             0.148            0.101
Chain 1:   9400       -10336.083             0.130            0.101
Chain 1:   9500        -9339.748             0.130            0.101
Chain 1:   9600       -10360.412             0.118            0.099
Chain 1:   9700       -12885.813             0.089            0.099
Chain 1:   9800       -11478.770             0.096            0.101
Chain 1:   9900        -8948.013             0.114            0.107
Chain 1:   10000       -11285.542             0.132            0.123
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001631 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57346.559             1.000            1.000
Chain 1:    200       -17863.558             1.605            2.210
Chain 1:    300        -8936.071             1.403            1.000
Chain 1:    400        -8250.145             1.073            1.000
Chain 1:    500        -8815.705             0.871            0.999
Chain 1:    600        -8663.818             0.729            0.999
Chain 1:    700        -8029.347             0.636            0.083
Chain 1:    800        -8309.408             0.561            0.083
Chain 1:    900        -8180.177             0.500            0.079
Chain 1:   1000        -7876.227             0.454            0.079
Chain 1:   1100        -7623.759             0.357            0.064
Chain 1:   1200        -7775.843             0.138            0.039
Chain 1:   1300        -7950.912             0.041            0.034
Chain 1:   1400        -7921.108             0.033            0.033
Chain 1:   1500        -7652.001             0.030            0.033
Chain 1:   1600        -7872.012             0.031            0.033
Chain 1:   1700        -7542.590             0.027            0.033
Chain 1:   1800        -7674.985             0.026            0.028
Chain 1:   1900        -7782.586             0.025            0.028
Chain 1:   2000        -7749.557             0.022            0.022
Chain 1:   2100        -7659.127             0.020            0.020
Chain 1:   2200        -7817.721             0.020            0.020
Chain 1:   2300        -7604.572             0.021            0.020
Chain 1:   2400        -7637.830             0.021            0.020
Chain 1:   2500        -7669.651             0.018            0.017
Chain 1:   2600        -7581.159             0.016            0.014
Chain 1:   2700        -7581.179             0.012            0.012
Chain 1:   2800        -7573.277             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003548 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86272.602             1.000            1.000
Chain 1:    200       -13867.197             3.111            5.221
Chain 1:    300       -10178.236             2.195            1.000
Chain 1:    400       -11347.843             1.672            1.000
Chain 1:    500        -9172.233             1.385            0.362
Chain 1:    600        -8621.442             1.165            0.362
Chain 1:    700        -8725.959             1.000            0.237
Chain 1:    800        -9039.161             0.879            0.237
Chain 1:    900        -8908.021             0.783            0.103
Chain 1:   1000        -8893.850             0.705            0.103
Chain 1:   1100        -8862.313             0.605            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8463.343             0.088            0.047
Chain 1:   1300        -8851.483             0.056            0.044
Chain 1:   1400        -8800.781             0.046            0.035
Chain 1:   1500        -8721.309             0.024            0.015
Chain 1:   1600        -8824.542             0.018            0.012
Chain 1:   1700        -8891.931             0.018            0.012
Chain 1:   1800        -8460.299             0.020            0.012
Chain 1:   1900        -8564.392             0.019            0.012
Chain 1:   2000        -8539.767             0.019            0.012
Chain 1:   2100        -8676.651             0.021            0.012
Chain 1:   2200        -8469.739             0.018            0.012
Chain 1:   2300        -8568.853             0.015            0.012
Chain 1:   2400        -8631.553             0.015            0.012
Chain 1:   2500        -8572.037             0.015            0.012
Chain 1:   2600        -8577.872             0.014            0.012
Chain 1:   2700        -8492.561             0.014            0.012
Chain 1:   2800        -8448.927             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399802.184             1.000            1.000
Chain 1:    200     -1585404.572             2.649            4.298
Chain 1:    300      -892353.254             2.025            1.000
Chain 1:    400      -458687.206             1.755            1.000
Chain 1:    500      -359019.663             1.460            0.945
Chain 1:    600      -233730.109             1.306            0.945
Chain 1:    700      -119767.813             1.255            0.945
Chain 1:    800       -86933.936             1.145            0.945
Chain 1:    900       -67244.954             1.051            0.777
Chain 1:   1000       -52026.687             0.975            0.777
Chain 1:   1100       -39484.377             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38660.831             0.479            0.378
Chain 1:   1300       -26592.122             0.447            0.378
Chain 1:   1400       -26309.698             0.353            0.318
Chain 1:   1500       -22890.445             0.340            0.318
Chain 1:   1600       -22105.422             0.290            0.293
Chain 1:   1700       -20975.845             0.201            0.293
Chain 1:   1800       -20919.472             0.163            0.149
Chain 1:   1900       -21245.850             0.135            0.054
Chain 1:   2000       -19754.888             0.114            0.054
Chain 1:   2100       -19993.316             0.083            0.036
Chain 1:   2200       -20220.275             0.082            0.036
Chain 1:   2300       -19836.989             0.039            0.019
Chain 1:   2400       -19608.980             0.039            0.019
Chain 1:   2500       -19411.095             0.025            0.015
Chain 1:   2600       -19040.892             0.023            0.015
Chain 1:   2700       -18997.773             0.018            0.012
Chain 1:   2800       -18714.550             0.019            0.015
Chain 1:   2900       -18995.955             0.019            0.015
Chain 1:   3000       -18982.086             0.012            0.012
Chain 1:   3100       -19067.125             0.011            0.012
Chain 1:   3200       -18757.613             0.011            0.015
Chain 1:   3300       -18962.499             0.011            0.012
Chain 1:   3400       -18437.099             0.012            0.015
Chain 1:   3500       -19049.517             0.014            0.015
Chain 1:   3600       -18355.539             0.016            0.015
Chain 1:   3700       -18742.818             0.018            0.017
Chain 1:   3800       -17701.540             0.023            0.021
Chain 1:   3900       -17697.692             0.021            0.021
Chain 1:   4000       -17814.963             0.022            0.021
Chain 1:   4100       -17728.685             0.022            0.021
Chain 1:   4200       -17544.731             0.021            0.021
Chain 1:   4300       -17683.251             0.021            0.021
Chain 1:   4400       -17639.894             0.018            0.010
Chain 1:   4500       -17542.429             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001519 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48161.376             1.000            1.000
Chain 1:    200       -14353.416             1.678            2.355
Chain 1:    300       -19660.549             1.208            1.000
Chain 1:    400       -18589.176             0.921            1.000
Chain 1:    500       -14492.179             0.793            0.283
Chain 1:    600       -18958.302             0.700            0.283
Chain 1:    700       -14431.645             0.645            0.283
Chain 1:    800       -10801.126             0.606            0.314
Chain 1:    900       -13694.159             0.562            0.283
Chain 1:   1000       -11640.094             0.524            0.283
Chain 1:   1100       -10541.943             0.434            0.270
Chain 1:   1200       -20199.873             0.247            0.270
Chain 1:   1300       -10839.611             0.306            0.283
Chain 1:   1400       -10697.888             0.301            0.283
Chain 1:   1500       -12231.086             0.286            0.236
Chain 1:   1600       -13705.912             0.273            0.211
Chain 1:   1700       -16387.018             0.258            0.176
Chain 1:   1800        -9891.202             0.290            0.176
Chain 1:   1900       -13474.294             0.295            0.176
Chain 1:   2000       -16064.426             0.294            0.164
Chain 1:   2100       -14540.954             0.294            0.164
Chain 1:   2200        -8904.099             0.310            0.164
Chain 1:   2300       -16369.131             0.269            0.164
Chain 1:   2400        -9147.716             0.346            0.266
Chain 1:   2500        -9313.828             0.336            0.266
Chain 1:   2600       -11455.034             0.344            0.266
Chain 1:   2700        -9145.225             0.352            0.266
Chain 1:   2800        -8722.922             0.292            0.253
Chain 1:   2900       -14392.274             0.304            0.253
Chain 1:   3000        -8469.999             0.358            0.394
Chain 1:   3100        -8988.188             0.354            0.394
Chain 1:   3200       -12405.803             0.318            0.275
Chain 1:   3300       -10875.354             0.286            0.253
Chain 1:   3400        -9863.247             0.218            0.187
Chain 1:   3500        -8866.626             0.227            0.187
Chain 1:   3600        -9495.369             0.215            0.141
Chain 1:   3700        -9109.485             0.194            0.112
Chain 1:   3800        -8933.272             0.191            0.112
Chain 1:   3900        -8476.688             0.157            0.103
Chain 1:   4000       -10944.654             0.110            0.103
Chain 1:   4100       -11790.464             0.111            0.103
Chain 1:   4200       -14143.206             0.100            0.103
Chain 1:   4300        -8773.908             0.147            0.103
Chain 1:   4400        -9084.304             0.140            0.072
Chain 1:   4500        -8693.718             0.134            0.066
Chain 1:   4600        -8772.956             0.128            0.054
Chain 1:   4700       -10854.834             0.143            0.072
Chain 1:   4800       -13698.307             0.162            0.166
Chain 1:   4900       -13053.324             0.161            0.166
Chain 1:   5000       -10297.324             0.165            0.166
Chain 1:   5100        -8436.696             0.180            0.192
Chain 1:   5200       -11986.037             0.193            0.208
Chain 1:   5300       -14134.261             0.147            0.192
Chain 1:   5400       -10356.562             0.180            0.208
Chain 1:   5500        -8979.244             0.191            0.208
Chain 1:   5600       -11777.072             0.214            0.221
Chain 1:   5700       -12561.347             0.201            0.221
Chain 1:   5800       -12979.954             0.184            0.221
Chain 1:   5900        -8516.240             0.231            0.238
Chain 1:   6000       -10244.200             0.221            0.221
Chain 1:   6100        -8313.509             0.222            0.232
Chain 1:   6200       -10156.287             0.211            0.181
Chain 1:   6300       -11712.916             0.209            0.181
Chain 1:   6400       -13102.354             0.183            0.169
Chain 1:   6500        -9979.549             0.199            0.181
Chain 1:   6600        -8439.370             0.194            0.181
Chain 1:   6700       -10728.075             0.209            0.182
Chain 1:   6800        -8128.490             0.237            0.213
Chain 1:   6900        -9767.933             0.202            0.182
Chain 1:   7000        -7957.129             0.208            0.213
Chain 1:   7100        -8178.350             0.187            0.182
Chain 1:   7200       -11178.076             0.196            0.213
Chain 1:   7300        -8383.841             0.216            0.228
Chain 1:   7400       -10972.624             0.229            0.236
Chain 1:   7500        -8283.898             0.230            0.236
Chain 1:   7600        -8246.214             0.212            0.236
Chain 1:   7700        -7966.198             0.194            0.236
Chain 1:   7800        -9542.243             0.179            0.228
Chain 1:   7900        -8074.804             0.180            0.228
Chain 1:   8000        -8853.880             0.166            0.182
Chain 1:   8100        -8227.951             0.171            0.182
Chain 1:   8200       -10233.025             0.164            0.182
Chain 1:   8300        -8068.888             0.158            0.182
Chain 1:   8400        -8555.911             0.140            0.165
Chain 1:   8500        -8099.814             0.113            0.088
Chain 1:   8600        -9579.833             0.128            0.154
Chain 1:   8700        -8446.474             0.138            0.154
Chain 1:   8800        -8051.947             0.126            0.134
Chain 1:   8900        -8129.411             0.109            0.088
Chain 1:   9000        -9569.391             0.115            0.134
Chain 1:   9100        -8355.148             0.122            0.145
Chain 1:   9200        -8322.135             0.103            0.134
Chain 1:   9300        -8027.985             0.080            0.057
Chain 1:   9400       -11325.994             0.103            0.134
Chain 1:   9500        -8005.075             0.139            0.145
Chain 1:   9600        -8070.008             0.124            0.134
Chain 1:   9700       -10364.438             0.133            0.145
Chain 1:   9800        -7967.881             0.158            0.150
Chain 1:   9900        -9684.896             0.175            0.177
Chain 1:   10000        -8003.808             0.181            0.210
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45614.000             1.000            1.000
Chain 1:    200       -14997.516             1.521            2.041
Chain 1:    300        -8479.982             1.270            1.000
Chain 1:    400        -8311.906             0.958            1.000
Chain 1:    500        -8172.097             0.769            0.769
Chain 1:    600        -7847.358             0.648            0.769
Chain 1:    700        -7830.991             0.556            0.041
Chain 1:    800        -8192.712             0.492            0.044
Chain 1:    900        -7914.605             0.441            0.041
Chain 1:   1000        -7934.997             0.397            0.041
Chain 1:   1100        -7641.316             0.301            0.038
Chain 1:   1200        -7762.016             0.099            0.035
Chain 1:   1300        -7703.090             0.022            0.020
Chain 1:   1400        -7849.849             0.022            0.019
Chain 1:   1500        -7632.399             0.023            0.028
Chain 1:   1600        -7545.778             0.020            0.019
Chain 1:   1700        -7526.699             0.020            0.019
Chain 1:   1800        -7548.825             0.016            0.016
Chain 1:   1900        -7620.359             0.014            0.011
Chain 1:   2000        -7621.417             0.014            0.011
Chain 1:   2100        -7676.357             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003041 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85753.946             1.000            1.000
Chain 1:    200       -13003.729             3.297            5.595
Chain 1:    300        -9520.180             2.320            1.000
Chain 1:    400       -10189.304             1.757            1.000
Chain 1:    500        -8415.428             1.447            0.366
Chain 1:    600        -8431.014             1.206            0.366
Chain 1:    700        -8457.436             1.035            0.211
Chain 1:    800        -8564.475             0.907            0.211
Chain 1:    900        -8437.820             0.808            0.066
Chain 1:   1000        -8182.415             0.730            0.066
Chain 1:   1100        -8457.923             0.633            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8220.583             0.077            0.031
Chain 1:   1300        -8303.217             0.041            0.029
Chain 1:   1400        -8297.414             0.035            0.015
Chain 1:   1500        -8207.785             0.015            0.012
Chain 1:   1600        -8290.930             0.015            0.012
Chain 1:   1700        -8388.823             0.016            0.012
Chain 1:   1800        -8017.954             0.020            0.015
Chain 1:   1900        -8114.375             0.019            0.012
Chain 1:   2000        -8085.738             0.017            0.012
Chain 1:   2100        -8233.429             0.015            0.012
Chain 1:   2200        -8009.489             0.015            0.012
Chain 1:   2300        -8092.227             0.015            0.012
Chain 1:   2400        -8160.511             0.016            0.012
Chain 1:   2500        -8121.883             0.015            0.012
Chain 1:   2600        -8115.190             0.014            0.012
Chain 1:   2700        -8027.499             0.014            0.011
Chain 1:   2800        -8013.051             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003177 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378888.681             1.000            1.000
Chain 1:    200     -1581603.105             2.649            4.298
Chain 1:    300      -890628.200             2.025            1.000
Chain 1:    400      -457359.884             1.755            1.000
Chain 1:    500      -357786.555             1.460            0.947
Chain 1:    600      -232835.004             1.306            0.947
Chain 1:    700      -118902.534             1.256            0.947
Chain 1:    800       -86035.216             1.147            0.947
Chain 1:    900       -66344.660             1.053            0.776
Chain 1:   1000       -51100.070             0.977            0.776
Chain 1:   1100       -38546.116             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37711.249             0.482            0.382
Chain 1:   1300       -25656.747             0.452            0.382
Chain 1:   1400       -25370.173             0.358            0.326
Chain 1:   1500       -21954.535             0.346            0.326
Chain 1:   1600       -21168.489             0.296            0.298
Chain 1:   1700       -20042.246             0.206            0.297
Chain 1:   1800       -19985.680             0.168            0.156
Chain 1:   1900       -20310.848             0.140            0.056
Chain 1:   2000       -18823.517             0.118            0.056
Chain 1:   2100       -19061.890             0.086            0.037
Chain 1:   2200       -19287.619             0.085            0.037
Chain 1:   2300       -18905.675             0.040            0.020
Chain 1:   2400       -18678.081             0.040            0.020
Chain 1:   2500       -18480.071             0.026            0.016
Chain 1:   2600       -18111.297             0.024            0.016
Chain 1:   2700       -18068.557             0.019            0.013
Chain 1:   2800       -17785.816             0.020            0.016
Chain 1:   2900       -18066.649             0.020            0.016
Chain 1:   3000       -18052.956             0.012            0.013
Chain 1:   3100       -18137.759             0.011            0.012
Chain 1:   3200       -17829.110             0.012            0.016
Chain 1:   3300       -18033.301             0.011            0.012
Chain 1:   3400       -17509.377             0.013            0.016
Chain 1:   3500       -18119.546             0.015            0.016
Chain 1:   3600       -17428.500             0.017            0.016
Chain 1:   3700       -17813.603             0.019            0.017
Chain 1:   3800       -16776.826             0.024            0.022
Chain 1:   3900       -16773.058             0.022            0.022
Chain 1:   4000       -16890.359             0.023            0.022
Chain 1:   4100       -16804.268             0.023            0.022
Chain 1:   4200       -16621.295             0.022            0.022
Chain 1:   4300       -16759.158             0.022            0.022
Chain 1:   4400       -16716.619             0.019            0.011
Chain 1:   4500       -16619.252             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001305 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48901.575             1.000            1.000
Chain 1:    200       -15860.593             1.542            2.083
Chain 1:    300       -15025.839             1.046            1.000
Chain 1:    400       -14433.187             0.795            1.000
Chain 1:    500       -26670.831             0.728            0.459
Chain 1:    600       -12142.439             0.806            1.000
Chain 1:    700       -15600.137             0.722            0.459
Chain 1:    800       -13057.862             0.656            0.459
Chain 1:    900       -11668.179             0.597            0.222
Chain 1:   1000       -13148.657             0.548            0.222
Chain 1:   1100       -13105.504             0.449            0.195
Chain 1:   1200       -11664.841             0.253            0.124
Chain 1:   1300       -12662.453             0.255            0.124
Chain 1:   1400       -15125.141             0.267            0.163
Chain 1:   1500       -17134.021             0.233            0.124
Chain 1:   1600       -10212.796             0.181            0.124
Chain 1:   1700       -11711.316             0.172            0.124
Chain 1:   1800       -15922.013             0.179            0.124
Chain 1:   1900       -18684.965             0.182            0.128
Chain 1:   2000       -12472.351             0.220            0.148
Chain 1:   2100       -10012.602             0.244            0.163
Chain 1:   2200       -10792.766             0.239            0.163
Chain 1:   2300       -10477.960             0.234            0.163
Chain 1:   2400        -9317.764             0.231            0.148
Chain 1:   2500        -9339.705             0.219            0.148
Chain 1:   2600        -9677.111             0.155            0.128
Chain 1:   2700        -9956.888             0.145            0.125
Chain 1:   2800       -10237.412             0.121            0.072
Chain 1:   2900        -9973.934             0.109            0.035
Chain 1:   3000        -9176.698             0.068            0.035
Chain 1:   3100        -9290.492             0.045            0.030
Chain 1:   3200       -15889.731             0.079            0.030
Chain 1:   3300       -10726.173             0.124            0.035
Chain 1:   3400        -9810.792             0.121            0.035
Chain 1:   3500        -9331.668             0.126            0.051
Chain 1:   3600        -9171.703             0.124            0.051
Chain 1:   3700        -9535.565             0.125            0.051
Chain 1:   3800       -10995.107             0.136            0.087
Chain 1:   3900       -10416.407             0.138            0.087
Chain 1:   4000        -9760.565             0.136            0.067
Chain 1:   4100        -8909.335             0.145            0.093
Chain 1:   4200       -16004.869             0.148            0.093
Chain 1:   4300        -9778.571             0.163            0.093
Chain 1:   4400        -8891.332             0.164            0.096
Chain 1:   4500       -10050.942             0.170            0.100
Chain 1:   4600        -9479.966             0.174            0.100
Chain 1:   4700        -9212.443             0.174            0.100
Chain 1:   4800        -8682.625             0.166            0.096
Chain 1:   4900       -15170.397             0.204            0.100
Chain 1:   5000       -10714.467             0.238            0.115
Chain 1:   5100        -9899.077             0.237            0.115
Chain 1:   5200        -9155.054             0.201            0.100
Chain 1:   5300       -10330.630             0.149            0.100
Chain 1:   5400        -8433.113             0.161            0.114
Chain 1:   5500       -11240.802             0.175            0.114
Chain 1:   5600       -11502.064             0.171            0.114
Chain 1:   5700       -10365.768             0.179            0.114
Chain 1:   5800       -10798.440             0.177            0.114
Chain 1:   5900        -8550.544             0.160            0.114
Chain 1:   6000       -11675.296             0.146            0.114
Chain 1:   6100       -12995.335             0.147            0.114
Chain 1:   6200        -8624.240             0.190            0.225
Chain 1:   6300        -8781.366             0.180            0.225
Chain 1:   6400       -10452.905             0.174            0.160
Chain 1:   6500        -9176.563             0.163            0.139
Chain 1:   6600        -8881.222             0.164            0.139
Chain 1:   6700        -8497.823             0.157            0.139
Chain 1:   6800       -11548.633             0.180            0.160
Chain 1:   6900       -12723.216             0.163            0.139
Chain 1:   7000       -12323.381             0.139            0.102
Chain 1:   7100        -8500.455             0.174            0.139
Chain 1:   7200        -8511.691             0.124            0.092
Chain 1:   7300       -10706.599             0.142            0.139
Chain 1:   7400        -9577.238             0.138            0.118
Chain 1:   7500       -11164.484             0.138            0.118
Chain 1:   7600        -9193.022             0.156            0.142
Chain 1:   7700        -9973.942             0.160            0.142
Chain 1:   7800       -11291.042             0.145            0.118
Chain 1:   7900        -9822.013             0.151            0.142
Chain 1:   8000        -8864.834             0.158            0.142
Chain 1:   8100        -8858.377             0.113            0.118
Chain 1:   8200        -9252.484             0.118            0.118
Chain 1:   8300        -8399.437             0.107            0.117
Chain 1:   8400       -12269.601             0.127            0.117
Chain 1:   8500        -8413.372             0.159            0.117
Chain 1:   8600       -11523.847             0.164            0.117
Chain 1:   8700        -8421.001             0.193            0.150
Chain 1:   8800        -8887.388             0.187            0.150
Chain 1:   8900        -9806.072             0.181            0.108
Chain 1:   9000        -8717.710             0.183            0.125
Chain 1:   9100        -8626.501             0.184            0.125
Chain 1:   9200        -8596.280             0.180            0.125
Chain 1:   9300        -8485.773             0.171            0.125
Chain 1:   9400       -11828.779             0.168            0.125
Chain 1:   9500        -8242.901             0.165            0.125
Chain 1:   9600       -10595.139             0.161            0.125
Chain 1:   9700        -9214.131             0.139            0.125
Chain 1:   9800        -9242.449             0.134            0.125
Chain 1:   9900        -8878.269             0.129            0.125
Chain 1:   10000        -8163.260             0.125            0.088
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57133.972             1.000            1.000
Chain 1:    200       -17600.706             1.623            2.246
Chain 1:    300        -8829.296             1.413            1.000
Chain 1:    400        -8416.099             1.072            1.000
Chain 1:    500        -8728.645             0.865            0.993
Chain 1:    600        -8528.828             0.725            0.993
Chain 1:    700        -8292.125             0.625            0.049
Chain 1:    800        -8139.845             0.549            0.049
Chain 1:    900        -7982.506             0.491            0.036
Chain 1:   1000        -7735.094             0.445            0.036
Chain 1:   1100        -7687.554             0.345            0.032
Chain 1:   1200        -7727.482             0.121            0.029
Chain 1:   1300        -7800.215             0.023            0.023
Chain 1:   1400        -7849.004             0.019            0.020
Chain 1:   1500        -7588.508             0.018            0.020
Chain 1:   1600        -7750.379             0.018            0.020
Chain 1:   1700        -7562.541             0.018            0.020
Chain 1:   1800        -7693.338             0.018            0.020
Chain 1:   1900        -7566.705             0.017            0.017
Chain 1:   2000        -7598.430             0.014            0.017
Chain 1:   2100        -7620.317             0.014            0.017
Chain 1:   2200        -7743.370             0.015            0.017
Chain 1:   2300        -7629.922             0.016            0.017
Chain 1:   2400        -7674.997             0.016            0.017
Chain 1:   2500        -7592.937             0.013            0.016
Chain 1:   2600        -7552.063             0.012            0.015
Chain 1:   2700        -7538.333             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002565 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86620.313             1.000            1.000
Chain 1:    200       -13666.911             3.169            5.338
Chain 1:    300       -10009.365             2.234            1.000
Chain 1:    400       -10869.232             1.696            1.000
Chain 1:    500        -9005.271             1.398            0.365
Chain 1:    600        -8513.512             1.175            0.365
Chain 1:    700        -8561.397             1.008            0.207
Chain 1:    800        -8905.292             0.886            0.207
Chain 1:    900        -8805.283             0.789            0.079
Chain 1:   1000        -8670.315             0.712            0.079
Chain 1:   1100        -8783.960             0.613            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8341.774             0.085            0.053
Chain 1:   1300        -8688.431             0.052            0.040
Chain 1:   1400        -8689.143             0.044            0.039
Chain 1:   1500        -8560.454             0.025            0.016
Chain 1:   1600        -8668.840             0.020            0.015
Chain 1:   1700        -8745.792             0.021            0.015
Chain 1:   1800        -8322.192             0.022            0.015
Chain 1:   1900        -8422.882             0.022            0.015
Chain 1:   2000        -8397.301             0.021            0.013
Chain 1:   2100        -8522.844             0.021            0.015
Chain 1:   2200        -8325.962             0.018            0.015
Chain 1:   2300        -8417.687             0.015            0.013
Chain 1:   2400        -8486.464             0.016            0.013
Chain 1:   2500        -8432.716             0.015            0.012
Chain 1:   2600        -8434.067             0.014            0.011
Chain 1:   2700        -8350.800             0.014            0.011
Chain 1:   2800        -8310.688             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386586.239             1.000            1.000
Chain 1:    200     -1581301.785             2.652            4.304
Chain 1:    300      -890111.381             2.027            1.000
Chain 1:    400      -456925.736             1.757            1.000
Chain 1:    500      -357469.212             1.461            0.948
Chain 1:    600      -232640.875             1.307            0.948
Chain 1:    700      -119210.276             1.256            0.948
Chain 1:    800       -86470.583             1.147            0.948
Chain 1:    900       -66867.079             1.052            0.777
Chain 1:   1000       -51698.764             0.976            0.777
Chain 1:   1100       -39199.934             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38385.221             0.480            0.379
Chain 1:   1300       -26358.286             0.448            0.379
Chain 1:   1400       -26080.287             0.354            0.319
Chain 1:   1500       -22670.741             0.341            0.319
Chain 1:   1600       -21888.778             0.291            0.293
Chain 1:   1700       -20763.978             0.201            0.293
Chain 1:   1800       -20708.792             0.164            0.150
Chain 1:   1900       -21035.147             0.136            0.054
Chain 1:   2000       -19546.750             0.114            0.054
Chain 1:   2100       -19785.193             0.083            0.036
Chain 1:   2200       -20011.549             0.082            0.036
Chain 1:   2300       -19628.800             0.039            0.019
Chain 1:   2400       -19400.863             0.039            0.019
Chain 1:   2500       -19202.745             0.025            0.016
Chain 1:   2600       -18832.917             0.023            0.016
Chain 1:   2700       -18789.955             0.018            0.012
Chain 1:   2800       -18506.660             0.019            0.015
Chain 1:   2900       -18787.973             0.019            0.015
Chain 1:   3000       -18774.205             0.012            0.012
Chain 1:   3100       -18859.181             0.011            0.012
Chain 1:   3200       -18549.801             0.012            0.015
Chain 1:   3300       -18754.623             0.011            0.012
Chain 1:   3400       -18229.368             0.012            0.015
Chain 1:   3500       -18841.438             0.015            0.015
Chain 1:   3600       -18147.924             0.016            0.015
Chain 1:   3700       -18534.837             0.018            0.017
Chain 1:   3800       -17494.172             0.023            0.021
Chain 1:   3900       -17490.315             0.021            0.021
Chain 1:   4000       -17607.629             0.022            0.021
Chain 1:   4100       -17521.312             0.022            0.021
Chain 1:   4200       -17337.565             0.021            0.021
Chain 1:   4300       -17475.998             0.021            0.021
Chain 1:   4400       -17432.774             0.018            0.011
Chain 1:   4500       -17335.281             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001438 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11950.494             1.000            1.000
Chain 1:    200        -8937.848             0.669            1.000
Chain 1:    300        -7817.002             0.493            0.337
Chain 1:    400        -7959.296             0.375            0.337
Chain 1:    500        -7791.500             0.304            0.143
Chain 1:    600        -7720.810             0.255            0.143
Chain 1:    700        -7653.239             0.220            0.022
Chain 1:    800        -7604.281             0.193            0.022
Chain 1:    900        -7765.977             0.174            0.021
Chain 1:   1000        -7681.839             0.158            0.021
Chain 1:   1100        -7744.504             0.058            0.018
Chain 1:   1200        -7680.287             0.026            0.011
Chain 1:   1300        -7629.502             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001467 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56545.351             1.000            1.000
Chain 1:    200       -16951.668             1.668            2.336
Chain 1:    300        -8461.577             1.446            1.003
Chain 1:    400        -8657.923             1.090            1.003
Chain 1:    500        -8344.796             0.880            1.000
Chain 1:    600        -8808.572             0.742            1.000
Chain 1:    700        -7815.808             0.654            0.127
Chain 1:    800        -8060.901             0.576            0.127
Chain 1:    900        -7780.877             0.516            0.053
Chain 1:   1000        -7623.866             0.467            0.053
Chain 1:   1100        -7571.373             0.367            0.038
Chain 1:   1200        -7511.312             0.135            0.036
Chain 1:   1300        -7607.397             0.035            0.030
Chain 1:   1400        -7740.305             0.035            0.030
Chain 1:   1500        -7508.928             0.034            0.030
Chain 1:   1600        -7446.790             0.030            0.021
Chain 1:   1700        -7432.450             0.017            0.017
Chain 1:   1800        -7463.000             0.015            0.013
Chain 1:   1900        -7502.013             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002707 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86406.495             1.000            1.000
Chain 1:    200       -13023.548             3.317            5.635
Chain 1:    300        -9498.276             2.335            1.000
Chain 1:    400       -10363.712             1.772            1.000
Chain 1:    500        -8392.455             1.465            0.371
Chain 1:    600        -8101.839             1.227            0.371
Chain 1:    700        -8154.186             1.052            0.235
Chain 1:    800        -8365.030             0.924            0.235
Chain 1:    900        -8382.954             0.822            0.084
Chain 1:   1000        -8590.596             0.742            0.084
Chain 1:   1100        -8238.851             0.646            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8052.967             0.085            0.036
Chain 1:   1300        -8102.454             0.048            0.025
Chain 1:   1400        -8095.842             0.040            0.024
Chain 1:   1500        -8126.604             0.017            0.023
Chain 1:   1600        -8132.848             0.014            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002687 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414407.473             1.000            1.000
Chain 1:    200     -1586259.035             2.652            4.305
Chain 1:    300      -890240.118             2.029            1.000
Chain 1:    400      -456684.646             1.759            1.000
Chain 1:    500      -356779.429             1.463            0.949
Chain 1:    600      -231817.722             1.309            0.949
Chain 1:    700      -118375.881             1.259            0.949
Chain 1:    800       -85674.630             1.149            0.949
Chain 1:    900       -66083.938             1.055            0.782
Chain 1:   1000       -50924.191             0.979            0.782
Chain 1:   1100       -38448.057             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37624.468             0.483            0.382
Chain 1:   1300       -25638.079             0.452            0.382
Chain 1:   1400       -25358.885             0.358            0.324
Chain 1:   1500       -21962.091             0.345            0.324
Chain 1:   1600       -21182.394             0.295            0.298
Chain 1:   1700       -20063.716             0.205            0.296
Chain 1:   1800       -20009.150             0.167            0.155
Chain 1:   1900       -20334.681             0.139            0.056
Chain 1:   2000       -18851.069             0.117            0.056
Chain 1:   2100       -19089.040             0.086            0.037
Chain 1:   2200       -19314.517             0.085            0.037
Chain 1:   2300       -18932.758             0.040            0.020
Chain 1:   2400       -18705.195             0.040            0.020
Chain 1:   2500       -18507.014             0.026            0.016
Chain 1:   2600       -18138.119             0.024            0.016
Chain 1:   2700       -18095.345             0.019            0.012
Chain 1:   2800       -17812.487             0.020            0.016
Chain 1:   2900       -18093.311             0.020            0.016
Chain 1:   3000       -18079.551             0.012            0.012
Chain 1:   3100       -18164.449             0.011            0.012
Chain 1:   3200       -17855.636             0.012            0.016
Chain 1:   3300       -18059.968             0.011            0.012
Chain 1:   3400       -17535.752             0.013            0.016
Chain 1:   3500       -18146.263             0.015            0.016
Chain 1:   3600       -17454.692             0.017            0.016
Chain 1:   3700       -17840.194             0.019            0.017
Chain 1:   3800       -16802.566             0.024            0.022
Chain 1:   3900       -16798.762             0.022            0.022
Chain 1:   4000       -16916.077             0.023            0.022
Chain 1:   4100       -16829.989             0.023            0.022
Chain 1:   4200       -16646.813             0.022            0.022
Chain 1:   4300       -16784.816             0.022            0.022
Chain 1:   4400       -16742.122             0.019            0.011
Chain 1:   4500       -16644.721             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48823.206             1.000            1.000
Chain 1:    200       -15621.324             1.563            2.125
Chain 1:    300       -15363.311             1.047            1.000
Chain 1:    400       -11175.012             0.879            1.000
Chain 1:    500       -14820.102             0.753            0.375
Chain 1:    600       -13332.996             0.646            0.375
Chain 1:    700       -14102.481             0.561            0.246
Chain 1:    800       -11100.643             0.525            0.270
Chain 1:    900       -12263.571             0.477            0.246
Chain 1:   1000       -13019.541             0.435            0.246
Chain 1:   1100       -27908.618             0.389            0.246
Chain 1:   1200       -11441.676             0.320            0.246
Chain 1:   1300       -12139.965             0.324            0.246
Chain 1:   1400       -10757.201             0.299            0.129
Chain 1:   1500       -11531.604             0.282            0.112
Chain 1:   1600       -12875.588             0.281            0.104
Chain 1:   1700        -9300.632             0.314            0.129
Chain 1:   1800       -18240.115             0.336            0.129
Chain 1:   1900       -10732.593             0.396            0.384
Chain 1:   2000       -10137.827             0.396            0.384
Chain 1:   2100       -11093.697             0.352            0.129
Chain 1:   2200        -9584.679             0.223            0.129
Chain 1:   2300        -9672.008             0.219            0.129
Chain 1:   2400        -9049.642             0.213            0.104
Chain 1:   2500       -16533.114             0.251            0.157
Chain 1:   2600        -9802.558             0.309            0.384
Chain 1:   2700        -9676.067             0.272            0.157
Chain 1:   2800        -9335.417             0.227            0.086
Chain 1:   2900        -9611.500             0.160            0.069
Chain 1:   3000        -8977.045             0.161            0.071
Chain 1:   3100        -8540.907             0.157            0.069
Chain 1:   3200        -9194.667             0.149            0.069
Chain 1:   3300       -10102.228             0.157            0.071
Chain 1:   3400        -9141.269             0.161            0.071
Chain 1:   3500       -10487.508             0.128            0.071
Chain 1:   3600        -9746.993             0.067            0.071
Chain 1:   3700        -8715.089             0.078            0.076
Chain 1:   3800        -8711.848             0.074            0.076
Chain 1:   3900        -9236.179             0.077            0.076
Chain 1:   4000        -9764.545             0.075            0.076
Chain 1:   4100        -9166.795             0.077            0.076
Chain 1:   4200       -12826.206             0.098            0.090
Chain 1:   4300       -13374.003             0.093            0.076
Chain 1:   4400        -9305.403             0.126            0.076
Chain 1:   4500        -9131.911             0.115            0.065
Chain 1:   4600       -12205.515             0.133            0.065
Chain 1:   4700       -11206.673             0.130            0.065
Chain 1:   4800        -8573.438             0.161            0.089
Chain 1:   4900        -8586.333             0.155            0.089
Chain 1:   5000        -9094.874             0.155            0.089
Chain 1:   5100       -11249.567             0.168            0.192
Chain 1:   5200        -9891.031             0.153            0.137
Chain 1:   5300       -12000.037             0.167            0.176
Chain 1:   5400       -16355.953             0.150            0.176
Chain 1:   5500       -10810.435             0.199            0.192
Chain 1:   5600        -8645.569             0.199            0.192
Chain 1:   5700       -14498.986             0.230            0.250
Chain 1:   5800        -8709.371             0.266            0.250
Chain 1:   5900        -8210.919             0.272            0.250
Chain 1:   6000        -9050.749             0.276            0.250
Chain 1:   6100        -9693.218             0.263            0.250
Chain 1:   6200       -11056.641             0.262            0.250
Chain 1:   6300        -8362.332             0.276            0.266
Chain 1:   6400        -8607.158             0.253            0.250
Chain 1:   6500       -11634.398             0.227            0.250
Chain 1:   6600       -10221.758             0.216            0.138
Chain 1:   6700        -9220.144             0.187            0.123
Chain 1:   6800        -8212.330             0.132            0.123
Chain 1:   6900       -14740.750             0.171            0.123
Chain 1:   7000        -9180.499             0.222            0.138
Chain 1:   7100        -8672.405             0.221            0.138
Chain 1:   7200        -8835.635             0.211            0.138
Chain 1:   7300        -9854.219             0.189            0.123
Chain 1:   7400       -10531.517             0.192            0.123
Chain 1:   7500        -9957.343             0.172            0.109
Chain 1:   7600        -8526.571             0.175            0.109
Chain 1:   7700        -8177.115             0.168            0.103
Chain 1:   7800       -12067.358             0.188            0.103
Chain 1:   7900        -8574.423             0.185            0.103
Chain 1:   8000        -8495.859             0.125            0.064
Chain 1:   8100        -8682.068             0.121            0.064
Chain 1:   8200        -9462.750             0.128            0.083
Chain 1:   8300       -10000.697             0.123            0.064
Chain 1:   8400       -10593.211             0.122            0.058
Chain 1:   8500        -8181.091             0.146            0.083
Chain 1:   8600        -9618.478             0.144            0.083
Chain 1:   8700        -8018.344             0.160            0.149
Chain 1:   8800        -8323.713             0.131            0.083
Chain 1:   8900        -9057.588             0.098            0.081
Chain 1:   9000        -8178.773             0.108            0.083
Chain 1:   9100       -10527.205             0.128            0.107
Chain 1:   9200        -8382.763             0.146            0.149
Chain 1:   9300        -8291.313             0.141            0.149
Chain 1:   9400        -8946.743             0.143            0.149
Chain 1:   9500       -10635.658             0.130            0.149
Chain 1:   9600        -8982.392             0.133            0.159
Chain 1:   9700       -10228.757             0.125            0.122
Chain 1:   9800       -10794.826             0.127            0.122
Chain 1:   9900       -10994.941             0.121            0.122
Chain 1:   10000        -7999.748             0.147            0.159
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57052.448             1.000            1.000
Chain 1:    200       -17457.118             1.634            2.268
Chain 1:    300        -8745.975             1.421            1.000
Chain 1:    400        -8378.348             1.077            1.000
Chain 1:    500        -8748.744             0.870            0.996
Chain 1:    600        -8726.874             0.725            0.996
Chain 1:    700        -8007.608             0.635            0.090
Chain 1:    800        -8070.296             0.556            0.090
Chain 1:    900        -7939.752             0.496            0.044
Chain 1:   1000        -7684.149             0.450            0.044
Chain 1:   1100        -7810.005             0.352            0.042
Chain 1:   1200        -7712.171             0.126            0.033
Chain 1:   1300        -7691.051             0.027            0.016
Chain 1:   1400        -7917.858             0.025            0.016
Chain 1:   1500        -7629.106             0.025            0.016
Chain 1:   1600        -7756.836             0.026            0.016
Chain 1:   1700        -7565.274             0.020            0.016
Chain 1:   1800        -7581.905             0.019            0.016
Chain 1:   1900        -7612.514             0.018            0.016
Chain 1:   2000        -7667.433             0.015            0.016
Chain 1:   2100        -7528.354             0.016            0.016
Chain 1:   2200        -7734.798             0.017            0.018
Chain 1:   2300        -7603.078             0.018            0.018
Chain 1:   2400        -7548.914             0.016            0.017
Chain 1:   2500        -7678.060             0.014            0.017
Chain 1:   2600        -7540.553             0.014            0.017
Chain 1:   2700        -7578.785             0.012            0.017
Chain 1:   2800        -7573.418             0.012            0.017
Chain 1:   2900        -7427.599             0.014            0.017
Chain 1:   3000        -7555.556             0.015            0.017
Chain 1:   3100        -7550.140             0.013            0.017
Chain 1:   3200        -7729.759             0.013            0.017
Chain 1:   3300        -7492.742             0.014            0.017
Chain 1:   3400        -7679.170             0.016            0.018
Chain 1:   3500        -7466.987             0.017            0.020
Chain 1:   3600        -7525.817             0.016            0.020
Chain 1:   3700        -7478.271             0.016            0.020
Chain 1:   3800        -7490.942             0.016            0.020
Chain 1:   3900        -7472.231             0.014            0.017
Chain 1:   4000        -7444.470             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004257 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 42.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86560.673             1.000            1.000
Chain 1:    200       -13499.954             3.206            5.412
Chain 1:    300        -9848.174             2.261            1.000
Chain 1:    400       -10719.170             1.716            1.000
Chain 1:    500        -8802.395             1.416            0.371
Chain 1:    600        -8579.594             1.185            0.371
Chain 1:    700        -8501.221             1.017            0.218
Chain 1:    800        -9379.645             0.901            0.218
Chain 1:    900        -8596.830             0.811            0.094
Chain 1:   1000        -8386.507             0.733            0.094
Chain 1:   1100        -8682.667             0.636            0.091   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8254.068             0.100            0.081
Chain 1:   1300        -8511.846             0.066            0.052
Chain 1:   1400        -8491.025             0.058            0.034
Chain 1:   1500        -8385.675             0.038            0.030
Chain 1:   1600        -8492.442             0.036            0.030
Chain 1:   1700        -8567.868             0.036            0.030
Chain 1:   1800        -8142.742             0.032            0.030
Chain 1:   1900        -8244.477             0.024            0.025
Chain 1:   2000        -8219.198             0.022            0.013
Chain 1:   2100        -8345.683             0.020            0.013
Chain 1:   2200        -8145.852             0.017            0.013
Chain 1:   2300        -8239.593             0.016            0.013
Chain 1:   2400        -8307.871             0.016            0.013
Chain 1:   2500        -8254.098             0.015            0.012
Chain 1:   2600        -8256.126             0.014            0.011
Chain 1:   2700        -8172.554             0.014            0.011
Chain 1:   2800        -8131.557             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002686 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399924.304             1.000            1.000
Chain 1:    200     -1584961.392             2.650            4.300
Chain 1:    300      -890201.014             2.027            1.000
Chain 1:    400      -457030.168             1.757            1.000
Chain 1:    500      -357443.874             1.461            0.948
Chain 1:    600      -232441.665             1.307            0.948
Chain 1:    700      -118963.660             1.257            0.948
Chain 1:    800       -86266.283             1.147            0.948
Chain 1:    900       -66661.739             1.052            0.780
Chain 1:   1000       -51501.520             0.977            0.780
Chain 1:   1100       -39016.798             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38198.354             0.481            0.379
Chain 1:   1300       -26186.540             0.449            0.379
Chain 1:   1400       -25908.995             0.355            0.320
Chain 1:   1500       -22504.932             0.342            0.320
Chain 1:   1600       -21724.441             0.292            0.294
Chain 1:   1700       -20601.651             0.202            0.294
Chain 1:   1800       -20546.714             0.164            0.151
Chain 1:   1900       -20873.090             0.137            0.054
Chain 1:   2000       -19385.801             0.115            0.054
Chain 1:   2100       -19624.047             0.084            0.036
Chain 1:   2200       -19850.437             0.083            0.036
Chain 1:   2300       -19467.637             0.039            0.020
Chain 1:   2400       -19239.715             0.039            0.020
Chain 1:   2500       -19041.635             0.025            0.016
Chain 1:   2600       -18671.792             0.023            0.016
Chain 1:   2700       -18628.730             0.018            0.012
Chain 1:   2800       -18345.545             0.020            0.015
Chain 1:   2900       -18626.763             0.019            0.015
Chain 1:   3000       -18612.954             0.012            0.012
Chain 1:   3100       -18697.988             0.011            0.012
Chain 1:   3200       -18388.584             0.012            0.015
Chain 1:   3300       -18593.370             0.011            0.012
Chain 1:   3400       -18068.136             0.013            0.015
Chain 1:   3500       -18680.231             0.015            0.015
Chain 1:   3600       -17986.572             0.017            0.015
Chain 1:   3700       -18373.630             0.019            0.017
Chain 1:   3800       -17332.826             0.023            0.021
Chain 1:   3900       -17328.939             0.021            0.021
Chain 1:   4000       -17446.255             0.022            0.021
Chain 1:   4100       -17360.028             0.022            0.021
Chain 1:   4200       -17176.135             0.022            0.021
Chain 1:   4300       -17314.638             0.021            0.021
Chain 1:   4400       -17271.369             0.019            0.011
Chain 1:   4500       -17173.867             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001189 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12724.421             1.000            1.000
Chain 1:    200        -9592.758             0.663            1.000
Chain 1:    300        -8207.269             0.498            0.326
Chain 1:    400        -8426.622             0.380            0.326
Chain 1:    500        -8408.635             0.305            0.169
Chain 1:    600        -8186.084             0.258            0.169
Chain 1:    700        -8147.033             0.222            0.027
Chain 1:    800        -8112.395             0.195            0.027
Chain 1:    900        -8091.751             0.174            0.026
Chain 1:   1000        -8190.966             0.157            0.026
Chain 1:   1100        -8212.382             0.058            0.012
Chain 1:   1200        -8153.306             0.026            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -52134.166             1.000            1.000
Chain 1:    200       -16783.354             1.553            2.106
Chain 1:    300        -8957.607             1.327            1.000
Chain 1:    400        -7855.169             1.030            1.000
Chain 1:    500        -8318.815             0.835            0.874
Chain 1:    600        -9113.694             0.711            0.874
Chain 1:    700        -8217.135             0.625            0.140
Chain 1:    800        -8377.413             0.549            0.140
Chain 1:    900        -8094.171             0.492            0.109
Chain 1:   1000        -7899.785             0.445            0.109
Chain 1:   1100        -7798.046             0.346            0.087
Chain 1:   1200        -7713.040             0.137            0.056
Chain 1:   1300        -7745.367             0.050            0.035
Chain 1:   1400        -7716.055             0.036            0.025
Chain 1:   1500        -7605.077             0.032            0.019
Chain 1:   1600        -7766.829             0.026            0.019
Chain 1:   1700        -7685.019             0.016            0.015
Chain 1:   1800        -7713.481             0.014            0.013
Chain 1:   1900        -7752.228             0.011            0.011
Chain 1:   2000        -7686.456             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002686 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86469.018             1.000            1.000
Chain 1:    200       -13892.545             3.112            5.224
Chain 1:    300       -10178.549             2.196            1.000
Chain 1:    400       -11552.243             1.677            1.000
Chain 1:    500        -9079.735             1.396            0.365
Chain 1:    600        -8947.947             1.166            0.365
Chain 1:    700        -8766.793             1.002            0.272
Chain 1:    800        -8452.655             0.882            0.272
Chain 1:    900        -8624.115             0.786            0.119
Chain 1:   1000        -8783.686             0.709            0.119
Chain 1:   1100        -8959.536             0.611            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8507.080             0.094            0.037
Chain 1:   1300        -8821.207             0.061            0.036
Chain 1:   1400        -8800.362             0.049            0.021
Chain 1:   1500        -8688.383             0.023            0.020
Chain 1:   1600        -8790.279             0.023            0.020
Chain 1:   1700        -8853.531             0.022            0.020
Chain 1:   1800        -8416.693             0.023            0.020
Chain 1:   1900        -8521.396             0.022            0.018
Chain 1:   2000        -8498.019             0.021            0.013
Chain 1:   2100        -8640.122             0.021            0.013
Chain 1:   2200        -8427.294             0.018            0.013
Chain 1:   2300        -8586.694             0.016            0.013
Chain 1:   2400        -8426.170             0.018            0.016
Chain 1:   2500        -8495.753             0.017            0.016
Chain 1:   2600        -8408.246             0.017            0.016
Chain 1:   2700        -8441.207             0.017            0.016
Chain 1:   2800        -8400.799             0.012            0.012
Chain 1:   2900        -8494.909             0.012            0.011
Chain 1:   3000        -8330.277             0.014            0.016
Chain 1:   3100        -8483.756             0.014            0.018
Chain 1:   3200        -8355.339             0.013            0.015
Chain 1:   3300        -8364.832             0.011            0.011
Chain 1:   3400        -8528.269             0.011            0.011
Chain 1:   3500        -8540.128             0.011            0.011
Chain 1:   3600        -8311.683             0.012            0.015
Chain 1:   3700        -8458.675             0.014            0.017
Chain 1:   3800        -8317.885             0.015            0.017
Chain 1:   3900        -8252.083             0.014            0.017
Chain 1:   4000        -8329.956             0.013            0.017
Chain 1:   4100        -8323.471             0.012            0.015
Chain 1:   4200        -8308.031             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00335 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403823.490             1.000            1.000
Chain 1:    200     -1583050.853             2.654            4.309
Chain 1:    300      -891120.119             2.028            1.000
Chain 1:    400      -458237.642             1.757            1.000
Chain 1:    500      -358614.359             1.462            0.945
Chain 1:    600      -233612.072             1.307            0.945
Chain 1:    700      -119744.041             1.256            0.945
Chain 1:    800       -86911.916             1.146            0.945
Chain 1:    900       -67239.187             1.052            0.776
Chain 1:   1000       -52029.732             0.976            0.776
Chain 1:   1100       -39494.362             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38675.453             0.479            0.378
Chain 1:   1300       -26614.586             0.446            0.378
Chain 1:   1400       -26334.840             0.353            0.317
Chain 1:   1500       -22916.785             0.340            0.317
Chain 1:   1600       -22132.216             0.290            0.293
Chain 1:   1700       -21003.549             0.200            0.292
Chain 1:   1800       -20947.583             0.163            0.149
Chain 1:   1900       -21274.080             0.135            0.054
Chain 1:   2000       -19783.260             0.113            0.054
Chain 1:   2100       -20021.826             0.083            0.035
Chain 1:   2200       -20248.621             0.082            0.035
Chain 1:   2300       -19865.458             0.038            0.019
Chain 1:   2400       -19637.376             0.039            0.019
Chain 1:   2500       -19439.363             0.025            0.015
Chain 1:   2600       -19069.086             0.023            0.015
Chain 1:   2700       -19026.003             0.018            0.012
Chain 1:   2800       -18742.581             0.019            0.015
Chain 1:   2900       -19024.126             0.019            0.015
Chain 1:   3000       -19010.322             0.012            0.012
Chain 1:   3100       -19095.301             0.011            0.012
Chain 1:   3200       -18785.707             0.011            0.015
Chain 1:   3300       -18990.691             0.011            0.012
Chain 1:   3400       -18465.053             0.012            0.015
Chain 1:   3500       -19077.682             0.014            0.015
Chain 1:   3600       -18383.490             0.016            0.015
Chain 1:   3700       -18770.899             0.018            0.016
Chain 1:   3800       -17729.122             0.022            0.021
Chain 1:   3900       -17725.234             0.021            0.021
Chain 1:   4000       -17842.564             0.022            0.021
Chain 1:   4100       -17756.159             0.022            0.021
Chain 1:   4200       -17572.155             0.021            0.021
Chain 1:   4300       -17710.741             0.021            0.021
Chain 1:   4400       -17667.304             0.018            0.010
Chain 1:   4500       -17569.802             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12244.797             1.000            1.000
Chain 1:    200        -9140.557             0.670            1.000
Chain 1:    300        -7920.615             0.498            0.340
Chain 1:    400        -7971.406             0.375            0.340
Chain 1:    500        -7938.698             0.301            0.154
Chain 1:    600        -7785.396             0.254            0.154
Chain 1:    700        -7695.119             0.219            0.020
Chain 1:    800        -7703.332             0.192            0.020
Chain 1:    900        -7625.251             0.172            0.012
Chain 1:   1000        -7804.568             0.157            0.020
Chain 1:   1100        -7830.472             0.057            0.012
Chain 1:   1200        -7728.012             0.025            0.012
Chain 1:   1300        -7662.866             0.010            0.010
Chain 1:   1400        -7686.417             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001673 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57891.945             1.000            1.000
Chain 1:    200       -17483.210             1.656            2.311
Chain 1:    300        -8549.503             1.452            1.045
Chain 1:    400        -8082.641             1.103            1.045
Chain 1:    500        -7992.688             0.885            1.000
Chain 1:    600        -8002.948             0.738            1.000
Chain 1:    700        -7774.810             0.637            0.058
Chain 1:    800        -8090.863             0.562            0.058
Chain 1:    900        -7867.270             0.503            0.039
Chain 1:   1000        -7704.353             0.454            0.039
Chain 1:   1100        -7566.065             0.356            0.029
Chain 1:   1200        -7538.601             0.126            0.028
Chain 1:   1300        -7541.869             0.021            0.021
Chain 1:   1400        -7767.685             0.018            0.021
Chain 1:   1500        -7497.136             0.021            0.028
Chain 1:   1600        -7536.757             0.021            0.028
Chain 1:   1700        -7448.482             0.019            0.021
Chain 1:   1800        -7456.012             0.016            0.018
Chain 1:   1900        -7488.079             0.013            0.012
Chain 1:   2000        -7524.552             0.011            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003098 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86540.541             1.000            1.000
Chain 1:    200       -13314.093             3.250            5.500
Chain 1:    300        -9686.642             2.291            1.000
Chain 1:    400       -10442.245             1.737            1.000
Chain 1:    500        -8659.812             1.431            0.374
Chain 1:    600        -8118.421             1.203            0.374
Chain 1:    700        -8460.193             1.037            0.206
Chain 1:    800        -9177.294             0.917            0.206
Chain 1:    900        -8533.476             0.824            0.078
Chain 1:   1000        -8263.391             0.745            0.078
Chain 1:   1100        -8461.313             0.647            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8029.719             0.102            0.072
Chain 1:   1300        -8313.916             0.068            0.067
Chain 1:   1400        -8331.859             0.061            0.054
Chain 1:   1500        -8266.278             0.041            0.040
Chain 1:   1600        -8364.484             0.036            0.034
Chain 1:   1700        -8434.839             0.033            0.033
Chain 1:   1800        -8024.240             0.030            0.033
Chain 1:   1900        -8120.068             0.024            0.023
Chain 1:   2000        -8093.318             0.021            0.012
Chain 1:   2100        -8216.054             0.020            0.012
Chain 1:   2200        -8036.100             0.017            0.012
Chain 1:   2300        -8115.210             0.014            0.012
Chain 1:   2400        -8184.847             0.015            0.012
Chain 1:   2500        -8130.203             0.015            0.012
Chain 1:   2600        -8129.585             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003632 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8425787.739             1.000            1.000
Chain 1:    200     -1588482.199             2.652            4.304
Chain 1:    300      -890249.372             2.030            1.000
Chain 1:    400      -456913.408             1.759            1.000
Chain 1:    500      -356980.596             1.463            0.948
Chain 1:    600      -232180.280             1.309            0.948
Chain 1:    700      -118726.304             1.259            0.948
Chain 1:    800       -85988.634             1.149            0.948
Chain 1:    900       -66403.430             1.054            0.784
Chain 1:   1000       -51249.075             0.978            0.784
Chain 1:   1100       -38774.165             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37956.679             0.482            0.381
Chain 1:   1300       -25966.447             0.450            0.381
Chain 1:   1400       -25690.696             0.356            0.322
Chain 1:   1500       -22290.898             0.343            0.322
Chain 1:   1600       -21511.164             0.293            0.296
Chain 1:   1700       -20391.312             0.203            0.295
Chain 1:   1800       -20336.885             0.165            0.153
Chain 1:   1900       -20663.049             0.137            0.055
Chain 1:   2000       -19177.359             0.116            0.055
Chain 1:   2100       -19415.793             0.085            0.036
Chain 1:   2200       -19641.570             0.084            0.036
Chain 1:   2300       -19259.328             0.039            0.020
Chain 1:   2400       -19031.455             0.040            0.020
Chain 1:   2500       -18833.165             0.025            0.016
Chain 1:   2600       -18463.798             0.024            0.016
Chain 1:   2700       -18420.840             0.018            0.012
Chain 1:   2800       -18137.567             0.020            0.016
Chain 1:   2900       -18418.708             0.020            0.015
Chain 1:   3000       -18405.050             0.012            0.012
Chain 1:   3100       -18490.010             0.011            0.012
Chain 1:   3200       -18180.805             0.012            0.015
Chain 1:   3300       -18385.411             0.011            0.012
Chain 1:   3400       -17860.420             0.013            0.015
Chain 1:   3500       -18472.077             0.015            0.016
Chain 1:   3600       -17778.975             0.017            0.016
Chain 1:   3700       -18165.579             0.019            0.017
Chain 1:   3800       -17125.575             0.023            0.021
Chain 1:   3900       -17121.645             0.022            0.021
Chain 1:   4000       -17239.024             0.022            0.021
Chain 1:   4100       -17152.783             0.022            0.021
Chain 1:   4200       -16969.055             0.022            0.021
Chain 1:   4300       -17107.485             0.021            0.021
Chain 1:   4400       -17064.377             0.019            0.011
Chain 1:   4500       -16966.834             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001306 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49722.904             1.000            1.000
Chain 1:    200       -23826.535             1.043            1.087
Chain 1:    300       -16323.578             0.849            1.000
Chain 1:    400       -20242.992             0.685            1.000
Chain 1:    500       -13071.771             0.658            0.549
Chain 1:    600       -18000.524             0.594            0.549
Chain 1:    700       -15733.630             0.530            0.460
Chain 1:    800       -16530.125             0.469            0.460
Chain 1:    900       -13089.359             0.446            0.274
Chain 1:   1000       -11172.938             0.419            0.274
Chain 1:   1100       -15403.345             0.346            0.274
Chain 1:   1200       -18839.818             0.256            0.263
Chain 1:   1300       -11298.287             0.277            0.263
Chain 1:   1400       -12528.257             0.267            0.263
Chain 1:   1500       -16622.839             0.237            0.246
Chain 1:   1600       -12263.332             0.245            0.246
Chain 1:   1700       -13816.214             0.242            0.246
Chain 1:   1800       -15953.363             0.251            0.246
Chain 1:   1900       -10421.048             0.277            0.246
Chain 1:   2000       -18016.362             0.302            0.275
Chain 1:   2100       -10875.089             0.341            0.355
Chain 1:   2200       -13119.717             0.339            0.355
Chain 1:   2300       -10857.861             0.293            0.246
Chain 1:   2400       -12015.577             0.293            0.246
Chain 1:   2500        -9908.881             0.290            0.213
Chain 1:   2600       -12090.675             0.272            0.208
Chain 1:   2700       -12049.877             0.262            0.208
Chain 1:   2800       -10143.080             0.267            0.208
Chain 1:   2900       -10557.043             0.218            0.188
Chain 1:   3000       -10007.257             0.181            0.180
Chain 1:   3100       -12969.290             0.138            0.180
Chain 1:   3200       -10638.561             0.143            0.188
Chain 1:   3300       -15563.954             0.154            0.188
Chain 1:   3400       -13231.597             0.162            0.188
Chain 1:   3500        -9966.308             0.173            0.188
Chain 1:   3600        -9714.183             0.158            0.188
Chain 1:   3700       -18455.374             0.205            0.219
Chain 1:   3800       -12223.174             0.237            0.228
Chain 1:   3900       -10944.658             0.245            0.228
Chain 1:   4000       -14939.681             0.266            0.267
Chain 1:   4100        -9825.102             0.295            0.316
Chain 1:   4200        -9717.502             0.275            0.316
Chain 1:   4300       -13113.195             0.269            0.267
Chain 1:   4400       -14684.219             0.262            0.267
Chain 1:   4500       -14654.973             0.229            0.259
Chain 1:   4600        -9273.479             0.285            0.267
Chain 1:   4700        -9360.076             0.238            0.259
Chain 1:   4800       -13432.674             0.218            0.259
Chain 1:   4900       -11880.287             0.219            0.259
Chain 1:   5000       -11131.855             0.199            0.131
Chain 1:   5100       -11090.861             0.147            0.107
Chain 1:   5200        -9741.058             0.160            0.131
Chain 1:   5300       -10100.706             0.138            0.107
Chain 1:   5400        -9134.242             0.138            0.106
Chain 1:   5500       -11497.153             0.158            0.131
Chain 1:   5600        -9229.965             0.125            0.131
Chain 1:   5700       -13114.694             0.153            0.139
Chain 1:   5800        -9874.931             0.156            0.139
Chain 1:   5900       -11758.325             0.159            0.160
Chain 1:   6000       -12559.025             0.158            0.160
Chain 1:   6100        -9678.473             0.188            0.206
Chain 1:   6200       -10502.211             0.182            0.206
Chain 1:   6300        -9275.583             0.191            0.206
Chain 1:   6400       -15725.535             0.222            0.246
Chain 1:   6500       -10480.047             0.251            0.296
Chain 1:   6600        -9416.415             0.238            0.296
Chain 1:   6700        -9022.689             0.213            0.160
Chain 1:   6800       -10490.681             0.194            0.140
Chain 1:   6900        -9152.016             0.193            0.140
Chain 1:   7000       -11993.461             0.210            0.146
Chain 1:   7100       -13799.986             0.193            0.140
Chain 1:   7200        -9138.824             0.236            0.146
Chain 1:   7300       -11903.244             0.246            0.232
Chain 1:   7400       -13667.410             0.218            0.146
Chain 1:   7500        -9061.490             0.219            0.146
Chain 1:   7600       -10312.971             0.220            0.146
Chain 1:   7700        -9242.455             0.227            0.146
Chain 1:   7800       -12619.795             0.240            0.232
Chain 1:   7900        -8890.160             0.267            0.237
Chain 1:   8000        -8828.271             0.244            0.232
Chain 1:   8100        -9044.299             0.233            0.232
Chain 1:   8200       -10160.484             0.193            0.129
Chain 1:   8300        -9652.264             0.176            0.121
Chain 1:   8400       -12572.371             0.186            0.121
Chain 1:   8500        -8890.811             0.176            0.121
Chain 1:   8600       -10152.018             0.177            0.124
Chain 1:   8700       -11500.269             0.177            0.124
Chain 1:   8800        -9868.382             0.167            0.124
Chain 1:   8900       -11621.747             0.140            0.124
Chain 1:   9000        -9481.686             0.162            0.151
Chain 1:   9100        -9003.613             0.165            0.151
Chain 1:   9200        -9262.196             0.156            0.151
Chain 1:   9300        -8920.867             0.155            0.151
Chain 1:   9400       -12493.406             0.160            0.151
Chain 1:   9500        -9280.037             0.153            0.151
Chain 1:   9600       -10788.098             0.155            0.151
Chain 1:   9700       -11974.064             0.153            0.151
Chain 1:   9800        -8808.403             0.173            0.151
Chain 1:   9900       -11824.980             0.183            0.226
Chain 1:   10000       -11522.245             0.163            0.140
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001436 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59016.669             1.000            1.000
Chain 1:    200       -18620.728             1.585            2.169
Chain 1:    300        -9094.445             1.406            1.047
Chain 1:    400        -8109.192             1.085            1.047
Chain 1:    500        -8218.040             0.870            1.000
Chain 1:    600        -8750.752             0.735            1.000
Chain 1:    700        -8552.390             0.634            0.121
Chain 1:    800        -8293.106             0.558            0.121
Chain 1:    900        -7930.630             0.501            0.061
Chain 1:   1000        -8216.661             0.455            0.061
Chain 1:   1100        -7708.064             0.361            0.061
Chain 1:   1200        -7765.132             0.145            0.046
Chain 1:   1300        -7886.363             0.042            0.035
Chain 1:   1400        -7680.361             0.032            0.031
Chain 1:   1500        -7585.053             0.032            0.031
Chain 1:   1600        -7703.585             0.028            0.027
Chain 1:   1700        -7528.929             0.028            0.027
Chain 1:   1800        -7594.801             0.026            0.023
Chain 1:   1900        -7529.832             0.022            0.015
Chain 1:   2000        -7736.338             0.021            0.015
Chain 1:   2100        -7556.679             0.017            0.015
Chain 1:   2200        -7836.719             0.020            0.023
Chain 1:   2300        -7630.374             0.021            0.024
Chain 1:   2400        -7765.946             0.020            0.023
Chain 1:   2500        -7548.910             0.022            0.024
Chain 1:   2600        -7570.497             0.020            0.024
Chain 1:   2700        -7460.309             0.019            0.024
Chain 1:   2800        -7682.577             0.021            0.027
Chain 1:   2900        -7453.285             0.024            0.027
Chain 1:   3000        -7574.942             0.023            0.027
Chain 1:   3100        -7556.096             0.020            0.027
Chain 1:   3200        -7762.028             0.020            0.027
Chain 1:   3300        -7420.767             0.021            0.027
Chain 1:   3400        -7546.094             0.021            0.027
Chain 1:   3500        -7472.965             0.019            0.017
Chain 1:   3600        -7488.963             0.019            0.017
Chain 1:   3700        -7413.299             0.019            0.017
Chain 1:   3800        -7457.755             0.017            0.016
Chain 1:   3900        -7453.022             0.014            0.010
Chain 1:   4000        -7409.834             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86632.582             1.000            1.000
Chain 1:    200       -14331.837             3.022            5.045
Chain 1:    300       -10588.872             2.133            1.000
Chain 1:    400       -12076.678             1.630            1.000
Chain 1:    500        -9571.631             1.357            0.353
Chain 1:    600        -9065.304             1.140            0.353
Chain 1:    700        -9819.877             0.988            0.262
Chain 1:    800        -8834.050             0.878            0.262
Chain 1:    900        -8981.484             0.783            0.123
Chain 1:   1000        -9295.120             0.708            0.123
Chain 1:   1100        -9367.055             0.609            0.112   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8920.944             0.109            0.077
Chain 1:   1300        -9251.152             0.077            0.056
Chain 1:   1400        -9019.803             0.068            0.050
Chain 1:   1500        -9078.597             0.042            0.036
Chain 1:   1600        -9184.636             0.038            0.034
Chain 1:   1700        -9241.979             0.031            0.026
Chain 1:   1800        -8794.318             0.024            0.026
Chain 1:   1900        -8902.430             0.024            0.026
Chain 1:   2000        -8886.778             0.021            0.012
Chain 1:   2100        -9024.485             0.022            0.015
Chain 1:   2200        -8797.611             0.019            0.015
Chain 1:   2300        -8894.739             0.017            0.012
Chain 1:   2400        -8969.932             0.015            0.012
Chain 1:   2500        -8909.953             0.015            0.012
Chain 1:   2600        -8926.706             0.014            0.011
Chain 1:   2700        -8832.869             0.014            0.011
Chain 1:   2800        -8778.529             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002882 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8379539.335             1.000            1.000
Chain 1:    200     -1584842.538             2.644            4.287
Chain 1:    300      -892972.849             2.021            1.000
Chain 1:    400      -459688.412             1.751            1.000
Chain 1:    500      -360222.131             1.456            0.943
Chain 1:    600      -234949.526             1.302            0.943
Chain 1:    700      -120628.058             1.252            0.943
Chain 1:    800       -87708.084             1.142            0.943
Chain 1:    900       -67949.338             1.048            0.775
Chain 1:   1000       -52673.119             0.972            0.775
Chain 1:   1100       -40075.966             0.903            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39246.072             0.477            0.375
Chain 1:   1300       -27119.082             0.444            0.375
Chain 1:   1400       -26832.284             0.351            0.314
Chain 1:   1500       -23397.718             0.338            0.314
Chain 1:   1600       -22608.105             0.288            0.291
Chain 1:   1700       -21471.566             0.198            0.290
Chain 1:   1800       -21413.569             0.161            0.147
Chain 1:   1900       -21740.261             0.134            0.053
Chain 1:   2000       -20244.713             0.112            0.053
Chain 1:   2100       -20483.553             0.082            0.035
Chain 1:   2200       -20711.301             0.081            0.035
Chain 1:   2300       -20327.169             0.038            0.019
Chain 1:   2400       -20098.944             0.038            0.019
Chain 1:   2500       -19901.267             0.024            0.015
Chain 1:   2600       -19530.633             0.023            0.015
Chain 1:   2700       -19487.240             0.018            0.012
Chain 1:   2800       -19204.013             0.019            0.015
Chain 1:   2900       -19485.611             0.019            0.014
Chain 1:   3000       -19471.702             0.011            0.012
Chain 1:   3100       -19556.834             0.011            0.011
Chain 1:   3200       -19247.036             0.011            0.014
Chain 1:   3300       -19452.076             0.010            0.011
Chain 1:   3400       -18926.305             0.012            0.014
Chain 1:   3500       -19539.403             0.014            0.015
Chain 1:   3600       -18844.478             0.016            0.015
Chain 1:   3700       -19232.562             0.018            0.016
Chain 1:   3800       -18189.891             0.022            0.020
Chain 1:   3900       -18185.994             0.021            0.020
Chain 1:   4000       -18303.261             0.021            0.020
Chain 1:   4100       -18216.981             0.021            0.020
Chain 1:   4200       -18032.623             0.021            0.020
Chain 1:   4300       -18171.404             0.020            0.020
Chain 1:   4400       -18127.807             0.018            0.010
Chain 1:   4500       -18030.280             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001168 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49411.159             1.000            1.000
Chain 1:    200       -23591.514             1.047            1.094
Chain 1:    300       -15179.538             0.883            1.000
Chain 1:    400       -20970.809             0.731            1.000
Chain 1:    500       -17119.083             0.630            0.554
Chain 1:    600       -42530.733             0.625            0.597
Chain 1:    700       -11346.925             0.928            0.597
Chain 1:    800       -16361.556             0.850            0.597
Chain 1:    900       -12041.133             0.796            0.554
Chain 1:   1000       -14203.648             0.731            0.554
Chain 1:   1100       -11640.290             0.653            0.359   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11052.903             0.549            0.306   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -11455.416             0.497            0.276
Chain 1:   1400       -11984.374             0.474            0.225
Chain 1:   1500       -12874.004             0.458            0.220
Chain 1:   1600       -12003.484             0.406            0.152
Chain 1:   1700       -10250.836             0.148            0.152
Chain 1:   1800       -10308.818             0.118            0.073
Chain 1:   1900       -11773.316             0.095            0.073
Chain 1:   2000       -12546.834             0.086            0.069
Chain 1:   2100        -9751.435             0.092            0.069
Chain 1:   2200       -11486.203             0.102            0.073
Chain 1:   2300       -10082.689             0.113            0.124
Chain 1:   2400       -15937.706             0.145            0.139
Chain 1:   2500       -10865.949             0.185            0.151
Chain 1:   2600       -11749.088             0.185            0.151
Chain 1:   2700       -10963.488             0.175            0.139
Chain 1:   2800       -11185.065             0.176            0.139
Chain 1:   2900        -9738.014             0.179            0.149
Chain 1:   3000        -9066.618             0.180            0.149
Chain 1:   3100       -10034.320             0.161            0.139
Chain 1:   3200       -17808.779             0.190            0.139
Chain 1:   3300        -9658.177             0.260            0.149
Chain 1:   3400        -8964.973             0.231            0.096
Chain 1:   3500       -10025.876             0.195            0.096
Chain 1:   3600        -9980.006             0.188            0.096
Chain 1:   3700       -10821.877             0.188            0.096
Chain 1:   3800        -9479.816             0.201            0.106
Chain 1:   3900       -15990.162             0.227            0.106
Chain 1:   4000        -9526.821             0.287            0.142
Chain 1:   4100       -12922.683             0.304            0.263
Chain 1:   4200       -10520.078             0.283            0.228
Chain 1:   4300       -11832.256             0.209            0.142
Chain 1:   4400        -8771.436             0.237            0.228
Chain 1:   4500        -8846.221             0.227            0.228
Chain 1:   4600        -8712.830             0.228            0.228
Chain 1:   4700        -8911.530             0.222            0.228
Chain 1:   4800        -9109.202             0.210            0.228
Chain 1:   4900        -9309.785             0.172            0.111
Chain 1:   5000       -14328.711             0.139            0.111
Chain 1:   5100        -9168.543             0.169            0.111
Chain 1:   5200       -15981.718             0.189            0.111
Chain 1:   5300       -11278.171             0.219            0.349
Chain 1:   5400       -15846.296             0.213            0.288
Chain 1:   5500       -10243.966             0.267            0.350
Chain 1:   5600       -12524.485             0.284            0.350
Chain 1:   5700        -9064.216             0.320            0.382
Chain 1:   5800       -10069.022             0.328            0.382
Chain 1:   5900       -17564.965             0.368            0.417
Chain 1:   6000       -11131.732             0.391            0.426
Chain 1:   6100        -9553.865             0.351            0.417
Chain 1:   6200        -8986.472             0.315            0.382
Chain 1:   6300       -15154.429             0.314            0.382
Chain 1:   6400       -11530.402             0.316            0.382
Chain 1:   6500       -10879.084             0.268            0.314
Chain 1:   6600       -10774.616             0.251            0.314
Chain 1:   6700       -11425.726             0.218            0.165
Chain 1:   6800        -8482.581             0.243            0.314
Chain 1:   6900       -11158.889             0.224            0.240
Chain 1:   7000        -9635.128             0.182            0.165
Chain 1:   7100        -8267.061             0.182            0.165
Chain 1:   7200       -10901.318             0.200            0.240
Chain 1:   7300       -11451.568             0.164            0.165
Chain 1:   7400       -10958.081             0.137            0.158
Chain 1:   7500        -9428.020             0.147            0.162
Chain 1:   7600        -8742.689             0.154            0.162
Chain 1:   7700        -8779.969             0.149            0.162
Chain 1:   7800       -11523.512             0.138            0.162
Chain 1:   7900        -8683.023             0.147            0.162
Chain 1:   8000        -8512.047             0.133            0.162
Chain 1:   8100        -9489.892             0.127            0.103
Chain 1:   8200        -9492.390             0.103            0.078
Chain 1:   8300        -8363.157             0.111            0.103
Chain 1:   8400        -8159.001             0.109            0.103
Chain 1:   8500       -11340.072             0.121            0.103
Chain 1:   8600        -8731.236             0.143            0.135
Chain 1:   8700        -8796.976             0.144            0.135
Chain 1:   8800       -12015.767             0.147            0.135
Chain 1:   8900       -10611.366             0.127            0.132
Chain 1:   9000        -9111.279             0.142            0.135
Chain 1:   9100        -9077.856             0.132            0.135
Chain 1:   9200        -8424.768             0.139            0.135
Chain 1:   9300        -8522.469             0.127            0.132
Chain 1:   9400        -8240.526             0.128            0.132
Chain 1:   9500       -11942.311             0.131            0.132
Chain 1:   9600        -8366.371             0.144            0.132
Chain 1:   9700        -9045.024             0.150            0.132
Chain 1:   9800        -8436.605             0.131            0.078
Chain 1:   9900        -9404.163             0.128            0.078
Chain 1:   10000        -8091.188             0.128            0.078
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001423 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58721.680             1.000            1.000
Chain 1:    200       -18052.459             1.626            2.253
Chain 1:    300        -8822.641             1.433            1.046
Chain 1:    400        -8115.328             1.097            1.046
Chain 1:    500        -9137.196             0.900            1.000
Chain 1:    600        -8088.813             0.771            1.000
Chain 1:    700        -8037.172             0.662            0.130
Chain 1:    800        -7873.521             0.582            0.130
Chain 1:    900        -7988.555             0.519            0.112
Chain 1:   1000        -7948.855             0.467            0.112
Chain 1:   1100        -7675.515             0.371            0.087
Chain 1:   1200        -7753.793             0.147            0.036
Chain 1:   1300        -7710.076             0.043            0.021
Chain 1:   1400        -7894.195             0.036            0.021
Chain 1:   1500        -7586.978             0.029            0.021
Chain 1:   1600        -7732.409             0.018            0.019
Chain 1:   1700        -7585.215             0.019            0.019
Chain 1:   1800        -7643.934             0.018            0.019
Chain 1:   1900        -7589.536             0.017            0.019
Chain 1:   2000        -7658.900             0.018            0.019
Chain 1:   2100        -7562.936             0.015            0.013
Chain 1:   2200        -7717.631             0.016            0.019
Chain 1:   2300        -7544.140             0.018            0.019
Chain 1:   2400        -7513.059             0.016            0.019
Chain 1:   2500        -7465.344             0.013            0.013
Chain 1:   2600        -7513.664             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85811.814             1.000            1.000
Chain 1:    200       -13787.928             3.112            5.224
Chain 1:    300       -10039.645             2.199            1.000
Chain 1:    400       -11749.068             1.686            1.000
Chain 1:    500        -8604.312             1.422            0.373
Chain 1:    600        -8744.716             1.187            0.373
Chain 1:    700        -9145.050             1.024            0.365
Chain 1:    800        -9591.770             0.902            0.365
Chain 1:    900        -8695.232             0.813            0.145
Chain 1:   1000        -8853.239             0.734            0.145
Chain 1:   1100        -8788.614             0.634            0.103   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8338.310             0.117            0.054
Chain 1:   1300        -8692.539             0.084            0.047
Chain 1:   1400        -8608.059             0.070            0.044
Chain 1:   1500        -8515.276             0.035            0.041
Chain 1:   1600        -8626.750             0.035            0.041
Chain 1:   1700        -8687.618             0.031            0.018
Chain 1:   1800        -8236.991             0.032            0.018
Chain 1:   1900        -8346.813             0.023            0.013
Chain 1:   2000        -8336.619             0.021            0.013
Chain 1:   2100        -8511.024             0.022            0.013
Chain 1:   2200        -8242.973             0.020            0.013
Chain 1:   2300        -8426.890             0.018            0.013
Chain 1:   2400        -8243.556             0.020            0.020
Chain 1:   2500        -8320.436             0.020            0.020
Chain 1:   2600        -8352.693             0.019            0.020
Chain 1:   2700        -8272.056             0.019            0.020
Chain 1:   2800        -8223.522             0.014            0.013
Chain 1:   2900        -8332.299             0.014            0.013
Chain 1:   3000        -8243.112             0.015            0.013
Chain 1:   3100        -8207.952             0.013            0.011
Chain 1:   3200        -8182.711             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003107 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397776.825             1.000            1.000
Chain 1:    200     -1583769.402             2.651            4.302
Chain 1:    300      -892424.329             2.026            1.000
Chain 1:    400      -458968.503             1.755            1.000
Chain 1:    500      -359192.865             1.460            0.944
Chain 1:    600      -233987.494             1.306            0.944
Chain 1:    700      -119890.787             1.255            0.944
Chain 1:    800       -86996.483             1.146            0.944
Chain 1:    900       -67279.091             1.051            0.775
Chain 1:   1000       -52041.172             0.975            0.775
Chain 1:   1100       -39476.430             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38654.840             0.479            0.378
Chain 1:   1300       -26560.553             0.447            0.378
Chain 1:   1400       -26278.224             0.353            0.318
Chain 1:   1500       -22851.228             0.341            0.318
Chain 1:   1600       -22064.524             0.291            0.293
Chain 1:   1700       -20931.507             0.201            0.293
Chain 1:   1800       -20874.610             0.163            0.150
Chain 1:   1900       -21201.350             0.136            0.054
Chain 1:   2000       -19707.847             0.114            0.054
Chain 1:   2100       -19946.493             0.083            0.036
Chain 1:   2200       -20173.890             0.082            0.036
Chain 1:   2300       -19790.153             0.039            0.019
Chain 1:   2400       -19561.955             0.039            0.019
Chain 1:   2500       -19364.111             0.025            0.015
Chain 1:   2600       -18993.472             0.023            0.015
Chain 1:   2700       -18950.247             0.018            0.012
Chain 1:   2800       -18666.814             0.019            0.015
Chain 1:   2900       -18948.497             0.019            0.015
Chain 1:   3000       -18934.622             0.012            0.012
Chain 1:   3100       -19019.663             0.011            0.012
Chain 1:   3200       -18709.905             0.011            0.015
Chain 1:   3300       -18915.009             0.011            0.012
Chain 1:   3400       -18389.118             0.012            0.015
Chain 1:   3500       -19002.238             0.015            0.015
Chain 1:   3600       -18307.421             0.016            0.015
Chain 1:   3700       -18695.323             0.018            0.017
Chain 1:   3800       -17652.662             0.023            0.021
Chain 1:   3900       -17648.787             0.021            0.021
Chain 1:   4000       -17766.084             0.022            0.021
Chain 1:   4100       -17679.679             0.022            0.021
Chain 1:   4200       -17495.455             0.021            0.021
Chain 1:   4300       -17634.169             0.021            0.021
Chain 1:   4400       -17590.571             0.018            0.011
Chain 1:   4500       -17493.074             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49650.180             1.000            1.000
Chain 1:    200       -16676.164             1.489            1.977
Chain 1:    300       -21526.600             1.068            1.000
Chain 1:    400       -22932.762             0.816            1.000
Chain 1:    500       -16346.953             0.733            0.403
Chain 1:    600       -13743.566             0.643            0.403
Chain 1:    700       -14973.342             0.563            0.225
Chain 1:    800       -13774.502             0.503            0.225
Chain 1:    900       -14178.002             0.450            0.189
Chain 1:   1000       -13054.966             0.414            0.189
Chain 1:   1100       -13255.109             0.316            0.087
Chain 1:   1200       -11793.329             0.130            0.087
Chain 1:   1300       -13192.554             0.118            0.087
Chain 1:   1400       -10803.158             0.134            0.106
Chain 1:   1500       -11108.877             0.097            0.087
Chain 1:   1600       -13021.986             0.092            0.087
Chain 1:   1700       -15734.201             0.101            0.106
Chain 1:   1800       -11640.620             0.128            0.124
Chain 1:   1900       -10377.866             0.137            0.124
Chain 1:   2000       -10500.289             0.130            0.124
Chain 1:   2100       -10045.413             0.133            0.124
Chain 1:   2200       -10527.777             0.125            0.122
Chain 1:   2300       -15429.243             0.146            0.147
Chain 1:   2400        -9714.865             0.183            0.147
Chain 1:   2500       -11662.673             0.197            0.167
Chain 1:   2600       -10001.778             0.199            0.167
Chain 1:   2700       -11448.583             0.194            0.166
Chain 1:   2800       -20513.497             0.203            0.166
Chain 1:   2900       -10072.523             0.295            0.167
Chain 1:   3000       -15234.584             0.327            0.318
Chain 1:   3100       -10288.860             0.371            0.339
Chain 1:   3200        -9344.757             0.376            0.339
Chain 1:   3300        -9907.536             0.350            0.339
Chain 1:   3400       -14153.010             0.322            0.300
Chain 1:   3500       -16536.988             0.319            0.300
Chain 1:   3600       -14882.404             0.314            0.300
Chain 1:   3700       -10362.755             0.345            0.339
Chain 1:   3800        -9226.925             0.313            0.300
Chain 1:   3900        -9457.936             0.212            0.144
Chain 1:   4000        -9440.682             0.178            0.123
Chain 1:   4100        -9901.545             0.135            0.111
Chain 1:   4200        -9273.742             0.131            0.111
Chain 1:   4300        -9685.505             0.130            0.111
Chain 1:   4400        -9706.936             0.100            0.068
Chain 1:   4500        -9156.098             0.092            0.060
Chain 1:   4600       -15251.486             0.120            0.060
Chain 1:   4700        -9550.467             0.137            0.060
Chain 1:   4800        -9233.682             0.128            0.047
Chain 1:   4900       -17716.839             0.173            0.060
Chain 1:   5000        -9288.802             0.264            0.068
Chain 1:   5100        -9662.654             0.263            0.068
Chain 1:   5200        -9164.945             0.261            0.060
Chain 1:   5300       -13818.602             0.291            0.337
Chain 1:   5400       -10538.021             0.322            0.337
Chain 1:   5500        -9954.686             0.322            0.337
Chain 1:   5600       -12956.285             0.305            0.311
Chain 1:   5700        -9128.886             0.287            0.311
Chain 1:   5800        -9057.657             0.284            0.311
Chain 1:   5900       -14901.581             0.276            0.311
Chain 1:   6000        -9482.430             0.242            0.311
Chain 1:   6100        -9708.116             0.241            0.311
Chain 1:   6200        -8897.824             0.244            0.311
Chain 1:   6300       -15460.855             0.253            0.311
Chain 1:   6400        -9722.568             0.281            0.392
Chain 1:   6500       -10364.499             0.281            0.392
Chain 1:   6600        -9313.516             0.269            0.392
Chain 1:   6700        -8819.941             0.233            0.113
Chain 1:   6800        -9061.447             0.235            0.113
Chain 1:   6900       -10448.821             0.209            0.113
Chain 1:   7000        -9979.438             0.157            0.091
Chain 1:   7100        -9776.201             0.156            0.091
Chain 1:   7200        -8801.184             0.158            0.111
Chain 1:   7300       -12010.899             0.143            0.111
Chain 1:   7400       -12236.762             0.085            0.062
Chain 1:   7500       -11872.171             0.082            0.056
Chain 1:   7600        -8937.870             0.104            0.056
Chain 1:   7700       -11593.397             0.121            0.111
Chain 1:   7800       -13609.529             0.133            0.133
Chain 1:   7900        -9339.242             0.166            0.148
Chain 1:   8000       -12207.131             0.185            0.229
Chain 1:   8100        -9292.079             0.214            0.235
Chain 1:   8200        -8668.004             0.210            0.235
Chain 1:   8300        -8639.520             0.184            0.229
Chain 1:   8400       -12628.050             0.213            0.235
Chain 1:   8500        -8663.624             0.256            0.314
Chain 1:   8600        -8741.390             0.224            0.235
Chain 1:   8700       -11078.152             0.222            0.235
Chain 1:   8800        -9154.329             0.228            0.235
Chain 1:   8900       -10692.536             0.197            0.211
Chain 1:   9000       -10849.954             0.175            0.210
Chain 1:   9100        -9577.815             0.157            0.144
Chain 1:   9200        -8793.434             0.159            0.144
Chain 1:   9300       -11673.346             0.183            0.210
Chain 1:   9400        -8809.262             0.184            0.210
Chain 1:   9500        -9191.340             0.142            0.144
Chain 1:   9600        -9452.430             0.144            0.144
Chain 1:   9700        -8464.955             0.135            0.133
Chain 1:   9800       -12010.803             0.143            0.133
Chain 1:   9900       -10622.562             0.142            0.131
Chain 1:   10000        -8622.437             0.164            0.133
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001392 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -52016.291             1.000            1.000
Chain 1:    200       -16988.608             1.531            2.062
Chain 1:    300        -9073.532             1.311            1.000
Chain 1:    400        -9662.196             0.999            1.000
Chain 1:    500        -9230.709             0.808            0.872
Chain 1:    600        -9029.545             0.677            0.872
Chain 1:    700        -8598.263             0.588            0.061
Chain 1:    800        -8492.119             0.516            0.061
Chain 1:    900        -8068.319             0.464            0.053
Chain 1:   1000        -7886.722             0.420            0.053
Chain 1:   1100        -7690.722             0.323            0.050
Chain 1:   1200        -7690.538             0.117            0.047
Chain 1:   1300        -7737.583             0.030            0.025
Chain 1:   1400        -7813.896             0.025            0.023
Chain 1:   1500        -7579.479             0.023            0.023
Chain 1:   1600        -7536.402             0.022            0.023
Chain 1:   1700        -7654.045             0.018            0.015
Chain 1:   1800        -7720.380             0.018            0.015
Chain 1:   1900        -7604.838             0.014            0.015
Chain 1:   2000        -7649.344             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002735 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87817.137             1.000            1.000
Chain 1:    200       -14174.308             3.098            5.196
Chain 1:    300       -10447.043             2.184            1.000
Chain 1:    400       -11801.663             1.667            1.000
Chain 1:    500        -9355.750             1.386            0.357
Chain 1:    600        -9170.827             1.158            0.357
Chain 1:    700        -9805.212             1.002            0.261
Chain 1:    800        -8756.427             0.892            0.261
Chain 1:    900        -8865.708             0.794            0.120
Chain 1:   1000        -9106.142             0.717            0.120
Chain 1:   1100        -9214.935             0.618            0.115   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8811.288             0.103            0.065
Chain 1:   1300        -9120.206             0.071            0.046
Chain 1:   1400        -9002.021             0.061            0.034
Chain 1:   1500        -8955.065             0.035            0.026
Chain 1:   1600        -9063.180             0.034            0.026
Chain 1:   1700        -9127.513             0.029            0.013
Chain 1:   1800        -8689.305             0.022            0.013
Chain 1:   1900        -8793.764             0.022            0.013
Chain 1:   2000        -8771.743             0.019            0.012
Chain 1:   2100        -8748.644             0.018            0.012
Chain 1:   2200        -8713.635             0.014            0.012
Chain 1:   2300        -8848.674             0.012            0.012
Chain 1:   2400        -8692.381             0.013            0.012
Chain 1:   2500        -8763.524             0.013            0.012
Chain 1:   2600        -8677.093             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002527 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414662.096             1.000            1.000
Chain 1:    200     -1585881.872             2.653            4.306
Chain 1:    300      -891431.784             2.028            1.000
Chain 1:    400      -457917.653             1.758            1.000
Chain 1:    500      -358237.993             1.462            0.947
Chain 1:    600      -233208.776             1.308            0.947
Chain 1:    700      -119698.926             1.256            0.947
Chain 1:    800       -86957.644             1.146            0.947
Chain 1:    900       -67352.551             1.051            0.779
Chain 1:   1000       -52191.922             0.975            0.779
Chain 1:   1100       -39701.566             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38886.615             0.478            0.377
Chain 1:   1300       -26868.704             0.445            0.377
Chain 1:   1400       -26591.949             0.351            0.315
Chain 1:   1500       -23185.232             0.338            0.315
Chain 1:   1600       -22404.033             0.288            0.291
Chain 1:   1700       -21280.473             0.199            0.290
Chain 1:   1800       -21225.510             0.161            0.147
Chain 1:   1900       -21552.138             0.134            0.053
Chain 1:   2000       -20063.749             0.112            0.053
Chain 1:   2100       -20302.182             0.082            0.035
Chain 1:   2200       -20528.695             0.081            0.035
Chain 1:   2300       -20145.726             0.038            0.019
Chain 1:   2400       -19917.695             0.038            0.019
Chain 1:   2500       -19719.474             0.024            0.015
Chain 1:   2600       -19349.442             0.023            0.015
Chain 1:   2700       -19306.335             0.018            0.012
Chain 1:   2800       -19022.940             0.019            0.015
Chain 1:   2900       -19304.318             0.019            0.015
Chain 1:   3000       -19290.545             0.011            0.012
Chain 1:   3100       -19375.587             0.011            0.011
Chain 1:   3200       -19066.011             0.011            0.015
Chain 1:   3300       -19270.930             0.010            0.011
Chain 1:   3400       -18745.323             0.012            0.015
Chain 1:   3500       -19357.936             0.014            0.015
Chain 1:   3600       -18663.643             0.016            0.015
Chain 1:   3700       -19051.152             0.018            0.016
Chain 1:   3800       -18009.294             0.022            0.020
Chain 1:   3900       -18005.359             0.021            0.020
Chain 1:   4000       -18122.701             0.021            0.020
Chain 1:   4100       -18036.387             0.021            0.020
Chain 1:   4200       -17852.277             0.021            0.020
Chain 1:   4300       -17990.958             0.020            0.020
Chain 1:   4400       -17947.510             0.018            0.010
Chain 1:   4500       -17849.954             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001325 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49413.413             1.000            1.000
Chain 1:    200       -14117.140             1.750            2.500
Chain 1:    300       -15639.746             1.199            1.000
Chain 1:    400       -19925.633             0.953            1.000
Chain 1:    500       -17437.056             0.791            0.215
Chain 1:    600       -26378.606             0.716            0.339
Chain 1:    700       -16652.802             0.697            0.339
Chain 1:    800       -14570.060             0.628            0.339
Chain 1:    900       -17952.708             0.579            0.215
Chain 1:   1000       -21542.746             0.538            0.215
Chain 1:   1100       -10760.536             0.538            0.215   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11564.926             0.295            0.188
Chain 1:   1300       -12842.283             0.295            0.188
Chain 1:   1400       -18643.041             0.305            0.188
Chain 1:   1500       -11964.883             0.346            0.311
Chain 1:   1600       -17024.298             0.342            0.297
Chain 1:   1700       -14985.868             0.297            0.188
Chain 1:   1800       -10862.984             0.321            0.297
Chain 1:   1900       -11285.371             0.306            0.297
Chain 1:   2000       -15559.619             0.317            0.297
Chain 1:   2100       -11102.279             0.256            0.297
Chain 1:   2200       -10820.324             0.252            0.297
Chain 1:   2300        -9566.385             0.255            0.297
Chain 1:   2400       -10087.260             0.229            0.275
Chain 1:   2500       -10292.106             0.176            0.136
Chain 1:   2600       -11219.141             0.154            0.131
Chain 1:   2700       -16514.165             0.173            0.131
Chain 1:   2800        -9617.284             0.206            0.131
Chain 1:   2900       -16490.253             0.244            0.275
Chain 1:   3000        -9794.902             0.285            0.321
Chain 1:   3100       -10649.910             0.253            0.131
Chain 1:   3200        -9458.360             0.263            0.131
Chain 1:   3300       -10021.894             0.255            0.126
Chain 1:   3400        -9342.554             0.258            0.126
Chain 1:   3500       -12083.426             0.278            0.227
Chain 1:   3600        -9370.243             0.299            0.290
Chain 1:   3700       -10792.792             0.280            0.227
Chain 1:   3800        -9097.477             0.227            0.186
Chain 1:   3900       -15249.939             0.226            0.186
Chain 1:   4000       -15542.318             0.159            0.132
Chain 1:   4100        -9631.476             0.213            0.186
Chain 1:   4200       -12973.368             0.226            0.227
Chain 1:   4300        -9043.118             0.264            0.258
Chain 1:   4400       -13749.636             0.291            0.290
Chain 1:   4500        -9101.723             0.319            0.342
Chain 1:   4600        -9527.080             0.294            0.342
Chain 1:   4700        -8929.158             0.288            0.342
Chain 1:   4800        -8940.518             0.269            0.342
Chain 1:   4900        -9298.233             0.233            0.258
Chain 1:   5000       -14573.270             0.267            0.342
Chain 1:   5100        -9524.839             0.259            0.342
Chain 1:   5200       -15906.972             0.273            0.362
Chain 1:   5300        -8956.389             0.307            0.362
Chain 1:   5400       -16091.640             0.317            0.401
Chain 1:   5500        -8938.364             0.346            0.401
Chain 1:   5600        -9119.124             0.344            0.401
Chain 1:   5700        -9186.877             0.338            0.401
Chain 1:   5800        -8967.788             0.340            0.401
Chain 1:   5900        -9565.438             0.343            0.401
Chain 1:   6000        -9055.641             0.312            0.401
Chain 1:   6100        -9370.161             0.262            0.062
Chain 1:   6200        -9360.497             0.222            0.056
Chain 1:   6300        -9393.925             0.145            0.034
Chain 1:   6400        -9331.498             0.102            0.024
Chain 1:   6500        -9551.286             0.024            0.023
Chain 1:   6600        -9281.018             0.025            0.024
Chain 1:   6700       -10111.984             0.032            0.029
Chain 1:   6800        -9334.029             0.038            0.034
Chain 1:   6900       -12122.237             0.055            0.034
Chain 1:   7000        -8993.894             0.084            0.034
Chain 1:   7100        -9000.095             0.081            0.029
Chain 1:   7200        -8848.124             0.082            0.029
Chain 1:   7300        -9413.207             0.088            0.060
Chain 1:   7400       -14267.550             0.121            0.082
Chain 1:   7500        -9954.481             0.162            0.083
Chain 1:   7600       -10928.893             0.168            0.089
Chain 1:   7700        -8794.986             0.184            0.230
Chain 1:   7800       -13610.487             0.211            0.243
Chain 1:   7900        -9723.551             0.228            0.340
Chain 1:   8000        -9253.464             0.199            0.243
Chain 1:   8100       -10566.614             0.211            0.243
Chain 1:   8200       -10013.009             0.215            0.243
Chain 1:   8300        -8963.546             0.221            0.243
Chain 1:   8400        -8563.289             0.191            0.124
Chain 1:   8500        -8943.922             0.152            0.117
Chain 1:   8600       -10943.614             0.162            0.124
Chain 1:   8700        -8626.706             0.164            0.124
Chain 1:   8800        -8845.469             0.131            0.117
Chain 1:   8900        -9210.701             0.095            0.055
Chain 1:   9000       -10679.707             0.104            0.117
Chain 1:   9100        -8646.459             0.115            0.117
Chain 1:   9200       -12537.190             0.141            0.138
Chain 1:   9300        -8540.787             0.176            0.183
Chain 1:   9400        -9307.079             0.179            0.183
Chain 1:   9500        -8508.669             0.184            0.183
Chain 1:   9600       -11407.070             0.191            0.235
Chain 1:   9700       -11754.000             0.168            0.138
Chain 1:   9800       -10502.369             0.177            0.138
Chain 1:   9900       -11213.225             0.179            0.138
Chain 1:   10000        -8583.513             0.196            0.235
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001471 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57598.067             1.000            1.000
Chain 1:    200       -17885.067             1.610            2.220
Chain 1:    300        -8963.851             1.405            1.000
Chain 1:    400        -8217.640             1.077            1.000
Chain 1:    500        -8624.907             0.871            0.995
Chain 1:    600        -9069.860             0.734            0.995
Chain 1:    700        -8099.960             0.646            0.120
Chain 1:    800        -8019.482             0.567            0.120
Chain 1:    900        -8118.778             0.505            0.091
Chain 1:   1000        -7797.360             0.459            0.091
Chain 1:   1100        -8267.821             0.364            0.057
Chain 1:   1200        -7655.715             0.150            0.057
Chain 1:   1300        -7884.307             0.054            0.049
Chain 1:   1400        -7890.931             0.045            0.047
Chain 1:   1500        -7604.369             0.044            0.041
Chain 1:   1600        -7794.618             0.041            0.038
Chain 1:   1700        -7542.623             0.033            0.033
Chain 1:   1800        -7694.180             0.034            0.033
Chain 1:   1900        -7669.442             0.033            0.033
Chain 1:   2000        -7710.475             0.029            0.029
Chain 1:   2100        -7612.969             0.025            0.024
Chain 1:   2200        -7778.305             0.019            0.021
Chain 1:   2300        -7623.719             0.018            0.020
Chain 1:   2400        -7598.272             0.018            0.020
Chain 1:   2500        -7691.171             0.016            0.020
Chain 1:   2600        -7586.570             0.015            0.014
Chain 1:   2700        -7498.437             0.012            0.013
Chain 1:   2800        -7679.157             0.013            0.013
Chain 1:   2900        -7436.680             0.016            0.014
Chain 1:   3000        -7594.454             0.017            0.020
Chain 1:   3100        -7578.700             0.016            0.020
Chain 1:   3200        -7793.726             0.017            0.020
Chain 1:   3300        -7504.212             0.019            0.021
Chain 1:   3400        -7746.797             0.021            0.024
Chain 1:   3500        -7493.035             0.024            0.028
Chain 1:   3600        -7558.251             0.023            0.028
Chain 1:   3700        -7509.305             0.023            0.028
Chain 1:   3800        -7508.805             0.020            0.028
Chain 1:   3900        -7469.011             0.017            0.021
Chain 1:   4000        -7460.760             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002623 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87092.942             1.000            1.000
Chain 1:    200       -13970.200             3.117            5.234
Chain 1:    300       -10293.080             2.197            1.000
Chain 1:    400       -11378.549             1.672            1.000
Chain 1:    500        -9196.947             1.385            0.357
Chain 1:    600        -8771.779             1.162            0.357
Chain 1:    700        -8790.075             0.996            0.237
Chain 1:    800        -9193.787             0.877            0.237
Chain 1:    900        -9026.704             0.782            0.095
Chain 1:   1000        -9032.199             0.704            0.095
Chain 1:   1100        -9112.040             0.605            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8615.402             0.087            0.048
Chain 1:   1300        -8952.290             0.055            0.044
Chain 1:   1400        -8955.216             0.046            0.038
Chain 1:   1500        -8843.423             0.023            0.019
Chain 1:   1600        -8957.311             0.019            0.013
Chain 1:   1700        -9028.227             0.020            0.013
Chain 1:   1800        -8601.343             0.021            0.013
Chain 1:   1900        -8703.675             0.020            0.013
Chain 1:   2000        -8678.671             0.020            0.013
Chain 1:   2100        -8805.547             0.021            0.013
Chain 1:   2200        -8604.825             0.017            0.013
Chain 1:   2300        -8699.018             0.015            0.013
Chain 1:   2400        -8767.069             0.015            0.013
Chain 1:   2500        -8713.333             0.015            0.012
Chain 1:   2600        -8715.641             0.013            0.011
Chain 1:   2700        -8631.877             0.014            0.011
Chain 1:   2800        -8590.586             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002848 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405706.148             1.000            1.000
Chain 1:    200     -1583247.829             2.655            4.309
Chain 1:    300      -890790.745             2.029            1.000
Chain 1:    400      -458210.233             1.758            1.000
Chain 1:    500      -358724.380             1.462            0.944
Chain 1:    600      -233592.227             1.307            0.944
Chain 1:    700      -119733.420             1.256            0.944
Chain 1:    800       -86958.381             1.146            0.944
Chain 1:    900       -67290.593             1.052            0.777
Chain 1:   1000       -52084.296             0.976            0.777
Chain 1:   1100       -39557.530             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38735.045             0.478            0.377
Chain 1:   1300       -26679.897             0.446            0.377
Chain 1:   1400       -26398.443             0.353            0.317
Chain 1:   1500       -22983.518             0.340            0.317
Chain 1:   1600       -22199.994             0.290            0.292
Chain 1:   1700       -21071.868             0.200            0.292
Chain 1:   1800       -21015.763             0.162            0.149
Chain 1:   1900       -21342.146             0.135            0.054
Chain 1:   2000       -19852.317             0.113            0.054
Chain 1:   2100       -20090.540             0.083            0.035
Chain 1:   2200       -20317.426             0.082            0.035
Chain 1:   2300       -19934.215             0.038            0.019
Chain 1:   2400       -19706.282             0.038            0.019
Chain 1:   2500       -19508.445             0.025            0.015
Chain 1:   2600       -19138.270             0.023            0.015
Chain 1:   2700       -19095.141             0.018            0.012
Chain 1:   2800       -18812.039             0.019            0.015
Chain 1:   2900       -19093.370             0.019            0.015
Chain 1:   3000       -19079.456             0.012            0.012
Chain 1:   3100       -19164.510             0.011            0.012
Chain 1:   3200       -18855.016             0.011            0.015
Chain 1:   3300       -19059.897             0.011            0.012
Chain 1:   3400       -18534.610             0.012            0.015
Chain 1:   3500       -19146.825             0.014            0.015
Chain 1:   3600       -18453.074             0.016            0.015
Chain 1:   3700       -18840.240             0.018            0.016
Chain 1:   3800       -17799.290             0.022            0.021
Chain 1:   3900       -17795.457             0.021            0.021
Chain 1:   4000       -17912.719             0.022            0.021
Chain 1:   4100       -17826.493             0.022            0.021
Chain 1:   4200       -17642.593             0.021            0.021
Chain 1:   4300       -17781.058             0.021            0.021
Chain 1:   4400       -17737.770             0.018            0.010
Chain 1:   4500       -17640.323             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001462 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12584.131             1.000            1.000
Chain 1:    200        -9514.443             0.661            1.000
Chain 1:    300        -8120.575             0.498            0.323
Chain 1:    400        -8307.221             0.379            0.323
Chain 1:    500        -8272.651             0.304            0.172
Chain 1:    600        -8064.080             0.258            0.172
Chain 1:    700        -7953.148             0.223            0.026
Chain 1:    800        -7984.467             0.196            0.026
Chain 1:    900        -8079.241             0.175            0.022
Chain 1:   1000        -8018.578             0.158            0.022
Chain 1:   1100        -8087.094             0.059            0.014
Chain 1:   1200        -7967.984             0.028            0.014
Chain 1:   1300        -7911.495             0.012            0.012
Chain 1:   1400        -7945.522             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56811.887             1.000            1.000
Chain 1:    200       -17680.175             1.607            2.213
Chain 1:    300        -8826.368             1.405            1.003
Chain 1:    400        -8263.313             1.071            1.003
Chain 1:    500        -8883.774             0.871            1.000
Chain 1:    600        -8331.543             0.737            1.000
Chain 1:    700        -8352.309             0.632            0.070
Chain 1:    800        -8044.120             0.558            0.070
Chain 1:    900        -7936.183             0.497            0.068
Chain 1:   1000        -7828.534             0.449            0.068
Chain 1:   1100        -7772.069             0.350            0.066
Chain 1:   1200        -7640.195             0.130            0.038
Chain 1:   1300        -7774.425             0.031            0.017
Chain 1:   1400        -7897.648             0.026            0.017
Chain 1:   1500        -7631.144             0.023            0.017
Chain 1:   1600        -7737.633             0.017            0.016
Chain 1:   1700        -7563.046             0.019            0.017
Chain 1:   1800        -7677.641             0.017            0.016
Chain 1:   1900        -7677.060             0.016            0.016
Chain 1:   2000        -7610.958             0.015            0.016
Chain 1:   2100        -7570.155             0.015            0.016
Chain 1:   2200        -7882.021             0.017            0.016
Chain 1:   2300        -7528.770             0.020            0.016
Chain 1:   2400        -7531.652             0.019            0.015
Chain 1:   2500        -7665.324             0.017            0.015
Chain 1:   2600        -7528.396             0.017            0.017
Chain 1:   2700        -7594.718             0.016            0.015
Chain 1:   2800        -7521.512             0.016            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003095 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86784.645             1.000            1.000
Chain 1:    200       -13732.873             3.160            5.319
Chain 1:    300       -10023.025             2.230            1.000
Chain 1:    400       -11189.291             1.698            1.000
Chain 1:    500        -9028.987             1.407            0.370
Chain 1:    600        -8634.066             1.180            0.370
Chain 1:    700        -8618.490             1.012            0.239
Chain 1:    800        -8946.698             0.890            0.239
Chain 1:    900        -8776.446             0.793            0.104
Chain 1:   1000        -8583.299             0.716            0.104
Chain 1:   1100        -8801.319             0.618            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8304.711             0.092            0.046
Chain 1:   1300        -8691.468             0.060            0.044
Chain 1:   1400        -8644.557             0.050            0.037
Chain 1:   1500        -8556.502             0.027            0.025
Chain 1:   1600        -8661.106             0.024            0.023
Chain 1:   1700        -8723.867             0.024            0.023
Chain 1:   1800        -8290.090             0.026            0.023
Chain 1:   1900        -8394.511             0.025            0.023
Chain 1:   2000        -8370.011             0.023            0.012
Chain 1:   2100        -8327.197             0.021            0.012
Chain 1:   2200        -8313.071             0.015            0.010
Chain 1:   2300        -8448.381             0.013            0.010
Chain 1:   2400        -8296.369             0.014            0.012
Chain 1:   2500        -8364.615             0.014            0.012
Chain 1:   2600        -8283.706             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003363 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8375073.339             1.000            1.000
Chain 1:    200     -1580302.582             2.650            4.300
Chain 1:    300      -891328.238             2.024            1.000
Chain 1:    400      -458204.262             1.754            1.000
Chain 1:    500      -359016.030             1.459            0.945
Chain 1:    600      -233852.592             1.305            0.945
Chain 1:    700      -119824.777             1.254            0.945
Chain 1:    800       -86928.093             1.145            0.945
Chain 1:    900       -67221.475             1.050            0.773
Chain 1:   1000       -51971.626             0.975            0.773
Chain 1:   1100       -39398.278             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38572.430             0.479            0.378
Chain 1:   1300       -26476.012             0.447            0.378
Chain 1:   1400       -26191.295             0.354            0.319
Chain 1:   1500       -22763.697             0.341            0.319
Chain 1:   1600       -21975.845             0.291            0.293
Chain 1:   1700       -20843.299             0.201            0.293
Chain 1:   1800       -20786.094             0.164            0.151
Chain 1:   1900       -21112.592             0.136            0.054
Chain 1:   2000       -19619.414             0.114            0.054
Chain 1:   2100       -19858.239             0.084            0.036
Chain 1:   2200       -20085.365             0.083            0.036
Chain 1:   2300       -19701.833             0.039            0.019
Chain 1:   2400       -19473.739             0.039            0.019
Chain 1:   2500       -19275.797             0.025            0.015
Chain 1:   2600       -18905.658             0.023            0.015
Chain 1:   2700       -18862.457             0.018            0.012
Chain 1:   2800       -18579.202             0.019            0.015
Chain 1:   2900       -18860.671             0.019            0.015
Chain 1:   3000       -18846.838             0.012            0.012
Chain 1:   3100       -18931.881             0.011            0.012
Chain 1:   3200       -18622.333             0.012            0.015
Chain 1:   3300       -18827.206             0.011            0.012
Chain 1:   3400       -18301.734             0.012            0.015
Chain 1:   3500       -18914.307             0.015            0.015
Chain 1:   3600       -18220.099             0.016            0.015
Chain 1:   3700       -18607.615             0.018            0.017
Chain 1:   3800       -17565.961             0.023            0.021
Chain 1:   3900       -17562.063             0.021            0.021
Chain 1:   4000       -17679.357             0.022            0.021
Chain 1:   4100       -17593.084             0.022            0.021
Chain 1:   4200       -17408.985             0.021            0.021
Chain 1:   4300       -17547.624             0.021            0.021
Chain 1:   4400       -17504.235             0.018            0.011
Chain 1:   4500       -17406.698             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002455 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49070.529             1.000            1.000
Chain 1:    200       -20833.761             1.178            1.355
Chain 1:    300       -18451.361             0.828            1.000
Chain 1:    400       -22487.573             0.666            1.000
Chain 1:    500       -14299.358             0.647            0.573
Chain 1:    600       -16167.388             0.559            0.573
Chain 1:    700       -13713.344             0.504            0.179
Chain 1:    800       -28498.415             0.506            0.519
Chain 1:    900       -11711.214             0.609            0.519
Chain 1:   1000       -12904.320             0.558            0.519
Chain 1:   1100       -27802.436             0.511            0.519   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10716.275             0.535            0.519   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -13253.144             0.541            0.519   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -10863.428             0.545            0.519   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -12067.106             0.498            0.220
Chain 1:   1600       -10357.761             0.503            0.220   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700       -12573.908             0.503            0.220   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800       -11860.152             0.457            0.191
Chain 1:   1900       -10971.627             0.322            0.176
Chain 1:   2000       -11771.347             0.319            0.176
Chain 1:   2100       -12240.510             0.269            0.165
Chain 1:   2200       -11605.119             0.115            0.100
Chain 1:   2300        -9396.248             0.120            0.100
Chain 1:   2400       -10697.598             0.110            0.100
Chain 1:   2500       -10926.147             0.102            0.081
Chain 1:   2600        -9477.403             0.101            0.081
Chain 1:   2700       -14124.922             0.116            0.081
Chain 1:   2800        -9411.050             0.160            0.122
Chain 1:   2900       -13962.316             0.185            0.153
Chain 1:   3000        -9571.942             0.224            0.235
Chain 1:   3100       -16802.872             0.263            0.326
Chain 1:   3200       -13705.857             0.280            0.326
Chain 1:   3300       -15966.429             0.271            0.326
Chain 1:   3400       -12073.345             0.291            0.326
Chain 1:   3500       -10611.376             0.303            0.326
Chain 1:   3600        -9901.644             0.294            0.326
Chain 1:   3700       -10561.476             0.268            0.322
Chain 1:   3800       -13354.517             0.239            0.226
Chain 1:   3900        -9419.575             0.248            0.226
Chain 1:   4000        -9630.189             0.204            0.209
Chain 1:   4100       -14020.432             0.192            0.209
Chain 1:   4200       -10207.891             0.207            0.209
Chain 1:   4300        -9983.555             0.195            0.209
Chain 1:   4400       -11344.601             0.175            0.138
Chain 1:   4500        -9478.726             0.181            0.197
Chain 1:   4600       -11977.013             0.195            0.209
Chain 1:   4700        -9320.313             0.217            0.209
Chain 1:   4800       -14487.548             0.232            0.285
Chain 1:   4900        -9025.978             0.250            0.285
Chain 1:   5000        -9774.486             0.256            0.285
Chain 1:   5100        -8622.890             0.238            0.209
Chain 1:   5200        -8924.783             0.204            0.197
Chain 1:   5300       -14279.302             0.239            0.209
Chain 1:   5400        -9953.220             0.271            0.285
Chain 1:   5500       -11185.642             0.262            0.285
Chain 1:   5600        -8647.317             0.270            0.294
Chain 1:   5700        -9106.221             0.247            0.294
Chain 1:   5800        -8673.695             0.216            0.134
Chain 1:   5900        -9408.633             0.164            0.110
Chain 1:   6000        -9417.542             0.156            0.110
Chain 1:   6100        -8590.050             0.152            0.096
Chain 1:   6200        -8445.047             0.151            0.096
Chain 1:   6300        -9253.169             0.122            0.087
Chain 1:   6400       -11372.457             0.097            0.087
Chain 1:   6500       -10972.289             0.090            0.078
Chain 1:   6600        -8505.087             0.089            0.078
Chain 1:   6700        -8523.322             0.084            0.078
Chain 1:   6800       -13889.496             0.118            0.087
Chain 1:   6900       -13518.751             0.113            0.087
Chain 1:   7000       -12692.833             0.119            0.087
Chain 1:   7100        -9327.628             0.146            0.087
Chain 1:   7200        -8687.843             0.152            0.087
Chain 1:   7300       -11179.243             0.165            0.186
Chain 1:   7400        -8407.648             0.179            0.223
Chain 1:   7500        -8319.639             0.177            0.223
Chain 1:   7600        -8668.875             0.152            0.074
Chain 1:   7700       -12235.786             0.181            0.223
Chain 1:   7800       -10504.334             0.159            0.165
Chain 1:   7900       -11024.324             0.161            0.165
Chain 1:   8000        -9860.127             0.166            0.165
Chain 1:   8100       -11928.398             0.147            0.165
Chain 1:   8200        -9902.731             0.160            0.173
Chain 1:   8300        -8627.676             0.153            0.165
Chain 1:   8400       -10845.441             0.140            0.165
Chain 1:   8500        -8467.707             0.167            0.173
Chain 1:   8600        -8686.079             0.166            0.173
Chain 1:   8700        -8721.244             0.137            0.165
Chain 1:   8800        -8877.000             0.122            0.148
Chain 1:   8900        -9704.391             0.126            0.148
Chain 1:   9000        -8459.327             0.129            0.148
Chain 1:   9100        -8788.461             0.115            0.147
Chain 1:   9200       -10969.726             0.115            0.147
Chain 1:   9300        -8436.471             0.130            0.147
Chain 1:   9400        -9288.495             0.119            0.092
Chain 1:   9500        -8680.636             0.098            0.085
Chain 1:   9600        -9748.464             0.106            0.092
Chain 1:   9700       -11043.710             0.118            0.110
Chain 1:   9800        -9558.221             0.131            0.117
Chain 1:   9900       -10877.412             0.135            0.121
Chain 1:   10000        -8359.558             0.150            0.121
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001409 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57492.198             1.000            1.000
Chain 1:    200       -17723.470             1.622            2.244
Chain 1:    300        -8827.805             1.417            1.008
Chain 1:    400        -8172.972             1.083            1.008
Chain 1:    500        -8750.806             0.880            1.000
Chain 1:    600        -8137.178             0.746            1.000
Chain 1:    700        -8306.859             0.642            0.080
Chain 1:    800        -7876.454             0.569            0.080
Chain 1:    900        -7940.731             0.506            0.075
Chain 1:   1000        -7914.330             0.456            0.075
Chain 1:   1100        -7745.881             0.358            0.066
Chain 1:   1200        -7591.034             0.136            0.055
Chain 1:   1300        -7792.164             0.038            0.026
Chain 1:   1400        -7915.696             0.031            0.022
Chain 1:   1500        -7561.638             0.029            0.022
Chain 1:   1600        -7756.903             0.024            0.022
Chain 1:   1700        -7513.520             0.025            0.025
Chain 1:   1800        -7626.149             0.021            0.022
Chain 1:   1900        -7591.613             0.021            0.022
Chain 1:   2000        -7643.298             0.021            0.022
Chain 1:   2100        -7607.000             0.020            0.020
Chain 1:   2200        -7749.672             0.020            0.018
Chain 1:   2300        -7520.097             0.020            0.018
Chain 1:   2400        -7558.013             0.019            0.018
Chain 1:   2500        -7570.811             0.014            0.015
Chain 1:   2600        -7522.043             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003408 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86886.899             1.000            1.000
Chain 1:    200       -13704.357             3.170            5.340
Chain 1:    300       -10071.738             2.234            1.000
Chain 1:    400       -11053.807             1.697            1.000
Chain 1:    500        -8970.909             1.404            0.361
Chain 1:    600        -8626.124             1.177            0.361
Chain 1:    700        -8925.891             1.014            0.232
Chain 1:    800        -9472.464             0.894            0.232
Chain 1:    900        -8927.436             0.802            0.089
Chain 1:   1000        -8687.613             0.724            0.089
Chain 1:   1100        -8861.677             0.626            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8432.251             0.097            0.058
Chain 1:   1300        -8637.708             0.064            0.051
Chain 1:   1400        -8778.964             0.056            0.040
Chain 1:   1500        -8635.185             0.035            0.034
Chain 1:   1600        -8750.959             0.032            0.028
Chain 1:   1700        -8831.154             0.030            0.024
Chain 1:   1800        -8418.997             0.029            0.024
Chain 1:   1900        -8514.864             0.024            0.020
Chain 1:   2000        -8488.147             0.021            0.017
Chain 1:   2100        -8610.682             0.021            0.016
Chain 1:   2200        -8430.757             0.018            0.016
Chain 1:   2300        -8509.913             0.016            0.014
Chain 1:   2400        -8579.583             0.016            0.013
Chain 1:   2500        -8524.983             0.015            0.011
Chain 1:   2600        -8524.343             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003154 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407266.859             1.000            1.000
Chain 1:    200     -1584914.568             2.652            4.305
Chain 1:    300      -890363.473             2.028            1.000
Chain 1:    400      -457382.609             1.758            1.000
Chain 1:    500      -357802.754             1.462            0.947
Chain 1:    600      -232783.763             1.308            0.947
Chain 1:    700      -119227.783             1.257            0.947
Chain 1:    800       -86489.610             1.147            0.947
Chain 1:    900       -66873.019             1.052            0.780
Chain 1:   1000       -51702.380             0.976            0.780
Chain 1:   1100       -39207.578             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38387.966             0.480            0.379
Chain 1:   1300       -26374.810             0.448            0.379
Chain 1:   1400       -26096.008             0.354            0.319
Chain 1:   1500       -22691.887             0.341            0.319
Chain 1:   1600       -21910.779             0.291            0.293
Chain 1:   1700       -20788.236             0.201            0.293
Chain 1:   1800       -20733.337             0.164            0.150
Chain 1:   1900       -21059.472             0.136            0.054
Chain 1:   2000       -19572.687             0.114            0.054
Chain 1:   2100       -19810.898             0.083            0.036
Chain 1:   2200       -20037.060             0.082            0.036
Chain 1:   2300       -19654.555             0.039            0.019
Chain 1:   2400       -19426.728             0.039            0.019
Chain 1:   2500       -19228.592             0.025            0.015
Chain 1:   2600       -18858.969             0.023            0.015
Chain 1:   2700       -18815.966             0.018            0.012
Chain 1:   2800       -18532.805             0.019            0.015
Chain 1:   2900       -18814.011             0.019            0.015
Chain 1:   3000       -18800.186             0.012            0.012
Chain 1:   3100       -18885.178             0.011            0.012
Chain 1:   3200       -18575.908             0.012            0.015
Chain 1:   3300       -18780.604             0.011            0.012
Chain 1:   3400       -18255.575             0.012            0.015
Chain 1:   3500       -18867.309             0.015            0.015
Chain 1:   3600       -18174.174             0.016            0.015
Chain 1:   3700       -18560.844             0.018            0.017
Chain 1:   3800       -17520.781             0.023            0.021
Chain 1:   3900       -17516.920             0.021            0.021
Chain 1:   4000       -17634.244             0.022            0.021
Chain 1:   4100       -17548.018             0.022            0.021
Chain 1:   4200       -17364.311             0.021            0.021
Chain 1:   4300       -17502.686             0.021            0.021
Chain 1:   4400       -17459.554             0.018            0.011
Chain 1:   4500       -17362.091             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001398 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48581.918             1.000            1.000
Chain 1:    200       -28590.818             0.850            1.000
Chain 1:    300       -18600.000             0.745            0.699
Chain 1:    400       -13946.429             0.643            0.699
Chain 1:    500       -17364.861             0.553            0.537
Chain 1:    600       -11203.296             0.553            0.550
Chain 1:    700       -12397.778             0.488            0.537
Chain 1:    800       -13595.856             0.438            0.537
Chain 1:    900       -11202.815             0.413            0.334
Chain 1:   1000       -10265.315             0.381            0.334
Chain 1:   1100        -9734.677             0.286            0.214
Chain 1:   1200       -10429.039             0.223            0.197
Chain 1:   1300       -18305.556             0.212            0.197
Chain 1:   1400       -10156.728             0.259            0.197
Chain 1:   1500        -9132.323             0.251            0.112
Chain 1:   1600       -11176.606             0.214            0.112
Chain 1:   1700        -9531.013             0.221            0.173
Chain 1:   1800       -11493.409             0.230            0.173
Chain 1:   1900       -10575.103             0.217            0.171
Chain 1:   2000       -19659.056             0.254            0.173
Chain 1:   2100        -9771.396             0.350            0.183
Chain 1:   2200        -9790.584             0.343            0.183
Chain 1:   2300        -9256.242             0.306            0.173
Chain 1:   2400        -9011.685             0.229            0.171
Chain 1:   2500       -10542.401             0.232            0.171
Chain 1:   2600        -9509.922             0.224            0.145
Chain 1:   2700        -9033.333             0.212            0.109
Chain 1:   2800       -10030.173             0.205            0.099
Chain 1:   2900        -9367.882             0.204            0.099
Chain 1:   3000        -8540.972             0.167            0.097
Chain 1:   3100       -17443.907             0.117            0.097
Chain 1:   3200       -18114.314             0.121            0.097
Chain 1:   3300       -13506.155             0.149            0.099
Chain 1:   3400        -9180.588             0.193            0.109
Chain 1:   3500       -17299.406             0.226            0.109
Chain 1:   3600        -8547.721             0.317            0.341
Chain 1:   3700       -10345.300             0.329            0.341
Chain 1:   3800        -9834.908             0.325            0.341
Chain 1:   3900        -8559.040             0.332            0.341
Chain 1:   4000        -8651.891             0.324            0.341
Chain 1:   4100        -8394.874             0.276            0.174
Chain 1:   4200       -10148.912             0.289            0.174
Chain 1:   4300       -12965.128             0.277            0.174
Chain 1:   4400        -9584.531             0.265            0.174
Chain 1:   4500       -11826.092             0.237            0.174
Chain 1:   4600       -13525.310             0.147            0.173
Chain 1:   4700        -8321.433             0.193            0.173
Chain 1:   4800        -8490.462             0.189            0.173
Chain 1:   4900       -13706.928             0.213            0.190
Chain 1:   5000        -8734.839             0.268            0.217
Chain 1:   5100        -8390.578             0.269            0.217
Chain 1:   5200        -8952.087             0.258            0.217
Chain 1:   5300        -9069.755             0.238            0.190
Chain 1:   5400        -8332.279             0.212            0.126
Chain 1:   5500       -11072.891             0.217            0.126
Chain 1:   5600       -11075.754             0.205            0.089
Chain 1:   5700        -8433.866             0.174            0.089
Chain 1:   5800        -8522.256             0.173            0.089
Chain 1:   5900        -9989.954             0.149            0.089
Chain 1:   6000       -10554.960             0.098            0.063
Chain 1:   6100        -9049.636             0.110            0.089
Chain 1:   6200        -8185.469             0.115            0.106
Chain 1:   6300       -11766.626             0.144            0.147
Chain 1:   6400        -9335.587             0.161            0.166
Chain 1:   6500       -12539.165             0.162            0.166
Chain 1:   6600       -12802.506             0.164            0.166
Chain 1:   6700       -10314.771             0.156            0.166
Chain 1:   6800        -7920.482             0.186            0.241
Chain 1:   6900       -11237.582             0.200            0.255
Chain 1:   7000        -8208.979             0.232            0.260
Chain 1:   7100        -8017.860             0.218            0.260
Chain 1:   7200       -10405.807             0.230            0.260
Chain 1:   7300        -7897.811             0.231            0.260
Chain 1:   7400        -8502.653             0.213            0.255
Chain 1:   7500       -10678.571             0.207            0.241
Chain 1:   7600       -10203.400             0.210            0.241
Chain 1:   7700        -8365.973             0.208            0.229
Chain 1:   7800       -10269.973             0.196            0.220
Chain 1:   7900        -8528.753             0.187            0.204
Chain 1:   8000       -10352.024             0.168            0.204
Chain 1:   8100        -7909.283             0.196            0.204
Chain 1:   8200        -8098.845             0.176            0.204
Chain 1:   8300        -7967.087             0.146            0.185
Chain 1:   8400        -8016.234             0.139            0.185
Chain 1:   8500        -8915.252             0.129            0.176
Chain 1:   8600        -9455.389             0.130            0.176
Chain 1:   8700        -9621.212             0.110            0.101
Chain 1:   8800        -9822.564             0.093            0.057
Chain 1:   8900       -12004.711             0.091            0.057
Chain 1:   9000        -9571.310             0.099            0.057
Chain 1:   9100        -8854.538             0.076            0.057
Chain 1:   9200        -8395.047             0.079            0.057
Chain 1:   9300        -7906.729             0.084            0.062
Chain 1:   9400        -8414.494             0.089            0.062
Chain 1:   9500        -8292.938             0.080            0.060
Chain 1:   9600        -7961.536             0.079            0.060
Chain 1:   9700        -8220.341             0.080            0.060
Chain 1:   9800        -8523.773             0.082            0.060
Chain 1:   9900        -8737.608             0.066            0.055
Chain 1:   10000        -8826.981             0.042            0.042
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001469 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50821.989             1.000            1.000
Chain 1:    200       -15951.624             1.593            2.186
Chain 1:    300        -8533.555             1.352            1.000
Chain 1:    400        -8190.951             1.024            1.000
Chain 1:    500        -8026.031             0.824            0.869
Chain 1:    600        -8483.629             0.695            0.869
Chain 1:    700        -7622.010             0.612            0.113
Chain 1:    800        -8088.740             0.543            0.113
Chain 1:    900        -7838.272             0.486            0.058
Chain 1:   1000        -7712.526             0.439            0.058
Chain 1:   1100        -7523.383             0.342            0.054
Chain 1:   1200        -7494.728             0.123            0.042
Chain 1:   1300        -7639.444             0.038            0.032
Chain 1:   1400        -7727.441             0.035            0.025
Chain 1:   1500        -7519.172             0.036            0.028
Chain 1:   1600        -7660.738             0.032            0.025
Chain 1:   1700        -7408.426             0.025            0.025
Chain 1:   1800        -7495.674             0.020            0.019
Chain 1:   1900        -7515.926             0.017            0.018
Chain 1:   2000        -7543.372             0.016            0.018
Chain 1:   2100        -7500.675             0.014            0.012
Chain 1:   2200        -7582.065             0.014            0.012
Chain 1:   2300        -7499.704             0.014            0.011
Chain 1:   2400        -7526.498             0.013            0.011
Chain 1:   2500        -7404.744             0.012            0.011
Chain 1:   2600        -7443.226             0.010            0.011
Chain 1:   2700        -7485.679             0.008            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002948 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85681.618             1.000            1.000
Chain 1:    200       -13167.056             3.254            5.507
Chain 1:    300        -9614.960             2.292            1.000
Chain 1:    400       -10414.573             1.738            1.000
Chain 1:    500        -8514.507             1.435            0.369
Chain 1:    600        -8160.371             1.203            0.369
Chain 1:    700        -8080.261             1.033            0.223
Chain 1:    800        -8697.550             0.913            0.223
Chain 1:    900        -8431.554             0.815            0.077
Chain 1:   1000        -8239.182             0.736            0.077
Chain 1:   1100        -8516.808             0.639            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8023.964             0.094            0.061
Chain 1:   1300        -8343.147             0.061            0.043
Chain 1:   1400        -8333.567             0.054            0.038
Chain 1:   1500        -8210.639             0.033            0.033
Chain 1:   1600        -8315.071             0.030            0.032
Chain 1:   1700        -8400.491             0.030            0.032
Chain 1:   1800        -8007.004             0.028            0.032
Chain 1:   1900        -8108.700             0.026            0.023
Chain 1:   2000        -8079.070             0.024            0.015
Chain 1:   2100        -8203.205             0.022            0.015
Chain 1:   2200        -7986.926             0.018            0.015
Chain 1:   2300        -8137.369             0.016            0.015
Chain 1:   2400        -8151.995             0.017            0.015
Chain 1:   2500        -8120.161             0.015            0.013
Chain 1:   2600        -8122.445             0.014            0.013
Chain 1:   2700        -8028.931             0.014            0.013
Chain 1:   2800        -8000.919             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002925 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399993.433             1.000            1.000
Chain 1:    200     -1583975.834             2.652            4.303
Chain 1:    300      -890574.588             2.027            1.000
Chain 1:    400      -457381.891             1.757            1.000
Chain 1:    500      -357778.655             1.461            0.947
Chain 1:    600      -232756.132             1.307            0.947
Chain 1:    700      -118922.977             1.257            0.947
Chain 1:    800       -86127.124             1.148            0.947
Chain 1:    900       -66456.289             1.053            0.779
Chain 1:   1000       -51243.349             0.978            0.779
Chain 1:   1100       -38714.975             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37885.345             0.482            0.381
Chain 1:   1300       -25844.783             0.450            0.381
Chain 1:   1400       -25561.427             0.357            0.324
Chain 1:   1500       -22150.215             0.344            0.324
Chain 1:   1600       -21366.282             0.294            0.297
Chain 1:   1700       -20241.012             0.204            0.296
Chain 1:   1800       -20185.033             0.166            0.154
Chain 1:   1900       -20510.764             0.138            0.056
Chain 1:   2000       -19023.178             0.117            0.056
Chain 1:   2100       -19261.404             0.085            0.037
Chain 1:   2200       -19487.568             0.084            0.037
Chain 1:   2300       -19105.138             0.040            0.020
Chain 1:   2400       -18877.398             0.040            0.020
Chain 1:   2500       -18679.395             0.026            0.016
Chain 1:   2600       -18310.039             0.024            0.016
Chain 1:   2700       -18267.103             0.019            0.012
Chain 1:   2800       -17984.180             0.020            0.016
Chain 1:   2900       -18265.211             0.020            0.015
Chain 1:   3000       -18251.426             0.012            0.012
Chain 1:   3100       -18336.351             0.011            0.012
Chain 1:   3200       -18027.302             0.012            0.015
Chain 1:   3300       -18231.796             0.011            0.012
Chain 1:   3400       -17707.222             0.013            0.015
Chain 1:   3500       -18318.355             0.015            0.016
Chain 1:   3600       -17626.006             0.017            0.016
Chain 1:   3700       -18012.096             0.019            0.017
Chain 1:   3800       -16973.305             0.023            0.021
Chain 1:   3900       -16969.482             0.022            0.021
Chain 1:   4000       -17086.780             0.022            0.021
Chain 1:   4100       -17000.640             0.023            0.021
Chain 1:   4200       -16817.188             0.022            0.021
Chain 1:   4300       -16955.367             0.022            0.021
Chain 1:   4400       -16912.459             0.019            0.011
Chain 1:   4500       -16815.048             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001445 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12540.350             1.000            1.000
Chain 1:    200        -9358.415             0.670            1.000
Chain 1:    300        -8045.762             0.501            0.340
Chain 1:    400        -8271.399             0.383            0.340
Chain 1:    500        -7945.542             0.314            0.163
Chain 1:    600        -8020.276             0.263            0.163
Chain 1:    700        -7942.149             0.227            0.041
Chain 1:    800        -7957.597             0.199            0.041
Chain 1:    900        -7880.201             0.178            0.027
Chain 1:   1000        -8001.549             0.162            0.027
Chain 1:   1100        -7985.692             0.062            0.015
Chain 1:   1200        -7965.985             0.028            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001465 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51951.180             1.000            1.000
Chain 1:    200       -16510.226             1.573            2.147
Chain 1:    300        -8797.955             1.341            1.000
Chain 1:    400        -8006.614             1.031            1.000
Chain 1:    500        -8113.594             0.827            0.877
Chain 1:    600        -8144.910             0.690            0.877
Chain 1:    700        -7942.339             0.595            0.099
Chain 1:    800        -8289.712             0.526            0.099
Chain 1:    900        -7795.781             0.474            0.063
Chain 1:   1000        -7994.707             0.429            0.063
Chain 1:   1100        -7721.898             0.333            0.042
Chain 1:   1200        -7623.192             0.120            0.035
Chain 1:   1300        -7832.601             0.035            0.027
Chain 1:   1400        -7734.178             0.026            0.026
Chain 1:   1500        -7586.855             0.027            0.026
Chain 1:   1600        -7776.636             0.029            0.026
Chain 1:   1700        -7508.712             0.030            0.027
Chain 1:   1800        -7645.230             0.027            0.025
Chain 1:   1900        -7572.743             0.022            0.024
Chain 1:   2000        -7608.901             0.020            0.019
Chain 1:   2100        -7602.498             0.016            0.018
Chain 1:   2200        -7717.145             0.017            0.018
Chain 1:   2300        -7604.284             0.015            0.015
Chain 1:   2400        -7654.139             0.015            0.015
Chain 1:   2500        -7570.036             0.014            0.015
Chain 1:   2600        -7530.587             0.012            0.011
Chain 1:   2700        -7523.320             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002953 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86446.278             1.000            1.000
Chain 1:    200       -13636.598             3.170            5.339
Chain 1:    300        -9974.069             2.235            1.000
Chain 1:    400       -10988.017             1.700            1.000
Chain 1:    500        -8953.106             1.405            0.367
Chain 1:    600        -8913.212             1.172            0.367
Chain 1:    700        -8466.861             1.012            0.227
Chain 1:    800        -8724.190             0.889            0.227
Chain 1:    900        -8699.827             0.791            0.092
Chain 1:   1000        -8620.367             0.712            0.092
Chain 1:   1100        -8795.493             0.614            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8404.085             0.085            0.047
Chain 1:   1300        -8644.835             0.051            0.029
Chain 1:   1400        -8668.565             0.042            0.028
Chain 1:   1500        -8511.106             0.021            0.020
Chain 1:   1600        -8625.818             0.022            0.020
Chain 1:   1700        -8700.022             0.018            0.019
Chain 1:   1800        -8274.960             0.020            0.019
Chain 1:   1900        -8376.934             0.021            0.019
Chain 1:   2000        -8351.563             0.020            0.019
Chain 1:   2100        -8477.854             0.020            0.015
Chain 1:   2200        -8278.532             0.018            0.015
Chain 1:   2300        -8371.895             0.016            0.013
Chain 1:   2400        -8440.334             0.017            0.013
Chain 1:   2500        -8386.567             0.015            0.012
Chain 1:   2600        -8388.439             0.014            0.011
Chain 1:   2700        -8304.935             0.014            0.011
Chain 1:   2800        -8264.138             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003098 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398299.503             1.000            1.000
Chain 1:    200     -1585919.674             2.648            4.296
Chain 1:    300      -892030.931             2.024            1.000
Chain 1:    400      -458108.736             1.755            1.000
Chain 1:    500      -358290.082             1.460            0.947
Chain 1:    600      -233168.636             1.306            0.947
Chain 1:    700      -119391.973             1.256            0.947
Chain 1:    800       -86583.679             1.146            0.947
Chain 1:    900       -66929.732             1.051            0.778
Chain 1:   1000       -51732.987             0.976            0.778
Chain 1:   1100       -39208.131             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38387.399             0.480            0.379
Chain 1:   1300       -26338.835             0.448            0.379
Chain 1:   1400       -26058.149             0.354            0.319
Chain 1:   1500       -22643.503             0.342            0.319
Chain 1:   1600       -21859.670             0.291            0.294
Chain 1:   1700       -20732.726             0.202            0.294
Chain 1:   1800       -20676.889             0.164            0.151
Chain 1:   1900       -21003.134             0.136            0.054
Chain 1:   2000       -19513.698             0.114            0.054
Chain 1:   2100       -19752.127             0.084            0.036
Chain 1:   2200       -19978.668             0.083            0.036
Chain 1:   2300       -19595.791             0.039            0.020
Chain 1:   2400       -19367.823             0.039            0.020
Chain 1:   2500       -19169.803             0.025            0.016
Chain 1:   2600       -18799.842             0.023            0.016
Chain 1:   2700       -18756.854             0.018            0.012
Chain 1:   2800       -18473.564             0.019            0.015
Chain 1:   2900       -18754.929             0.019            0.015
Chain 1:   3000       -18741.143             0.012            0.012
Chain 1:   3100       -18826.109             0.011            0.012
Chain 1:   3200       -18516.717             0.012            0.015
Chain 1:   3300       -18721.542             0.011            0.012
Chain 1:   3400       -18196.256             0.012            0.015
Chain 1:   3500       -18808.406             0.015            0.015
Chain 1:   3600       -18114.822             0.017            0.015
Chain 1:   3700       -18501.746             0.018            0.017
Chain 1:   3800       -17461.007             0.023            0.021
Chain 1:   3900       -17457.165             0.021            0.021
Chain 1:   4000       -17574.463             0.022            0.021
Chain 1:   4100       -17488.129             0.022            0.021
Chain 1:   4200       -17304.364             0.021            0.021
Chain 1:   4300       -17442.794             0.021            0.021
Chain 1:   4400       -17399.525             0.018            0.011
Chain 1:   4500       -17302.065             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001427 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12858.626             1.000            1.000
Chain 1:    200        -9465.530             0.679            1.000
Chain 1:    300        -8260.147             0.501            0.358
Chain 1:    400        -8429.043             0.381            0.358
Chain 1:    500        -8072.588             0.314            0.146
Chain 1:    600        -8136.373             0.263            0.146
Chain 1:    700        -8044.052             0.227            0.044
Chain 1:    800        -8066.903             0.199            0.044
Chain 1:    900        -8092.548             0.177            0.020
Chain 1:   1000        -8106.299             0.160            0.020
Chain 1:   1100        -8186.922             0.061            0.011
Chain 1:   1200        -8061.982             0.026            0.011
Chain 1:   1300        -8006.584             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001409 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55313.331             1.000            1.000
Chain 1:    200       -17678.594             1.564            2.129
Chain 1:    300        -8952.143             1.368            1.000
Chain 1:    400        -8533.366             1.038            1.000
Chain 1:    500        -8844.419             0.838            0.975
Chain 1:    600        -8661.256             0.702            0.975
Chain 1:    700        -8504.646             0.604            0.049
Chain 1:    800        -8200.122             0.533            0.049
Chain 1:    900        -8401.253             0.477            0.037
Chain 1:   1000        -7808.336             0.436            0.049
Chain 1:   1100        -7746.879             0.337            0.037
Chain 1:   1200        -7935.809             0.127            0.035
Chain 1:   1300        -7885.590             0.030            0.024
Chain 1:   1400        -7924.166             0.025            0.024
Chain 1:   1500        -7563.370             0.027            0.024
Chain 1:   1600        -7844.712             0.028            0.024
Chain 1:   1700        -7573.412             0.030            0.036
Chain 1:   1800        -7614.680             0.027            0.024
Chain 1:   1900        -7590.326             0.025            0.024
Chain 1:   2000        -7667.495             0.018            0.010
Chain 1:   2100        -7589.190             0.018            0.010
Chain 1:   2200        -7791.057             0.019            0.010
Chain 1:   2300        -7562.572             0.021            0.026
Chain 1:   2400        -7548.585             0.021            0.026
Chain 1:   2500        -7592.750             0.016            0.010
Chain 1:   2600        -7557.751             0.013            0.010
Chain 1:   2700        -7474.925             0.011            0.010
Chain 1:   2800        -7654.148             0.013            0.010
Chain 1:   2900        -7397.856             0.016            0.011
Chain 1:   3000        -7561.383             0.017            0.022
Chain 1:   3100        -7547.452             0.016            0.022
Chain 1:   3200        -7767.371             0.016            0.022
Chain 1:   3300        -7469.376             0.017            0.022
Chain 1:   3400        -7725.543             0.020            0.023
Chain 1:   3500        -7466.327             0.023            0.028
Chain 1:   3600        -7524.417             0.024            0.028
Chain 1:   3700        -7481.301             0.023            0.028
Chain 1:   3800        -7489.258             0.021            0.028
Chain 1:   3900        -7453.712             0.018            0.022
Chain 1:   4000        -7425.290             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003024 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86467.375             1.000            1.000
Chain 1:    200       -14002.085             3.088            5.175
Chain 1:    300       -10228.668             2.181            1.000
Chain 1:    400       -11877.434             1.671            1.000
Chain 1:    500        -8954.375             1.402            0.369
Chain 1:    600        -9302.263             1.174            0.369
Chain 1:    700        -8749.942             1.016            0.326
Chain 1:    800        -8420.480             0.894            0.326
Chain 1:    900        -8442.472             0.795            0.139
Chain 1:   1000        -8610.285             0.717            0.139
Chain 1:   1100        -8933.054             0.621            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8517.181             0.108            0.049
Chain 1:   1300        -8789.209             0.074            0.039
Chain 1:   1400        -8788.707             0.060            0.037
Chain 1:   1500        -8693.307             0.029            0.036
Chain 1:   1600        -8745.524             0.026            0.031
Chain 1:   1700        -8838.408             0.020            0.019
Chain 1:   1800        -8399.296             0.022            0.019
Chain 1:   1900        -8495.995             0.023            0.019
Chain 1:   2000        -8506.259             0.021            0.011
Chain 1:   2100        -8601.895             0.018            0.011
Chain 1:   2200        -8386.072             0.016            0.011
Chain 1:   2300        -8547.533             0.015            0.011
Chain 1:   2400        -8392.389             0.017            0.011
Chain 1:   2500        -8467.000             0.016            0.011
Chain 1:   2600        -8377.773             0.017            0.011
Chain 1:   2700        -8412.056             0.016            0.011
Chain 1:   2800        -8363.288             0.012            0.011
Chain 1:   2900        -8477.399             0.012            0.011
Chain 1:   3000        -8394.971             0.013            0.011
Chain 1:   3100        -8355.562             0.012            0.011
Chain 1:   3200        -8327.973             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003094 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393620.824             1.000            1.000
Chain 1:    200     -1585766.479             2.647            4.293
Chain 1:    300      -891882.415             2.024            1.000
Chain 1:    400      -458682.716             1.754            1.000
Chain 1:    500      -358901.082             1.459            0.944
Chain 1:    600      -233823.019             1.305            0.944
Chain 1:    700      -119891.859             1.254            0.944
Chain 1:    800       -87081.052             1.144            0.944
Chain 1:    900       -67402.659             1.050            0.778
Chain 1:   1000       -52194.167             0.974            0.778
Chain 1:   1100       -39658.814             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38841.237             0.478            0.377
Chain 1:   1300       -26769.576             0.446            0.377
Chain 1:   1400       -26490.809             0.352            0.316
Chain 1:   1500       -23069.662             0.339            0.316
Chain 1:   1600       -22285.062             0.289            0.292
Chain 1:   1700       -21154.409             0.200            0.291
Chain 1:   1800       -21098.220             0.162            0.148
Chain 1:   1900       -21425.080             0.134            0.053
Chain 1:   2000       -19932.541             0.113            0.053
Chain 1:   2100       -20171.288             0.082            0.035
Chain 1:   2200       -20398.532             0.081            0.035
Chain 1:   2300       -20014.803             0.038            0.019
Chain 1:   2400       -19786.510             0.038            0.019
Chain 1:   2500       -19588.646             0.025            0.015
Chain 1:   2600       -19217.937             0.023            0.015
Chain 1:   2700       -19174.678             0.018            0.012
Chain 1:   2800       -18891.171             0.019            0.015
Chain 1:   2900       -19172.835             0.019            0.015
Chain 1:   3000       -19159.006             0.012            0.012
Chain 1:   3100       -19244.089             0.011            0.012
Chain 1:   3200       -18934.246             0.011            0.015
Chain 1:   3300       -19139.391             0.011            0.012
Chain 1:   3400       -18613.364             0.012            0.015
Chain 1:   3500       -19226.720             0.014            0.015
Chain 1:   3600       -18531.477             0.016            0.015
Chain 1:   3700       -18919.651             0.018            0.016
Chain 1:   3800       -17876.450             0.022            0.021
Chain 1:   3900       -17872.518             0.021            0.021
Chain 1:   4000       -17989.824             0.021            0.021
Chain 1:   4100       -17903.401             0.022            0.021
Chain 1:   4200       -17719.036             0.021            0.021
Chain 1:   4300       -17857.869             0.021            0.021
Chain 1:   4400       -17814.142             0.018            0.010
Chain 1:   4500       -17716.582             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13471.172             1.000            1.000
Chain 1:    200       -10288.413             0.655            1.000
Chain 1:    300        -8815.331             0.492            0.309
Chain 1:    400        -9025.919             0.375            0.309
Chain 1:    500        -8568.033             0.311            0.167
Chain 1:    600        -8709.751             0.262            0.167
Chain 1:    700        -8701.251             0.224            0.053
Chain 1:    800        -8667.344             0.197            0.053
Chain 1:    900        -8697.207             0.175            0.023
Chain 1:   1000        -8710.783             0.158            0.023
Chain 1:   1100        -8711.565             0.058            0.016
Chain 1:   1200        -8629.223             0.028            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001585 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47286.080             1.000            1.000
Chain 1:    200       -16601.612             1.424            1.848
Chain 1:    300        -9271.511             1.213            1.000
Chain 1:    400        -8304.873             0.939            1.000
Chain 1:    500        -8849.983             0.763            0.791
Chain 1:    600        -9057.915             0.640            0.791
Chain 1:    700        -7912.180             0.569            0.145
Chain 1:    800        -8466.828             0.506            0.145
Chain 1:    900        -8333.014             0.452            0.116
Chain 1:   1000        -7641.524             0.416            0.116
Chain 1:   1100        -7885.211             0.319            0.090
Chain 1:   1200        -8104.492             0.137            0.066
Chain 1:   1300        -7833.076             0.061            0.062
Chain 1:   1400        -7572.933             0.053            0.035
Chain 1:   1500        -7725.677             0.049            0.034
Chain 1:   1600        -7764.671             0.047            0.034
Chain 1:   1700        -7424.519             0.037            0.034
Chain 1:   1800        -7659.722             0.033            0.031
Chain 1:   1900        -7550.489             0.033            0.031
Chain 1:   2000        -8003.684             0.030            0.031
Chain 1:   2100        -7687.886             0.031            0.034
Chain 1:   2200        -7606.553             0.029            0.034
Chain 1:   2300        -7710.993             0.027            0.031
Chain 1:   2400        -7700.188             0.024            0.020
Chain 1:   2500        -7636.952             0.023            0.014
Chain 1:   2600        -7593.038             0.023            0.014
Chain 1:   2700        -7597.643             0.018            0.014
Chain 1:   2800        -7575.840             0.016            0.011
Chain 1:   2900        -7418.425             0.016            0.011
Chain 1:   3000        -7581.379             0.013            0.011
Chain 1:   3100        -7563.173             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003065 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87809.522             1.000            1.000
Chain 1:    200       -14628.051             3.001            5.003
Chain 1:    300       -10816.148             2.118            1.000
Chain 1:    400       -12780.980             1.627            1.000
Chain 1:    500        -9229.270             1.379            0.385
Chain 1:    600        -9155.235             1.150            0.385
Chain 1:    700        -9329.040             0.989            0.352
Chain 1:    800        -9380.616             0.866            0.352
Chain 1:    900        -9480.139             0.771            0.154
Chain 1:   1000        -9370.963             0.695            0.154
Chain 1:   1100        -9533.065             0.597            0.019   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9104.433             0.101            0.019
Chain 1:   1300        -9451.742             0.069            0.019
Chain 1:   1400        -9353.813             0.055            0.017
Chain 1:   1500        -9297.597             0.017            0.012
Chain 1:   1600        -9332.880             0.017            0.012
Chain 1:   1700        -9413.777             0.016            0.010
Chain 1:   1800        -8970.368             0.020            0.012
Chain 1:   1900        -9070.072             0.020            0.012
Chain 1:   2000        -9089.467             0.019            0.011
Chain 1:   2100        -9175.276             0.018            0.010
Chain 1:   2200        -8955.657             0.016            0.010
Chain 1:   2300        -9153.217             0.015            0.010
Chain 1:   2400        -8966.573             0.016            0.011
Chain 1:   2500        -9040.659             0.016            0.011
Chain 1:   2600        -8951.319             0.017            0.011
Chain 1:   2700        -8985.012             0.016            0.011
Chain 1:   2800        -8935.845             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003053 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399489.241             1.000            1.000
Chain 1:    200     -1585560.024             2.649            4.297
Chain 1:    300      -892512.495             2.025            1.000
Chain 1:    400      -459116.886             1.754            1.000
Chain 1:    500      -359572.595             1.459            0.944
Chain 1:    600      -234474.441             1.305            0.944
Chain 1:    700      -120544.124             1.253            0.944
Chain 1:    800       -87714.120             1.143            0.944
Chain 1:    900       -68038.291             1.049            0.777
Chain 1:   1000       -52824.713             0.972            0.777
Chain 1:   1100       -40280.270             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39463.625             0.476            0.374
Chain 1:   1300       -27383.652             0.442            0.374
Chain 1:   1400       -27103.601             0.349            0.311
Chain 1:   1500       -23680.706             0.336            0.311
Chain 1:   1600       -22895.164             0.286            0.289
Chain 1:   1700       -21763.855             0.197            0.288
Chain 1:   1800       -21707.330             0.159            0.145
Chain 1:   1900       -22034.358             0.132            0.052
Chain 1:   2000       -20541.164             0.110            0.052
Chain 1:   2100       -20779.895             0.080            0.034
Chain 1:   2200       -21007.314             0.079            0.034
Chain 1:   2300       -20623.438             0.037            0.019
Chain 1:   2400       -20395.160             0.037            0.019
Chain 1:   2500       -20197.228             0.024            0.015
Chain 1:   2600       -19826.466             0.022            0.015
Chain 1:   2700       -19783.148             0.017            0.011
Chain 1:   2800       -19499.649             0.018            0.015
Chain 1:   2900       -19781.369             0.018            0.014
Chain 1:   3000       -19767.491             0.011            0.011
Chain 1:   3100       -19852.595             0.011            0.011
Chain 1:   3200       -19542.671             0.011            0.014
Chain 1:   3300       -19747.875             0.010            0.011
Chain 1:   3400       -19221.728             0.012            0.014
Chain 1:   3500       -19835.218             0.014            0.015
Chain 1:   3600       -19139.835             0.016            0.015
Chain 1:   3700       -19528.190             0.017            0.016
Chain 1:   3800       -18484.647             0.022            0.020
Chain 1:   3900       -18480.709             0.020            0.020
Chain 1:   4000       -18598.029             0.021            0.020
Chain 1:   4100       -18511.609             0.021            0.020
Chain 1:   4200       -18327.147             0.020            0.020
Chain 1:   4300       -18466.040             0.020            0.020
Chain 1:   4400       -18422.282             0.017            0.010
Chain 1:   4500       -18324.712             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001157 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12295.611             1.000            1.000
Chain 1:    200        -9209.372             0.668            1.000
Chain 1:    300        -8056.654             0.493            0.335
Chain 1:    400        -8202.166             0.374            0.335
Chain 1:    500        -8097.383             0.302            0.143
Chain 1:    600        -7979.816             0.254            0.143
Chain 1:    700        -7908.844             0.219            0.018
Chain 1:    800        -7919.726             0.192            0.018
Chain 1:    900        -7820.503             0.172            0.015
Chain 1:   1000        -8012.067             0.157            0.018
Chain 1:   1100        -7966.286             0.058            0.015
Chain 1:   1200        -7952.966             0.024            0.013
Chain 1:   1300        -7880.616             0.011            0.013
Chain 1:   1400        -7904.422             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001475 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51069.337             1.000            1.000
Chain 1:    200       -16128.647             1.583            2.166
Chain 1:    300        -8625.474             1.345            1.000
Chain 1:    400        -8238.302             1.021            1.000
Chain 1:    500        -8196.926             0.818            0.870
Chain 1:    600        -8437.278             0.686            0.870
Chain 1:    700        -7738.284             0.601            0.090
Chain 1:    800        -8072.798             0.531            0.090
Chain 1:    900        -8005.925             0.473            0.047
Chain 1:   1000        -7630.716             0.431            0.049
Chain 1:   1100        -7659.410             0.331            0.047
Chain 1:   1200        -7689.162             0.115            0.041
Chain 1:   1300        -7588.958             0.029            0.028
Chain 1:   1400        -7731.081             0.026            0.018
Chain 1:   1500        -7541.270             0.028            0.025
Chain 1:   1600        -7688.750             0.027            0.019
Chain 1:   1700        -7426.887             0.022            0.019
Chain 1:   1800        -7506.670             0.019            0.018
Chain 1:   1900        -7490.686             0.018            0.018
Chain 1:   2000        -7521.471             0.014            0.013
Chain 1:   2100        -7507.691             0.013            0.013
Chain 1:   2200        -7615.611             0.014            0.014
Chain 1:   2300        -7524.329             0.014            0.014
Chain 1:   2400        -7560.316             0.013            0.012
Chain 1:   2500        -7556.624             0.010            0.011
Chain 1:   2600        -7465.328             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003052 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86054.973             1.000            1.000
Chain 1:    200       -13357.747             3.221            5.442
Chain 1:    300        -9799.831             2.268            1.000
Chain 1:    400       -10531.358             1.719            1.000
Chain 1:    500        -8698.466             1.417            0.363
Chain 1:    600        -8400.797             1.187            0.363
Chain 1:    700        -8450.904             1.018            0.211
Chain 1:    800        -8619.243             0.893            0.211
Chain 1:    900        -8674.811             0.795            0.069
Chain 1:   1000        -8391.480             0.719            0.069
Chain 1:   1100        -8707.873             0.622            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8428.117             0.081            0.035
Chain 1:   1300        -8554.649             0.047            0.034
Chain 1:   1400        -8460.105             0.041            0.033
Chain 1:   1500        -8407.978             0.020            0.020
Chain 1:   1600        -8395.479             0.017            0.015
Chain 1:   1700        -8322.364             0.017            0.015
Chain 1:   1800        -8209.434             0.017            0.014
Chain 1:   1900        -8326.878             0.017            0.014
Chain 1:   2000        -8287.255             0.014            0.014
Chain 1:   2100        -8415.967             0.012            0.014
Chain 1:   2200        -8206.845             0.012            0.014
Chain 1:   2300        -8349.496             0.012            0.014
Chain 1:   2400        -8363.630             0.011            0.014
Chain 1:   2500        -8331.528             0.011            0.014
Chain 1:   2600        -8330.761             0.010            0.014
Chain 1:   2700        -8238.786             0.011            0.014
Chain 1:   2800        -8213.710             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003262 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414958.887             1.000            1.000
Chain 1:    200     -1586071.962             2.653            4.306
Chain 1:    300      -891105.333             2.028            1.000
Chain 1:    400      -457586.979             1.758            1.000
Chain 1:    500      -357645.481             1.462            0.947
Chain 1:    600      -232750.253             1.308            0.947
Chain 1:    700      -118993.588             1.258            0.947
Chain 1:    800       -86224.257             1.148            0.947
Chain 1:    900       -66574.533             1.053            0.780
Chain 1:   1000       -51377.044             0.978            0.780
Chain 1:   1100       -38864.746             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38036.666             0.481            0.380
Chain 1:   1300       -26011.161             0.450            0.380
Chain 1:   1400       -25730.045             0.356            0.322
Chain 1:   1500       -22322.536             0.343            0.322
Chain 1:   1600       -21539.728             0.293            0.296
Chain 1:   1700       -20416.185             0.203            0.295
Chain 1:   1800       -20360.581             0.165            0.153
Chain 1:   1900       -20686.231             0.138            0.055
Chain 1:   2000       -19199.594             0.116            0.055
Chain 1:   2100       -19437.833             0.085            0.036
Chain 1:   2200       -19663.814             0.084            0.036
Chain 1:   2300       -19281.553             0.039            0.020
Chain 1:   2400       -19053.829             0.040            0.020
Chain 1:   2500       -18855.812             0.025            0.016
Chain 1:   2600       -18486.610             0.024            0.016
Chain 1:   2700       -18443.681             0.018            0.012
Chain 1:   2800       -18160.805             0.020            0.016
Chain 1:   2900       -18441.743             0.020            0.015
Chain 1:   3000       -18428.018             0.012            0.012
Chain 1:   3100       -18512.944             0.011            0.012
Chain 1:   3200       -18203.949             0.012            0.015
Chain 1:   3300       -18408.366             0.011            0.012
Chain 1:   3400       -17883.929             0.013            0.015
Chain 1:   3500       -18494.858             0.015            0.016
Chain 1:   3600       -17802.706             0.017            0.016
Chain 1:   3700       -18188.670             0.019            0.017
Chain 1:   3800       -17150.209             0.023            0.021
Chain 1:   3900       -17146.361             0.022            0.021
Chain 1:   4000       -17263.686             0.022            0.021
Chain 1:   4100       -17177.581             0.022            0.021
Chain 1:   4200       -16994.163             0.022            0.021
Chain 1:   4300       -17132.320             0.021            0.021
Chain 1:   4400       -17089.467             0.019            0.011
Chain 1:   4500       -16992.035             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001285 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49423.680             1.000            1.000
Chain 1:    200       -25069.587             0.986            1.000
Chain 1:    300       -35042.348             0.752            0.971
Chain 1:    400       -20149.693             0.749            0.971
Chain 1:    500       -13991.331             0.687            0.739
Chain 1:    600       -26092.591             0.650            0.739
Chain 1:    700       -11960.788             0.726            0.739
Chain 1:    800       -13207.655             0.647            0.739
Chain 1:    900       -13372.198             0.576            0.464
Chain 1:   1000       -13618.507             0.521            0.464
Chain 1:   1100       -10920.790             0.445            0.440
Chain 1:   1200       -10658.035             0.351            0.285
Chain 1:   1300       -10710.976             0.323            0.247
Chain 1:   1400       -10509.112             0.251            0.094
Chain 1:   1500       -10539.857             0.207            0.025
Chain 1:   1600       -12196.160             0.174            0.025
Chain 1:   1700       -12775.381             0.060            0.025
Chain 1:   1800       -10605.380             0.071            0.025
Chain 1:   1900       -11196.061             0.076            0.045
Chain 1:   2000       -12252.608             0.082            0.053
Chain 1:   2100        -9494.265             0.087            0.053
Chain 1:   2200        -9721.190             0.087            0.053
Chain 1:   2300        -9270.672             0.091            0.053
Chain 1:   2400       -20296.833             0.143            0.086
Chain 1:   2500       -10087.329             0.244            0.136
Chain 1:   2600        -9411.787             0.238            0.086
Chain 1:   2700       -10641.928             0.245            0.116
Chain 1:   2800       -10951.343             0.227            0.086
Chain 1:   2900       -10391.799             0.227            0.086
Chain 1:   3000        -9166.444             0.232            0.116
Chain 1:   3100        -9834.995             0.210            0.072
Chain 1:   3200       -13135.048             0.233            0.116
Chain 1:   3300       -10582.902             0.252            0.134
Chain 1:   3400       -17388.710             0.237            0.134
Chain 1:   3500        -9632.127             0.216            0.134
Chain 1:   3600       -10508.905             0.217            0.134
Chain 1:   3700        -9185.653             0.220            0.144
Chain 1:   3800       -15213.655             0.257            0.241
Chain 1:   3900       -15137.880             0.252            0.241
Chain 1:   4000       -10292.462             0.286            0.251
Chain 1:   4100       -10065.814             0.281            0.251
Chain 1:   4200       -10146.148             0.257            0.241
Chain 1:   4300       -11394.370             0.244            0.144
Chain 1:   4400       -10172.238             0.216            0.120
Chain 1:   4500        -9777.719             0.140            0.110
Chain 1:   4600        -9152.330             0.138            0.110
Chain 1:   4700       -13791.367             0.158            0.110
Chain 1:   4800       -12452.585             0.129            0.108
Chain 1:   4900        -8846.275             0.169            0.110
Chain 1:   5000       -11787.895             0.147            0.110
Chain 1:   5100        -8650.208             0.181            0.120
Chain 1:   5200        -8876.299             0.183            0.120
Chain 1:   5300        -8553.092             0.176            0.120
Chain 1:   5400       -13526.934             0.200            0.250
Chain 1:   5500        -9423.920             0.240            0.336
Chain 1:   5600        -8732.624             0.241            0.336
Chain 1:   5700       -18036.006             0.259            0.363
Chain 1:   5800        -9035.014             0.348            0.368
Chain 1:   5900        -8645.328             0.311            0.363
Chain 1:   6000        -9396.187             0.295            0.363
Chain 1:   6100       -10566.243             0.269            0.111
Chain 1:   6200       -11483.113             0.275            0.111
Chain 1:   6300        -8918.244             0.300            0.288
Chain 1:   6400        -8253.858             0.271            0.111
Chain 1:   6500        -9200.779             0.238            0.103
Chain 1:   6600        -8797.875             0.234            0.103
Chain 1:   6700        -9608.097             0.191            0.084
Chain 1:   6800       -13033.641             0.118            0.084
Chain 1:   6900       -10289.258             0.140            0.103
Chain 1:   7000       -10570.308             0.135            0.103
Chain 1:   7100        -9232.485             0.138            0.103
Chain 1:   7200        -8390.173             0.140            0.103
Chain 1:   7300        -8612.362             0.114            0.100
Chain 1:   7400        -8709.322             0.107            0.100
Chain 1:   7500       -10971.688             0.117            0.100
Chain 1:   7600        -8265.976             0.146            0.145
Chain 1:   7700        -8561.743             0.141            0.145
Chain 1:   7800        -8758.174             0.117            0.100
Chain 1:   7900        -8812.865             0.091            0.035
Chain 1:   8000        -8627.405             0.090            0.035
Chain 1:   8100       -12938.015             0.109            0.035
Chain 1:   8200        -8654.789             0.148            0.035
Chain 1:   8300        -8280.015             0.150            0.045
Chain 1:   8400        -8536.861             0.152            0.045
Chain 1:   8500        -8279.579             0.135            0.035
Chain 1:   8600       -10446.551             0.123            0.035
Chain 1:   8700       -10697.576             0.122            0.031
Chain 1:   8800       -11021.894             0.122            0.031
Chain 1:   8900        -8459.750             0.152            0.045
Chain 1:   9000        -9742.198             0.163            0.132
Chain 1:   9100        -8585.066             0.143            0.132
Chain 1:   9200       -13016.237             0.128            0.132
Chain 1:   9300       -10480.203             0.147            0.135
Chain 1:   9400       -12815.030             0.163            0.182
Chain 1:   9500        -8247.569             0.215            0.207
Chain 1:   9600        -9740.309             0.209            0.182
Chain 1:   9700        -8115.055             0.227            0.200
Chain 1:   9800        -8236.554             0.226            0.200
Chain 1:   9900        -9265.052             0.206            0.182
Chain 1:   10000       -11877.010             0.215            0.200
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63825.820             1.000            1.000
Chain 1:    200       -18544.887             1.721            2.442
Chain 1:    300        -8930.901             1.506            1.076
Chain 1:    400        -8049.391             1.157            1.076
Chain 1:    500        -9024.190             0.947            1.000
Chain 1:    600        -8654.272             0.796            1.000
Chain 1:    700        -8544.766             0.684            0.110
Chain 1:    800        -8468.727             0.600            0.110
Chain 1:    900        -7976.280             0.540            0.108
Chain 1:   1000        -7893.800             0.487            0.108
Chain 1:   1100        -7658.938             0.390            0.062
Chain 1:   1200        -7678.908             0.146            0.043
Chain 1:   1300        -7571.499             0.040            0.031
Chain 1:   1400        -7741.545             0.031            0.022
Chain 1:   1500        -7592.715             0.023            0.020
Chain 1:   1600        -7770.245             0.021            0.020
Chain 1:   1700        -7584.352             0.022            0.022
Chain 1:   1800        -7705.402             0.022            0.022
Chain 1:   1900        -7686.421             0.017            0.020
Chain 1:   2000        -7644.007             0.016            0.020
Chain 1:   2100        -7631.149             0.013            0.016
Chain 1:   2200        -7769.398             0.015            0.018
Chain 1:   2300        -7613.396             0.015            0.020
Chain 1:   2400        -7708.053             0.014            0.018
Chain 1:   2500        -7524.270             0.015            0.018
Chain 1:   2600        -7567.673             0.013            0.016
Chain 1:   2700        -7589.872             0.011            0.012
Chain 1:   2800        -7655.314             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002991 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85437.641             1.000            1.000
Chain 1:    200       -13721.459             3.113            5.227
Chain 1:    300        -9996.371             2.200            1.000
Chain 1:    400       -11539.191             1.683            1.000
Chain 1:    500        -8836.351             1.408            0.373
Chain 1:    600        -8374.065             1.182            0.373
Chain 1:    700        -8275.231             1.015            0.306
Chain 1:    800        -8763.420             0.895            0.306
Chain 1:    900        -8709.908             0.796            0.134
Chain 1:   1000        -8395.269             0.721            0.134
Chain 1:   1100        -8794.143             0.625            0.056   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8332.031             0.108            0.055
Chain 1:   1300        -8661.681             0.074            0.055
Chain 1:   1400        -8438.485             0.064            0.045
Chain 1:   1500        -8479.110             0.034            0.038
Chain 1:   1600        -8583.807             0.029            0.037
Chain 1:   1700        -8647.148             0.029            0.037
Chain 1:   1800        -8199.838             0.029            0.037
Chain 1:   1900        -8308.067             0.029            0.037
Chain 1:   2000        -8292.570             0.026            0.026
Chain 1:   2100        -8429.202             0.023            0.016
Chain 1:   2200        -8203.917             0.020            0.016
Chain 1:   2300        -8319.023             0.018            0.014
Chain 1:   2400        -8376.141             0.016            0.013
Chain 1:   2500        -8316.886             0.016            0.013
Chain 1:   2600        -8330.035             0.015            0.013
Chain 1:   2700        -8237.869             0.015            0.013
Chain 1:   2800        -8184.599             0.011            0.011
Chain 1:   2900        -8288.014             0.011            0.011
Chain 1:   3000        -8126.365             0.012            0.012
Chain 1:   3100        -8269.136             0.012            0.012
Chain 1:   3200        -8138.533             0.011            0.012
Chain 1:   3300        -8354.458             0.012            0.012
Chain 1:   3400        -8400.176             0.012            0.012
Chain 1:   3500        -8223.933             0.014            0.016
Chain 1:   3600        -8097.136             0.015            0.016
Chain 1:   3700        -8240.370             0.016            0.017
Chain 1:   3800        -8254.043             0.015            0.017
Chain 1:   3900        -8030.551             0.017            0.017
Chain 1:   4000        -8195.383             0.017            0.017
Chain 1:   4100        -8105.153             0.016            0.017
Chain 1:   4200        -8091.299             0.015            0.017
Chain 1:   4300        -8124.900             0.013            0.016
Chain 1:   4400        -8078.795             0.013            0.016
Chain 1:   4500        -8179.400             0.012            0.012
Chain 1:   4600        -8071.495             0.012            0.012
Chain 1:   4700        -8275.928             0.012            0.012
Chain 1:   4800        -8160.253             0.014            0.013
Chain 1:   4900        -8169.772             0.011            0.012
Chain 1:   5000        -8103.411             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003041 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408563.116             1.000            1.000
Chain 1:    200     -1585809.926             2.651            4.302
Chain 1:    300      -891176.750             2.027            1.000
Chain 1:    400      -458309.390             1.757            1.000
Chain 1:    500      -358688.310             1.461            0.944
Chain 1:    600      -233551.371             1.307            0.944
Chain 1:    700      -119641.959             1.256            0.944
Chain 1:    800       -86814.688             1.146            0.944
Chain 1:    900       -67126.993             1.051            0.779
Chain 1:   1000       -51915.556             0.976            0.779
Chain 1:   1100       -39376.279             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38553.766             0.479            0.378
Chain 1:   1300       -26484.061             0.447            0.378
Chain 1:   1400       -26202.942             0.354            0.318
Chain 1:   1500       -22783.306             0.341            0.318
Chain 1:   1600       -21998.692             0.291            0.293
Chain 1:   1700       -20868.755             0.201            0.293
Chain 1:   1800       -20812.508             0.164            0.150
Chain 1:   1900       -21139.244             0.136            0.054
Chain 1:   2000       -19647.412             0.114            0.054
Chain 1:   2100       -19885.871             0.083            0.036
Chain 1:   2200       -20113.132             0.082            0.036
Chain 1:   2300       -19729.520             0.039            0.019
Chain 1:   2400       -19501.366             0.039            0.019
Chain 1:   2500       -19303.511             0.025            0.015
Chain 1:   2600       -18932.960             0.023            0.015
Chain 1:   2700       -18889.704             0.018            0.012
Chain 1:   2800       -18606.333             0.019            0.015
Chain 1:   2900       -18887.889             0.019            0.015
Chain 1:   3000       -18874.017             0.012            0.012
Chain 1:   3100       -18959.120             0.011            0.012
Chain 1:   3200       -18649.380             0.012            0.015
Chain 1:   3300       -18854.415             0.011            0.012
Chain 1:   3400       -18328.599             0.012            0.015
Chain 1:   3500       -18941.645             0.015            0.015
Chain 1:   3600       -18246.815             0.016            0.015
Chain 1:   3700       -18634.754             0.018            0.017
Chain 1:   3800       -17592.161             0.023            0.021
Chain 1:   3900       -17588.269             0.021            0.021
Chain 1:   4000       -17705.563             0.022            0.021
Chain 1:   4100       -17619.231             0.022            0.021
Chain 1:   4200       -17434.968             0.021            0.021
Chain 1:   4300       -17573.707             0.021            0.021
Chain 1:   4400       -17530.106             0.018            0.011
Chain 1:   4500       -17432.610             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001187 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13172.959             1.000            1.000
Chain 1:    200        -9826.028             0.670            1.000
Chain 1:    300        -8332.882             0.507            0.341
Chain 1:    400        -8351.968             0.381            0.341
Chain 1:    500        -8287.538             0.306            0.179
Chain 1:    600        -8185.574             0.257            0.179
Chain 1:    700        -8093.722             0.222            0.012
Chain 1:    800        -8127.881             0.195            0.012
Chain 1:    900        -7998.690             0.175            0.012
Chain 1:   1000        -8149.234             0.159            0.016
Chain 1:   1100        -8228.969             0.060            0.012
Chain 1:   1200        -8102.718             0.028            0.012
Chain 1:   1300        -8105.125             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002288 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 22.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58124.163             1.000            1.000
Chain 1:    200       -17768.244             1.636            2.271
Chain 1:    300        -8731.058             1.435            1.035
Chain 1:    400        -8102.768             1.096            1.035
Chain 1:    500        -7995.072             0.879            1.000
Chain 1:    600        -7856.828             0.736            1.000
Chain 1:    700        -7872.807             0.631            0.078
Chain 1:    800        -7682.959             0.555            0.078
Chain 1:    900        -8470.618             0.504            0.078
Chain 1:   1000        -7627.582             0.465            0.093
Chain 1:   1100        -7669.174             0.365            0.078
Chain 1:   1200        -7654.924             0.138            0.025
Chain 1:   1300        -7645.149             0.035            0.018
Chain 1:   1400        -7769.705             0.029            0.016
Chain 1:   1500        -7559.454             0.030            0.018
Chain 1:   1600        -7763.064             0.031            0.025
Chain 1:   1700        -7522.197             0.034            0.026
Chain 1:   1800        -7578.200             0.032            0.026
Chain 1:   1900        -7587.644             0.023            0.016
Chain 1:   2000        -7583.754             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003113 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86309.150             1.000            1.000
Chain 1:    200       -13656.216             3.160            5.320
Chain 1:    300       -10048.767             2.226            1.000
Chain 1:    400       -10757.282             1.686            1.000
Chain 1:    500        -8986.325             1.388            0.359
Chain 1:    600        -8563.317             1.165            0.359
Chain 1:    700        -8584.472             0.999            0.197
Chain 1:    800        -8859.229             0.878            0.197
Chain 1:    900        -8765.321             0.782            0.066
Chain 1:   1000        -8604.755             0.705            0.066
Chain 1:   1100        -8911.140             0.609            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8507.503             0.082            0.047
Chain 1:   1300        -8738.071             0.048            0.034
Chain 1:   1400        -8723.731             0.042            0.031
Chain 1:   1500        -8629.441             0.023            0.026
Chain 1:   1600        -8735.924             0.020            0.019
Chain 1:   1700        -8824.590             0.020            0.019
Chain 1:   1800        -8417.707             0.022            0.019
Chain 1:   1900        -8514.703             0.022            0.019
Chain 1:   2000        -8486.786             0.021            0.012
Chain 1:   2100        -8607.257             0.019            0.012
Chain 1:   2200        -8416.814             0.016            0.012
Chain 1:   2300        -8554.188             0.015            0.012
Chain 1:   2400        -8561.378             0.015            0.012
Chain 1:   2500        -8528.026             0.014            0.012
Chain 1:   2600        -8526.076             0.013            0.011
Chain 1:   2700        -8439.944             0.013            0.011
Chain 1:   2800        -8405.261             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003004 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410780.205             1.000            1.000
Chain 1:    200     -1585230.203             2.653            4.306
Chain 1:    300      -890638.178             2.029            1.000
Chain 1:    400      -457546.466             1.758            1.000
Chain 1:    500      -357674.315             1.462            0.947
Chain 1:    600      -232756.589             1.308            0.947
Chain 1:    700      -119205.198             1.257            0.947
Chain 1:    800       -86443.210             1.147            0.947
Chain 1:    900       -66825.394             1.053            0.780
Chain 1:   1000       -51649.210             0.977            0.780
Chain 1:   1100       -39153.786             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38335.143             0.480            0.379
Chain 1:   1300       -26321.749             0.448            0.379
Chain 1:   1400       -26043.626             0.354            0.319
Chain 1:   1500       -22638.010             0.341            0.319
Chain 1:   1600       -21856.858             0.291            0.294
Chain 1:   1700       -20734.180             0.201            0.294
Chain 1:   1800       -20679.252             0.164            0.150
Chain 1:   1900       -21005.264             0.136            0.054
Chain 1:   2000       -19518.675             0.114            0.054
Chain 1:   2100       -19756.948             0.084            0.036
Chain 1:   2200       -19982.905             0.083            0.036
Chain 1:   2300       -19600.593             0.039            0.020
Chain 1:   2400       -19372.775             0.039            0.020
Chain 1:   2500       -19174.648             0.025            0.016
Chain 1:   2600       -18805.111             0.023            0.016
Chain 1:   2700       -18762.262             0.018            0.012
Chain 1:   2800       -18479.046             0.019            0.015
Chain 1:   2900       -18760.242             0.019            0.015
Chain 1:   3000       -18746.499             0.012            0.012
Chain 1:   3100       -18831.410             0.011            0.012
Chain 1:   3200       -18522.262             0.012            0.015
Chain 1:   3300       -18726.908             0.011            0.012
Chain 1:   3400       -18202.001             0.012            0.015
Chain 1:   3500       -18813.513             0.015            0.015
Chain 1:   3600       -18120.730             0.017            0.015
Chain 1:   3700       -18507.060             0.018            0.017
Chain 1:   3800       -17467.519             0.023            0.021
Chain 1:   3900       -17463.685             0.021            0.021
Chain 1:   4000       -17581.009             0.022            0.021
Chain 1:   4100       -17494.719             0.022            0.021
Chain 1:   4200       -17311.221             0.021            0.021
Chain 1:   4300       -17449.472             0.021            0.021
Chain 1:   4400       -17406.444             0.018            0.011
Chain 1:   4500       -17308.991             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49081.641             1.000            1.000
Chain 1:    200       -12899.491             1.902            2.805
Chain 1:    300       -16361.841             1.339            1.000
Chain 1:    400       -19250.466             1.042            1.000
Chain 1:    500       -15124.313             0.888            0.273
Chain 1:    600       -28154.324             0.817            0.463
Chain 1:    700       -16477.859             0.802            0.463
Chain 1:    800       -11427.438             0.757            0.463
Chain 1:    900       -11256.612             0.674            0.442
Chain 1:   1000       -11955.501             0.613            0.442
Chain 1:   1100       -10161.695             0.530            0.273   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11618.917             0.262            0.212
Chain 1:   1300       -11087.508             0.246            0.177
Chain 1:   1400        -9912.877             0.243            0.177
Chain 1:   1500       -10850.972             0.224            0.125
Chain 1:   1600       -11031.262             0.180            0.118
Chain 1:   1700       -13128.188             0.125            0.118
Chain 1:   1800       -13217.252             0.081            0.086
Chain 1:   1900       -13694.212             0.083            0.086
Chain 1:   2000       -20802.380             0.111            0.118
Chain 1:   2100        -9738.997             0.207            0.118
Chain 1:   2200       -17549.663             0.239            0.118
Chain 1:   2300       -22405.062             0.256            0.160
Chain 1:   2400        -9134.959             0.390            0.217
Chain 1:   2500       -15762.693             0.423            0.342
Chain 1:   2600       -15490.853             0.423            0.342
Chain 1:   2700        -9802.557             0.465            0.420
Chain 1:   2800       -10640.526             0.472            0.420
Chain 1:   2900       -15040.681             0.498            0.420
Chain 1:   3000        -8939.549             0.532            0.445   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   3100       -10619.096             0.434            0.420
Chain 1:   3200       -13468.576             0.411            0.293
Chain 1:   3300        -9448.321             0.432            0.420
Chain 1:   3400       -17408.565             0.332            0.420
Chain 1:   3500       -15752.929             0.301            0.293
Chain 1:   3600        -8919.427             0.376            0.425
Chain 1:   3700        -9169.914             0.320            0.293
Chain 1:   3800       -11390.494             0.332            0.293
Chain 1:   3900       -10164.706             0.315            0.212
Chain 1:   4000       -10161.141             0.247            0.195
Chain 1:   4100        -9262.972             0.241            0.195
Chain 1:   4200        -9043.252             0.222            0.121
Chain 1:   4300       -14404.333             0.217            0.121
Chain 1:   4400       -12483.151             0.186            0.121
Chain 1:   4500       -11392.092             0.185            0.121
Chain 1:   4600        -8539.338             0.142            0.121
Chain 1:   4700        -8633.548             0.140            0.121
Chain 1:   4800        -8813.557             0.123            0.097
Chain 1:   4900        -8646.638             0.113            0.096
Chain 1:   5000       -13276.588             0.148            0.097
Chain 1:   5100        -8643.764             0.192            0.154
Chain 1:   5200        -8925.459             0.192            0.154
Chain 1:   5300       -10257.020             0.168            0.130
Chain 1:   5400        -8608.815             0.172            0.130
Chain 1:   5500       -12943.186             0.196            0.191
Chain 1:   5600       -10445.647             0.186            0.191
Chain 1:   5700        -9417.271             0.196            0.191
Chain 1:   5800        -9091.624             0.198            0.191
Chain 1:   5900       -11921.297             0.219            0.237
Chain 1:   6000        -8563.675             0.224            0.237
Chain 1:   6100       -12293.756             0.200            0.237
Chain 1:   6200        -8345.385             0.245            0.239
Chain 1:   6300        -8954.269             0.238            0.239
Chain 1:   6400       -10667.478             0.235            0.239
Chain 1:   6500        -8794.769             0.223            0.237
Chain 1:   6600        -8635.648             0.201            0.213
Chain 1:   6700        -8249.453             0.195            0.213
Chain 1:   6800        -8936.432             0.199            0.213
Chain 1:   6900       -11539.309             0.198            0.213
Chain 1:   7000       -10716.827             0.166            0.161
Chain 1:   7100        -9515.844             0.149            0.126
Chain 1:   7200        -8612.653             0.112            0.105
Chain 1:   7300        -8745.641             0.106            0.105
Chain 1:   7400       -11596.622             0.115            0.105
Chain 1:   7500        -8155.833             0.136            0.105
Chain 1:   7600       -11441.786             0.163            0.126
Chain 1:   7700        -8382.112             0.195            0.226
Chain 1:   7800        -8812.465             0.192            0.226
Chain 1:   7900        -8650.110             0.171            0.126
Chain 1:   8000        -8502.010             0.165            0.126
Chain 1:   8100       -11524.550             0.179            0.246
Chain 1:   8200        -8293.264             0.207            0.262
Chain 1:   8300        -8414.477             0.207            0.262
Chain 1:   8400        -8693.175             0.186            0.262
Chain 1:   8500        -8160.241             0.150            0.065
Chain 1:   8600        -8330.004             0.123            0.049
Chain 1:   8700        -9825.908             0.102            0.049
Chain 1:   8800       -11286.322             0.110            0.065
Chain 1:   8900       -12655.067             0.119            0.108
Chain 1:   9000        -8323.900             0.169            0.129
Chain 1:   9100        -8761.697             0.148            0.108
Chain 1:   9200        -8021.333             0.118            0.092
Chain 1:   9300        -8777.915             0.126            0.092
Chain 1:   9400        -8284.477             0.128            0.092
Chain 1:   9500        -8889.037             0.129            0.092
Chain 1:   9600        -8408.793             0.132            0.092
Chain 1:   9700        -8763.186             0.121            0.086
Chain 1:   9800       -10675.513             0.126            0.086
Chain 1:   9900       -10185.845             0.120            0.068
Chain 1:   10000        -8133.746             0.093            0.068
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57947.939             1.000            1.000
Chain 1:    200       -17741.901             1.633            2.266
Chain 1:    300        -8703.983             1.435            1.038
Chain 1:    400        -8115.991             1.094            1.038
Chain 1:    500        -8605.046             0.887            1.000
Chain 1:    600        -7992.027             0.752            1.000
Chain 1:    700        -7757.182             0.649            0.077
Chain 1:    800        -8100.883             0.573            0.077
Chain 1:    900        -7990.947             0.511            0.072
Chain 1:   1000        -7858.327             0.461            0.072
Chain 1:   1100        -7838.239             0.362            0.057
Chain 1:   1200        -7848.342             0.135            0.042
Chain 1:   1300        -7788.814             0.032            0.030
Chain 1:   1400        -7681.065             0.026            0.017
Chain 1:   1500        -7535.879             0.022            0.017
Chain 1:   1600        -7569.860             0.015            0.014
Chain 1:   1700        -7554.148             0.012            0.014
Chain 1:   1800        -7628.986             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002565 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86029.635             1.000            1.000
Chain 1:    200       -13575.644             3.169            5.337
Chain 1:    300        -9898.182             2.236            1.000
Chain 1:    400       -10847.092             1.699            1.000
Chain 1:    500        -8888.158             1.403            0.372
Chain 1:    600        -8370.826             1.180            0.372
Chain 1:    700        -8757.547             1.017            0.220
Chain 1:    800        -8881.652             0.892            0.220
Chain 1:    900        -8719.785             0.795            0.087
Chain 1:   1000        -8505.774             0.718            0.087
Chain 1:   1100        -8692.083             0.620            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8305.121             0.091            0.047
Chain 1:   1300        -8580.611             0.057            0.044
Chain 1:   1400        -8591.634             0.049            0.032
Chain 1:   1500        -8435.931             0.028            0.025
Chain 1:   1600        -8550.155             0.024            0.021
Chain 1:   1700        -8621.959             0.020            0.019
Chain 1:   1800        -8193.298             0.024            0.021
Chain 1:   1900        -8296.734             0.023            0.021
Chain 1:   2000        -8271.705             0.021            0.018
Chain 1:   2100        -8400.535             0.020            0.015
Chain 1:   2200        -8197.934             0.018            0.015
Chain 1:   2300        -8293.128             0.016            0.013
Chain 1:   2400        -8359.775             0.017            0.013
Chain 1:   2500        -8305.699             0.016            0.012
Chain 1:   2600        -8308.926             0.014            0.011
Chain 1:   2700        -8224.741             0.014            0.011
Chain 1:   2800        -8182.465             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002625 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410719.658             1.000            1.000
Chain 1:    200     -1587851.341             2.648            4.297
Chain 1:    300      -890756.970             2.027            1.000
Chain 1:    400      -457399.135             1.757            1.000
Chain 1:    500      -357497.870             1.461            0.947
Chain 1:    600      -232657.024             1.307            0.947
Chain 1:    700      -119115.519             1.257            0.947
Chain 1:    800       -86360.474             1.147            0.947
Chain 1:    900       -66752.585             1.052            0.783
Chain 1:   1000       -51585.990             0.976            0.783
Chain 1:   1100       -39093.272             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38277.776             0.481            0.379
Chain 1:   1300       -26263.641             0.448            0.379
Chain 1:   1400       -25987.310             0.355            0.320
Chain 1:   1500       -22580.930             0.342            0.320
Chain 1:   1600       -21799.673             0.292            0.294
Chain 1:   1700       -20676.725             0.202            0.294
Chain 1:   1800       -20621.853             0.164            0.151
Chain 1:   1900       -20948.230             0.136            0.054
Chain 1:   2000       -19460.517             0.114            0.054
Chain 1:   2100       -19699.056             0.084            0.036
Chain 1:   2200       -19925.253             0.083            0.036
Chain 1:   2300       -19542.589             0.039            0.020
Chain 1:   2400       -19314.603             0.039            0.020
Chain 1:   2500       -19116.420             0.025            0.016
Chain 1:   2600       -18746.677             0.023            0.016
Chain 1:   2700       -18703.659             0.018            0.012
Chain 1:   2800       -18420.268             0.019            0.015
Chain 1:   2900       -18701.603             0.019            0.015
Chain 1:   3000       -18687.901             0.012            0.012
Chain 1:   3100       -18772.875             0.011            0.012
Chain 1:   3200       -18463.490             0.012            0.015
Chain 1:   3300       -18668.269             0.011            0.012
Chain 1:   3400       -18142.949             0.012            0.015
Chain 1:   3500       -18755.103             0.015            0.015
Chain 1:   3600       -18061.426             0.017            0.015
Chain 1:   3700       -18448.450             0.018            0.017
Chain 1:   3800       -17407.524             0.023            0.021
Chain 1:   3900       -17403.602             0.021            0.021
Chain 1:   4000       -17520.967             0.022            0.021
Chain 1:   4100       -17434.642             0.022            0.021
Chain 1:   4200       -17250.762             0.021            0.021
Chain 1:   4300       -17389.286             0.021            0.021
Chain 1:   4400       -17346.015             0.018            0.011
Chain 1:   4500       -17248.476             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12718.030             1.000            1.000
Chain 1:    200        -9538.106             0.667            1.000
Chain 1:    300        -8195.145             0.499            0.333
Chain 1:    400        -8297.788             0.377            0.333
Chain 1:    500        -8289.326             0.302            0.164
Chain 1:    600        -8123.422             0.255            0.164
Chain 1:    700        -8041.012             0.220            0.020
Chain 1:    800        -8046.361             0.193            0.020
Chain 1:    900        -7943.562             0.173            0.013
Chain 1:   1000        -8105.687             0.157            0.020
Chain 1:   1100        -8074.407             0.058            0.013
Chain 1:   1200        -8065.849             0.025            0.012
Chain 1:   1300        -8010.498             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49472.827             1.000            1.000
Chain 1:    200       -16128.388             1.534            2.067
Chain 1:    300        -8781.812             1.301            1.000
Chain 1:    400        -8501.917             0.984            1.000
Chain 1:    500        -8249.772             0.793            0.837
Chain 1:    600        -8232.872             0.662            0.837
Chain 1:    700        -8134.364             0.569            0.033
Chain 1:    800        -8076.364             0.499            0.033
Chain 1:    900        -7737.652             0.448            0.033
Chain 1:   1000        -7849.753             0.405            0.033
Chain 1:   1100        -7797.023             0.305            0.031
Chain 1:   1200        -7671.204             0.100            0.016
Chain 1:   1300        -7816.903             0.018            0.016
Chain 1:   1400        -7706.316             0.017            0.014
Chain 1:   1500        -7565.713             0.015            0.014
Chain 1:   1600        -7833.842             0.019            0.016
Chain 1:   1700        -7427.899             0.023            0.019
Chain 1:   1800        -7645.019             0.025            0.019
Chain 1:   1900        -7508.290             0.022            0.019
Chain 1:   2000        -7631.934             0.023            0.019
Chain 1:   2100        -7618.590             0.022            0.019
Chain 1:   2200        -7702.694             0.022            0.019
Chain 1:   2300        -7593.831             0.021            0.018
Chain 1:   2400        -7634.172             0.020            0.018
Chain 1:   2500        -7611.298             0.019            0.016
Chain 1:   2600        -7525.425             0.016            0.014
Chain 1:   2700        -7583.570             0.012            0.011
Chain 1:   2800        -7499.927             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002952 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86766.708             1.000            1.000
Chain 1:    200       -13649.959             3.178            5.357
Chain 1:    300       -10015.571             2.240            1.000
Chain 1:    400       -10785.942             1.698            1.000
Chain 1:    500        -8985.822             1.398            0.363
Chain 1:    600        -8495.771             1.175            0.363
Chain 1:    700        -8719.479             1.011            0.200
Chain 1:    800        -9333.818             0.893            0.200
Chain 1:    900        -8821.477             0.800            0.071
Chain 1:   1000        -8664.129             0.722            0.071
Chain 1:   1100        -8832.981             0.624            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8526.908             0.092            0.058
Chain 1:   1300        -8670.127             0.057            0.058
Chain 1:   1400        -8707.595             0.050            0.036
Chain 1:   1500        -8581.932             0.032            0.026
Chain 1:   1600        -8690.189             0.027            0.019
Chain 1:   1700        -8780.184             0.026            0.018
Chain 1:   1800        -8368.361             0.024            0.018
Chain 1:   1900        -8464.327             0.019            0.017
Chain 1:   2000        -8437.326             0.018            0.015
Chain 1:   2100        -8559.430             0.017            0.014
Chain 1:   2200        -8378.175             0.016            0.014
Chain 1:   2300        -8459.805             0.015            0.012
Chain 1:   2400        -8529.087             0.015            0.012
Chain 1:   2500        -8474.393             0.015            0.011
Chain 1:   2600        -8473.429             0.013            0.010
Chain 1:   2700        -8390.712             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003126 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406311.594             1.000            1.000
Chain 1:    200     -1585388.105             2.651            4.302
Chain 1:    300      -891732.505             2.027            1.000
Chain 1:    400      -457803.342             1.757            1.000
Chain 1:    500      -357942.168             1.461            0.948
Chain 1:    600      -232914.818             1.307            0.948
Chain 1:    700      -119301.475             1.257            0.948
Chain 1:    800       -86503.229             1.147            0.948
Chain 1:    900       -66866.600             1.052            0.778
Chain 1:   1000       -51676.350             0.976            0.778
Chain 1:   1100       -39165.918             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38345.865             0.480            0.379
Chain 1:   1300       -26321.590             0.448            0.379
Chain 1:   1400       -26042.041             0.354            0.319
Chain 1:   1500       -22633.258             0.341            0.319
Chain 1:   1600       -21850.841             0.291            0.294
Chain 1:   1700       -20727.260             0.202            0.294
Chain 1:   1800       -20672.091             0.164            0.151
Chain 1:   1900       -20998.088             0.136            0.054
Chain 1:   2000       -19510.632             0.114            0.054
Chain 1:   2100       -19749.139             0.084            0.036
Chain 1:   2200       -19975.095             0.083            0.036
Chain 1:   2300       -19592.755             0.039            0.020
Chain 1:   2400       -19364.892             0.039            0.020
Chain 1:   2500       -19166.655             0.025            0.016
Chain 1:   2600       -18797.152             0.023            0.016
Chain 1:   2700       -18754.283             0.018            0.012
Chain 1:   2800       -18470.990             0.019            0.015
Chain 1:   2900       -18752.224             0.019            0.015
Chain 1:   3000       -18738.477             0.012            0.012
Chain 1:   3100       -18823.419             0.011            0.012
Chain 1:   3200       -18514.211             0.012            0.015
Chain 1:   3300       -18718.886             0.011            0.012
Chain 1:   3400       -18193.874             0.012            0.015
Chain 1:   3500       -18805.527             0.015            0.015
Chain 1:   3600       -18112.545             0.017            0.015
Chain 1:   3700       -18499.042             0.018            0.017
Chain 1:   3800       -17459.169             0.023            0.021
Chain 1:   3900       -17455.300             0.021            0.021
Chain 1:   4000       -17572.640             0.022            0.021
Chain 1:   4100       -17486.322             0.022            0.021
Chain 1:   4200       -17302.744             0.021            0.021
Chain 1:   4300       -17441.071             0.021            0.021
Chain 1:   4400       -17397.993             0.018            0.011
Chain 1:   4500       -17300.492             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001284 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13324.672             1.000            1.000
Chain 1:    200       -10237.443             0.651            1.000
Chain 1:    300        -8753.922             0.490            0.302
Chain 1:    400        -8978.015             0.374            0.302
Chain 1:    500        -8621.095             0.307            0.169
Chain 1:    600        -8673.701             0.257            0.169
Chain 1:    700        -8568.860             0.222            0.041
Chain 1:    800        -8490.122             0.196            0.041
Chain 1:    900        -8608.004             0.175            0.025
Chain 1:   1000        -8628.191             0.158            0.025
Chain 1:   1100        -8716.925             0.059            0.014
Chain 1:   1200        -8590.311             0.030            0.014
Chain 1:   1300        -8523.611             0.014            0.012
Chain 1:   1400        -8540.405             0.012            0.010
Chain 1:   1500        -8661.855             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63316.678             1.000            1.000
Chain 1:    200       -19015.534             1.665            2.330
Chain 1:    300        -9496.626             1.444            1.002
Chain 1:    400        -8951.925             1.098            1.002
Chain 1:    500        -9239.104             0.885            1.000
Chain 1:    600        -9473.893             0.741            1.000
Chain 1:    700        -8173.857             0.658            0.159
Chain 1:    800        -8482.843             0.581            0.159
Chain 1:    900        -8141.550             0.521            0.061
Chain 1:   1000        -7816.503             0.473            0.061
Chain 1:   1100        -8164.792             0.377            0.043
Chain 1:   1200        -7913.250             0.147            0.042
Chain 1:   1300        -8190.913             0.050            0.042
Chain 1:   1400        -7952.979             0.047            0.036
Chain 1:   1500        -7649.592             0.048            0.040
Chain 1:   1600        -7897.893             0.049            0.040
Chain 1:   1700        -7901.567             0.033            0.036
Chain 1:   1800        -7930.516             0.030            0.034
Chain 1:   1900        -7771.538             0.028            0.032
Chain 1:   2000        -7955.503             0.026            0.031
Chain 1:   2100        -7778.409             0.024            0.030
Chain 1:   2200        -7976.642             0.023            0.025
Chain 1:   2300        -7738.508             0.023            0.025
Chain 1:   2400        -7860.617             0.021            0.023
Chain 1:   2500        -7753.711             0.019            0.023
Chain 1:   2600        -7674.550             0.017            0.020
Chain 1:   2700        -7666.249             0.017            0.020
Chain 1:   2800        -7698.610             0.017            0.020
Chain 1:   2900        -7522.068             0.017            0.023
Chain 1:   3000        -7683.800             0.017            0.021
Chain 1:   3100        -7648.521             0.015            0.016
Chain 1:   3200        -7754.597             0.014            0.014
Chain 1:   3300        -7590.732             0.013            0.014
Chain 1:   3400        -7823.482             0.014            0.014
Chain 1:   3500        -7603.923             0.016            0.021
Chain 1:   3600        -7630.394             0.015            0.021
Chain 1:   3700        -7547.337             0.016            0.021
Chain 1:   3800        -7636.549             0.017            0.021
Chain 1:   3900        -7537.167             0.016            0.014
Chain 1:   4000        -7521.139             0.014            0.013
Chain 1:   4100        -7530.369             0.014            0.013
Chain 1:   4200        -7670.273             0.014            0.013
Chain 1:   4300        -7510.465             0.014            0.013
Chain 1:   4400        -7562.119             0.012            0.012
Chain 1:   4500        -7713.760             0.011            0.012
Chain 1:   4600        -7591.066             0.012            0.013
Chain 1:   4700        -7584.229             0.011            0.013
Chain 1:   4800        -7539.963             0.011            0.013
Chain 1:   4900        -7660.488             0.011            0.016
Chain 1:   5000        -7720.182             0.011            0.016
Chain 1:   5100        -7617.214             0.013            0.016
Chain 1:   5200        -7637.028             0.011            0.014
Chain 1:   5300        -7609.568             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002614 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87023.580             1.000            1.000
Chain 1:    200       -14569.007             2.987            4.973
Chain 1:    300       -10778.060             2.108            1.000
Chain 1:    400       -12630.945             1.618            1.000
Chain 1:    500        -9166.328             1.370            0.378
Chain 1:    600        -9020.656             1.144            0.378
Chain 1:    700        -9397.667             0.987            0.352
Chain 1:    800        -9307.187             0.864            0.352
Chain 1:    900        -9609.160             0.772            0.147
Chain 1:   1000        -9012.119             0.701            0.147
Chain 1:   1100        -9543.307             0.607            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9010.275             0.115            0.059
Chain 1:   1300        -9353.374             0.084            0.056
Chain 1:   1400        -9231.656             0.071            0.040
Chain 1:   1500        -9226.591             0.033            0.037
Chain 1:   1600        -9303.735             0.032            0.037
Chain 1:   1700        -9356.357             0.029            0.031
Chain 1:   1800        -8903.203             0.033            0.037
Chain 1:   1900        -9011.497             0.031            0.037
Chain 1:   2000        -9032.846             0.024            0.013
Chain 1:   2100        -9120.997             0.020            0.012
Chain 1:   2200        -8896.327             0.016            0.012
Chain 1:   2300        -9101.223             0.015            0.012
Chain 1:   2400        -8909.920             0.016            0.012
Chain 1:   2500        -8982.246             0.017            0.012
Chain 1:   2600        -8891.757             0.017            0.012
Chain 1:   2700        -8925.641             0.017            0.012
Chain 1:   2800        -8876.811             0.012            0.010
Chain 1:   2900        -8991.687             0.012            0.010
Chain 1:   3000        -8900.944             0.013            0.010
Chain 1:   3100        -8868.181             0.012            0.010
Chain 1:   3200        -8839.178             0.010            0.010
Chain 1:   3300        -9103.049             0.011            0.010
Chain 1:   3400        -9149.928             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002898 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390340.605             1.000            1.000
Chain 1:    200     -1580053.726             2.655            4.310
Chain 1:    300      -890310.063             2.028            1.000
Chain 1:    400      -458273.124             1.757            1.000
Chain 1:    500      -359337.190             1.461            0.943
Chain 1:    600      -234367.602             1.306            0.943
Chain 1:    700      -120500.889             1.254            0.943
Chain 1:    800       -87727.085             1.144            0.943
Chain 1:    900       -68032.991             1.049            0.775
Chain 1:   1000       -52815.538             0.973            0.775
Chain 1:   1100       -40266.749             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39447.506             0.475            0.374
Chain 1:   1300       -27353.460             0.442            0.374
Chain 1:   1400       -27072.173             0.349            0.312
Chain 1:   1500       -23647.074             0.336            0.312
Chain 1:   1600       -22861.693             0.286            0.289
Chain 1:   1700       -21727.795             0.197            0.288
Chain 1:   1800       -21671.022             0.160            0.145
Chain 1:   1900       -21998.132             0.132            0.052
Chain 1:   2000       -20503.974             0.111            0.052
Chain 1:   2100       -20742.432             0.081            0.034
Chain 1:   2200       -20970.394             0.080            0.034
Chain 1:   2300       -20586.098             0.037            0.019
Chain 1:   2400       -20357.793             0.037            0.019
Chain 1:   2500       -20160.189             0.024            0.015
Chain 1:   2600       -19789.045             0.022            0.015
Chain 1:   2700       -19745.614             0.017            0.011
Chain 1:   2800       -19462.252             0.019            0.015
Chain 1:   2900       -19744.022             0.018            0.014
Chain 1:   3000       -19729.951             0.011            0.011
Chain 1:   3100       -19815.150             0.011            0.011
Chain 1:   3200       -19505.117             0.011            0.014
Chain 1:   3300       -19710.418             0.010            0.011
Chain 1:   3400       -19184.234             0.012            0.014
Chain 1:   3500       -19797.906             0.014            0.015
Chain 1:   3600       -19102.246             0.016            0.015
Chain 1:   3700       -19490.866             0.017            0.016
Chain 1:   3800       -18447.042             0.022            0.020
Chain 1:   3900       -18443.161             0.020            0.020
Chain 1:   4000       -18560.400             0.021            0.020
Chain 1:   4100       -18474.053             0.021            0.020
Chain 1:   4200       -18289.497             0.020            0.020
Chain 1:   4300       -18428.400             0.020            0.020
Chain 1:   4400       -18384.574             0.018            0.010
Chain 1:   4500       -18287.052             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001176 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49341.377             1.000            1.000
Chain 1:    200      -107731.999             0.771            1.000
Chain 1:    300       -22124.714             1.804            1.000
Chain 1:    400       -12735.012             1.537            1.000
Chain 1:    500       -16246.573             1.273            0.737
Chain 1:    600       -17756.585             1.075            0.737
Chain 1:    700       -14835.583             0.950            0.542
Chain 1:    800       -28785.445             0.891            0.542
Chain 1:    900       -11517.784             0.959            0.542
Chain 1:   1000       -10975.550             0.868            0.542
Chain 1:   1100       -10446.769             0.773            0.485   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12360.233             0.734            0.216   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -12384.499             0.348            0.197
Chain 1:   1400       -19667.836             0.311            0.197
Chain 1:   1500       -11779.191             0.356            0.197
Chain 1:   1600       -12972.415             0.357            0.197
Chain 1:   1700       -13991.143             0.345            0.155
Chain 1:   1800       -18132.766             0.319            0.155
Chain 1:   1900       -16760.477             0.177            0.092
Chain 1:   2000       -11192.044             0.222            0.155
Chain 1:   2100       -10876.928             0.220            0.155
Chain 1:   2200        -9928.831             0.214            0.095
Chain 1:   2300       -14401.095             0.245            0.228
Chain 1:   2400        -9968.190             0.252            0.228
Chain 1:   2500       -10682.297             0.192            0.095
Chain 1:   2600       -12039.823             0.194            0.113
Chain 1:   2700       -10519.283             0.201            0.145
Chain 1:   2800       -12288.350             0.193            0.144
Chain 1:   2900       -17251.918             0.213            0.145
Chain 1:   3000        -9243.178             0.250            0.145
Chain 1:   3100        -9054.894             0.249            0.145
Chain 1:   3200       -15196.349             0.280            0.288
Chain 1:   3300       -14639.525             0.253            0.145
Chain 1:   3400       -13404.632             0.218            0.144
Chain 1:   3500        -9593.813             0.251            0.145
Chain 1:   3600        -9567.574             0.240            0.145
Chain 1:   3700        -8944.988             0.232            0.144
Chain 1:   3800        -9615.574             0.225            0.092
Chain 1:   3900        -9636.865             0.196            0.070
Chain 1:   4000       -12502.489             0.133            0.070
Chain 1:   4100       -15007.557             0.147            0.092
Chain 1:   4200        -9647.366             0.162            0.092
Chain 1:   4300        -8952.557             0.166            0.092
Chain 1:   4400       -12383.116             0.185            0.167
Chain 1:   4500        -9561.454             0.175            0.167
Chain 1:   4600        -9392.220             0.176            0.167
Chain 1:   4700        -9254.006             0.171            0.167
Chain 1:   4800        -9108.107             0.165            0.167
Chain 1:   4900        -8903.565             0.167            0.167
Chain 1:   5000        -8963.611             0.145            0.078
Chain 1:   5100        -9356.853             0.133            0.042
Chain 1:   5200        -9049.725             0.080            0.034
Chain 1:   5300       -14496.276             0.110            0.034
Chain 1:   5400        -8861.936             0.146            0.034
Chain 1:   5500       -11523.124             0.140            0.034
Chain 1:   5600        -8614.900             0.172            0.042
Chain 1:   5700        -9635.786             0.181            0.106
Chain 1:   5800       -12960.894             0.205            0.231
Chain 1:   5900        -8833.529             0.249            0.257
Chain 1:   6000        -9240.494             0.253            0.257
Chain 1:   6100        -9229.581             0.249            0.257
Chain 1:   6200        -8617.964             0.253            0.257
Chain 1:   6300       -11987.956             0.243            0.257
Chain 1:   6400       -14664.899             0.198            0.231
Chain 1:   6500        -9664.649             0.226            0.257
Chain 1:   6600        -9272.927             0.197            0.183
Chain 1:   6700       -10808.170             0.201            0.183
Chain 1:   6800        -8729.711             0.199            0.183
Chain 1:   6900       -13520.727             0.187            0.183
Chain 1:   7000        -8494.245             0.242            0.238
Chain 1:   7100        -8515.489             0.242            0.238
Chain 1:   7200        -8806.848             0.239            0.238
Chain 1:   7300        -8606.897             0.213            0.183
Chain 1:   7400        -8479.234             0.196            0.142
Chain 1:   7500        -8501.354             0.144            0.042
Chain 1:   7600        -8833.684             0.144            0.038
Chain 1:   7700        -8802.394             0.130            0.033
Chain 1:   7800        -8501.982             0.110            0.033
Chain 1:   7900        -9071.083             0.081            0.033
Chain 1:   8000        -8597.489             0.027            0.033
Chain 1:   8100        -9101.091             0.032            0.035
Chain 1:   8200       -12333.576             0.055            0.038
Chain 1:   8300       -13021.014             0.058            0.053
Chain 1:   8400       -11154.493             0.073            0.055
Chain 1:   8500        -8460.656             0.105            0.055
Chain 1:   8600        -9503.648             0.112            0.063
Chain 1:   8700        -9597.683             0.113            0.063
Chain 1:   8800        -9811.872             0.112            0.063
Chain 1:   8900       -12478.760             0.127            0.110
Chain 1:   9000       -11372.744             0.131            0.110
Chain 1:   9100        -8787.277             0.155            0.167
Chain 1:   9200        -9179.904             0.133            0.110
Chain 1:   9300        -8637.798             0.134            0.110
Chain 1:   9400        -8625.746             0.117            0.097
Chain 1:   9500        -9713.234             0.097            0.097
Chain 1:   9600       -12194.046             0.106            0.097
Chain 1:   9700       -10456.043             0.122            0.112
Chain 1:   9800        -8236.096             0.146            0.166
Chain 1:   9900        -8973.334             0.133            0.112
Chain 1:   10000       -10629.182             0.139            0.156
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58758.476             1.000            1.000
Chain 1:    200       -18260.045             1.609            2.218
Chain 1:    300        -8950.893             1.419            1.040
Chain 1:    400        -8175.249             1.088            1.040
Chain 1:    500        -8246.333             0.872            1.000
Chain 1:    600        -8128.693             0.729            1.000
Chain 1:    700        -7847.916             0.630            0.095
Chain 1:    800        -8094.249             0.555            0.095
Chain 1:    900        -7972.068             0.495            0.036
Chain 1:   1000        -7770.337             0.448            0.036
Chain 1:   1100        -7812.376             0.349            0.030
Chain 1:   1200        -7685.346             0.129            0.026
Chain 1:   1300        -7595.992             0.026            0.017
Chain 1:   1400        -8069.442             0.022            0.017
Chain 1:   1500        -7585.494             0.028            0.026
Chain 1:   1600        -7994.611             0.031            0.030
Chain 1:   1700        -7479.752             0.035            0.030
Chain 1:   1800        -7662.585             0.034            0.026
Chain 1:   1900        -7730.318             0.033            0.026
Chain 1:   2000        -7715.876             0.031            0.024
Chain 1:   2100        -7617.746             0.032            0.024
Chain 1:   2200        -7795.270             0.032            0.024
Chain 1:   2300        -7672.606             0.033            0.024
Chain 1:   2400        -7590.851             0.028            0.023
Chain 1:   2500        -7629.612             0.022            0.016
Chain 1:   2600        -7585.076             0.018            0.013
Chain 1:   2700        -7572.802             0.011            0.011
Chain 1:   2800        -7715.324             0.010            0.011
Chain 1:   2900        -7428.282             0.013            0.013
Chain 1:   3000        -7582.480             0.015            0.016
Chain 1:   3100        -7576.412             0.014            0.016
Chain 1:   3200        -7798.406             0.015            0.016
Chain 1:   3300        -7507.141             0.017            0.018
Chain 1:   3400        -7750.799             0.019            0.020
Chain 1:   3500        -7489.872             0.022            0.028
Chain 1:   3600        -7552.730             0.022            0.028
Chain 1:   3700        -7505.449             0.023            0.028
Chain 1:   3800        -7499.000             0.021            0.028
Chain 1:   3900        -7460.443             0.018            0.020
Chain 1:   4000        -7453.995             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002563 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86717.337             1.000            1.000
Chain 1:    200       -13995.946             3.098            5.196
Chain 1:    300       -10212.143             2.189            1.000
Chain 1:    400       -11831.957             1.676            1.000
Chain 1:    500        -8952.994             1.405            0.371
Chain 1:    600        -9934.234             1.187            0.371
Chain 1:    700        -9081.169             1.031            0.322
Chain 1:    800        -8381.889             0.913            0.322
Chain 1:    900        -8443.166             0.812            0.137
Chain 1:   1000        -8719.320             0.734            0.137
Chain 1:   1100        -8925.494             0.636            0.099   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8452.938             0.122            0.094
Chain 1:   1300        -8777.304             0.089            0.083
Chain 1:   1400        -8732.731             0.076            0.056
Chain 1:   1500        -8658.239             0.044            0.037
Chain 1:   1600        -8725.550             0.035            0.032
Chain 1:   1700        -8810.832             0.027            0.023
Chain 1:   1800        -8366.064             0.024            0.023
Chain 1:   1900        -8466.302             0.024            0.023
Chain 1:   2000        -8482.850             0.021            0.012
Chain 1:   2100        -8569.923             0.020            0.010
Chain 1:   2200        -8353.928             0.017            0.010
Chain 1:   2300        -8514.842             0.015            0.010
Chain 1:   2400        -8362.909             0.017            0.012
Chain 1:   2500        -8436.490             0.017            0.012
Chain 1:   2600        -8347.129             0.017            0.012
Chain 1:   2700        -8381.294             0.016            0.012
Chain 1:   2800        -8332.503             0.012            0.011
Chain 1:   2900        -8447.233             0.012            0.011
Chain 1:   3000        -8360.248             0.013            0.011
Chain 1:   3100        -8324.688             0.012            0.011
Chain 1:   3200        -8296.534             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002863 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401921.544             1.000            1.000
Chain 1:    200     -1584318.652             2.652            4.303
Chain 1:    300      -890100.257             2.028            1.000
Chain 1:    400      -457538.625             1.757            1.000
Chain 1:    500      -358186.883             1.461            0.945
Chain 1:    600      -233359.289             1.307            0.945
Chain 1:    700      -119714.849             1.256            0.945
Chain 1:    800       -86948.546             1.146            0.945
Chain 1:    900       -67314.511             1.051            0.780
Chain 1:   1000       -52132.237             0.975            0.780
Chain 1:   1100       -39615.377             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38800.957             0.478            0.377
Chain 1:   1300       -26748.985             0.445            0.377
Chain 1:   1400       -26471.314             0.352            0.316
Chain 1:   1500       -23055.371             0.339            0.316
Chain 1:   1600       -22272.109             0.289            0.292
Chain 1:   1700       -21143.899             0.199            0.291
Chain 1:   1800       -21088.255             0.162            0.148
Chain 1:   1900       -21415.261             0.134            0.053
Chain 1:   2000       -19923.649             0.113            0.053
Chain 1:   2100       -20162.371             0.082            0.035
Chain 1:   2200       -20389.511             0.081            0.035
Chain 1:   2300       -20005.865             0.038            0.019
Chain 1:   2400       -19777.618             0.038            0.019
Chain 1:   2500       -19579.602             0.025            0.015
Chain 1:   2600       -19209.030             0.023            0.015
Chain 1:   2700       -19165.725             0.018            0.012
Chain 1:   2800       -18882.179             0.019            0.015
Chain 1:   2900       -19163.846             0.019            0.015
Chain 1:   3000       -19149.982             0.012            0.012
Chain 1:   3100       -19235.115             0.011            0.012
Chain 1:   3200       -18925.249             0.011            0.015
Chain 1:   3300       -19130.391             0.011            0.012
Chain 1:   3400       -18604.319             0.012            0.015
Chain 1:   3500       -19217.715             0.014            0.015
Chain 1:   3600       -18522.383             0.016            0.015
Chain 1:   3700       -18910.697             0.018            0.016
Chain 1:   3800       -17867.299             0.022            0.021
Chain 1:   3900       -17863.341             0.021            0.021
Chain 1:   4000       -17980.664             0.021            0.021
Chain 1:   4100       -17894.273             0.022            0.021
Chain 1:   4200       -17709.817             0.021            0.021
Chain 1:   4300       -17848.728             0.021            0.021
Chain 1:   4400       -17805.001             0.018            0.010
Chain 1:   4500       -17707.402             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49456.226             1.000            1.000
Chain 1:    200       -16886.018             1.464            1.929
Chain 1:    300       -23797.136             1.073            1.000
Chain 1:    400       -17968.976             0.886            1.000
Chain 1:    500       -12326.036             0.800            0.458
Chain 1:    600       -15996.496             0.705            0.458
Chain 1:    700       -14242.533             0.622            0.324
Chain 1:    800       -12565.521             0.561            0.324
Chain 1:    900       -22307.035             0.547            0.324
Chain 1:   1000       -11605.882             0.585            0.437
Chain 1:   1100       -15295.908             0.509            0.324   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12158.429             0.342            0.290
Chain 1:   1300       -13397.378             0.322            0.258
Chain 1:   1400       -10887.521             0.312            0.241
Chain 1:   1500       -10877.751             0.267            0.231
Chain 1:   1600       -13482.944             0.263            0.231
Chain 1:   1700       -10739.480             0.276            0.241
Chain 1:   1800       -13016.461             0.281            0.241
Chain 1:   1900       -10523.915             0.261            0.237
Chain 1:   2000       -11130.860             0.174            0.231
Chain 1:   2100        -9723.919             0.164            0.193
Chain 1:   2200       -10569.479             0.146            0.175
Chain 1:   2300       -15546.019             0.169            0.193
Chain 1:   2400       -10472.899             0.195            0.193
Chain 1:   2500       -12405.954             0.210            0.193
Chain 1:   2600       -10238.880             0.212            0.212
Chain 1:   2700       -17352.118             0.227            0.212
Chain 1:   2800       -13820.727             0.235            0.237
Chain 1:   2900        -9483.282             0.257            0.256
Chain 1:   3000        -9320.492             0.254            0.256
Chain 1:   3100        -9448.495             0.241            0.256
Chain 1:   3200        -9271.305             0.234            0.256
Chain 1:   3300       -15762.412             0.244            0.256
Chain 1:   3400       -10236.744             0.249            0.256
Chain 1:   3500       -14923.407             0.265            0.314
Chain 1:   3600        -9625.850             0.299            0.410
Chain 1:   3700        -9332.616             0.261            0.314
Chain 1:   3800       -16116.504             0.278            0.412
Chain 1:   3900       -10792.310             0.281            0.412
Chain 1:   4000       -11811.102             0.288            0.412
Chain 1:   4100        -9556.674             0.310            0.412
Chain 1:   4200       -14087.989             0.341            0.412
Chain 1:   4300       -17042.842             0.317            0.322
Chain 1:   4400        -9630.074             0.340            0.322
Chain 1:   4500        -9948.769             0.311            0.322
Chain 1:   4600       -13164.655             0.281            0.244
Chain 1:   4700       -10319.001             0.305            0.276
Chain 1:   4800        -8920.398             0.279            0.244
Chain 1:   4900        -9267.989             0.233            0.236
Chain 1:   5000       -10419.419             0.236            0.236
Chain 1:   5100        -9714.308             0.219            0.173
Chain 1:   5200       -10900.828             0.198            0.157
Chain 1:   5300       -12449.989             0.193            0.124
Chain 1:   5400        -9206.015             0.152            0.124
Chain 1:   5500       -14759.006             0.186            0.157
Chain 1:   5600       -13460.074             0.171            0.124
Chain 1:   5700        -9717.299             0.182            0.124
Chain 1:   5800        -9300.423             0.171            0.111
Chain 1:   5900       -10456.045             0.178            0.111
Chain 1:   6000        -8881.670             0.185            0.124
Chain 1:   6100        -9661.856             0.186            0.124
Chain 1:   6200        -8601.605             0.187            0.124
Chain 1:   6300        -9033.273             0.179            0.123
Chain 1:   6400        -9110.823             0.145            0.111
Chain 1:   6500        -9139.545             0.108            0.097
Chain 1:   6600        -8949.267             0.100            0.081
Chain 1:   6700        -8776.623             0.064            0.048
Chain 1:   6800        -8667.790             0.060            0.048
Chain 1:   6900       -12998.832             0.083            0.048
Chain 1:   7000        -9020.544             0.109            0.048
Chain 1:   7100       -10351.579             0.114            0.048
Chain 1:   7200        -9065.758             0.116            0.048
Chain 1:   7300       -11911.043             0.135            0.129
Chain 1:   7400       -13635.156             0.147            0.129
Chain 1:   7500        -8573.014             0.205            0.142
Chain 1:   7600        -9029.205             0.208            0.142
Chain 1:   7700        -9189.267             0.208            0.142
Chain 1:   7800        -9281.248             0.208            0.142
Chain 1:   7900        -8614.515             0.182            0.129
Chain 1:   8000       -11121.372             0.161            0.129
Chain 1:   8100        -9191.800             0.169            0.142
Chain 1:   8200        -9254.393             0.155            0.126
Chain 1:   8300        -8807.869             0.136            0.077
Chain 1:   8400       -11300.103             0.146            0.077
Chain 1:   8500       -10668.163             0.093            0.059
Chain 1:   8600       -10378.889             0.091            0.059
Chain 1:   8700        -8789.218             0.107            0.077
Chain 1:   8800        -8693.192             0.107            0.077
Chain 1:   8900       -12853.010             0.132            0.181
Chain 1:   9000        -9892.024             0.139            0.181
Chain 1:   9100        -8528.928             0.134            0.160
Chain 1:   9200        -8778.094             0.136            0.160
Chain 1:   9300        -8484.947             0.135            0.160
Chain 1:   9400        -8742.881             0.115            0.059
Chain 1:   9500        -9838.092             0.121            0.111
Chain 1:   9600        -9275.738             0.124            0.111
Chain 1:   9700        -9059.307             0.108            0.061
Chain 1:   9800        -8738.262             0.111            0.061
Chain 1:   9900        -9633.968             0.088            0.061
Chain 1:   10000        -8378.651             0.073            0.061
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001422 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46614.724             1.000            1.000
Chain 1:    200       -15945.315             1.462            1.923
Chain 1:    300        -8910.428             1.238            1.000
Chain 1:    400        -8100.394             0.953            1.000
Chain 1:    500        -8777.180             0.778            0.790
Chain 1:    600        -8788.142             0.649            0.790
Chain 1:    700        -7927.196             0.571            0.109
Chain 1:    800        -8172.745             0.504            0.109
Chain 1:    900        -7687.769             0.455            0.100
Chain 1:   1000        -7972.994             0.413            0.100
Chain 1:   1100        -7777.410             0.315            0.077
Chain 1:   1200        -7780.482             0.123            0.063
Chain 1:   1300        -7726.454             0.045            0.036
Chain 1:   1400        -7928.307             0.037            0.030
Chain 1:   1500        -7546.017             0.035            0.030
Chain 1:   1600        -7656.775             0.036            0.030
Chain 1:   1700        -7564.090             0.026            0.025
Chain 1:   1800        -7544.871             0.024            0.025
Chain 1:   1900        -7515.965             0.018            0.014
Chain 1:   2000        -7672.372             0.016            0.014
Chain 1:   2100        -7442.907             0.017            0.014
Chain 1:   2200        -7866.174             0.022            0.020
Chain 1:   2300        -7526.418             0.026            0.025
Chain 1:   2400        -7497.128             0.024            0.020
Chain 1:   2500        -7590.769             0.020            0.014
Chain 1:   2600        -7497.170             0.020            0.012
Chain 1:   2700        -7480.528             0.019            0.012
Chain 1:   2800        -7495.312             0.019            0.012
Chain 1:   2900        -7342.940             0.020            0.020
Chain 1:   3000        -7493.635             0.020            0.020
Chain 1:   3100        -7500.568             0.017            0.012
Chain 1:   3200        -7715.363             0.015            0.012
Chain 1:   3300        -7411.603             0.014            0.012
Chain 1:   3400        -7672.665             0.017            0.020
Chain 1:   3500        -7410.311             0.020            0.021
Chain 1:   3600        -7469.294             0.019            0.021
Chain 1:   3700        -7424.937             0.020            0.021
Chain 1:   3800        -7424.926             0.019            0.021
Chain 1:   3900        -7376.704             0.018            0.020
Chain 1:   4000        -7370.540             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003016 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87123.427             1.000            1.000
Chain 1:    200       -14008.330             3.110            5.219
Chain 1:    300       -10279.182             2.194            1.000
Chain 1:    400       -11351.156             1.669            1.000
Chain 1:    500        -9220.913             1.382            0.363
Chain 1:    600        -9520.948             1.157            0.363
Chain 1:    700        -9248.868             0.996            0.231
Chain 1:    800        -8607.219             0.880            0.231
Chain 1:    900        -8634.964             0.783            0.094
Chain 1:   1000        -8757.826             0.706            0.094
Chain 1:   1100        -9074.540             0.610            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8629.396             0.093            0.052
Chain 1:   1300        -8949.621             0.060            0.036
Chain 1:   1400        -8940.345             0.051            0.035
Chain 1:   1500        -8784.469             0.029            0.032
Chain 1:   1600        -8893.298             0.027            0.029
Chain 1:   1700        -8955.522             0.025            0.018
Chain 1:   1800        -8517.327             0.023            0.018
Chain 1:   1900        -8621.767             0.024            0.018
Chain 1:   2000        -8601.191             0.023            0.018
Chain 1:   2100        -8739.807             0.021            0.016
Chain 1:   2200        -8522.570             0.018            0.016
Chain 1:   2300        -8688.709             0.016            0.016
Chain 1:   2400        -8523.672             0.018            0.018
Chain 1:   2500        -8594.477             0.017            0.016
Chain 1:   2600        -8506.697             0.017            0.016
Chain 1:   2700        -8540.817             0.017            0.016
Chain 1:   2800        -8499.350             0.012            0.012
Chain 1:   2900        -8595.660             0.012            0.011
Chain 1:   3000        -8434.618             0.014            0.016
Chain 1:   3100        -8583.537             0.014            0.017
Chain 1:   3200        -8454.565             0.013            0.015
Chain 1:   3300        -8466.797             0.011            0.011
Chain 1:   3400        -8642.981             0.011            0.011
Chain 1:   3500        -8646.133             0.010            0.011
Chain 1:   3600        -8410.559             0.012            0.015
Chain 1:   3700        -8559.006             0.014            0.017
Chain 1:   3800        -8416.352             0.015            0.017
Chain 1:   3900        -8350.044             0.014            0.017
Chain 1:   4000        -8433.244             0.013            0.017
Chain 1:   4100        -8422.168             0.012            0.015
Chain 1:   4200        -8407.436             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002963 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396509.384             1.000            1.000
Chain 1:    200     -1587257.180             2.645            4.290
Chain 1:    300      -891930.785             2.023            1.000
Chain 1:    400      -458198.995             1.754            1.000
Chain 1:    500      -358404.129             1.459            0.947
Chain 1:    600      -233318.058             1.305            0.947
Chain 1:    700      -119646.312             1.254            0.947
Chain 1:    800       -86873.742             1.145            0.947
Chain 1:    900       -67247.215             1.050            0.780
Chain 1:   1000       -52072.584             0.974            0.780
Chain 1:   1100       -39566.208             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38750.402             0.479            0.377
Chain 1:   1300       -26717.457             0.446            0.377
Chain 1:   1400       -26439.955             0.352            0.316
Chain 1:   1500       -23028.800             0.339            0.316
Chain 1:   1600       -22246.300             0.289            0.292
Chain 1:   1700       -21121.123             0.200            0.291
Chain 1:   1800       -21065.805             0.162            0.148
Chain 1:   1900       -21392.422             0.134            0.053
Chain 1:   2000       -19902.986             0.113            0.053
Chain 1:   2100       -20141.622             0.082            0.035
Chain 1:   2200       -20368.185             0.081            0.035
Chain 1:   2300       -19985.130             0.038            0.019
Chain 1:   2400       -19757.035             0.038            0.019
Chain 1:   2500       -19558.876             0.024            0.015
Chain 1:   2600       -19188.805             0.023            0.015
Chain 1:   2700       -19145.702             0.018            0.012
Chain 1:   2800       -18862.253             0.019            0.015
Chain 1:   2900       -19143.714             0.019            0.015
Chain 1:   3000       -19129.941             0.012            0.012
Chain 1:   3100       -19214.969             0.011            0.012
Chain 1:   3200       -18905.404             0.011            0.015
Chain 1:   3300       -19110.322             0.011            0.012
Chain 1:   3400       -18584.688             0.012            0.015
Chain 1:   3500       -19197.363             0.014            0.015
Chain 1:   3600       -18503.014             0.016            0.015
Chain 1:   3700       -18890.541             0.018            0.016
Chain 1:   3800       -17848.604             0.022            0.021
Chain 1:   3900       -17844.669             0.021            0.021
Chain 1:   4000       -17962.018             0.021            0.021
Chain 1:   4100       -17875.657             0.022            0.021
Chain 1:   4200       -17691.554             0.021            0.021
Chain 1:   4300       -17830.233             0.021            0.021
Chain 1:   4400       -17786.763             0.018            0.010
Chain 1:   4500       -17689.199             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00129 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49023.417             1.000            1.000
Chain 1:    200       -20074.813             1.221            1.442
Chain 1:    300       -20947.815             0.828            1.000
Chain 1:    400       -17195.229             0.675            1.000
Chain 1:    500       -22122.248             0.585            0.223
Chain 1:    600       -13878.875             0.586            0.594
Chain 1:    700       -11644.765             0.530            0.223
Chain 1:    800       -10860.683             0.473            0.223
Chain 1:    900       -13144.705             0.440            0.218
Chain 1:   1000       -13495.691             0.398            0.218
Chain 1:   1100       -10402.703             0.328            0.218
Chain 1:   1200       -18291.503             0.227            0.218
Chain 1:   1300       -11107.040             0.287            0.223
Chain 1:   1400       -16678.395             0.299            0.297
Chain 1:   1500       -11083.702             0.327            0.334
Chain 1:   1600        -9606.481             0.283            0.297
Chain 1:   1700       -20768.797             0.318            0.334
Chain 1:   1800        -9178.502             0.437            0.431
Chain 1:   1900        -9618.068             0.424            0.431
Chain 1:   2000       -10388.185             0.429            0.431
Chain 1:   2100       -20027.635             0.447            0.481
Chain 1:   2200        -9751.218             0.509            0.505   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300       -13018.555             0.470            0.481
Chain 1:   2400        -8889.546             0.483            0.481
Chain 1:   2500        -9926.167             0.443            0.464
Chain 1:   2600        -9184.693             0.436            0.464
Chain 1:   2700        -8920.313             0.385            0.251
Chain 1:   2800       -10582.693             0.274            0.157
Chain 1:   2900       -11728.678             0.279            0.157
Chain 1:   3000        -9882.993             0.291            0.187
Chain 1:   3100        -9806.343             0.243            0.157
Chain 1:   3200        -8649.260             0.151            0.134
Chain 1:   3300       -15217.960             0.169            0.134
Chain 1:   3400       -10325.151             0.170            0.134
Chain 1:   3500        -9391.032             0.170            0.134
Chain 1:   3600        -8794.559             0.169            0.134
Chain 1:   3700        -8608.621             0.168            0.134
Chain 1:   3800        -8796.353             0.154            0.099
Chain 1:   3900        -9539.575             0.152            0.099
Chain 1:   4000       -10007.573             0.138            0.078
Chain 1:   4100        -8692.349             0.153            0.099
Chain 1:   4200       -11765.762             0.165            0.099
Chain 1:   4300       -12455.309             0.128            0.078
Chain 1:   4400       -16617.703             0.105            0.078
Chain 1:   4500       -18510.601             0.106            0.078
Chain 1:   4600       -13320.069             0.138            0.102
Chain 1:   4700       -12543.555             0.142            0.102
Chain 1:   4800        -8534.713             0.187            0.151
Chain 1:   4900        -8535.938             0.179            0.151
Chain 1:   5000       -12122.717             0.204            0.250
Chain 1:   5100        -8528.820             0.231            0.261
Chain 1:   5200        -8902.352             0.209            0.250
Chain 1:   5300        -9178.564             0.206            0.250
Chain 1:   5400        -8565.899             0.188            0.102
Chain 1:   5500        -8284.754             0.182            0.072
Chain 1:   5600        -8233.368             0.143            0.062
Chain 1:   5700       -12765.924             0.173            0.072
Chain 1:   5800        -9242.966             0.164            0.072
Chain 1:   5900        -9038.704             0.166            0.072
Chain 1:   6000        -8506.671             0.143            0.063
Chain 1:   6100        -9782.942             0.114            0.063
Chain 1:   6200        -8106.877             0.130            0.072
Chain 1:   6300        -8402.848             0.131            0.072
Chain 1:   6400       -10690.997             0.145            0.130
Chain 1:   6500        -8414.716             0.168            0.207
Chain 1:   6600        -8565.561             0.170            0.207
Chain 1:   6700        -8138.659             0.139            0.130
Chain 1:   6800        -8740.929             0.108            0.069
Chain 1:   6900        -8943.732             0.108            0.069
Chain 1:   7000       -10530.637             0.117            0.130
Chain 1:   7100        -8174.689             0.133            0.151
Chain 1:   7200        -8453.987             0.115            0.069
Chain 1:   7300        -8518.973             0.113            0.069
Chain 1:   7400        -8317.369             0.094            0.052
Chain 1:   7500       -11909.706             0.097            0.052
Chain 1:   7600        -9509.561             0.120            0.069
Chain 1:   7700        -8182.572             0.131            0.151
Chain 1:   7800       -14281.913             0.167            0.162
Chain 1:   7900        -8290.352             0.237            0.252
Chain 1:   8000        -8755.728             0.227            0.252
Chain 1:   8100        -8066.377             0.207            0.162
Chain 1:   8200        -9919.383             0.222            0.187
Chain 1:   8300        -9395.366             0.227            0.187
Chain 1:   8400        -8455.371             0.236            0.187
Chain 1:   8500       -10542.553             0.225            0.187
Chain 1:   8600       -11924.375             0.212            0.162
Chain 1:   8700       -10095.021             0.214            0.181
Chain 1:   8800        -8305.943             0.193            0.181
Chain 1:   8900        -8958.416             0.128            0.116
Chain 1:   9000        -8844.979             0.124            0.116
Chain 1:   9100        -8115.604             0.124            0.116
Chain 1:   9200        -8320.143             0.108            0.111
Chain 1:   9300        -8684.192             0.106            0.111
Chain 1:   9400        -9181.999             0.101            0.090
Chain 1:   9500        -8159.629             0.093            0.090
Chain 1:   9600        -8163.323             0.082            0.073
Chain 1:   9700        -8857.051             0.072            0.073
Chain 1:   9800        -8583.061             0.053            0.054
Chain 1:   9900        -9923.875             0.059            0.054
Chain 1:   10000        -7904.366             0.084            0.078
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57394.483             1.000            1.000
Chain 1:    200       -17128.719             1.675            2.351
Chain 1:    300        -8672.174             1.442            1.000
Chain 1:    400        -8519.334             1.086            1.000
Chain 1:    500        -8380.517             0.872            0.975
Chain 1:    600        -8443.727             0.728            0.975
Chain 1:    700        -7777.762             0.636            0.086
Chain 1:    800        -8301.436             0.565            0.086
Chain 1:    900        -7991.529             0.506            0.063
Chain 1:   1000        -7840.458             0.457            0.063
Chain 1:   1100        -7759.179             0.359            0.039
Chain 1:   1200        -7659.263             0.125            0.019
Chain 1:   1300        -7683.878             0.028            0.018
Chain 1:   1400        -7956.707             0.029            0.019
Chain 1:   1500        -7641.268             0.032            0.034
Chain 1:   1600        -7650.753             0.031            0.034
Chain 1:   1700        -7531.274             0.024            0.019
Chain 1:   1800        -7605.680             0.019            0.016
Chain 1:   1900        -7578.882             0.015            0.013
Chain 1:   2000        -7661.270             0.014            0.011
Chain 1:   2100        -7628.303             0.014            0.011
Chain 1:   2200        -7709.591             0.013            0.011
Chain 1:   2300        -7624.738             0.014            0.011
Chain 1:   2400        -7663.230             0.011            0.011
Chain 1:   2500        -7590.912             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002993 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86532.557             1.000            1.000
Chain 1:    200       -13348.374             3.241            5.483
Chain 1:    300        -9709.795             2.286            1.000
Chain 1:    400       -10808.867             1.740            1.000
Chain 1:    500        -8504.189             1.446            0.375
Chain 1:    600        -8244.897             1.210            0.375
Chain 1:    700        -8381.325             1.040            0.271
Chain 1:    800        -8489.975             0.911            0.271
Chain 1:    900        -8484.936             0.810            0.102
Chain 1:   1000        -8427.667             0.730            0.102
Chain 1:   1100        -8554.530             0.631            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8161.824             0.088            0.031
Chain 1:   1300        -8388.388             0.053            0.027
Chain 1:   1400        -8406.815             0.043            0.016
Chain 1:   1500        -8254.327             0.018            0.016
Chain 1:   1600        -8368.456             0.016            0.015
Chain 1:   1700        -8447.527             0.015            0.014
Chain 1:   1800        -8028.936             0.019            0.015
Chain 1:   1900        -8127.659             0.020            0.015
Chain 1:   2000        -8101.535             0.020            0.015
Chain 1:   2100        -8225.662             0.020            0.015
Chain 1:   2200        -8038.477             0.018            0.015
Chain 1:   2300        -8122.230             0.016            0.014
Chain 1:   2400        -8191.696             0.017            0.014
Chain 1:   2500        -8137.617             0.015            0.012
Chain 1:   2600        -8137.995             0.014            0.010
Chain 1:   2700        -8055.168             0.014            0.010
Chain 1:   2800        -8016.628             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003076 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8453774.629             1.000            1.000
Chain 1:    200     -1595846.594             2.649            4.297
Chain 1:    300      -892486.803             2.028            1.000
Chain 1:    400      -457504.107             1.759            1.000
Chain 1:    500      -356840.964             1.464            0.951
Chain 1:    600      -231556.701             1.310            0.951
Chain 1:    700      -118340.162             1.259            0.951
Chain 1:    800       -85695.722             1.150            0.951
Chain 1:    900       -66176.514             1.055            0.788
Chain 1:   1000       -51108.308             0.979            0.788
Chain 1:   1100       -38703.107             0.911            0.541   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37895.477             0.483            0.381
Chain 1:   1300       -25969.435             0.450            0.381
Chain 1:   1400       -25699.928             0.356            0.321
Chain 1:   1500       -22317.644             0.343            0.321
Chain 1:   1600       -21543.300             0.293            0.295
Chain 1:   1700       -20431.218             0.202            0.295
Chain 1:   1800       -20378.688             0.165            0.152
Chain 1:   1900       -20704.902             0.137            0.054
Chain 1:   2000       -19223.254             0.115            0.054
Chain 1:   2100       -19461.248             0.084            0.036
Chain 1:   2200       -19686.562             0.083            0.036
Chain 1:   2300       -19304.780             0.039            0.020
Chain 1:   2400       -19077.020             0.039            0.020
Chain 1:   2500       -18878.480             0.025            0.016
Chain 1:   2600       -18509.161             0.024            0.016
Chain 1:   2700       -18466.338             0.018            0.012
Chain 1:   2800       -18182.933             0.020            0.016
Chain 1:   2900       -18464.020             0.020            0.015
Chain 1:   3000       -18450.370             0.012            0.012
Chain 1:   3100       -18535.327             0.011            0.012
Chain 1:   3200       -18226.110             0.012            0.015
Chain 1:   3300       -18430.767             0.011            0.012
Chain 1:   3400       -17905.633             0.013            0.015
Chain 1:   3500       -18517.367             0.015            0.016
Chain 1:   3600       -17824.233             0.017            0.016
Chain 1:   3700       -18210.760             0.019            0.017
Chain 1:   3800       -17170.625             0.023            0.021
Chain 1:   3900       -17166.708             0.022            0.021
Chain 1:   4000       -17284.093             0.022            0.021
Chain 1:   4100       -17197.811             0.022            0.021
Chain 1:   4200       -17014.134             0.022            0.021
Chain 1:   4300       -17152.539             0.021            0.021
Chain 1:   4400       -17109.381             0.019            0.011
Chain 1:   4500       -17011.873             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12721.449             1.000            1.000
Chain 1:    200        -9465.951             0.672            1.000
Chain 1:    300        -7992.849             0.509            0.344
Chain 1:    400        -8206.058             0.389            0.344
Chain 1:    500        -8209.951             0.311            0.184
Chain 1:    600        -7889.433             0.266            0.184
Chain 1:    700        -7817.160             0.229            0.041
Chain 1:    800        -7790.771             0.201            0.041
Chain 1:    900        -7893.732             0.180            0.026
Chain 1:   1000        -7867.970             0.162            0.026
Chain 1:   1100        -7857.680             0.063            0.013
Chain 1:   1200        -7803.440             0.029            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001519 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57679.007             1.000            1.000
Chain 1:    200       -17863.392             1.614            2.229
Chain 1:    300        -8929.314             1.410            1.001
Chain 1:    400        -8292.059             1.077            1.001
Chain 1:    500        -9221.800             0.881            1.000
Chain 1:    600        -8605.711             0.746            1.000
Chain 1:    700        -8483.092             0.642            0.101
Chain 1:    800        -8363.081             0.563            0.101
Chain 1:    900        -7989.936             0.506            0.077
Chain 1:   1000        -7972.214             0.456            0.077
Chain 1:   1100        -7581.570             0.361            0.072
Chain 1:   1200        -7667.208             0.139            0.052
Chain 1:   1300        -7619.506             0.040            0.047
Chain 1:   1400        -7924.489             0.036            0.038
Chain 1:   1500        -7557.808             0.031            0.038
Chain 1:   1600        -7738.743             0.026            0.023
Chain 1:   1700        -7671.933             0.025            0.023
Chain 1:   1800        -7719.218             0.024            0.023
Chain 1:   1900        -7466.717             0.023            0.023
Chain 1:   2000        -7592.312             0.024            0.023
Chain 1:   2100        -7541.976             0.020            0.017
Chain 1:   2200        -7704.159             0.021            0.021
Chain 1:   2300        -7564.227             0.022            0.021
Chain 1:   2400        -7511.765             0.019            0.018
Chain 1:   2500        -7556.044             0.015            0.017
Chain 1:   2600        -7511.518             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003089 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86608.657             1.000            1.000
Chain 1:    200       -13831.924             3.131            5.262
Chain 1:    300       -10020.240             2.214            1.000
Chain 1:    400       -12139.649             1.704            1.000
Chain 1:    500        -8412.931             1.452            0.443
Chain 1:    600        -8905.641             1.219            0.443
Chain 1:    700        -8603.006             1.050            0.380
Chain 1:    800        -8162.235             0.925            0.380
Chain 1:    900        -8195.171             0.823            0.175
Chain 1:   1000        -8827.783             0.748            0.175
Chain 1:   1100        -8512.140             0.652            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8936.134             0.130            0.055
Chain 1:   1300        -8301.624             0.100            0.055
Chain 1:   1400        -8357.599             0.083            0.054
Chain 1:   1500        -8389.886             0.039            0.047
Chain 1:   1600        -8349.847             0.034            0.037
Chain 1:   1700        -8214.875             0.032            0.037
Chain 1:   1800        -8267.905             0.027            0.016
Chain 1:   1900        -8307.287             0.028            0.016
Chain 1:   2000        -8453.002             0.022            0.016
Chain 1:   2100        -8184.299             0.022            0.016
Chain 1:   2200        -8205.840             0.017            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002911 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8427211.566             1.000            1.000
Chain 1:    200     -1585345.201             2.658            4.316
Chain 1:    300      -891551.309             2.031            1.000
Chain 1:    400      -458198.900             1.760            1.000
Chain 1:    500      -358576.180             1.463            0.946
Chain 1:    600      -233333.188             1.309            0.946
Chain 1:    700      -119558.553             1.258            0.946
Chain 1:    800       -86817.044             1.148            0.946
Chain 1:    900       -67157.276             1.053            0.778
Chain 1:   1000       -51974.162             0.977            0.778
Chain 1:   1100       -39464.137             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38649.090             0.479            0.377
Chain 1:   1300       -26592.936             0.447            0.377
Chain 1:   1400       -26315.468             0.353            0.317
Chain 1:   1500       -22899.671             0.340            0.317
Chain 1:   1600       -22117.421             0.290            0.293
Chain 1:   1700       -20987.936             0.200            0.292
Chain 1:   1800       -20932.184             0.163            0.149
Chain 1:   1900       -21259.308             0.135            0.054
Chain 1:   2000       -19767.461             0.113            0.054
Chain 1:   2100       -20005.804             0.083            0.035
Chain 1:   2200       -20233.378             0.082            0.035
Chain 1:   2300       -19849.382             0.038            0.019
Chain 1:   2400       -19621.115             0.039            0.019
Chain 1:   2500       -19423.320             0.025            0.015
Chain 1:   2600       -19052.294             0.023            0.015
Chain 1:   2700       -19008.929             0.018            0.012
Chain 1:   2800       -18725.475             0.019            0.015
Chain 1:   2900       -19007.140             0.019            0.015
Chain 1:   3000       -18993.192             0.012            0.012
Chain 1:   3100       -19078.383             0.011            0.012
Chain 1:   3200       -18768.338             0.011            0.015
Chain 1:   3300       -18973.639             0.011            0.012
Chain 1:   3400       -18447.402             0.012            0.015
Chain 1:   3500       -19061.094             0.014            0.015
Chain 1:   3600       -18365.361             0.016            0.015
Chain 1:   3700       -18753.959             0.018            0.017
Chain 1:   3800       -17710.045             0.023            0.021
Chain 1:   3900       -17706.128             0.021            0.021
Chain 1:   4000       -17823.393             0.022            0.021
Chain 1:   4100       -17737.028             0.022            0.021
Chain 1:   4200       -17552.476             0.021            0.021
Chain 1:   4300       -17691.408             0.021            0.021
Chain 1:   4400       -17647.555             0.018            0.011
Chain 1:   4500       -17550.016             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001187 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48628.922             1.000            1.000
Chain 1:    200       -19169.419             1.268            1.537
Chain 1:    300       -15624.094             0.921            1.000
Chain 1:    400       -17518.365             0.718            1.000
Chain 1:    500       -12551.329             0.654            0.396
Chain 1:    600       -13982.871             0.562            0.396
Chain 1:    700       -15132.819             0.492            0.227
Chain 1:    800       -12908.990             0.452            0.227
Chain 1:    900       -13733.442             0.409            0.172
Chain 1:   1000       -12286.740             0.380            0.172
Chain 1:   1100       -13459.699             0.288            0.118
Chain 1:   1200       -12650.187             0.141            0.108
Chain 1:   1300        -9809.550             0.147            0.108
Chain 1:   1400       -11092.257             0.148            0.116
Chain 1:   1500       -10002.754             0.119            0.109
Chain 1:   1600       -10182.117             0.111            0.109
Chain 1:   1700       -11184.544             0.112            0.109
Chain 1:   1800       -11669.258             0.099            0.090
Chain 1:   1900       -10611.177             0.103            0.100
Chain 1:   2000       -10416.238             0.093            0.090
Chain 1:   2100       -10310.651             0.086            0.090
Chain 1:   2200        -9677.177             0.086            0.090
Chain 1:   2300        -9100.852             0.063            0.065
Chain 1:   2400       -17860.161             0.101            0.065
Chain 1:   2500       -10413.092             0.161            0.065
Chain 1:   2600       -11653.430             0.170            0.090
Chain 1:   2700        -8946.791             0.191            0.100
Chain 1:   2800       -10751.039             0.204            0.106
Chain 1:   2900        -9643.793             0.205            0.115
Chain 1:   3000        -9015.274             0.211            0.115
Chain 1:   3100       -10343.339             0.222            0.128
Chain 1:   3200       -12044.214             0.230            0.141
Chain 1:   3300        -9430.781             0.251            0.168
Chain 1:   3400        -9450.160             0.203            0.141
Chain 1:   3500        -9314.540             0.132            0.128
Chain 1:   3600        -9248.233             0.123            0.128
Chain 1:   3700        -8749.943             0.098            0.115
Chain 1:   3800       -12139.258             0.109            0.115
Chain 1:   3900       -10371.840             0.115            0.128
Chain 1:   4000        -8729.181             0.127            0.141
Chain 1:   4100        -8493.541             0.116            0.141
Chain 1:   4200       -12207.828             0.133            0.170
Chain 1:   4300        -9348.008             0.136            0.170
Chain 1:   4400       -13613.931             0.167            0.188
Chain 1:   4500       -10056.493             0.201            0.279
Chain 1:   4600        -9102.265             0.210            0.279
Chain 1:   4700        -8856.937             0.208            0.279
Chain 1:   4800        -8892.505             0.180            0.188
Chain 1:   4900        -8724.545             0.165            0.188
Chain 1:   5000        -9692.885             0.156            0.105
Chain 1:   5100       -14247.721             0.185            0.304
Chain 1:   5200        -8891.372             0.215            0.306
Chain 1:   5300       -12726.129             0.215            0.301
Chain 1:   5400        -8311.114             0.236            0.301
Chain 1:   5500       -11630.546             0.230            0.285
Chain 1:   5600        -8294.207             0.259            0.301
Chain 1:   5700        -8536.324             0.259            0.301
Chain 1:   5800        -8422.938             0.260            0.301
Chain 1:   5900       -13653.671             0.297            0.320
Chain 1:   6000        -8486.024             0.348            0.383
Chain 1:   6100       -12639.861             0.349            0.383
Chain 1:   6200        -8099.891             0.344            0.383
Chain 1:   6300        -8141.753             0.315            0.383
Chain 1:   6400       -13396.181             0.301            0.383
Chain 1:   6500        -9017.737             0.321            0.392
Chain 1:   6600       -10248.131             0.293            0.383
Chain 1:   6700        -8921.423             0.305            0.383
Chain 1:   6800       -10053.872             0.315            0.383
Chain 1:   6900        -8774.449             0.291            0.329
Chain 1:   7000        -9361.048             0.236            0.149
Chain 1:   7100       -13120.482             0.232            0.149
Chain 1:   7200       -13155.776             0.176            0.146
Chain 1:   7300       -10606.644             0.200            0.149
Chain 1:   7400        -8125.204             0.191            0.149
Chain 1:   7500        -9924.152             0.161            0.149
Chain 1:   7600        -8439.319             0.166            0.176
Chain 1:   7700        -8441.400             0.151            0.176
Chain 1:   7800        -8424.950             0.140            0.176
Chain 1:   7900       -11121.540             0.150            0.181
Chain 1:   8000        -8173.636             0.180            0.240
Chain 1:   8100        -8833.134             0.159            0.181
Chain 1:   8200        -8471.766             0.163            0.181
Chain 1:   8300       -11792.568             0.167            0.181
Chain 1:   8400        -8159.065             0.181            0.181
Chain 1:   8500        -8111.998             0.163            0.176
Chain 1:   8600        -8156.736             0.146            0.075
Chain 1:   8700       -10090.193             0.165            0.192
Chain 1:   8800        -8336.114             0.186            0.210
Chain 1:   8900        -9389.386             0.173            0.192
Chain 1:   9000        -9065.588             0.141            0.112
Chain 1:   9100        -9168.393             0.134            0.112
Chain 1:   9200        -8299.996             0.140            0.112
Chain 1:   9300        -8031.019             0.116            0.105
Chain 1:   9400        -7912.256             0.073            0.036
Chain 1:   9500        -8026.417             0.073            0.036
Chain 1:   9600        -8353.600             0.077            0.039
Chain 1:   9700        -8428.342             0.058            0.036
Chain 1:   9800        -9242.944             0.046            0.036
Chain 1:   9900        -8505.199             0.044            0.036
Chain 1:   10000        -8211.036             0.044            0.036
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61314.102             1.000            1.000
Chain 1:    200       -17524.115             1.749            2.499
Chain 1:    300        -8651.273             1.508            1.026
Chain 1:    400        -8904.034             1.138            1.026
Chain 1:    500        -7725.708             0.941            1.000
Chain 1:    600        -8708.729             0.803            1.000
Chain 1:    700        -8156.775             0.698            0.153
Chain 1:    800        -8030.319             0.613            0.153
Chain 1:    900        -7879.465             0.547            0.113
Chain 1:   1000        -7782.176             0.493            0.113
Chain 1:   1100        -7700.447             0.394            0.068
Chain 1:   1200        -7672.661             0.145            0.028
Chain 1:   1300        -7611.187             0.043            0.019
Chain 1:   1400        -7591.255             0.041            0.016
Chain 1:   1500        -7598.123             0.025            0.013
Chain 1:   1600        -7491.850             0.016            0.013
Chain 1:   1700        -7476.524             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85235.107             1.000            1.000
Chain 1:    200       -13126.590             3.247            5.493
Chain 1:    300        -9628.058             2.286            1.000
Chain 1:    400       -10451.697             1.734            1.000
Chain 1:    500        -8495.252             1.433            0.363
Chain 1:    600        -8224.983             1.200            0.363
Chain 1:    700        -8589.378             1.034            0.230
Chain 1:    800        -8793.527             0.908            0.230
Chain 1:    900        -8495.637             0.811            0.079
Chain 1:   1000        -8216.145             0.733            0.079
Chain 1:   1100        -8466.715             0.636            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8225.116             0.090            0.035
Chain 1:   1300        -8393.231             0.056            0.034
Chain 1:   1400        -8326.586             0.048            0.033
Chain 1:   1500        -8271.871             0.026            0.030
Chain 1:   1600        -8268.575             0.023            0.029
Chain 1:   1700        -8207.290             0.019            0.023
Chain 1:   1800        -8087.664             0.019            0.020
Chain 1:   1900        -8200.929             0.016            0.015
Chain 1:   2000        -8162.725             0.013            0.014
Chain 1:   2100        -8304.728             0.012            0.014
Chain 1:   2200        -8088.403             0.012            0.014
Chain 1:   2300        -8229.652             0.012            0.014
Chain 1:   2400        -8116.481             0.012            0.014
Chain 1:   2500        -8172.159             0.012            0.014
Chain 1:   2600        -8185.397             0.012            0.014
Chain 1:   2700        -8106.908             0.013            0.014
Chain 1:   2800        -8091.158             0.011            0.014
Chain 1:   2900        -8088.463             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003042 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400981.222             1.000            1.000
Chain 1:    200     -1582620.111             2.654            4.308
Chain 1:    300      -890362.720             2.029            1.000
Chain 1:    400      -457125.442             1.758            1.000
Chain 1:    500      -357472.179             1.462            0.948
Chain 1:    600      -232628.729             1.308            0.948
Chain 1:    700      -118854.711             1.258            0.948
Chain 1:    800       -86057.544             1.148            0.948
Chain 1:    900       -66393.688             1.054            0.778
Chain 1:   1000       -51177.434             0.978            0.778
Chain 1:   1100       -38648.042             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37817.257             0.482            0.381
Chain 1:   1300       -25778.470             0.451            0.381
Chain 1:   1400       -25494.770             0.357            0.324
Chain 1:   1500       -22084.042             0.345            0.324
Chain 1:   1600       -21300.052             0.295            0.297
Chain 1:   1700       -20175.151             0.205            0.296
Chain 1:   1800       -20119.257             0.167            0.154
Chain 1:   1900       -20444.583             0.139            0.056
Chain 1:   2000       -18958.075             0.117            0.056
Chain 1:   2100       -19196.156             0.086            0.037
Chain 1:   2200       -19422.003             0.085            0.037
Chain 1:   2300       -19040.009             0.040            0.020
Chain 1:   2400       -18812.419             0.040            0.020
Chain 1:   2500       -18614.471             0.026            0.016
Chain 1:   2600       -18245.383             0.024            0.016
Chain 1:   2700       -18202.639             0.019            0.012
Chain 1:   2800       -17919.832             0.020            0.016
Chain 1:   2900       -18200.733             0.020            0.015
Chain 1:   3000       -18186.994             0.012            0.012
Chain 1:   3100       -18271.818             0.011            0.012
Chain 1:   3200       -17963.025             0.012            0.015
Chain 1:   3300       -18167.365             0.011            0.012
Chain 1:   3400       -17643.210             0.013            0.015
Chain 1:   3500       -18253.662             0.015            0.016
Chain 1:   3600       -17562.287             0.017            0.016
Chain 1:   3700       -17947.609             0.019            0.017
Chain 1:   3800       -16910.282             0.023            0.021
Chain 1:   3900       -16906.536             0.022            0.021
Chain 1:   4000       -17023.819             0.023            0.021
Chain 1:   4100       -16937.681             0.023            0.021
Chain 1:   4200       -16754.646             0.022            0.021
Chain 1:   4300       -16892.532             0.022            0.021
Chain 1:   4400       -16849.873             0.019            0.011
Chain 1:   4500       -16752.552             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48813.279             1.000            1.000
Chain 1:    200       -23035.144             1.060            1.119
Chain 1:    300       -16868.869             0.828            1.000
Chain 1:    400       -12996.729             0.696            1.000
Chain 1:    500       -12875.987             0.558            0.366
Chain 1:    600       -15226.535             0.491            0.366
Chain 1:    700       -27162.134             0.484            0.366
Chain 1:    800       -14987.667             0.525            0.439
Chain 1:    900       -14189.653             0.473            0.366
Chain 1:   1000       -12903.413             0.435            0.366
Chain 1:   1100        -9644.912             0.369            0.338
Chain 1:   1200       -10843.011             0.268            0.298
Chain 1:   1300       -13093.141             0.249            0.172
Chain 1:   1400       -10349.312             0.246            0.172
Chain 1:   1500       -11910.669             0.258            0.172
Chain 1:   1600       -19681.635             0.282            0.265
Chain 1:   1700       -16532.675             0.257            0.190
Chain 1:   1800       -10866.205             0.228            0.190
Chain 1:   1900       -10093.339             0.230            0.190
Chain 1:   2000        -9739.582             0.224            0.190
Chain 1:   2100        -9662.202             0.191            0.172
Chain 1:   2200        -9733.563             0.180            0.172
Chain 1:   2300        -8932.272             0.172            0.131
Chain 1:   2400        -9755.657             0.154            0.090
Chain 1:   2500       -10109.915             0.144            0.084
Chain 1:   2600       -10250.719             0.106            0.077
Chain 1:   2700       -13206.628             0.110            0.077
Chain 1:   2800       -11077.837             0.077            0.077
Chain 1:   2900        -9732.473             0.083            0.084
Chain 1:   3000       -13164.366             0.105            0.090
Chain 1:   3100        -9380.587             0.145            0.138
Chain 1:   3200        -8776.952             0.151            0.138
Chain 1:   3300        -9429.296             0.149            0.138
Chain 1:   3400        -9283.006             0.142            0.138
Chain 1:   3500       -11830.415             0.160            0.192
Chain 1:   3600        -9587.632             0.182            0.215
Chain 1:   3700        -8880.908             0.168            0.192
Chain 1:   3800        -8582.660             0.152            0.138
Chain 1:   3900       -13240.653             0.173            0.215
Chain 1:   4000        -9915.298             0.181            0.215
Chain 1:   4100        -9984.577             0.141            0.080
Chain 1:   4200        -8702.807             0.149            0.147
Chain 1:   4300        -8453.207             0.145            0.147
Chain 1:   4400        -8625.615             0.145            0.147
Chain 1:   4500        -9904.011             0.137            0.129
Chain 1:   4600        -8490.732             0.130            0.129
Chain 1:   4700       -10189.105             0.139            0.147
Chain 1:   4800        -8890.150             0.150            0.147
Chain 1:   4900        -8794.231             0.116            0.146
Chain 1:   5000       -10365.678             0.097            0.146
Chain 1:   5100        -8343.227             0.121            0.147
Chain 1:   5200        -8631.500             0.110            0.146
Chain 1:   5300       -11614.956             0.132            0.152
Chain 1:   5400        -8244.705             0.171            0.166
Chain 1:   5500       -12571.503             0.193            0.167
Chain 1:   5600        -8311.657             0.227            0.242
Chain 1:   5700       -11206.110             0.237            0.257
Chain 1:   5800        -8829.553             0.249            0.258
Chain 1:   5900        -8523.188             0.251            0.258
Chain 1:   6000       -11307.406             0.261            0.258
Chain 1:   6100       -11563.417             0.239            0.258
Chain 1:   6200        -9910.946             0.252            0.258
Chain 1:   6300        -8443.359             0.244            0.258
Chain 1:   6400       -13565.994             0.241            0.258
Chain 1:   6500        -8520.561             0.265            0.258
Chain 1:   6600       -10684.555             0.234            0.246
Chain 1:   6700        -8283.526             0.238            0.246
Chain 1:   6800       -13225.305             0.248            0.246
Chain 1:   6900       -11257.236             0.262            0.246
Chain 1:   7000        -8684.565             0.267            0.290
Chain 1:   7100       -11964.773             0.292            0.290
Chain 1:   7200        -9360.609             0.303            0.290
Chain 1:   7300        -8367.519             0.298            0.290
Chain 1:   7400       -13727.142             0.299            0.290
Chain 1:   7500       -10007.598             0.277            0.290
Chain 1:   7600        -8255.313             0.278            0.290
Chain 1:   7700       -10245.132             0.268            0.278
Chain 1:   7800        -8477.931             0.252            0.274
Chain 1:   7900        -8101.114             0.239            0.274
Chain 1:   8000       -12231.270             0.243            0.274
Chain 1:   8100        -7956.481             0.270            0.278
Chain 1:   8200        -8098.824             0.243            0.212
Chain 1:   8300        -8225.909             0.233            0.212
Chain 1:   8400        -8184.713             0.195            0.208
Chain 1:   8500        -8202.741             0.158            0.194
Chain 1:   8600        -7799.404             0.142            0.052
Chain 1:   8700        -8359.701             0.129            0.052
Chain 1:   8800        -9577.817             0.121            0.052
Chain 1:   8900        -8322.863             0.131            0.067
Chain 1:   9000       -10119.141             0.115            0.067
Chain 1:   9100        -9954.766             0.063            0.052
Chain 1:   9200        -9070.315             0.071            0.067
Chain 1:   9300        -8010.127             0.083            0.098
Chain 1:   9400        -8078.343             0.083            0.098
Chain 1:   9500        -8312.443             0.086            0.098
Chain 1:   9600        -8098.352             0.083            0.098
Chain 1:   9700        -9940.337             0.095            0.127
Chain 1:   9800       -10928.187             0.091            0.098
Chain 1:   9900        -7941.278             0.114            0.098
Chain 1:   10000       -10631.081             0.121            0.098
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001405 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61201.806             1.000            1.000
Chain 1:    200       -17487.483             1.750            2.500
Chain 1:    300        -8701.506             1.503            1.010
Chain 1:    400        -8177.802             1.143            1.010
Chain 1:    500        -8267.566             0.917            1.000
Chain 1:    600        -8113.406             0.767            1.000
Chain 1:    700        -7742.034             0.664            0.064
Chain 1:    800        -8135.965             0.587            0.064
Chain 1:    900        -7811.367             0.527            0.048
Chain 1:   1000        -7753.467             0.475            0.048
Chain 1:   1100        -7672.020             0.376            0.048
Chain 1:   1200        -7676.088             0.126            0.042
Chain 1:   1300        -7726.945             0.026            0.019
Chain 1:   1400        -7834.770             0.021            0.014
Chain 1:   1500        -7571.969             0.023            0.019
Chain 1:   1600        -7515.943             0.022            0.014
Chain 1:   1700        -7473.629             0.018            0.011
Chain 1:   1800        -7513.598             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002907 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86228.932             1.000            1.000
Chain 1:    200       -13155.579             3.277            5.555
Chain 1:    300        -9583.456             2.309            1.000
Chain 1:    400       -10398.116             1.751            1.000
Chain 1:    500        -8516.963             1.445            0.373
Chain 1:    600        -8121.833             1.213            0.373
Chain 1:    700        -8219.288             1.041            0.221
Chain 1:    800        -8819.595             0.919            0.221
Chain 1:    900        -8349.322             0.823            0.078
Chain 1:   1000        -8190.899             0.743            0.078
Chain 1:   1100        -8442.091             0.646            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8145.405             0.094            0.056
Chain 1:   1300        -8286.102             0.059            0.049
Chain 1:   1400        -8305.520             0.051            0.036
Chain 1:   1500        -8190.097             0.030            0.030
Chain 1:   1600        -8292.123             0.027            0.019
Chain 1:   1700        -8379.824             0.027            0.019
Chain 1:   1800        -7984.385             0.025            0.019
Chain 1:   1900        -8086.250             0.020            0.017
Chain 1:   2000        -8056.563             0.019            0.014
Chain 1:   2100        -8180.047             0.017            0.014
Chain 1:   2200        -7962.867             0.016            0.014
Chain 1:   2300        -8114.816             0.017            0.014
Chain 1:   2400        -8129.070             0.017            0.014
Chain 1:   2500        -8097.823             0.016            0.013
Chain 1:   2600        -8100.247             0.014            0.013
Chain 1:   2700        -8006.614             0.014            0.013
Chain 1:   2800        -7978.265             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003153 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8421396.676             1.000            1.000
Chain 1:    200     -1586700.852             2.654            4.307
Chain 1:    300      -891597.465             2.029            1.000
Chain 1:    400      -457922.702             1.759            1.000
Chain 1:    500      -358014.782             1.463            0.947
Chain 1:    600      -232733.173             1.309            0.947
Chain 1:    700      -118872.835             1.258            0.947
Chain 1:    800       -86081.383             1.149            0.947
Chain 1:    900       -66410.377             1.054            0.780
Chain 1:   1000       -51201.198             0.978            0.780
Chain 1:   1100       -38682.726             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37854.270             0.482            0.381
Chain 1:   1300       -25820.959             0.451            0.381
Chain 1:   1400       -25539.027             0.357            0.324
Chain 1:   1500       -22129.976             0.345            0.324
Chain 1:   1600       -21347.228             0.295            0.297
Chain 1:   1700       -20222.479             0.204            0.296
Chain 1:   1800       -20166.778             0.166            0.154
Chain 1:   1900       -20492.517             0.138            0.056
Chain 1:   2000       -19005.460             0.117            0.056
Chain 1:   2100       -19243.540             0.085            0.037
Chain 1:   2200       -19469.723             0.084            0.037
Chain 1:   2300       -19087.306             0.040            0.020
Chain 1:   2400       -18859.561             0.040            0.020
Chain 1:   2500       -18661.631             0.026            0.016
Chain 1:   2600       -18292.132             0.024            0.016
Chain 1:   2700       -18249.226             0.019            0.012
Chain 1:   2800       -17966.291             0.020            0.016
Chain 1:   2900       -18247.333             0.020            0.015
Chain 1:   3000       -18233.518             0.012            0.012
Chain 1:   3100       -18318.457             0.011            0.012
Chain 1:   3200       -18009.389             0.012            0.015
Chain 1:   3300       -18213.926             0.011            0.012
Chain 1:   3400       -17689.316             0.013            0.015
Chain 1:   3500       -18300.495             0.015            0.016
Chain 1:   3600       -17608.092             0.017            0.016
Chain 1:   3700       -17994.187             0.019            0.017
Chain 1:   3800       -16955.351             0.023            0.021
Chain 1:   3900       -16951.549             0.022            0.021
Chain 1:   4000       -17068.830             0.023            0.021
Chain 1:   4100       -16982.675             0.023            0.021
Chain 1:   4200       -16799.241             0.022            0.021
Chain 1:   4300       -16937.393             0.022            0.021
Chain 1:   4400       -16894.466             0.019            0.011
Chain 1:   4500       -16797.070             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00137 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48772.355             1.000            1.000
Chain 1:    200       -19022.324             1.282            1.564
Chain 1:    300       -14580.263             0.956            1.000
Chain 1:    400       -13339.060             0.740            1.000
Chain 1:    500       -12898.106             0.599            0.305
Chain 1:    600       -19761.849             0.557            0.347
Chain 1:    700       -12102.806             0.568            0.347
Chain 1:    800       -11071.550             0.509            0.347
Chain 1:    900       -14285.474             0.477            0.305
Chain 1:   1000       -12701.040             0.442            0.305
Chain 1:   1100       -29752.485             0.399            0.305
Chain 1:   1200       -13715.529             0.360            0.305
Chain 1:   1300       -12301.862             0.341            0.225
Chain 1:   1400       -10371.990             0.350            0.225
Chain 1:   1500       -12356.609             0.363            0.225
Chain 1:   1600        -9878.067             0.353            0.225
Chain 1:   1700       -14672.547             0.322            0.225
Chain 1:   1800        -9777.835             0.363            0.251
Chain 1:   1900       -16575.410             0.382            0.327
Chain 1:   2000       -10608.375             0.425            0.410
Chain 1:   2100       -15333.056             0.399            0.327
Chain 1:   2200       -10499.617             0.328            0.327
Chain 1:   2300        -9508.288             0.327            0.327
Chain 1:   2400        -9785.295             0.311            0.327
Chain 1:   2500       -14639.731             0.328            0.332
Chain 1:   2600       -19510.091             0.328            0.332
Chain 1:   2700        -9578.862             0.399            0.410
Chain 1:   2800       -10393.750             0.357            0.332
Chain 1:   2900       -10743.928             0.319            0.308
Chain 1:   3000        -9029.177             0.282            0.250
Chain 1:   3100        -9380.213             0.255            0.190
Chain 1:   3200       -10311.796             0.218            0.104
Chain 1:   3300       -13913.700             0.233            0.190
Chain 1:   3400       -12766.630             0.240            0.190
Chain 1:   3500        -9176.823             0.245            0.190
Chain 1:   3600        -9543.749             0.224            0.090
Chain 1:   3700        -9548.556             0.121            0.090
Chain 1:   3800        -8663.037             0.123            0.090
Chain 1:   3900       -10065.696             0.134            0.102
Chain 1:   4000       -18098.508             0.159            0.102
Chain 1:   4100       -10106.604             0.235            0.139
Chain 1:   4200       -13095.096             0.248            0.228
Chain 1:   4300       -15729.480             0.239            0.167
Chain 1:   4400       -11018.656             0.273            0.228
Chain 1:   4500        -8792.427             0.259            0.228
Chain 1:   4600       -12169.729             0.283            0.253
Chain 1:   4700       -13350.264             0.292            0.253
Chain 1:   4800        -8601.557             0.337            0.278
Chain 1:   4900        -8590.032             0.323            0.278
Chain 1:   5000       -14675.257             0.320            0.278
Chain 1:   5100        -8874.212             0.306            0.278
Chain 1:   5200        -9868.675             0.294            0.278
Chain 1:   5300       -13377.026             0.303            0.278
Chain 1:   5400        -9629.303             0.299            0.278
Chain 1:   5500        -8442.088             0.288            0.278
Chain 1:   5600        -9100.526             0.268            0.262
Chain 1:   5700       -13645.245             0.292            0.333
Chain 1:   5800        -8870.661             0.291            0.333
Chain 1:   5900        -9243.435             0.295            0.333
Chain 1:   6000        -8639.594             0.260            0.262
Chain 1:   6100        -8640.102             0.195            0.141
Chain 1:   6200        -9939.589             0.198            0.141
Chain 1:   6300        -8775.491             0.185            0.133
Chain 1:   6400        -9677.559             0.155            0.131
Chain 1:   6500        -9031.885             0.148            0.093
Chain 1:   6600        -9554.115             0.146            0.093
Chain 1:   6700       -13022.538             0.140            0.093
Chain 1:   6800        -8328.738             0.142            0.093
Chain 1:   6900        -8569.009             0.141            0.093
Chain 1:   7000       -13533.836             0.171            0.131
Chain 1:   7100        -8246.482             0.235            0.133
Chain 1:   7200        -8461.730             0.224            0.133
Chain 1:   7300        -9020.159             0.217            0.093
Chain 1:   7400        -8530.488             0.214            0.071
Chain 1:   7500        -9234.082             0.214            0.076
Chain 1:   7600        -8628.597             0.216            0.076
Chain 1:   7700       -11558.543             0.214            0.076
Chain 1:   7800        -8327.645             0.197            0.076
Chain 1:   7900        -8927.634             0.201            0.076
Chain 1:   8000        -8193.123             0.173            0.076
Chain 1:   8100       -12761.350             0.145            0.076
Chain 1:   8200        -8379.367             0.194            0.090
Chain 1:   8300        -8805.502             0.193            0.090
Chain 1:   8400        -8907.788             0.189            0.090
Chain 1:   8500       -10217.547             0.194            0.128
Chain 1:   8600        -8889.290             0.202            0.149
Chain 1:   8700        -9483.333             0.183            0.128
Chain 1:   8800        -8067.494             0.161            0.128
Chain 1:   8900       -11054.986             0.182            0.149
Chain 1:   9000        -8819.266             0.198            0.175
Chain 1:   9100        -9359.801             0.168            0.149
Chain 1:   9200        -8307.521             0.128            0.128
Chain 1:   9300       -10505.292             0.144            0.149
Chain 1:   9400        -8989.378             0.160            0.169
Chain 1:   9500        -8256.788             0.156            0.169
Chain 1:   9600        -8800.271             0.147            0.169
Chain 1:   9700        -8235.691             0.148            0.169
Chain 1:   9800        -8302.658             0.131            0.127
Chain 1:   9900       -10293.235             0.124            0.127
Chain 1:   10000        -8522.937             0.119            0.127
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00143 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46310.179             1.000            1.000
Chain 1:    200       -15450.225             1.499            1.997
Chain 1:    300        -8704.841             1.257            1.000
Chain 1:    400        -8609.318             0.946            1.000
Chain 1:    500        -8398.205             0.762            0.775
Chain 1:    600        -7805.300             0.647            0.775
Chain 1:    700        -7818.700             0.555            0.076
Chain 1:    800        -8228.726             0.492            0.076
Chain 1:    900        -8115.469             0.439            0.050
Chain 1:   1000        -8113.589             0.395            0.050
Chain 1:   1100        -7752.818             0.300            0.047
Chain 1:   1200        -7621.098             0.102            0.025
Chain 1:   1300        -7861.827             0.027            0.025
Chain 1:   1400        -7691.270             0.028            0.025
Chain 1:   1500        -7623.597             0.027            0.022
Chain 1:   1600        -7824.686             0.022            0.022
Chain 1:   1700        -7559.285             0.025            0.026
Chain 1:   1800        -7656.846             0.021            0.022
Chain 1:   1900        -7661.757             0.020            0.022
Chain 1:   2000        -7618.719             0.021            0.022
Chain 1:   2100        -7647.361             0.016            0.017
Chain 1:   2200        -7752.114             0.016            0.014
Chain 1:   2300        -7642.678             0.014            0.014
Chain 1:   2400        -7692.039             0.013            0.013
Chain 1:   2500        -7626.433             0.013            0.013
Chain 1:   2600        -7587.305             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002993 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86254.762             1.000            1.000
Chain 1:    200       -13531.208             3.187            5.375
Chain 1:    300        -9863.827             2.249            1.000
Chain 1:    400       -10697.912             1.706            1.000
Chain 1:    500        -8651.506             1.412            0.372
Chain 1:    600        -8279.736             1.184            0.372
Chain 1:    700        -8498.231             1.019            0.237
Chain 1:    800        -8670.810             0.894            0.237
Chain 1:    900        -8647.150             0.795            0.078
Chain 1:   1000        -8298.278             0.720            0.078
Chain 1:   1100        -8693.493             0.624            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8272.575             0.092            0.045
Chain 1:   1300        -8471.006             0.057            0.045
Chain 1:   1400        -8528.682             0.050            0.042
Chain 1:   1500        -8386.874             0.028            0.026
Chain 1:   1600        -8495.240             0.025            0.023
Chain 1:   1700        -8577.527             0.023            0.020
Chain 1:   1800        -8149.835             0.026            0.023
Chain 1:   1900        -8253.006             0.027            0.023
Chain 1:   2000        -8227.832             0.023            0.017
Chain 1:   2100        -8355.647             0.020            0.015
Chain 1:   2200        -8153.761             0.018            0.015
Chain 1:   2300        -8248.662             0.017            0.013
Chain 1:   2400        -8316.057             0.017            0.013
Chain 1:   2500        -8262.150             0.016            0.013
Chain 1:   2600        -8264.939             0.014            0.012
Chain 1:   2700        -8180.988             0.014            0.012
Chain 1:   2800        -8139.154             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003117 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388875.145             1.000            1.000
Chain 1:    200     -1581080.468             2.653            4.306
Chain 1:    300      -889976.203             2.027            1.000
Chain 1:    400      -457546.280             1.757            1.000
Chain 1:    500      -358109.853             1.461            0.945
Chain 1:    600      -233109.578             1.307            0.945
Chain 1:    700      -119295.406             1.256            0.945
Chain 1:    800       -86514.741             1.147            0.945
Chain 1:    900       -66847.865             1.052            0.777
Chain 1:   1000       -51644.611             0.976            0.777
Chain 1:   1100       -39117.237             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38294.499             0.480            0.379
Chain 1:   1300       -26238.720             0.448            0.379
Chain 1:   1400       -25957.652             0.355            0.320
Chain 1:   1500       -22542.036             0.342            0.320
Chain 1:   1600       -21758.118             0.292            0.294
Chain 1:   1700       -20629.936             0.202            0.294
Chain 1:   1800       -20573.819             0.165            0.152
Chain 1:   1900       -20900.197             0.137            0.055
Chain 1:   2000       -19410.119             0.115            0.055
Chain 1:   2100       -19648.500             0.084            0.036
Chain 1:   2200       -19875.311             0.083            0.036
Chain 1:   2300       -19492.150             0.039            0.020
Chain 1:   2400       -19264.150             0.039            0.020
Chain 1:   2500       -19066.261             0.025            0.016
Chain 1:   2600       -18696.131             0.024            0.016
Chain 1:   2700       -18653.040             0.018            0.012
Chain 1:   2800       -18369.874             0.020            0.015
Chain 1:   2900       -18651.217             0.019            0.015
Chain 1:   3000       -18637.369             0.012            0.012
Chain 1:   3100       -18722.385             0.011            0.012
Chain 1:   3200       -18412.916             0.012            0.015
Chain 1:   3300       -18617.772             0.011            0.012
Chain 1:   3400       -18092.471             0.013            0.015
Chain 1:   3500       -18704.712             0.015            0.015
Chain 1:   3600       -18010.929             0.017            0.015
Chain 1:   3700       -18398.084             0.018            0.017
Chain 1:   3800       -17357.080             0.023            0.021
Chain 1:   3900       -17353.225             0.021            0.021
Chain 1:   4000       -17470.516             0.022            0.021
Chain 1:   4100       -17384.246             0.022            0.021
Chain 1:   4200       -17200.347             0.021            0.021
Chain 1:   4300       -17338.836             0.021            0.021
Chain 1:   4400       -17295.525             0.019            0.011
Chain 1:   4500       -17198.049             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13058.002             1.000            1.000
Chain 1:    200        -9881.541             0.661            1.000
Chain 1:    300        -8417.026             0.498            0.321
Chain 1:    400        -8594.714             0.379            0.321
Chain 1:    500        -8205.491             0.313            0.174
Chain 1:    600        -8312.697             0.263            0.174
Chain 1:    700        -8274.326             0.226            0.047
Chain 1:    800        -8264.024             0.198            0.047
Chain 1:    900        -8302.777             0.176            0.021
Chain 1:   1000        -8350.997             0.159            0.021
Chain 1:   1100        -8336.549             0.059            0.013
Chain 1:   1200        -8259.354             0.028            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58831.419             1.000            1.000
Chain 1:    200       -18456.891             1.594            2.188
Chain 1:    300        -9093.949             1.406            1.030
Chain 1:    400        -8115.829             1.084            1.030
Chain 1:    500        -8580.733             0.878            1.000
Chain 1:    600        -8504.571             0.733            1.000
Chain 1:    700        -8223.795             0.634            0.121
Chain 1:    800        -8645.467             0.560            0.121
Chain 1:    900        -8277.304             0.503            0.054
Chain 1:   1000        -8205.441             0.454            0.054
Chain 1:   1100        -7872.326             0.358            0.049
Chain 1:   1200        -7786.049             0.140            0.044
Chain 1:   1300        -7797.849             0.037            0.042
Chain 1:   1400        -7764.553             0.026            0.034
Chain 1:   1500        -7650.121             0.022            0.015
Chain 1:   1600        -7882.501             0.024            0.029
Chain 1:   1700        -7901.605             0.021            0.015
Chain 1:   1800        -7706.216             0.018            0.015
Chain 1:   1900        -7770.075             0.015            0.011
Chain 1:   2000        -7830.304             0.015            0.011
Chain 1:   2100        -7732.124             0.012            0.011
Chain 1:   2200        -8140.177             0.016            0.013
Chain 1:   2300        -7695.402             0.021            0.015
Chain 1:   2400        -7779.910             0.022            0.015
Chain 1:   2500        -7727.109             0.021            0.013
Chain 1:   2600        -7622.169             0.020            0.013
Chain 1:   2700        -7591.059             0.020            0.013
Chain 1:   2800        -7621.429             0.018            0.011
Chain 1:   2900        -7462.042             0.019            0.013
Chain 1:   3000        -7628.487             0.020            0.014
Chain 1:   3100        -7615.661             0.019            0.014
Chain 1:   3200        -7835.978             0.017            0.014
Chain 1:   3300        -7526.553             0.015            0.014
Chain 1:   3400        -7750.167             0.017            0.021
Chain 1:   3500        -7533.759             0.019            0.022
Chain 1:   3600        -7597.951             0.019            0.022
Chain 1:   3700        -7549.990             0.019            0.022
Chain 1:   3800        -7522.326             0.019            0.022
Chain 1:   3900        -7497.197             0.017            0.022
Chain 1:   4000        -7492.530             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003461 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87148.930             1.000            1.000
Chain 1:    200       -14229.027             3.062            5.125
Chain 1:    300       -10421.980             2.163            1.000
Chain 1:    400       -12450.844             1.663            1.000
Chain 1:    500        -8944.344             1.409            0.392
Chain 1:    600        -9647.884             1.186            0.392
Chain 1:    700        -8801.754             1.031            0.365
Chain 1:    800        -8966.501             0.904            0.365
Chain 1:    900        -9005.516             0.804            0.163
Chain 1:   1000        -9199.088             0.726            0.163
Chain 1:   1100        -9150.518             0.626            0.096   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8639.182             0.120            0.073
Chain 1:   1300        -9033.919             0.088            0.059
Chain 1:   1400        -8860.716             0.073            0.044
Chain 1:   1500        -8876.928             0.034            0.021
Chain 1:   1600        -8971.691             0.028            0.020
Chain 1:   1700        -9017.627             0.019            0.018
Chain 1:   1800        -8545.062             0.023            0.020
Chain 1:   1900        -8669.584             0.024            0.020
Chain 1:   2000        -8688.822             0.022            0.014
Chain 1:   2100        -8773.531             0.022            0.014
Chain 1:   2200        -8551.882             0.019            0.014
Chain 1:   2300        -8775.234             0.017            0.014
Chain 1:   2400        -8560.034             0.018            0.014
Chain 1:   2500        -8637.132             0.018            0.014
Chain 1:   2600        -8546.523             0.018            0.014
Chain 1:   2700        -8582.489             0.018            0.014
Chain 1:   2800        -8534.035             0.013            0.011
Chain 1:   2900        -8648.482             0.013            0.011
Chain 1:   3000        -8557.923             0.014            0.011
Chain 1:   3100        -8525.115             0.013            0.011
Chain 1:   3200        -8496.152             0.011            0.011
Chain 1:   3300        -8759.422             0.012            0.011
Chain 1:   3400        -8805.680             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003086 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8441191.115             1.000            1.000
Chain 1:    200     -1591276.706             2.652            4.305
Chain 1:    300      -892288.678             2.029            1.000
Chain 1:    400      -459201.779             1.758            1.000
Chain 1:    500      -358957.495             1.462            0.943
Chain 1:    600      -233694.820             1.308            0.943
Chain 1:    700      -119903.402             1.256            0.943
Chain 1:    800       -87130.749             1.146            0.943
Chain 1:    900       -67488.973             1.051            0.783
Chain 1:   1000       -52314.347             0.975            0.783
Chain 1:   1100       -39814.943             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39000.443             0.478            0.376
Chain 1:   1300       -26967.124             0.445            0.376
Chain 1:   1400       -26691.264             0.351            0.314
Chain 1:   1500       -23280.604             0.338            0.314
Chain 1:   1600       -22499.152             0.288            0.291
Chain 1:   1700       -21373.275             0.198            0.290
Chain 1:   1800       -21318.078             0.161            0.147
Chain 1:   1900       -21645.064             0.133            0.053
Chain 1:   2000       -20154.968             0.112            0.053
Chain 1:   2100       -20393.401             0.081            0.035
Chain 1:   2200       -20620.427             0.080            0.035
Chain 1:   2300       -20236.911             0.038            0.019
Chain 1:   2400       -20008.704             0.038            0.019
Chain 1:   2500       -19810.660             0.024            0.015
Chain 1:   2600       -19439.989             0.023            0.015
Chain 1:   2700       -19396.713             0.018            0.012
Chain 1:   2800       -19113.141             0.019            0.015
Chain 1:   2900       -19394.775             0.019            0.015
Chain 1:   3000       -19380.954             0.011            0.012
Chain 1:   3100       -19466.069             0.011            0.011
Chain 1:   3200       -19156.162             0.011            0.015
Chain 1:   3300       -19361.341             0.010            0.011
Chain 1:   3400       -18835.185             0.012            0.015
Chain 1:   3500       -19448.621             0.014            0.015
Chain 1:   3600       -18753.257             0.016            0.015
Chain 1:   3700       -19141.545             0.018            0.016
Chain 1:   3800       -18098.050             0.022            0.020
Chain 1:   3900       -18094.095             0.021            0.020
Chain 1:   4000       -18211.435             0.021            0.020
Chain 1:   4100       -18125.021             0.021            0.020
Chain 1:   4200       -17940.567             0.021            0.020
Chain 1:   4300       -18079.469             0.020            0.020
Chain 1:   4400       -18035.707             0.018            0.010
Chain 1:   4500       -17938.137             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48345.944             1.000            1.000
Chain 1:    200       -23451.930             1.031            1.061
Chain 1:    300       -29379.352             0.754            1.000
Chain 1:    400       -19373.565             0.695            1.000
Chain 1:    500       -18369.974             0.567            0.516
Chain 1:    600       -13072.859             0.540            0.516
Chain 1:    700       -13483.816             0.467            0.405
Chain 1:    800       -17120.071             0.435            0.405
Chain 1:    900       -16323.204             0.392            0.212
Chain 1:   1000       -11494.765             0.395            0.405
Chain 1:   1100       -10235.445             0.307            0.212
Chain 1:   1200       -10003.129             0.204            0.202
Chain 1:   1300       -10902.309             0.192            0.123
Chain 1:   1400       -13546.717             0.160            0.123
Chain 1:   1500        -9936.952             0.190            0.195
Chain 1:   1600       -10614.427             0.156            0.123
Chain 1:   1700       -10258.693             0.157            0.123
Chain 1:   1800       -11167.060             0.144            0.082
Chain 1:   1900       -10074.780             0.150            0.108
Chain 1:   2000       -10542.394             0.112            0.082
Chain 1:   2100        -8789.869             0.120            0.082
Chain 1:   2200        -9404.148             0.124            0.082
Chain 1:   2300        -9674.677             0.118            0.081
Chain 1:   2400        -8997.005             0.106            0.075
Chain 1:   2500        -9123.549             0.071            0.065
Chain 1:   2600        -9145.822             0.065            0.065
Chain 1:   2700       -10119.218             0.071            0.075
Chain 1:   2800        -9619.104             0.069            0.065
Chain 1:   2900        -9292.087             0.061            0.052
Chain 1:   3000        -8554.762             0.065            0.065
Chain 1:   3100        -8365.738             0.048            0.052
Chain 1:   3200        -8546.378             0.043            0.035
Chain 1:   3300       -15624.091             0.086            0.052
Chain 1:   3400        -8627.120             0.159            0.052
Chain 1:   3500        -9059.540             0.163            0.052
Chain 1:   3600        -9233.065             0.164            0.052
Chain 1:   3700        -8364.348             0.165            0.052
Chain 1:   3800        -8588.626             0.163            0.048
Chain 1:   3900        -8451.293             0.161            0.048
Chain 1:   4000        -9349.803             0.162            0.048
Chain 1:   4100        -8686.518             0.167            0.076
Chain 1:   4200        -9748.432             0.176            0.096
Chain 1:   4300       -13525.889             0.158            0.096
Chain 1:   4400       -10921.670             0.101            0.096
Chain 1:   4500        -8512.952             0.125            0.104
Chain 1:   4600       -13036.147             0.158            0.109
Chain 1:   4700        -8307.945             0.204            0.238
Chain 1:   4800        -8305.860             0.201            0.238
Chain 1:   4900       -12551.514             0.234            0.279
Chain 1:   5000       -11124.022             0.237            0.279
Chain 1:   5100        -8752.878             0.256            0.279
Chain 1:   5200        -8442.793             0.249            0.279
Chain 1:   5300       -14162.253             0.262            0.283
Chain 1:   5400       -10769.363             0.269            0.315
Chain 1:   5500        -8210.201             0.272            0.315
Chain 1:   5600        -9027.029             0.246            0.312
Chain 1:   5700       -13080.703             0.221            0.310
Chain 1:   5800        -8220.470             0.280            0.312
Chain 1:   5900       -10024.289             0.264            0.310
Chain 1:   6000        -8197.370             0.273            0.310
Chain 1:   6100        -9483.300             0.260            0.310
Chain 1:   6200        -9560.360             0.257            0.310
Chain 1:   6300       -10163.896             0.222            0.223
Chain 1:   6400       -11323.128             0.201            0.180
Chain 1:   6500       -11037.168             0.173            0.136
Chain 1:   6600        -8276.737             0.197            0.180
Chain 1:   6700       -12420.827             0.199            0.180
Chain 1:   6800        -8277.147             0.190            0.180
Chain 1:   6900        -8402.077             0.174            0.136
Chain 1:   7000        -9090.539             0.159            0.102
Chain 1:   7100       -11947.655             0.169            0.102
Chain 1:   7200        -8307.848             0.212            0.239
Chain 1:   7300        -9249.683             0.217            0.239
Chain 1:   7400       -12161.147             0.230            0.239
Chain 1:   7500        -9551.239             0.255            0.273
Chain 1:   7600        -8270.745             0.237            0.239
Chain 1:   7700        -8202.998             0.205            0.239
Chain 1:   7800        -9529.158             0.168            0.155
Chain 1:   7900        -8060.144             0.185            0.182
Chain 1:   8000        -8105.474             0.178            0.182
Chain 1:   8100        -8479.852             0.159            0.155
Chain 1:   8200        -8160.881             0.119            0.139
Chain 1:   8300        -9543.468             0.123            0.145
Chain 1:   8400       -11712.442             0.118            0.145
Chain 1:   8500        -8306.827             0.131            0.145
Chain 1:   8600        -8170.864             0.118            0.139
Chain 1:   8700        -8230.780             0.117            0.139
Chain 1:   8800        -7860.088             0.108            0.047
Chain 1:   8900        -8291.809             0.095            0.047
Chain 1:   9000        -9410.173             0.107            0.052
Chain 1:   9100       -10171.436             0.110            0.075
Chain 1:   9200       -12200.324             0.122            0.119
Chain 1:   9300        -9932.707             0.131            0.119
Chain 1:   9400        -8008.389             0.136            0.119
Chain 1:   9500        -8696.640             0.103            0.079
Chain 1:   9600        -8863.787             0.103            0.079
Chain 1:   9700        -8429.695             0.108            0.079
Chain 1:   9800        -8517.065             0.104            0.079
Chain 1:   9900        -8185.638             0.103            0.079
Chain 1:   10000        -8095.663             0.092            0.075
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56484.754             1.000            1.000
Chain 1:    200       -16886.511             1.672            2.345
Chain 1:    300        -8473.880             1.446            1.000
Chain 1:    400        -8616.002             1.089            1.000
Chain 1:    500        -7948.965             0.888            0.993
Chain 1:    600        -8364.047             0.748            0.993
Chain 1:    700        -7763.070             0.652            0.084
Chain 1:    800        -7908.924             0.573            0.084
Chain 1:    900        -7798.048             0.511            0.077
Chain 1:   1000        -7667.180             0.461            0.077
Chain 1:   1100        -7558.848             0.363            0.050
Chain 1:   1200        -7711.066             0.130            0.020
Chain 1:   1300        -7550.433             0.033            0.020
Chain 1:   1400        -7572.345             0.032            0.020
Chain 1:   1500        -7546.458             0.024            0.018
Chain 1:   1600        -7454.057             0.020            0.017
Chain 1:   1700        -7442.455             0.013            0.014
Chain 1:   1800        -7467.249             0.011            0.014
Chain 1:   1900        -7538.439             0.011            0.012
Chain 1:   2000        -7519.687             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002815 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85799.836             1.000            1.000
Chain 1:    200       -12998.804             3.300            5.601
Chain 1:    300        -9468.352             2.324            1.000
Chain 1:    400       -10379.490             1.765            1.000
Chain 1:    500        -8367.999             1.460            0.373
Chain 1:    600        -8035.760             1.224            0.373
Chain 1:    700        -8267.438             1.053            0.240
Chain 1:    800        -8468.185             0.924            0.240
Chain 1:    900        -8330.753             0.823            0.088
Chain 1:   1000        -8067.983             0.744            0.088
Chain 1:   1100        -8347.087             0.648            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8008.019             0.092            0.041
Chain 1:   1300        -8162.681             0.057            0.033
Chain 1:   1400        -8258.426             0.049            0.033
Chain 1:   1500        -8116.058             0.027            0.028
Chain 1:   1600        -8215.679             0.024            0.024
Chain 1:   1700        -8302.638             0.022            0.019
Chain 1:   1800        -7921.689             0.024            0.019
Chain 1:   1900        -8023.059             0.024            0.019
Chain 1:   2000        -7992.573             0.021            0.018
Chain 1:   2100        -8131.607             0.019            0.017
Chain 1:   2200        -7913.132             0.018            0.017
Chain 1:   2300        -8055.266             0.018            0.017
Chain 1:   2400        -8065.316             0.017            0.017
Chain 1:   2500        -8030.202             0.016            0.013
Chain 1:   2600        -8027.378             0.014            0.013
Chain 1:   2700        -7937.638             0.014            0.013
Chain 1:   2800        -7918.044             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002604 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398310.281             1.000            1.000
Chain 1:    200     -1585265.415             2.649            4.298
Chain 1:    300      -890012.153             2.026            1.000
Chain 1:    400      -456444.701             1.757            1.000
Chain 1:    500      -356808.036             1.462            0.950
Chain 1:    600      -232019.266             1.308            0.950
Chain 1:    700      -118495.457             1.258            0.950
Chain 1:    800       -85740.826             1.148            0.950
Chain 1:    900       -66131.858             1.054            0.781
Chain 1:   1000       -50956.652             0.978            0.781
Chain 1:   1100       -38459.116             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37635.313             0.483            0.382
Chain 1:   1300       -25630.829             0.452            0.382
Chain 1:   1400       -25350.405             0.358            0.325
Chain 1:   1500       -21947.726             0.345            0.325
Chain 1:   1600       -21166.115             0.295            0.298
Chain 1:   1700       -20045.503             0.205            0.297
Chain 1:   1800       -19990.546             0.167            0.155
Chain 1:   1900       -20316.069             0.139            0.056
Chain 1:   2000       -18831.155             0.117            0.056
Chain 1:   2100       -19069.409             0.086            0.037
Chain 1:   2200       -19294.849             0.085            0.037
Chain 1:   2300       -18913.096             0.040            0.020
Chain 1:   2400       -18685.492             0.040            0.020
Chain 1:   2500       -18487.241             0.026            0.016
Chain 1:   2600       -18118.372             0.024            0.016
Chain 1:   2700       -18075.637             0.019            0.012
Chain 1:   2800       -17792.657             0.020            0.016
Chain 1:   2900       -18073.569             0.020            0.016
Chain 1:   3000       -18059.886             0.012            0.012
Chain 1:   3100       -18144.737             0.011            0.012
Chain 1:   3200       -17835.919             0.012            0.016
Chain 1:   3300       -18040.261             0.011            0.012
Chain 1:   3400       -17515.974             0.013            0.016
Chain 1:   3500       -18126.561             0.015            0.016
Chain 1:   3600       -17434.955             0.017            0.016
Chain 1:   3700       -17820.448             0.019            0.017
Chain 1:   3800       -16782.707             0.024            0.022
Chain 1:   3900       -16778.889             0.022            0.022
Chain 1:   4000       -16896.224             0.023            0.022
Chain 1:   4100       -16810.064             0.023            0.022
Chain 1:   4200       -16626.912             0.022            0.022
Chain 1:   4300       -16764.926             0.022            0.022
Chain 1:   4400       -16722.221             0.019            0.011
Chain 1:   4500       -16624.810             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001356 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11983.296             1.000            1.000
Chain 1:    200        -8902.119             0.673            1.000
Chain 1:    300        -7916.141             0.490            0.346
Chain 1:    400        -7993.422             0.370            0.346
Chain 1:    500        -7828.289             0.300            0.125
Chain 1:    600        -7720.280             0.253            0.125
Chain 1:    700        -7652.914             0.218            0.021
Chain 1:    800        -7671.792             0.191            0.021
Chain 1:    900        -7762.171             0.171            0.014
Chain 1:   1000        -7680.693             0.155            0.014
Chain 1:   1100        -7781.855             0.056            0.013
Chain 1:   1200        -7687.777             0.023            0.012
Chain 1:   1300        -7623.005             0.011            0.012
Chain 1:   1400        -7644.584             0.011            0.012
Chain 1:   1500        -7730.188             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62682.839             1.000            1.000
Chain 1:    200       -17691.030             1.772            2.543
Chain 1:    300        -8562.558             1.536            1.066
Chain 1:    400        -8166.922             1.164            1.066
Chain 1:    500        -8423.957             0.938            1.000
Chain 1:    600        -8569.879             0.784            1.000
Chain 1:    700        -7767.044             0.687            0.103
Chain 1:    800        -7791.780             0.601            0.103
Chain 1:    900        -7769.043             0.535            0.048
Chain 1:   1000        -7693.708             0.482            0.048
Chain 1:   1100        -7611.768             0.384            0.031
Chain 1:   1200        -7578.758             0.130            0.017
Chain 1:   1300        -7623.730             0.024            0.011
Chain 1:   1400        -7847.571             0.022            0.011
Chain 1:   1500        -7569.132             0.022            0.011
Chain 1:   1600        -7474.765             0.022            0.011
Chain 1:   1700        -7461.218             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002938 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85771.212             1.000            1.000
Chain 1:    200       -13058.368             3.284            5.568
Chain 1:    300        -9526.171             2.313            1.000
Chain 1:    400       -10450.285             1.757            1.000
Chain 1:    500        -8458.549             1.453            0.371
Chain 1:    600        -8332.291             1.213            0.371
Chain 1:    700        -8321.255             1.040            0.235
Chain 1:    800        -8880.474             0.918            0.235
Chain 1:    900        -8330.960             0.823            0.088
Chain 1:   1000        -8232.796             0.742            0.088
Chain 1:   1100        -8433.476             0.644            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8154.658             0.091            0.063
Chain 1:   1300        -8318.161             0.056            0.034
Chain 1:   1400        -8278.828             0.048            0.024
Chain 1:   1500        -8153.550             0.026            0.020
Chain 1:   1600        -8254.507             0.025            0.020
Chain 1:   1700        -8345.471             0.026            0.020
Chain 1:   1800        -7959.549             0.025            0.020
Chain 1:   1900        -8061.802             0.019            0.015
Chain 1:   2000        -8031.895             0.019            0.015
Chain 1:   2100        -8166.972             0.018            0.015
Chain 1:   2200        -7950.924             0.017            0.015
Chain 1:   2300        -8092.200             0.017            0.015
Chain 1:   2400        -8102.949             0.017            0.015
Chain 1:   2500        -8071.300             0.015            0.013
Chain 1:   2600        -8069.253             0.014            0.013
Chain 1:   2700        -7978.619             0.014            0.013
Chain 1:   2800        -7957.206             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8423285.821             1.000            1.000
Chain 1:    200     -1587203.522             2.653            4.307
Chain 1:    300      -890054.942             2.030            1.000
Chain 1:    400      -456532.972             1.760            1.000
Chain 1:    500      -356542.042             1.464            0.950
Chain 1:    600      -231600.849             1.310            0.950
Chain 1:    700      -118298.770             1.260            0.950
Chain 1:    800       -85621.207             1.150            0.950
Chain 1:    900       -66054.619             1.055            0.783
Chain 1:   1000       -50920.286             0.979            0.783
Chain 1:   1100       -38464.392             0.912            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37645.333             0.483            0.382
Chain 1:   1300       -25678.155             0.451            0.382
Chain 1:   1400       -25401.816             0.358            0.324
Chain 1:   1500       -22009.555             0.345            0.324
Chain 1:   1600       -21231.548             0.295            0.297
Chain 1:   1700       -20115.121             0.204            0.296
Chain 1:   1800       -20061.362             0.166            0.154
Chain 1:   1900       -20386.963             0.138            0.056
Chain 1:   2000       -18904.465             0.117            0.056
Chain 1:   2100       -19142.317             0.085            0.037
Chain 1:   2200       -19367.597             0.084            0.037
Chain 1:   2300       -18986.044             0.040            0.020
Chain 1:   2400       -18758.487             0.040            0.020
Chain 1:   2500       -18560.181             0.026            0.016
Chain 1:   2600       -18191.187             0.024            0.016
Chain 1:   2700       -18148.515             0.019            0.012
Chain 1:   2800       -17865.469             0.020            0.016
Chain 1:   2900       -18146.397             0.020            0.015
Chain 1:   3000       -18132.646             0.012            0.012
Chain 1:   3100       -18217.498             0.011            0.012
Chain 1:   3200       -17908.654             0.012            0.015
Chain 1:   3300       -18113.069             0.011            0.012
Chain 1:   3400       -17588.650             0.013            0.015
Chain 1:   3500       -18199.349             0.015            0.016
Chain 1:   3600       -17507.634             0.017            0.016
Chain 1:   3700       -17893.160             0.019            0.017
Chain 1:   3800       -16855.204             0.024            0.022
Chain 1:   3900       -16851.401             0.022            0.022
Chain 1:   4000       -16968.733             0.023            0.022
Chain 1:   4100       -16882.540             0.023            0.022
Chain 1:   4200       -16699.383             0.022            0.022
Chain 1:   4300       -16837.392             0.022            0.022
Chain 1:   4400       -16794.638             0.019            0.011
Chain 1:   4500       -16697.253             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11979.001             1.000            1.000
Chain 1:    200        -8933.070             0.670            1.000
Chain 1:    300        -7882.570             0.491            0.341
Chain 1:    400        -7998.739             0.372            0.341
Chain 1:    500        -7832.144             0.302            0.133
Chain 1:    600        -7755.931             0.253            0.133
Chain 1:    700        -7688.313             0.218            0.021
Chain 1:    800        -7642.421             0.192            0.021
Chain 1:    900        -7670.337             0.171            0.015
Chain 1:   1000        -7751.812             0.155            0.015
Chain 1:   1100        -7793.533             0.055            0.011
Chain 1:   1200        -7706.995             0.022            0.011
Chain 1:   1300        -7663.856             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001418 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56427.212             1.000            1.000
Chain 1:    200       -16977.398             1.662            2.324
Chain 1:    300        -8491.942             1.441            1.000
Chain 1:    400        -8819.347             1.090            1.000
Chain 1:    500        -8439.269             0.881            0.999
Chain 1:    600        -9088.492             0.746            0.999
Chain 1:    700        -7889.441             0.661            0.152
Chain 1:    800        -8258.222             0.584            0.152
Chain 1:    900        -7649.830             0.528            0.080
Chain 1:   1000        -7791.249             0.477            0.080
Chain 1:   1100        -7484.363             0.381            0.071
Chain 1:   1200        -7753.594             0.152            0.045
Chain 1:   1300        -7620.317             0.054            0.045
Chain 1:   1400        -7840.889             0.053            0.045
Chain 1:   1500        -7541.172             0.053            0.041
Chain 1:   1600        -7453.425             0.047            0.040
Chain 1:   1700        -7448.958             0.032            0.035
Chain 1:   1800        -7480.788             0.028            0.028
Chain 1:   1900        -7414.134             0.020            0.018
Chain 1:   2000        -7524.020             0.020            0.017
Chain 1:   2100        -7591.668             0.017            0.015
Chain 1:   2200        -7600.960             0.014            0.012
Chain 1:   2300        -7496.724             0.013            0.012
Chain 1:   2400        -7528.392             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85745.529             1.000            1.000
Chain 1:    200       -13051.759             3.285            5.570
Chain 1:    300        -9535.140             2.313            1.000
Chain 1:    400       -10323.633             1.754            1.000
Chain 1:    500        -8473.854             1.447            0.369
Chain 1:    600        -8324.786             1.209            0.369
Chain 1:    700        -8354.343             1.036            0.218
Chain 1:    800        -8384.229             0.907            0.218
Chain 1:    900        -8389.366             0.807            0.076
Chain 1:   1000        -8111.612             0.729            0.076
Chain 1:   1100        -8460.225             0.633            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8199.634             0.080            0.034
Chain 1:   1300        -8329.841             0.044            0.032
Chain 1:   1400        -8300.484             0.037            0.018
Chain 1:   1500        -8184.172             0.017            0.016
Chain 1:   1600        -8279.041             0.016            0.014
Chain 1:   1700        -8382.741             0.017            0.014
Chain 1:   1800        -7992.935             0.021            0.016
Chain 1:   1900        -8093.513             0.023            0.016
Chain 1:   2000        -8063.627             0.020            0.014
Chain 1:   2100        -8204.215             0.017            0.014
Chain 1:   2200        -7984.608             0.017            0.014
Chain 1:   2300        -8127.134             0.017            0.014
Chain 1:   2400        -8015.582             0.018            0.014
Chain 1:   2500        -8070.217             0.017            0.014
Chain 1:   2600        -8083.894             0.016            0.014
Chain 1:   2700        -8006.020             0.016            0.014
Chain 1:   2800        -7988.493             0.011            0.012
Chain 1:   2900        -8008.690             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003157 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397578.350             1.000            1.000
Chain 1:    200     -1589122.062             2.642            4.284
Chain 1:    300      -891954.636             2.022            1.000
Chain 1:    400      -457340.081             1.754            1.000
Chain 1:    500      -357046.731             1.459            0.950
Chain 1:    600      -231981.606             1.306            0.950
Chain 1:    700      -118480.457             1.256            0.950
Chain 1:    800       -85723.507             1.147            0.950
Chain 1:    900       -66129.841             1.053            0.782
Chain 1:   1000       -50972.202             0.977            0.782
Chain 1:   1100       -38488.925             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37668.858             0.483            0.382
Chain 1:   1300       -25678.663             0.452            0.382
Chain 1:   1400       -25400.483             0.358            0.324
Chain 1:   1500       -22000.913             0.345            0.324
Chain 1:   1600       -21220.330             0.295            0.297
Chain 1:   1700       -20101.316             0.205            0.296
Chain 1:   1800       -20046.803             0.167            0.155
Chain 1:   1900       -20372.284             0.139            0.056
Chain 1:   2000       -18888.239             0.117            0.056
Chain 1:   2100       -19126.507             0.086            0.037
Chain 1:   2200       -19351.736             0.085            0.037
Chain 1:   2300       -18970.200             0.040            0.020
Chain 1:   2400       -18742.584             0.040            0.020
Chain 1:   2500       -18544.293             0.026            0.016
Chain 1:   2600       -18175.503             0.024            0.016
Chain 1:   2700       -18132.850             0.019            0.012
Chain 1:   2800       -17849.776             0.020            0.016
Chain 1:   2900       -18130.730             0.020            0.015
Chain 1:   3000       -18117.074             0.012            0.012
Chain 1:   3100       -18201.874             0.011            0.012
Chain 1:   3200       -17893.128             0.012            0.015
Chain 1:   3300       -18097.462             0.011            0.012
Chain 1:   3400       -17573.186             0.013            0.015
Chain 1:   3500       -18183.699             0.015            0.016
Chain 1:   3600       -17492.261             0.017            0.016
Chain 1:   3700       -17877.557             0.019            0.017
Chain 1:   3800       -16840.043             0.024            0.022
Chain 1:   3900       -16836.236             0.022            0.022
Chain 1:   4000       -16953.588             0.023            0.022
Chain 1:   4100       -16867.369             0.023            0.022
Chain 1:   4200       -16684.316             0.022            0.022
Chain 1:   4300       -16822.273             0.022            0.022
Chain 1:   4400       -16779.607             0.019            0.011
Chain 1:   4500       -16682.195             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49255.491             1.000            1.000
Chain 1:    200       -16531.946             1.490            1.979
Chain 1:    300       -21224.020             1.067            1.000
Chain 1:    400       -17110.617             0.860            1.000
Chain 1:    500       -24033.259             0.746            0.288
Chain 1:    600       -12440.442             0.777            0.932
Chain 1:    700       -12518.939             0.667            0.288
Chain 1:    800       -14318.892             0.599            0.288
Chain 1:    900       -13983.716             0.535            0.240
Chain 1:   1000       -12558.137             0.493            0.240
Chain 1:   1100       -10539.728             0.412            0.221
Chain 1:   1200       -12777.305             0.232            0.192
Chain 1:   1300       -13276.367             0.213            0.175
Chain 1:   1400       -11406.360             0.206            0.164
Chain 1:   1500       -10317.916             0.187            0.126
Chain 1:   1600       -10485.780             0.096            0.114
Chain 1:   1700       -16950.956             0.133            0.126
Chain 1:   1800       -16463.251             0.124            0.114
Chain 1:   1900       -11135.618             0.169            0.164
Chain 1:   2000       -10169.962             0.167            0.164
Chain 1:   2100        -9795.555             0.152            0.105
Chain 1:   2200       -10000.565             0.137            0.095
Chain 1:   2300       -13543.677             0.159            0.105
Chain 1:   2400        -9352.314             0.187            0.105
Chain 1:   2500       -19057.964             0.228            0.262
Chain 1:   2600       -10291.755             0.311            0.381
Chain 1:   2700        -9234.511             0.285            0.262
Chain 1:   2800       -19079.303             0.333            0.448
Chain 1:   2900        -9640.848             0.383            0.448
Chain 1:   3000       -10724.063             0.384            0.448
Chain 1:   3100       -11487.843             0.387            0.448
Chain 1:   3200        -9170.323             0.410            0.448
Chain 1:   3300       -17944.004             0.433            0.489
Chain 1:   3400       -12638.085             0.430            0.489
Chain 1:   3500       -18909.738             0.412            0.420
Chain 1:   3600        -9931.860             0.417            0.420
Chain 1:   3700        -8979.671             0.417            0.420
Chain 1:   3800        -9327.482             0.369            0.332
Chain 1:   3900       -13406.978             0.301            0.304
Chain 1:   4000       -10716.530             0.316            0.304
Chain 1:   4100        -9527.404             0.322            0.304
Chain 1:   4200       -12263.357             0.319            0.304
Chain 1:   4300       -10295.715             0.289            0.251
Chain 1:   4400       -14673.678             0.277            0.251
Chain 1:   4500       -11982.060             0.266            0.225
Chain 1:   4600        -9658.389             0.200            0.225
Chain 1:   4700       -13534.238             0.218            0.241
Chain 1:   4800        -8850.653             0.267            0.251
Chain 1:   4900       -12574.436             0.267            0.251
Chain 1:   5000        -9396.103             0.275            0.286
Chain 1:   5100        -8568.569             0.272            0.286
Chain 1:   5200        -9249.415             0.257            0.286
Chain 1:   5300       -13602.318             0.270            0.296
Chain 1:   5400       -13995.585             0.243            0.286
Chain 1:   5500       -14027.732             0.221            0.286
Chain 1:   5600       -12855.364             0.206            0.286
Chain 1:   5700       -12706.630             0.179            0.097
Chain 1:   5800        -9129.160             0.165            0.097
Chain 1:   5900        -9136.525             0.135            0.091
Chain 1:   6000        -9144.495             0.102            0.074
Chain 1:   6100       -11410.069             0.112            0.074
Chain 1:   6200        -8464.508             0.139            0.091
Chain 1:   6300        -8461.786             0.107            0.028
Chain 1:   6400       -10541.731             0.124            0.091
Chain 1:   6500        -9813.407             0.131            0.091
Chain 1:   6600        -8621.342             0.136            0.138
Chain 1:   6700        -9489.521             0.144            0.138
Chain 1:   6800       -11892.993             0.125            0.138
Chain 1:   6900       -12549.759             0.130            0.138
Chain 1:   7000        -9645.830             0.160            0.197
Chain 1:   7100       -10513.423             0.149            0.138
Chain 1:   7200        -8622.540             0.136            0.138
Chain 1:   7300       -10937.993             0.157            0.197
Chain 1:   7400        -8776.128             0.162            0.202
Chain 1:   7500        -8435.393             0.159            0.202
Chain 1:   7600       -10382.501             0.163            0.202
Chain 1:   7700        -8432.889             0.177            0.212
Chain 1:   7800        -8693.182             0.160            0.212
Chain 1:   7900        -8511.862             0.157            0.212
Chain 1:   8000        -8511.877             0.127            0.188
Chain 1:   8100        -8383.174             0.120            0.188
Chain 1:   8200       -11212.422             0.124            0.188
Chain 1:   8300        -8337.899             0.137            0.188
Chain 1:   8400        -8687.220             0.116            0.040
Chain 1:   8500       -12530.353             0.143            0.188
Chain 1:   8600        -8393.754             0.173            0.231
Chain 1:   8700        -8782.387             0.155            0.044
Chain 1:   8800       -10454.505             0.168            0.160
Chain 1:   8900       -11579.151             0.175            0.160
Chain 1:   9000        -8686.200             0.209            0.252
Chain 1:   9100       -11323.891             0.230            0.252
Chain 1:   9200       -10749.762             0.211            0.233
Chain 1:   9300        -9120.849             0.194            0.179
Chain 1:   9400       -10353.355             0.202            0.179
Chain 1:   9500        -8310.340             0.196            0.179
Chain 1:   9600        -9632.189             0.160            0.160
Chain 1:   9700        -8519.647             0.169            0.160
Chain 1:   9800       -11351.595             0.178            0.179
Chain 1:   9900       -10099.091             0.180            0.179
Chain 1:   10000        -8211.770             0.170            0.179
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001392 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62245.505             1.000            1.000
Chain 1:    200       -18175.471             1.712            2.425
Chain 1:    300        -8996.096             1.482            1.020
Chain 1:    400        -9610.333             1.127            1.020
Chain 1:    500        -8518.165             0.927            1.000
Chain 1:    600        -8684.070             0.776            1.000
Chain 1:    700        -7943.721             0.679            0.128
Chain 1:    800        -8231.353             0.598            0.128
Chain 1:    900        -8167.898             0.532            0.093
Chain 1:   1000        -7797.372             0.484            0.093
Chain 1:   1100        -7849.230             0.385            0.064
Chain 1:   1200        -7757.866             0.143            0.048
Chain 1:   1300        -7840.515             0.042            0.035
Chain 1:   1400        -7810.406             0.036            0.019
Chain 1:   1500        -7585.521             0.026            0.019
Chain 1:   1600        -7779.314             0.027            0.025
Chain 1:   1700        -7524.193             0.021            0.025
Chain 1:   1800        -7574.762             0.018            0.012
Chain 1:   1900        -7578.511             0.018            0.012
Chain 1:   2000        -7643.795             0.014            0.011
Chain 1:   2100        -7570.731             0.014            0.011
Chain 1:   2200        -7749.002             0.015            0.011
Chain 1:   2300        -7566.784             0.016            0.023
Chain 1:   2400        -7620.546             0.017            0.023
Chain 1:   2500        -7608.602             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002595 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85577.190             1.000            1.000
Chain 1:    200       -13765.386             3.108            5.217
Chain 1:    300       -10084.500             2.194            1.000
Chain 1:    400       -11419.058             1.675            1.000
Chain 1:    500        -9044.801             1.392            0.365
Chain 1:    600        -8978.461             1.161            0.365
Chain 1:    700        -8680.681             1.000            0.262
Chain 1:    800        -8923.039             0.879            0.262
Chain 1:    900        -8786.903             0.783            0.117
Chain 1:   1000        -8858.892             0.705            0.117
Chain 1:   1100        -8841.619             0.606            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8358.340             0.090            0.034
Chain 1:   1300        -8759.757             0.058            0.034
Chain 1:   1400        -8760.347             0.046            0.027
Chain 1:   1500        -8599.445             0.022            0.019
Chain 1:   1600        -8717.546             0.022            0.019
Chain 1:   1700        -8781.540             0.020            0.015
Chain 1:   1800        -8349.230             0.022            0.015
Chain 1:   1900        -8452.526             0.022            0.014
Chain 1:   2000        -8427.779             0.021            0.014
Chain 1:   2100        -8565.862             0.023            0.016
Chain 1:   2200        -8358.169             0.019            0.016
Chain 1:   2300        -8506.412             0.016            0.016
Chain 1:   2400        -8357.295             0.018            0.017
Chain 1:   2500        -8426.465             0.017            0.016
Chain 1:   2600        -8340.006             0.017            0.016
Chain 1:   2700        -8372.641             0.017            0.016
Chain 1:   2800        -8333.984             0.012            0.012
Chain 1:   2900        -8425.795             0.012            0.011
Chain 1:   3000        -8251.365             0.014            0.016
Chain 1:   3100        -8415.911             0.014            0.017
Chain 1:   3200        -8288.876             0.013            0.015
Chain 1:   3300        -8298.254             0.011            0.011
Chain 1:   3400        -8449.236             0.011            0.011
Chain 1:   3500        -8437.930             0.011            0.011
Chain 1:   3600        -8246.277             0.012            0.015
Chain 1:   3700        -8389.066             0.013            0.017
Chain 1:   3800        -8253.153             0.014            0.017
Chain 1:   3900        -8188.447             0.014            0.017
Chain 1:   4000        -8262.783             0.013            0.016
Chain 1:   4100        -8253.638             0.011            0.015
Chain 1:   4200        -8242.138             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003101 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399207.756             1.000            1.000
Chain 1:    200     -1581008.889             2.656            4.313
Chain 1:    300      -890950.544             2.029            1.000
Chain 1:    400      -458317.602             1.758            1.000
Chain 1:    500      -358852.569             1.462            0.944
Chain 1:    600      -233864.766             1.307            0.944
Chain 1:    700      -119815.369             1.256            0.944
Chain 1:    800       -86976.283             1.147            0.944
Chain 1:    900       -67259.308             1.052            0.775
Chain 1:   1000       -52014.397             0.976            0.775
Chain 1:   1100       -39448.437             0.908            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38621.728             0.479            0.378
Chain 1:   1300       -26523.648             0.447            0.378
Chain 1:   1400       -26239.090             0.353            0.319
Chain 1:   1500       -22813.050             0.341            0.319
Chain 1:   1600       -22026.532             0.291            0.293
Chain 1:   1700       -20893.189             0.201            0.293
Chain 1:   1800       -20836.085             0.164            0.150
Chain 1:   1900       -21162.538             0.136            0.054
Chain 1:   2000       -19669.922             0.114            0.054
Chain 1:   2100       -19908.121             0.083            0.036
Chain 1:   2200       -20135.576             0.082            0.036
Chain 1:   2300       -19751.954             0.039            0.019
Chain 1:   2400       -19523.919             0.039            0.019
Chain 1:   2500       -19326.290             0.025            0.015
Chain 1:   2600       -18955.634             0.023            0.015
Chain 1:   2700       -18912.482             0.018            0.012
Chain 1:   2800       -18629.313             0.019            0.015
Chain 1:   2900       -18910.859             0.019            0.015
Chain 1:   3000       -18896.889             0.012            0.012
Chain 1:   3100       -18981.907             0.011            0.012
Chain 1:   3200       -18672.276             0.012            0.015
Chain 1:   3300       -18877.315             0.011            0.012
Chain 1:   3400       -18351.751             0.012            0.015
Chain 1:   3500       -18964.369             0.015            0.015
Chain 1:   3600       -18270.245             0.016            0.015
Chain 1:   3700       -18657.650             0.018            0.017
Chain 1:   3800       -17616.067             0.023            0.021
Chain 1:   3900       -17612.289             0.021            0.021
Chain 1:   4000       -17729.527             0.022            0.021
Chain 1:   4100       -17643.189             0.022            0.021
Chain 1:   4200       -17459.243             0.021            0.021
Chain 1:   4300       -17597.724             0.021            0.021
Chain 1:   4400       -17554.302             0.018            0.011
Chain 1:   4500       -17456.910             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12519.074             1.000            1.000
Chain 1:    200        -9491.187             0.660            1.000
Chain 1:    300        -8179.673             0.493            0.319
Chain 1:    400        -8377.055             0.376            0.319
Chain 1:    500        -8228.990             0.304            0.160
Chain 1:    600        -8142.923             0.255            0.160
Chain 1:    700        -8053.921             0.220            0.024
Chain 1:    800        -8093.802             0.193            0.024
Chain 1:    900        -8221.309             0.174            0.018
Chain 1:   1000        -8133.177             0.157            0.018
Chain 1:   1100        -8087.216             0.058            0.016
Chain 1:   1200        -8074.142             0.026            0.011
Chain 1:   1300        -8025.342             0.011            0.011
Chain 1:   1400        -8054.032             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45698.432             1.000            1.000
Chain 1:    200       -15656.787             1.459            1.919
Chain 1:    300        -8761.175             1.235            1.000
Chain 1:    400        -8669.865             0.929            1.000
Chain 1:    500        -8710.699             0.744            0.787
Chain 1:    600        -8548.860             0.623            0.787
Chain 1:    700        -7842.293             0.547            0.090
Chain 1:    800        -8172.373             0.484            0.090
Chain 1:    900        -7999.696             0.432            0.040
Chain 1:   1000        -7867.660             0.391            0.040
Chain 1:   1100        -7967.565             0.292            0.022
Chain 1:   1200        -7677.642             0.104            0.022
Chain 1:   1300        -7894.525             0.028            0.022
Chain 1:   1400        -7849.782             0.028            0.022
Chain 1:   1500        -7668.601             0.029            0.024
Chain 1:   1600        -7665.011             0.028            0.024
Chain 1:   1700        -7579.830             0.020            0.022
Chain 1:   1800        -7626.662             0.016            0.017
Chain 1:   1900        -7653.378             0.015            0.013
Chain 1:   2000        -7663.262             0.013            0.011
Chain 1:   2100        -7649.872             0.012            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00259 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87266.612             1.000            1.000
Chain 1:    200       -13633.904             3.200            5.401
Chain 1:    300       -10005.925             2.254            1.000
Chain 1:    400       -10744.349             1.708            1.000
Chain 1:    500        -8974.449             1.406            0.363
Chain 1:    600        -8527.277             1.180            0.363
Chain 1:    700        -8418.362             1.014            0.197
Chain 1:    800        -9049.280             0.896            0.197
Chain 1:    900        -8879.365             0.798            0.070
Chain 1:   1000        -8596.515             0.722            0.070
Chain 1:   1100        -8847.028             0.624            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8503.333             0.088            0.052
Chain 1:   1300        -8723.554             0.055            0.040
Chain 1:   1400        -8726.592             0.048            0.033
Chain 1:   1500        -8581.947             0.030            0.028
Chain 1:   1600        -8694.526             0.026            0.025
Chain 1:   1700        -8780.692             0.026            0.025
Chain 1:   1800        -8373.231             0.023            0.025
Chain 1:   1900        -8469.205             0.023            0.025
Chain 1:   2000        -8441.666             0.020            0.017
Chain 1:   2100        -8562.626             0.018            0.014
Chain 1:   2200        -8454.403             0.016            0.013
Chain 1:   2300        -8509.341             0.014            0.013
Chain 1:   2400        -8398.417             0.015            0.013
Chain 1:   2500        -8445.509             0.014            0.013
Chain 1:   2600        -8472.725             0.013            0.011
Chain 1:   2700        -8388.926             0.013            0.011
Chain 1:   2800        -8357.879             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002477 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414985.120             1.000            1.000
Chain 1:    200     -1586046.573             2.653            4.306
Chain 1:    300      -891977.191             2.028            1.000
Chain 1:    400      -457826.638             1.758            1.000
Chain 1:    500      -358152.396             1.462            0.948
Chain 1:    600      -232946.974             1.308            0.948
Chain 1:    700      -119309.586             1.257            0.948
Chain 1:    800       -86501.168             1.147            0.948
Chain 1:    900       -66859.355             1.053            0.778
Chain 1:   1000       -51665.657             0.977            0.778
Chain 1:   1100       -39150.451             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38328.712             0.480            0.379
Chain 1:   1300       -26300.897             0.448            0.379
Chain 1:   1400       -26019.512             0.354            0.320
Chain 1:   1500       -22610.646             0.342            0.320
Chain 1:   1600       -21827.940             0.292            0.294
Chain 1:   1700       -20704.278             0.202            0.294
Chain 1:   1800       -20648.909             0.164            0.151
Chain 1:   1900       -20974.911             0.136            0.054
Chain 1:   2000       -19487.563             0.114            0.054
Chain 1:   2100       -19725.880             0.084            0.036
Chain 1:   2200       -19951.934             0.083            0.036
Chain 1:   2300       -19569.557             0.039            0.020
Chain 1:   2400       -19341.776             0.039            0.020
Chain 1:   2500       -19143.539             0.025            0.016
Chain 1:   2600       -18774.039             0.023            0.016
Chain 1:   2700       -18731.164             0.018            0.012
Chain 1:   2800       -18447.962             0.019            0.015
Chain 1:   2900       -18729.179             0.019            0.015
Chain 1:   3000       -18715.344             0.012            0.012
Chain 1:   3100       -18800.300             0.011            0.012
Chain 1:   3200       -18491.112             0.012            0.015
Chain 1:   3300       -18695.779             0.011            0.012
Chain 1:   3400       -18170.819             0.012            0.015
Chain 1:   3500       -18782.382             0.015            0.015
Chain 1:   3600       -18089.561             0.017            0.015
Chain 1:   3700       -18475.987             0.018            0.017
Chain 1:   3800       -17436.296             0.023            0.021
Chain 1:   3900       -17432.458             0.021            0.021
Chain 1:   4000       -17549.783             0.022            0.021
Chain 1:   4100       -17463.501             0.022            0.021
Chain 1:   4200       -17279.953             0.021            0.021
Chain 1:   4300       -17418.239             0.021            0.021
Chain 1:   4400       -17375.214             0.018            0.011
Chain 1:   4500       -17277.739             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13014.113             1.000            1.000
Chain 1:    200        -9945.907             0.654            1.000
Chain 1:    300        -8394.059             0.498            0.308
Chain 1:    400        -8614.930             0.380            0.308
Chain 1:    500        -8333.448             0.311            0.185
Chain 1:    600        -8256.863             0.260            0.185
Chain 1:    700        -8301.416             0.224            0.034
Chain 1:    800        -8288.018             0.196            0.034
Chain 1:    900        -8207.251             0.175            0.026
Chain 1:   1000        -8236.380             0.158            0.026
Chain 1:   1100        -8375.049             0.060            0.017
Chain 1:   1200        -8247.499             0.031            0.015
Chain 1:   1300        -8197.870             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001413 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49787.170             1.000            1.000
Chain 1:    200       -16752.816             1.486            1.972
Chain 1:    300        -9176.986             1.266            1.000
Chain 1:    400        -9742.870             0.964            1.000
Chain 1:    500        -8901.321             0.790            0.826
Chain 1:    600        -8793.353             0.660            0.826
Chain 1:    700        -8309.984             0.574            0.095
Chain 1:    800        -8591.798             0.507            0.095
Chain 1:    900        -8249.982             0.455            0.058
Chain 1:   1000        -7848.090             0.415            0.058
Chain 1:   1100        -7900.226             0.315            0.058
Chain 1:   1200        -8149.299             0.121            0.051
Chain 1:   1300        -8142.631             0.039            0.041
Chain 1:   1400        -7932.870             0.035            0.033
Chain 1:   1500        -7742.654             0.028            0.031
Chain 1:   1600        -7862.954             0.029            0.031
Chain 1:   1700        -7787.524             0.024            0.026
Chain 1:   1800        -7803.836             0.021            0.025
Chain 1:   1900        -7832.533             0.017            0.015
Chain 1:   2000        -7880.411             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002505 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86875.544             1.000            1.000
Chain 1:    200       -14241.731             3.050            5.100
Chain 1:    300       -10418.989             2.156            1.000
Chain 1:    400       -12244.506             1.654            1.000
Chain 1:    500        -9189.260             1.390            0.367
Chain 1:    600        -8734.590             1.167            0.367
Chain 1:    700        -9254.840             1.008            0.332
Chain 1:    800        -9167.546             0.883            0.332
Chain 1:    900        -9068.744             0.786            0.149
Chain 1:   1000        -9273.538             0.710            0.149
Chain 1:   1100        -9145.994             0.611            0.056   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8706.990             0.106            0.052
Chain 1:   1300        -9019.079             0.073            0.050
Chain 1:   1400        -8891.433             0.060            0.035
Chain 1:   1500        -8898.674             0.026            0.022
Chain 1:   1600        -8962.774             0.022            0.014
Chain 1:   1700        -9020.879             0.017            0.014
Chain 1:   1800        -8558.850             0.021            0.014
Chain 1:   1900        -8678.130             0.022            0.014
Chain 1:   2000        -8697.644             0.020            0.014
Chain 1:   2100        -8783.109             0.019            0.014
Chain 1:   2200        -8562.251             0.017            0.014
Chain 1:   2300        -8769.212             0.016            0.014
Chain 1:   2400        -8575.892             0.017            0.014
Chain 1:   2500        -8648.363             0.017            0.014
Chain 1:   2600        -8558.653             0.018            0.014
Chain 1:   2700        -8592.290             0.017            0.014
Chain 1:   2800        -8542.956             0.013            0.010
Chain 1:   2900        -8658.175             0.013            0.010
Chain 1:   3000        -8568.533             0.013            0.010
Chain 1:   3100        -8534.970             0.013            0.010
Chain 1:   3200        -8506.264             0.011            0.010
Chain 1:   3300        -8768.380             0.011            0.010
Chain 1:   3400        -8813.336             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408635.466             1.000            1.000
Chain 1:    200     -1583827.495             2.655            4.309
Chain 1:    300      -893097.638             2.027            1.000
Chain 1:    400      -459279.836             1.757            1.000
Chain 1:    500      -359796.709             1.461            0.945
Chain 1:    600      -234532.086             1.306            0.945
Chain 1:    700      -120421.782             1.255            0.945
Chain 1:    800       -87503.125             1.145            0.945
Chain 1:    900       -67773.995             1.050            0.773
Chain 1:   1000       -52521.913             0.974            0.773
Chain 1:   1100       -39943.826             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39122.815             0.477            0.376
Chain 1:   1300       -27015.816             0.444            0.376
Chain 1:   1400       -26732.179             0.351            0.315
Chain 1:   1500       -23301.776             0.338            0.315
Chain 1:   1600       -22513.923             0.288            0.291
Chain 1:   1700       -21379.795             0.199            0.290
Chain 1:   1800       -21322.613             0.161            0.147
Chain 1:   1900       -21649.545             0.134            0.053
Chain 1:   2000       -20154.849             0.112            0.053
Chain 1:   2100       -20393.705             0.082            0.035
Chain 1:   2200       -20621.227             0.081            0.035
Chain 1:   2300       -20237.304             0.038            0.019
Chain 1:   2400       -20009.029             0.038            0.019
Chain 1:   2500       -19811.079             0.024            0.015
Chain 1:   2600       -19440.276             0.023            0.015
Chain 1:   2700       -19396.995             0.018            0.012
Chain 1:   2800       -19113.421             0.019            0.015
Chain 1:   2900       -19395.235             0.019            0.015
Chain 1:   3000       -19381.324             0.011            0.012
Chain 1:   3100       -19466.416             0.011            0.011
Chain 1:   3200       -19156.476             0.011            0.015
Chain 1:   3300       -19361.723             0.010            0.011
Chain 1:   3400       -18835.508             0.012            0.015
Chain 1:   3500       -19449.033             0.014            0.015
Chain 1:   3600       -18753.689             0.016            0.015
Chain 1:   3700       -19142.021             0.018            0.016
Chain 1:   3800       -18098.451             0.022            0.020
Chain 1:   3900       -18094.536             0.021            0.020
Chain 1:   4000       -18211.848             0.021            0.020
Chain 1:   4100       -18125.364             0.021            0.020
Chain 1:   4200       -17940.963             0.021            0.020
Chain 1:   4300       -18079.821             0.020            0.020
Chain 1:   4400       -18036.089             0.018            0.010
Chain 1:   4500       -17938.521             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49302.195             1.000            1.000
Chain 1:    200       -17151.023             1.437            1.875
Chain 1:    300       -16345.236             0.975            1.000
Chain 1:    400       -19103.770             0.767            1.000
Chain 1:    500       -16178.172             0.650            0.181
Chain 1:    600       -12060.335             0.598            0.341
Chain 1:    700       -14696.432             0.539            0.181
Chain 1:    800       -12928.222             0.488            0.181
Chain 1:    900       -17690.319             0.464            0.181
Chain 1:   1000       -12744.507             0.456            0.269
Chain 1:   1100       -12692.212             0.357            0.181
Chain 1:   1200       -12880.326             0.171            0.179
Chain 1:   1300       -10644.095             0.187            0.181
Chain 1:   1400       -10443.567             0.174            0.181
Chain 1:   1500       -10465.594             0.156            0.179
Chain 1:   1600       -10209.981             0.125            0.137
Chain 1:   1700       -12218.176             0.123            0.137
Chain 1:   1800       -10905.743             0.122            0.120
Chain 1:   1900       -10989.977             0.096            0.025
Chain 1:   2000       -18588.898             0.098            0.025
Chain 1:   2100       -10467.883             0.175            0.120
Chain 1:   2200       -20345.174             0.222            0.164
Chain 1:   2300        -9710.124             0.310            0.164
Chain 1:   2400        -9967.171             0.311            0.164
Chain 1:   2500       -10091.214             0.312            0.164
Chain 1:   2600       -12695.714             0.330            0.205
Chain 1:   2700       -15547.896             0.332            0.205
Chain 1:   2800       -20890.558             0.346            0.256
Chain 1:   2900       -16104.306             0.374            0.297
Chain 1:   3000        -9141.157             0.410            0.297
Chain 1:   3100       -10404.007             0.344            0.256
Chain 1:   3200       -10179.852             0.298            0.205
Chain 1:   3300        -9957.867             0.191            0.183
Chain 1:   3400        -9488.437             0.193            0.183
Chain 1:   3500        -9716.749             0.194            0.183
Chain 1:   3600       -10246.654             0.179            0.121
Chain 1:   3700       -10289.712             0.161            0.052
Chain 1:   3800       -10004.481             0.138            0.049
Chain 1:   3900        -9466.782             0.114            0.049
Chain 1:   4000       -20807.216             0.092            0.049
Chain 1:   4100        -9131.722             0.208            0.049
Chain 1:   4200       -10455.142             0.219            0.052
Chain 1:   4300       -14617.302             0.245            0.057
Chain 1:   4400       -13019.388             0.252            0.123
Chain 1:   4500       -11267.878             0.265            0.127
Chain 1:   4600       -11494.240             0.262            0.127
Chain 1:   4700        -9022.128             0.289            0.155
Chain 1:   4800        -9409.146             0.290            0.155
Chain 1:   4900       -11412.784             0.302            0.176
Chain 1:   5000       -10392.421             0.258            0.155
Chain 1:   5100        -9257.157             0.142            0.127
Chain 1:   5200       -12173.868             0.153            0.155
Chain 1:   5300       -12421.262             0.127            0.123
Chain 1:   5400       -12108.714             0.117            0.123
Chain 1:   5500       -10754.007             0.114            0.123
Chain 1:   5600       -10290.493             0.117            0.123
Chain 1:   5700       -11436.179             0.099            0.100
Chain 1:   5800       -11174.582             0.098            0.100
Chain 1:   5900        -9654.755             0.096            0.100
Chain 1:   6000        -9509.217             0.088            0.100
Chain 1:   6100       -11370.115             0.092            0.100
Chain 1:   6200       -10450.061             0.076            0.088
Chain 1:   6300        -8825.350             0.093            0.100
Chain 1:   6400       -13270.444             0.124            0.126
Chain 1:   6500       -12773.803             0.115            0.100
Chain 1:   6600        -9226.641             0.149            0.157
Chain 1:   6700       -12971.562             0.168            0.164
Chain 1:   6800       -12550.111             0.169            0.164
Chain 1:   6900        -8690.192             0.198            0.184
Chain 1:   7000        -8641.522             0.197            0.184
Chain 1:   7100        -8536.729             0.181            0.184
Chain 1:   7200        -8879.430             0.177            0.184
Chain 1:   7300       -11392.879             0.180            0.221
Chain 1:   7400        -8937.015             0.174            0.221
Chain 1:   7500       -10254.333             0.183            0.221
Chain 1:   7600       -11995.417             0.159            0.145
Chain 1:   7700        -9074.578             0.163            0.145
Chain 1:   7800        -8992.577             0.160            0.145
Chain 1:   7900        -8981.120             0.116            0.128
Chain 1:   8000        -8607.442             0.120            0.128
Chain 1:   8100        -9311.868             0.126            0.128
Chain 1:   8200        -9360.402             0.123            0.128
Chain 1:   8300       -10402.401             0.111            0.100
Chain 1:   8400       -11755.448             0.095            0.100
Chain 1:   8500        -9899.808             0.100            0.100
Chain 1:   8600       -11339.578             0.099            0.100
Chain 1:   8700        -9626.399             0.084            0.100
Chain 1:   8800        -9955.576             0.087            0.100
Chain 1:   8900        -8473.604             0.104            0.115
Chain 1:   9000       -11858.649             0.128            0.127
Chain 1:   9100        -8351.289             0.163            0.175
Chain 1:   9200        -9047.323             0.170            0.175
Chain 1:   9300        -9132.195             0.161            0.175
Chain 1:   9400       -11235.162             0.168            0.178
Chain 1:   9500       -11291.499             0.150            0.175
Chain 1:   9600        -8529.489             0.169            0.178
Chain 1:   9700        -8930.404             0.156            0.175
Chain 1:   9800        -8617.254             0.156            0.175
Chain 1:   9900        -9598.636             0.149            0.102
Chain 1:   10000        -9200.966             0.125            0.077
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001374 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62075.655             1.000            1.000
Chain 1:    200       -18159.117             1.709            2.418
Chain 1:    300        -9054.615             1.475            1.006
Chain 1:    400        -9677.719             1.122            1.006
Chain 1:    500        -8577.698             0.923            1.000
Chain 1:    600        -8525.301             0.770            1.000
Chain 1:    700        -8293.847             0.664            0.128
Chain 1:    800        -8335.800             0.582            0.128
Chain 1:    900        -7728.967             0.526            0.079
Chain 1:   1000        -7870.069             0.475            0.079
Chain 1:   1100        -7754.829             0.377            0.064
Chain 1:   1200        -7705.078             0.135            0.028
Chain 1:   1300        -7863.627             0.037            0.020
Chain 1:   1400        -7786.955             0.032            0.018
Chain 1:   1500        -7616.926             0.021            0.018
Chain 1:   1600        -7771.222             0.022            0.020
Chain 1:   1700        -7538.327             0.023            0.020
Chain 1:   1800        -7674.904             0.024            0.020
Chain 1:   1900        -7747.482             0.017            0.018
Chain 1:   2000        -7749.783             0.015            0.018
Chain 1:   2100        -7637.773             0.015            0.018
Chain 1:   2200        -7802.350             0.017            0.020
Chain 1:   2300        -7603.293             0.017            0.020
Chain 1:   2400        -7601.662             0.016            0.020
Chain 1:   2500        -7676.736             0.015            0.018
Chain 1:   2600        -7594.321             0.014            0.015
Chain 1:   2700        -7534.057             0.012            0.011
Chain 1:   2800        -7572.030             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002515 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85844.968             1.000            1.000
Chain 1:    200       -13834.582             3.103            5.205
Chain 1:    300       -10149.601             2.189            1.000
Chain 1:    400       -11317.176             1.668            1.000
Chain 1:    500        -9147.471             1.382            0.363
Chain 1:    600        -8788.777             1.158            0.363
Chain 1:    700        -8819.584             0.993            0.237
Chain 1:    800        -9391.169             0.877            0.237
Chain 1:    900        -8895.526             0.785            0.103
Chain 1:   1000        -8649.718             0.710            0.103
Chain 1:   1100        -8945.363             0.613            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8393.875             0.099            0.061
Chain 1:   1300        -8801.004             0.067            0.056
Chain 1:   1400        -8833.585             0.058            0.046
Chain 1:   1500        -8659.271             0.036            0.041
Chain 1:   1600        -8770.866             0.033            0.033
Chain 1:   1700        -8833.036             0.033            0.033
Chain 1:   1800        -8396.430             0.032            0.033
Chain 1:   1900        -8501.553             0.028            0.028
Chain 1:   2000        -8477.407             0.026            0.020
Chain 1:   2100        -8434.232             0.023            0.013
Chain 1:   2200        -8420.823             0.016            0.012
Chain 1:   2300        -8557.745             0.013            0.012
Chain 1:   2400        -8402.356             0.015            0.013
Chain 1:   2500        -8471.506             0.014            0.012
Chain 1:   2600        -8389.515             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8394705.870             1.000            1.000
Chain 1:    200     -1580304.011             2.656            4.312
Chain 1:    300      -890280.487             2.029            1.000
Chain 1:    400      -457886.418             1.758            1.000
Chain 1:    500      -358525.886             1.462            0.944
Chain 1:    600      -233586.635             1.307            0.944
Chain 1:    700      -119707.001             1.256            0.944
Chain 1:    800       -86932.610             1.146            0.944
Chain 1:    900       -67247.128             1.052            0.775
Chain 1:   1000       -52028.479             0.976            0.775
Chain 1:   1100       -39487.488             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38664.630             0.478            0.377
Chain 1:   1300       -26584.214             0.446            0.377
Chain 1:   1400       -26302.700             0.353            0.318
Chain 1:   1500       -22880.929             0.340            0.318
Chain 1:   1600       -22096.205             0.290            0.293
Chain 1:   1700       -20964.361             0.201            0.293
Chain 1:   1800       -20907.774             0.163            0.150
Chain 1:   1900       -21234.314             0.135            0.054
Chain 1:   2000       -19742.388             0.114            0.054
Chain 1:   2100       -19980.639             0.083            0.036
Chain 1:   2200       -20208.033             0.082            0.036
Chain 1:   2300       -19824.384             0.039            0.019
Chain 1:   2400       -19596.302             0.039            0.019
Chain 1:   2500       -19398.710             0.025            0.015
Chain 1:   2600       -19028.069             0.023            0.015
Chain 1:   2700       -18984.882             0.018            0.012
Chain 1:   2800       -18701.726             0.019            0.015
Chain 1:   2900       -18983.198             0.019            0.015
Chain 1:   3000       -18969.258             0.012            0.012
Chain 1:   3100       -19054.325             0.011            0.012
Chain 1:   3200       -18744.668             0.011            0.015
Chain 1:   3300       -18949.703             0.011            0.012
Chain 1:   3400       -18424.150             0.012            0.015
Chain 1:   3500       -19036.831             0.014            0.015
Chain 1:   3600       -18342.502             0.016            0.015
Chain 1:   3700       -18730.061             0.018            0.017
Chain 1:   3800       -17688.308             0.023            0.021
Chain 1:   3900       -17684.501             0.021            0.021
Chain 1:   4000       -17801.724             0.022            0.021
Chain 1:   4100       -17715.431             0.022            0.021
Chain 1:   4200       -17531.408             0.021            0.021
Chain 1:   4300       -17669.947             0.021            0.021
Chain 1:   4400       -17626.479             0.018            0.010
Chain 1:   4500       -17529.051             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48645.438             1.000            1.000
Chain 1:    200       -22586.428             1.077            1.154
Chain 1:    300       -25875.039             0.760            1.000
Chain 1:    400       -28777.918             0.595            1.000
Chain 1:    500       -22626.821             0.531            0.272
Chain 1:    600       -20939.191             0.456            0.272
Chain 1:    700       -11549.211             0.507            0.272
Chain 1:    800       -13801.358             0.464            0.272
Chain 1:    900       -16755.845             0.432            0.176
Chain 1:   1000       -13049.933             0.417            0.272
Chain 1:   1100       -22327.489             0.359            0.272
Chain 1:   1200       -10796.570             0.350            0.272
Chain 1:   1300       -11595.941             0.344            0.272
Chain 1:   1400        -9857.729             0.352            0.272
Chain 1:   1500       -16848.481             0.366            0.284
Chain 1:   1600       -11151.873             0.409            0.415
Chain 1:   1700        -9454.007             0.346            0.284
Chain 1:   1800       -13092.963             0.357            0.284
Chain 1:   1900        -9219.870             0.382            0.415
Chain 1:   2000       -14855.481             0.391            0.415
Chain 1:   2100        -9501.980             0.406            0.415
Chain 1:   2200       -10428.213             0.308            0.379
Chain 1:   2300        -9374.079             0.312            0.379
Chain 1:   2400       -10685.387             0.307            0.379
Chain 1:   2500        -9034.735             0.284            0.278
Chain 1:   2600        -9978.554             0.242            0.183
Chain 1:   2700       -10411.075             0.228            0.183
Chain 1:   2800        -9873.201             0.206            0.123
Chain 1:   2900        -9241.817             0.171            0.112
Chain 1:   3000        -9824.691             0.139            0.095
Chain 1:   3100        -9072.205             0.091            0.089
Chain 1:   3200        -9199.924             0.083            0.083
Chain 1:   3300        -9335.176             0.073            0.068
Chain 1:   3400        -9921.040             0.067            0.059
Chain 1:   3500        -8752.908             0.062            0.059
Chain 1:   3600        -9326.311             0.059            0.059
Chain 1:   3700        -8812.133             0.061            0.059
Chain 1:   3800        -9935.552             0.066            0.061
Chain 1:   3900       -11995.991             0.077            0.061
Chain 1:   4000        -8546.646             0.111            0.083
Chain 1:   4100       -13326.874             0.139            0.113
Chain 1:   4200        -8823.424             0.188            0.133
Chain 1:   4300       -12005.309             0.213            0.172
Chain 1:   4400       -10694.140             0.220            0.172
Chain 1:   4500        -8894.804             0.227            0.202
Chain 1:   4600       -11583.620             0.244            0.232
Chain 1:   4700        -9277.542             0.263            0.249
Chain 1:   4800        -8386.857             0.262            0.249
Chain 1:   4900       -13397.350             0.282            0.265
Chain 1:   5000        -9384.889             0.285            0.265
Chain 1:   5100       -16055.083             0.290            0.265
Chain 1:   5200        -8658.230             0.325            0.265
Chain 1:   5300        -9133.580             0.304            0.249
Chain 1:   5400       -11133.789             0.309            0.249
Chain 1:   5500        -9236.253             0.310            0.249
Chain 1:   5600       -10946.122             0.302            0.249
Chain 1:   5700       -12716.190             0.291            0.205
Chain 1:   5800        -8680.144             0.327            0.374
Chain 1:   5900       -11748.375             0.316            0.261
Chain 1:   6000       -10713.901             0.283            0.205
Chain 1:   6100        -8404.679             0.268            0.205
Chain 1:   6200        -8278.905             0.185            0.180
Chain 1:   6300        -8346.435             0.180            0.180
Chain 1:   6400       -11064.370             0.187            0.205
Chain 1:   6500       -12844.604             0.180            0.156
Chain 1:   6600        -8332.048             0.219            0.246
Chain 1:   6700        -9782.521             0.219            0.246
Chain 1:   6800       -10341.096             0.178            0.148
Chain 1:   6900        -8284.211             0.177            0.148
Chain 1:   7000       -10366.979             0.188            0.201
Chain 1:   7100        -8437.411             0.183            0.201
Chain 1:   7200        -8386.348             0.182            0.201
Chain 1:   7300        -9338.397             0.191            0.201
Chain 1:   7400       -10142.407             0.175            0.148
Chain 1:   7500       -11862.348             0.175            0.148
Chain 1:   7600        -9307.986             0.149            0.148
Chain 1:   7700        -8479.459             0.144            0.145
Chain 1:   7800       -11864.467             0.167            0.201
Chain 1:   7900        -8234.030             0.186            0.201
Chain 1:   8000        -8254.774             0.166            0.145
Chain 1:   8100       -11650.410             0.172            0.145
Chain 1:   8200        -8166.628             0.215            0.274
Chain 1:   8300        -8086.501             0.205            0.274
Chain 1:   8400        -8376.695             0.201            0.274
Chain 1:   8500        -8765.319             0.191            0.274
Chain 1:   8600        -8458.791             0.167            0.098
Chain 1:   8700        -8549.926             0.158            0.044
Chain 1:   8800        -8599.326             0.130            0.036
Chain 1:   8900       -12122.902             0.115            0.036
Chain 1:   9000        -9312.084             0.145            0.044
Chain 1:   9100        -8153.567             0.130            0.044
Chain 1:   9200        -8205.913             0.088            0.036
Chain 1:   9300        -8097.078             0.089            0.036
Chain 1:   9400       -11615.336             0.115            0.044
Chain 1:   9500        -9597.920             0.132            0.142
Chain 1:   9600        -9720.740             0.130            0.142
Chain 1:   9700       -10499.754             0.136            0.142
Chain 1:   9800        -8957.582             0.153            0.172
Chain 1:   9900        -9836.197             0.133            0.142
Chain 1:   10000       -10809.889             0.111            0.090
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001417 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57674.210             1.000            1.000
Chain 1:    200       -17442.605             1.653            2.307
Chain 1:    300        -8567.201             1.447            1.036
Chain 1:    400        -8120.534             1.099            1.036
Chain 1:    500        -8338.796             0.885            1.000
Chain 1:    600        -8910.575             0.748            1.000
Chain 1:    700        -7721.780             0.663            0.154
Chain 1:    800        -7948.643             0.584            0.154
Chain 1:    900        -7900.084             0.520            0.064
Chain 1:   1000        -7700.429             0.470            0.064
Chain 1:   1100        -7815.395             0.372            0.055
Chain 1:   1200        -7601.168             0.144            0.029
Chain 1:   1300        -7563.540             0.041            0.028
Chain 1:   1400        -7846.175             0.039            0.028
Chain 1:   1500        -7589.594             0.040            0.029
Chain 1:   1600        -7525.033             0.034            0.028
Chain 1:   1700        -7487.289             0.019            0.026
Chain 1:   1800        -7535.699             0.017            0.015
Chain 1:   1900        -7551.243             0.017            0.015
Chain 1:   2000        -7546.689             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004143 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86175.761             1.000            1.000
Chain 1:    200       -13261.039             3.249            5.498
Chain 1:    300        -9697.495             2.289            1.000
Chain 1:    400       -10506.797             1.736            1.000
Chain 1:    500        -8598.928             1.433            0.367
Chain 1:    600        -8261.433             1.201            0.367
Chain 1:    700        -8173.779             1.031            0.222
Chain 1:    800        -8633.366             0.909            0.222
Chain 1:    900        -8515.296             0.809            0.077
Chain 1:   1000        -8351.357             0.730            0.077
Chain 1:   1100        -8604.059             0.633            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8129.701             0.089            0.053
Chain 1:   1300        -8286.188             0.054            0.041
Chain 1:   1400        -8441.197             0.049            0.029
Chain 1:   1500        -8307.806             0.028            0.020
Chain 1:   1600        -8416.895             0.025            0.019
Chain 1:   1700        -8497.560             0.025            0.019
Chain 1:   1800        -8106.505             0.025            0.019
Chain 1:   1900        -8209.063             0.024            0.019
Chain 1:   2000        -8179.092             0.023            0.018
Chain 1:   2100        -8306.455             0.021            0.016
Chain 1:   2200        -8092.773             0.018            0.016
Chain 1:   2300        -8237.711             0.018            0.016
Chain 1:   2400        -8252.824             0.016            0.015
Chain 1:   2500        -8219.377             0.015            0.013
Chain 1:   2600        -8221.032             0.014            0.012
Chain 1:   2700        -8128.125             0.014            0.012
Chain 1:   2800        -8101.751             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003616 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8382358.032             1.000            1.000
Chain 1:    200     -1582872.855             2.648            4.296
Chain 1:    300      -890758.364             2.024            1.000
Chain 1:    400      -457294.904             1.755            1.000
Chain 1:    500      -357789.647             1.460            0.948
Chain 1:    600      -232811.138             1.306            0.948
Chain 1:    700      -119034.433             1.256            0.948
Chain 1:    800       -86196.396             1.147            0.948
Chain 1:    900       -66539.883             1.052            0.777
Chain 1:   1000       -51326.858             0.976            0.777
Chain 1:   1100       -38792.953             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37965.829             0.481            0.381
Chain 1:   1300       -25925.134             0.450            0.381
Chain 1:   1400       -25642.501             0.356            0.323
Chain 1:   1500       -22229.431             0.344            0.323
Chain 1:   1600       -21444.825             0.294            0.296
Chain 1:   1700       -20319.834             0.204            0.295
Chain 1:   1800       -20263.866             0.166            0.154
Chain 1:   1900       -20589.586             0.138            0.055
Chain 1:   2000       -19101.722             0.116            0.055
Chain 1:   2100       -19340.298             0.085            0.037
Chain 1:   2200       -19566.181             0.084            0.037
Chain 1:   2300       -19183.947             0.040            0.020
Chain 1:   2400       -18956.187             0.040            0.020
Chain 1:   2500       -18758.011             0.025            0.016
Chain 1:   2600       -18388.914             0.024            0.016
Chain 1:   2700       -18346.050             0.019            0.012
Chain 1:   2800       -18063.006             0.020            0.016
Chain 1:   2900       -18344.041             0.020            0.015
Chain 1:   3000       -18330.383             0.012            0.012
Chain 1:   3100       -18415.269             0.011            0.012
Chain 1:   3200       -18106.301             0.012            0.015
Chain 1:   3300       -18310.724             0.011            0.012
Chain 1:   3400       -17786.192             0.013            0.015
Chain 1:   3500       -18397.228             0.015            0.016
Chain 1:   3600       -17705.018             0.017            0.016
Chain 1:   3700       -18090.990             0.019            0.017
Chain 1:   3800       -17052.368             0.023            0.021
Chain 1:   3900       -17048.513             0.022            0.021
Chain 1:   4000       -17165.848             0.022            0.021
Chain 1:   4100       -17079.675             0.022            0.021
Chain 1:   4200       -16896.276             0.022            0.021
Chain 1:   4300       -17034.459             0.022            0.021
Chain 1:   4400       -16991.609             0.019            0.011
Chain 1:   4500       -16894.144             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004333 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12201.518             1.000            1.000
Chain 1:    200        -9127.683             0.668            1.000
Chain 1:    300        -7858.020             0.499            0.337
Chain 1:    400        -8045.933             0.380            0.337
Chain 1:    500        -7907.807             0.308            0.162
Chain 1:    600        -7815.678             0.258            0.162
Chain 1:    700        -7727.544             0.223            0.023
Chain 1:    800        -7767.674             0.196            0.023
Chain 1:    900        -7895.041             0.176            0.017
Chain 1:   1000        -7807.470             0.159            0.017
Chain 1:   1100        -7824.134             0.060            0.016
Chain 1:   1200        -7746.982             0.027            0.012
Chain 1:   1300        -7696.015             0.012            0.011
Chain 1:   1400        -7718.476             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001554 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56654.034             1.000            1.000
Chain 1:    200       -17200.783             1.647            2.294
Chain 1:    300        -8597.869             1.431            1.001
Chain 1:    400        -8213.005             1.085            1.001
Chain 1:    500        -8036.852             0.873            1.000
Chain 1:    600        -8567.295             0.737            1.000
Chain 1:    700        -7898.378             0.644            0.085
Chain 1:    800        -8188.608             0.568            0.085
Chain 1:    900        -7817.028             0.510            0.062
Chain 1:   1000        -7660.994             0.461            0.062
Chain 1:   1100        -7699.675             0.362            0.048
Chain 1:   1200        -7572.618             0.134            0.047
Chain 1:   1300        -7751.629             0.036            0.035
Chain 1:   1400        -7603.525             0.034            0.023
Chain 1:   1500        -7531.711             0.032            0.023
Chain 1:   1600        -7640.419             0.028            0.020
Chain 1:   1700        -7450.650             0.022            0.020
Chain 1:   1800        -7506.391             0.019            0.019
Chain 1:   1900        -7496.063             0.014            0.017
Chain 1:   2000        -7525.367             0.013            0.014
Chain 1:   2100        -7520.993             0.012            0.014
Chain 1:   2200        -7623.984             0.012            0.014
Chain 1:   2300        -7550.492             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003583 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86459.584             1.000            1.000
Chain 1:    200       -13278.140             3.256            5.511
Chain 1:    300        -9673.389             2.295            1.000
Chain 1:    400       -10395.511             1.738            1.000
Chain 1:    500        -8635.066             1.431            0.373
Chain 1:    600        -8277.708             1.200            0.373
Chain 1:    700        -8275.925             1.029            0.204
Chain 1:    800        -8570.916             0.904            0.204
Chain 1:    900        -8414.131             0.806            0.069
Chain 1:   1000        -8259.282             0.727            0.069
Chain 1:   1100        -8374.601             0.629            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8185.672             0.080            0.034
Chain 1:   1300        -8392.074             0.045            0.025
Chain 1:   1400        -8377.686             0.038            0.023
Chain 1:   1500        -8251.593             0.019            0.019
Chain 1:   1600        -8361.194             0.016            0.019
Chain 1:   1700        -8448.022             0.017            0.019
Chain 1:   1800        -8042.982             0.019            0.019
Chain 1:   1900        -8140.321             0.018            0.015
Chain 1:   2000        -8112.173             0.017            0.014
Chain 1:   2100        -8232.397             0.017            0.015
Chain 1:   2200        -8028.663             0.017            0.015
Chain 1:   2300        -8176.775             0.016            0.015
Chain 1:   2400        -8183.637             0.016            0.015
Chain 1:   2500        -8153.895             0.015            0.013
Chain 1:   2600        -8152.519             0.014            0.012
Chain 1:   2700        -8065.090             0.014            0.012
Chain 1:   2800        -8030.992             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003108 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403593.642             1.000            1.000
Chain 1:    200     -1583428.509             2.654            4.307
Chain 1:    300      -889876.481             2.029            1.000
Chain 1:    400      -456813.876             1.759            1.000
Chain 1:    500      -357157.143             1.463            0.948
Chain 1:    600      -232397.472             1.308            0.948
Chain 1:    700      -118819.179             1.258            0.948
Chain 1:    800       -86082.940             1.148            0.948
Chain 1:    900       -66461.576             1.054            0.779
Chain 1:   1000       -51285.258             0.978            0.779
Chain 1:   1100       -38785.005             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37964.504             0.481            0.380
Chain 1:   1300       -25943.522             0.450            0.380
Chain 1:   1400       -25664.586             0.356            0.322
Chain 1:   1500       -22257.643             0.344            0.322
Chain 1:   1600       -21475.813             0.293            0.296
Chain 1:   1700       -20352.171             0.203            0.295
Chain 1:   1800       -20296.882             0.166            0.153
Chain 1:   1900       -20622.892             0.138            0.055
Chain 1:   2000       -19135.811             0.116            0.055
Chain 1:   2100       -19374.088             0.085            0.036
Chain 1:   2200       -19600.208             0.084            0.036
Chain 1:   2300       -19217.760             0.040            0.020
Chain 1:   2400       -18989.930             0.040            0.020
Chain 1:   2500       -18791.880             0.025            0.016
Chain 1:   2600       -18422.329             0.024            0.016
Chain 1:   2700       -18379.420             0.018            0.012
Chain 1:   2800       -18096.329             0.020            0.016
Chain 1:   2900       -18377.470             0.020            0.015
Chain 1:   3000       -18363.700             0.012            0.012
Chain 1:   3100       -18448.636             0.011            0.012
Chain 1:   3200       -18139.469             0.012            0.015
Chain 1:   3300       -18344.101             0.011            0.012
Chain 1:   3400       -17819.262             0.013            0.015
Chain 1:   3500       -18430.732             0.015            0.016
Chain 1:   3600       -17737.965             0.017            0.016
Chain 1:   3700       -18124.334             0.019            0.017
Chain 1:   3800       -17084.856             0.023            0.021
Chain 1:   3900       -17081.015             0.022            0.021
Chain 1:   4000       -17198.327             0.022            0.021
Chain 1:   4100       -17112.100             0.022            0.021
Chain 1:   4200       -16928.557             0.022            0.021
Chain 1:   4300       -17066.820             0.021            0.021
Chain 1:   4400       -17023.797             0.019            0.011
Chain 1:   4500       -16926.343             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001308 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12826.751             1.000            1.000
Chain 1:    200        -9719.596             0.660            1.000
Chain 1:    300        -8228.300             0.500            0.320
Chain 1:    400        -8466.957             0.382            0.320
Chain 1:    500        -8354.919             0.309            0.181
Chain 1:    600        -8251.790             0.259            0.181
Chain 1:    700        -8060.146             0.226            0.028
Chain 1:    800        -8057.285             0.197            0.028
Chain 1:    900        -8124.655             0.176            0.024
Chain 1:   1000        -8172.879             0.159            0.024
Chain 1:   1100        -8142.966             0.060            0.013
Chain 1:   1200        -8088.463             0.028            0.012
Chain 1:   1300        -8007.121             0.011            0.010
Chain 1:   1400        -8032.222             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46979.582             1.000            1.000
Chain 1:    200       -16095.276             1.459            1.919
Chain 1:    300        -8991.120             1.236            1.000
Chain 1:    400        -8278.805             0.949            1.000
Chain 1:    500        -8730.817             0.769            0.790
Chain 1:    600        -8743.160             0.641            0.790
Chain 1:    700        -8631.020             0.552            0.086
Chain 1:    800        -8623.485             0.483            0.086
Chain 1:    900        -8243.582             0.434            0.052
Chain 1:   1000        -8011.780             0.394            0.052
Chain 1:   1100        -7989.483             0.294            0.046
Chain 1:   1200        -7775.039             0.105            0.029
Chain 1:   1300        -7830.975             0.027            0.028
Chain 1:   1400        -7768.692             0.019            0.013
Chain 1:   1500        -7719.113             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003668 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86503.804             1.000            1.000
Chain 1:    200       -14019.489             3.085            5.170
Chain 1:    300       -10230.612             2.180            1.000
Chain 1:    400       -12007.766             1.672            1.000
Chain 1:    500        -8698.404             1.414            0.380
Chain 1:    600        -8760.509             1.179            0.380
Chain 1:    700        -9086.442             1.016            0.370
Chain 1:    800        -8789.047             0.893            0.370
Chain 1:    900        -8945.678             0.796            0.148
Chain 1:   1000        -9213.647             0.719            0.148
Chain 1:   1100        -8928.098             0.622            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8632.147             0.109            0.034
Chain 1:   1300        -8858.463             0.074            0.034
Chain 1:   1400        -8687.016             0.062            0.032
Chain 1:   1500        -8691.945             0.024            0.029
Chain 1:   1600        -8804.502             0.024            0.029
Chain 1:   1700        -8849.570             0.021            0.026
Chain 1:   1800        -8391.076             0.023            0.026
Chain 1:   1900        -8502.848             0.023            0.026
Chain 1:   2000        -8516.897             0.020            0.020
Chain 1:   2100        -8608.146             0.018            0.013
Chain 1:   2200        -8392.588             0.017            0.013
Chain 1:   2300        -8600.589             0.017            0.013
Chain 1:   2400        -8397.900             0.017            0.013
Chain 1:   2500        -8474.610             0.018            0.013
Chain 1:   2600        -8384.548             0.018            0.013
Chain 1:   2700        -8417.596             0.018            0.013
Chain 1:   2800        -8368.651             0.013            0.011
Chain 1:   2900        -8483.082             0.013            0.011
Chain 1:   3000        -8399.648             0.014            0.011
Chain 1:   3100        -8360.953             0.013            0.011
Chain 1:   3200        -8333.201             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003704 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8366451.121             1.000            1.000
Chain 1:    200     -1580854.599             2.646            4.292
Chain 1:    300      -891120.599             2.022            1.000
Chain 1:    400      -457919.656             1.753            1.000
Chain 1:    500      -358732.721             1.458            0.946
Chain 1:    600      -233781.998             1.304            0.946
Chain 1:    700      -119974.015             1.253            0.946
Chain 1:    800       -87125.320             1.144            0.946
Chain 1:    900       -67457.281             1.049            0.774
Chain 1:   1000       -52242.241             0.973            0.774
Chain 1:   1100       -39690.385             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38875.332             0.478            0.377
Chain 1:   1300       -26789.794             0.445            0.377
Chain 1:   1400       -26509.417             0.352            0.316
Chain 1:   1500       -23083.440             0.339            0.316
Chain 1:   1600       -22297.248             0.289            0.292
Chain 1:   1700       -21165.265             0.200            0.291
Chain 1:   1800       -21108.748             0.162            0.148
Chain 1:   1900       -21435.718             0.135            0.053
Chain 1:   2000       -19942.078             0.113            0.053
Chain 1:   2100       -20181.010             0.082            0.035
Chain 1:   2200       -20408.272             0.081            0.035
Chain 1:   2300       -20024.532             0.038            0.019
Chain 1:   2400       -19796.251             0.038            0.019
Chain 1:   2500       -19598.263             0.025            0.015
Chain 1:   2600       -19227.640             0.023            0.015
Chain 1:   2700       -19184.426             0.018            0.012
Chain 1:   2800       -18900.808             0.019            0.015
Chain 1:   2900       -19182.600             0.019            0.015
Chain 1:   3000       -19168.743             0.012            0.012
Chain 1:   3100       -19253.789             0.011            0.012
Chain 1:   3200       -18943.980             0.011            0.015
Chain 1:   3300       -19149.147             0.011            0.012
Chain 1:   3400       -18623.048             0.012            0.015
Chain 1:   3500       -19236.458             0.014            0.015
Chain 1:   3600       -18541.265             0.016            0.015
Chain 1:   3700       -18929.419             0.018            0.016
Chain 1:   3800       -17886.157             0.022            0.021
Chain 1:   3900       -17882.236             0.021            0.021
Chain 1:   4000       -17999.551             0.021            0.021
Chain 1:   4100       -17913.074             0.022            0.021
Chain 1:   4200       -17728.744             0.021            0.021
Chain 1:   4300       -17867.578             0.021            0.021
Chain 1:   4400       -17823.884             0.018            0.010
Chain 1:   4500       -17726.313             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12612.061             1.000            1.000
Chain 1:    200        -9420.375             0.669            1.000
Chain 1:    300        -8027.783             0.504            0.339
Chain 1:    400        -8157.600             0.382            0.339
Chain 1:    500        -7992.837             0.310            0.173
Chain 1:    600        -7920.151             0.260            0.173
Chain 1:    700        -7813.470             0.225            0.021
Chain 1:    800        -7820.037             0.197            0.021
Chain 1:    900        -7764.738             0.176            0.016
Chain 1:   1000        -7943.359             0.160            0.021
Chain 1:   1100        -7953.818             0.060            0.016
Chain 1:   1200        -7836.571             0.028            0.015
Chain 1:   1300        -7803.185             0.011            0.014
Chain 1:   1400        -7814.513             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49619.008             1.000            1.000
Chain 1:    200       -16007.986             1.550            2.100
Chain 1:    300        -8722.820             1.312            1.000
Chain 1:    400        -8533.102             0.989            1.000
Chain 1:    500        -8528.345             0.792            0.835
Chain 1:    600        -8464.169             0.661            0.835
Chain 1:    700        -7717.465             0.580            0.097
Chain 1:    800        -8063.742             0.513            0.097
Chain 1:    900        -7867.077             0.459            0.043
Chain 1:   1000        -7847.902             0.413            0.043
Chain 1:   1100        -7850.754             0.313            0.025
Chain 1:   1200        -7653.051             0.106            0.025
Chain 1:   1300        -7783.814             0.024            0.022
Chain 1:   1400        -7706.453             0.023            0.017
Chain 1:   1500        -7546.305             0.025            0.021
Chain 1:   1600        -7829.320             0.028            0.025
Chain 1:   1700        -7581.724             0.021            0.025
Chain 1:   1800        -7653.762             0.018            0.021
Chain 1:   1900        -7640.066             0.016            0.017
Chain 1:   2000        -7624.343             0.016            0.017
Chain 1:   2100        -7581.009             0.016            0.017
Chain 1:   2200        -7722.795             0.015            0.017
Chain 1:   2300        -7603.794             0.015            0.016
Chain 1:   2400        -7639.959             0.015            0.016
Chain 1:   2500        -7594.639             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003102 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87374.811             1.000            1.000
Chain 1:    200       -13567.796             3.220            5.440
Chain 1:    300        -9862.900             2.272            1.000
Chain 1:    400       -10937.629             1.728            1.000
Chain 1:    500        -8854.643             1.430            0.376
Chain 1:    600        -8328.572             1.202            0.376
Chain 1:    700        -8753.893             1.037            0.235
Chain 1:    800        -9477.540             0.917            0.235
Chain 1:    900        -8622.844             0.826            0.099
Chain 1:   1000        -8300.568             0.748            0.099
Chain 1:   1100        -8691.761             0.652            0.098   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8249.351             0.113            0.076
Chain 1:   1300        -8559.845             0.079            0.063
Chain 1:   1400        -8507.130             0.070            0.054
Chain 1:   1500        -8401.073             0.048            0.049
Chain 1:   1600        -8510.212             0.043            0.045
Chain 1:   1700        -8581.213             0.039            0.039
Chain 1:   1800        -8150.267             0.037            0.039
Chain 1:   1900        -8254.418             0.028            0.036
Chain 1:   2000        -8229.552             0.024            0.013
Chain 1:   2100        -8366.523             0.021            0.013
Chain 1:   2200        -8159.534             0.019            0.013
Chain 1:   2300        -8258.557             0.016            0.013
Chain 1:   2400        -8321.233             0.016            0.013
Chain 1:   2500        -8261.890             0.016            0.013
Chain 1:   2600        -8267.613             0.015            0.012
Chain 1:   2700        -8182.391             0.015            0.012
Chain 1:   2800        -8138.737             0.010            0.010
Chain 1:   2900        -8227.619             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 50.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8427149.376             1.000            1.000
Chain 1:    200     -1588641.066             2.652            4.305
Chain 1:    300      -891012.664             2.029            1.000
Chain 1:    400      -457142.176             1.759            1.000
Chain 1:    500      -357156.855             1.463            0.949
Chain 1:    600      -232077.091             1.309            0.949
Chain 1:    700      -118804.313             1.258            0.949
Chain 1:    800       -86119.587             1.149            0.949
Chain 1:    900       -66564.315             1.054            0.783
Chain 1:   1000       -51446.894             0.978            0.783
Chain 1:   1100       -38997.872             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38188.473             0.481            0.380
Chain 1:   1300       -26217.869             0.449            0.380
Chain 1:   1400       -25945.048             0.355            0.319
Chain 1:   1500       -22550.612             0.342            0.319
Chain 1:   1600       -21772.839             0.291            0.294
Chain 1:   1700       -20655.313             0.202            0.294
Chain 1:   1800       -20601.699             0.164            0.151
Chain 1:   1900       -20928.137             0.136            0.054
Chain 1:   2000       -19443.167             0.114            0.054
Chain 1:   2100       -19681.498             0.084            0.036
Chain 1:   2200       -19907.292             0.083            0.036
Chain 1:   2300       -19524.995             0.039            0.020
Chain 1:   2400       -19297.087             0.039            0.020
Chain 1:   2500       -19098.624             0.025            0.016
Chain 1:   2600       -18728.999             0.023            0.016
Chain 1:   2700       -18686.060             0.018            0.012
Chain 1:   2800       -18402.586             0.019            0.015
Chain 1:   2900       -18683.868             0.019            0.015
Chain 1:   3000       -18670.142             0.012            0.012
Chain 1:   3100       -18755.132             0.011            0.012
Chain 1:   3200       -18445.737             0.012            0.015
Chain 1:   3300       -18650.540             0.011            0.012
Chain 1:   3400       -18125.140             0.012            0.015
Chain 1:   3500       -18737.311             0.015            0.015
Chain 1:   3600       -18043.598             0.017            0.015
Chain 1:   3700       -18430.627             0.018            0.017
Chain 1:   3800       -17389.565             0.023            0.021
Chain 1:   3900       -17385.617             0.021            0.021
Chain 1:   4000       -17503.002             0.022            0.021
Chain 1:   4100       -17416.667             0.022            0.021
Chain 1:   4200       -17232.777             0.021            0.021
Chain 1:   4300       -17371.330             0.021            0.021
Chain 1:   4400       -17328.035             0.019            0.011
Chain 1:   4500       -17230.473             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001297 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48044.270             1.000            1.000
Chain 1:    200       -14345.258             1.675            2.349
Chain 1:    300       -16210.682             1.155            1.000
Chain 1:    400       -11545.010             0.967            1.000
Chain 1:    500       -19784.742             0.857            0.416
Chain 1:    600       -17382.165             0.737            0.416
Chain 1:    700       -25988.964             0.679            0.404
Chain 1:    800       -21321.855             0.622            0.404
Chain 1:    900       -12560.840             0.630            0.404
Chain 1:   1000       -12368.422             0.569            0.404
Chain 1:   1100       -17172.325             0.497            0.331
Chain 1:   1200       -13924.642             0.285            0.280
Chain 1:   1300       -11054.232             0.299            0.280
Chain 1:   1400        -9764.443             0.272            0.260
Chain 1:   1500        -9530.056             0.233            0.233
Chain 1:   1600       -11379.520             0.235            0.233
Chain 1:   1700       -18184.128             0.240            0.233
Chain 1:   1800       -14471.494             0.244            0.257
Chain 1:   1900        -8745.366             0.239            0.257
Chain 1:   2000       -15791.916             0.282            0.260
Chain 1:   2100        -9324.857             0.324            0.260
Chain 1:   2200        -9048.873             0.303            0.260
Chain 1:   2300        -8656.164             0.282            0.257
Chain 1:   2400        -8635.786             0.269            0.257
Chain 1:   2500       -14349.823             0.306            0.374
Chain 1:   2600        -8756.518             0.354            0.398
Chain 1:   2700        -8331.252             0.322            0.398
Chain 1:   2800       -16447.658             0.345            0.446
Chain 1:   2900        -8780.525             0.367            0.446
Chain 1:   3000       -13494.552             0.358            0.398
Chain 1:   3100       -11669.289             0.304            0.349
Chain 1:   3200       -12526.336             0.308            0.349
Chain 1:   3300        -9977.202             0.329            0.349
Chain 1:   3400        -9308.443             0.336            0.349
Chain 1:   3500        -8616.366             0.304            0.255
Chain 1:   3600        -8764.576             0.242            0.156
Chain 1:   3700        -8168.398             0.244            0.156
Chain 1:   3800       -11777.606             0.225            0.156
Chain 1:   3900        -8875.455             0.171            0.156
Chain 1:   4000        -8727.287             0.137            0.080
Chain 1:   4100        -8428.439             0.125            0.073
Chain 1:   4200        -8248.879             0.121            0.073
Chain 1:   4300       -11435.912             0.123            0.073
Chain 1:   4400        -8136.294             0.156            0.080
Chain 1:   4500       -10459.085             0.170            0.222
Chain 1:   4600        -9194.802             0.182            0.222
Chain 1:   4700        -8087.073             0.189            0.222
Chain 1:   4800       -10188.628             0.179            0.206
Chain 1:   4900        -9635.248             0.152            0.137
Chain 1:   5000        -9734.555             0.151            0.137
Chain 1:   5100        -8082.496             0.168            0.204
Chain 1:   5200       -10499.308             0.189            0.206
Chain 1:   5300       -12429.550             0.177            0.204
Chain 1:   5400        -8440.886             0.183            0.204
Chain 1:   5500        -7983.740             0.167            0.155
Chain 1:   5600       -12555.364             0.189            0.204
Chain 1:   5700       -12452.507             0.177            0.204
Chain 1:   5800        -8535.028             0.202            0.204
Chain 1:   5900        -7973.727             0.203            0.204
Chain 1:   6000        -9157.418             0.215            0.204
Chain 1:   6100        -8070.453             0.208            0.155
Chain 1:   6200        -8771.063             0.193            0.135
Chain 1:   6300       -11604.747             0.202            0.135
Chain 1:   6400       -11999.317             0.158            0.129
Chain 1:   6500        -8625.980             0.191            0.135
Chain 1:   6600        -8239.793             0.160            0.129
Chain 1:   6700        -8087.298             0.161            0.129
Chain 1:   6800        -9708.955             0.132            0.129
Chain 1:   6900        -8403.157             0.140            0.135
Chain 1:   7000        -7958.541             0.133            0.135
Chain 1:   7100        -8097.809             0.121            0.080
Chain 1:   7200        -7821.293             0.116            0.056
Chain 1:   7300        -7697.706             0.094            0.047
Chain 1:   7400        -8255.327             0.097            0.056
Chain 1:   7500        -7660.089             0.066            0.056
Chain 1:   7600       -10500.431             0.088            0.068
Chain 1:   7700        -7863.900             0.120            0.078
Chain 1:   7800        -9267.755             0.118            0.078
Chain 1:   7900        -7962.187             0.119            0.078
Chain 1:   8000        -7599.724             0.118            0.078
Chain 1:   8100        -7812.813             0.119            0.078
Chain 1:   8200        -7730.399             0.117            0.078
Chain 1:   8300        -9240.983             0.132            0.151
Chain 1:   8400        -8866.805             0.129            0.151
Chain 1:   8500        -7829.303             0.135            0.151
Chain 1:   8600       -10957.498             0.136            0.151
Chain 1:   8700       -10106.098             0.111            0.133
Chain 1:   8800        -7653.210             0.128            0.133
Chain 1:   8900        -7930.643             0.115            0.084
Chain 1:   9000        -8654.223             0.118            0.084
Chain 1:   9100        -8277.337             0.120            0.084
Chain 1:   9200        -8954.989             0.127            0.084
Chain 1:   9300       -10589.984             0.126            0.084
Chain 1:   9400       -11169.234             0.127            0.084
Chain 1:   9500        -7723.226             0.158            0.084
Chain 1:   9600        -7730.647             0.130            0.084
Chain 1:   9700        -8693.568             0.132            0.084
Chain 1:   9800        -7732.562             0.113            0.084
Chain 1:   9900        -9133.488             0.125            0.111
Chain 1:   10000        -9227.398             0.117            0.111
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002017 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 20.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56200.214             1.000            1.000
Chain 1:    200       -16617.793             1.691            2.382
Chain 1:    300        -8355.006             1.457            1.000
Chain 1:    400        -7981.017             1.104            1.000
Chain 1:    500        -8195.767             0.889            0.989
Chain 1:    600        -8707.561             0.750            0.989
Chain 1:    700        -7702.051             0.662            0.131
Chain 1:    800        -7855.316             0.582            0.131
Chain 1:    900        -7498.052             0.522            0.059
Chain 1:   1000        -7593.094             0.471            0.059
Chain 1:   1100        -7537.426             0.372            0.048
Chain 1:   1200        -7495.712             0.134            0.047
Chain 1:   1300        -7589.902             0.037            0.026
Chain 1:   1400        -7737.071             0.034            0.020
Chain 1:   1500        -7519.289             0.034            0.020
Chain 1:   1600        -7433.108             0.030            0.019
Chain 1:   1700        -7415.260             0.017            0.013
Chain 1:   1800        -7449.665             0.015            0.012
Chain 1:   1900        -7498.510             0.011            0.012
Chain 1:   2000        -7487.738             0.010            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003492 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85711.100             1.000            1.000
Chain 1:    200       -12694.681             3.376            5.752
Chain 1:    300        -9226.268             2.376            1.000
Chain 1:    400        -9758.897             1.796            1.000
Chain 1:    500        -8140.535             1.476            0.376
Chain 1:    600        -8231.541             1.232            0.376
Chain 1:    700        -8049.200             1.059            0.199
Chain 1:    800        -8190.115             0.929            0.199
Chain 1:    900        -8154.953             0.826            0.055
Chain 1:   1000        -7876.792             0.747            0.055
Chain 1:   1100        -8136.396             0.650            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7859.374             0.079            0.035
Chain 1:   1300        -8036.920             0.043            0.032
Chain 1:   1400        -7964.677             0.039            0.023
Chain 1:   1500        -7912.646             0.020            0.022
Chain 1:   1600        -7904.814             0.019            0.022
Chain 1:   1700        -7855.100             0.017            0.017
Chain 1:   1800        -7734.601             0.017            0.016
Chain 1:   1900        -7843.977             0.018            0.016
Chain 1:   2000        -7810.050             0.015            0.014
Chain 1:   2100        -7959.063             0.013            0.014
Chain 1:   2200        -7736.272             0.013            0.014
Chain 1:   2300        -7817.517             0.011            0.010
Chain 1:   2400        -7882.087             0.011            0.010
Chain 1:   2500        -7845.259             0.011            0.010
Chain 1:   2600        -7838.000             0.011            0.010
Chain 1:   2700        -7750.412             0.012            0.011
Chain 1:   2800        -7738.739             0.010            0.010
Chain 1:   2900        -7743.668             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004088 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414654.313             1.000            1.000
Chain 1:    200     -1587973.079             2.649            4.299
Chain 1:    300      -891290.255             2.027            1.000
Chain 1:    400      -457258.166             1.757            1.000
Chain 1:    500      -357181.020             1.462            0.949
Chain 1:    600      -231950.906             1.308            0.949
Chain 1:    700      -118245.884             1.259            0.949
Chain 1:    800       -85464.873             1.149            0.949
Chain 1:    900       -65830.173             1.055            0.782
Chain 1:   1000       -50635.973             0.979            0.782
Chain 1:   1100       -38134.762             0.912            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37304.306             0.484            0.384
Chain 1:   1300       -25305.031             0.454            0.384
Chain 1:   1400       -25022.289             0.360            0.328
Chain 1:   1500       -21622.554             0.348            0.328
Chain 1:   1600       -20841.091             0.297            0.300
Chain 1:   1700       -19721.632             0.207            0.298
Chain 1:   1800       -19666.612             0.169            0.157
Chain 1:   1900       -19991.692             0.141            0.057
Chain 1:   2000       -18508.346             0.119            0.057
Chain 1:   2100       -18746.303             0.087            0.037
Chain 1:   2200       -18971.522             0.086            0.037
Chain 1:   2300       -18590.088             0.041            0.021
Chain 1:   2400       -18362.671             0.041            0.021
Chain 1:   2500       -18164.480             0.026            0.016
Chain 1:   2600       -17795.957             0.025            0.016
Chain 1:   2700       -17753.275             0.019            0.013
Chain 1:   2800       -17470.567             0.020            0.016
Chain 1:   2900       -17751.239             0.020            0.016
Chain 1:   3000       -17737.535             0.012            0.013
Chain 1:   3100       -17822.365             0.012            0.012
Chain 1:   3200       -17513.788             0.012            0.016
Chain 1:   3300       -17717.919             0.011            0.012
Chain 1:   3400       -17194.131             0.013            0.016
Chain 1:   3500       -17803.993             0.015            0.016
Chain 1:   3600       -17113.295             0.017            0.016
Chain 1:   3700       -17498.139             0.019            0.018
Chain 1:   3800       -16461.850             0.024            0.022
Chain 1:   3900       -16458.081             0.022            0.022
Chain 1:   4000       -16575.401             0.023            0.022
Chain 1:   4100       -16489.364             0.023            0.022
Chain 1:   4200       -16306.486             0.023            0.022
Chain 1:   4300       -16444.274             0.022            0.022
Chain 1:   4400       -16401.814             0.019            0.011
Chain 1:   4500       -16304.481             0.017            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48877.479             1.000            1.000
Chain 1:    200       -14625.798             1.671            2.342
Chain 1:    300       -20800.116             1.213            1.000
Chain 1:    400       -72398.294             1.088            1.000
Chain 1:    500       -11267.508             1.955            1.000
Chain 1:    600       -16180.892             1.680            1.000
Chain 1:    700       -10888.672             1.509            0.713
Chain 1:    800       -13834.715             1.347            0.713
Chain 1:    900       -17174.524             1.219            0.486
Chain 1:   1000       -13042.300             1.129            0.486
Chain 1:   1100       -10003.503             1.059            0.317   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11537.349             0.839            0.304   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -12212.865             0.814            0.304   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -12023.556             0.745            0.304   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500        -9990.220             0.223            0.213
Chain 1:   1600       -15344.862             0.227            0.213
Chain 1:   1700       -15721.857             0.181            0.204
Chain 1:   1800       -18298.563             0.174            0.194
Chain 1:   1900        -9852.828             0.240            0.204
Chain 1:   2000       -16366.946             0.248            0.204
Chain 1:   2100        -9002.054             0.299            0.204
Chain 1:   2200       -13982.387             0.322            0.349
Chain 1:   2300       -13262.671             0.322            0.349
Chain 1:   2400        -9121.859             0.366            0.356
Chain 1:   2500       -15254.671             0.385            0.398
Chain 1:   2600        -9034.642             0.419            0.402
Chain 1:   2700       -13855.118             0.452            0.402
Chain 1:   2800        -9766.158             0.479            0.419
Chain 1:   2900        -9414.460             0.397            0.402
Chain 1:   3000        -9398.652             0.358            0.402
Chain 1:   3100       -12466.018             0.301            0.356
Chain 1:   3200        -8439.141             0.313            0.402
Chain 1:   3300        -9593.246             0.319            0.402
Chain 1:   3400       -12273.513             0.296            0.348
Chain 1:   3500       -13503.821             0.265            0.246
Chain 1:   3600        -9459.812             0.239            0.246
Chain 1:   3700        -8695.446             0.213            0.218
Chain 1:   3800        -8588.899             0.172            0.120
Chain 1:   3900        -8641.284             0.169            0.120
Chain 1:   4000        -8493.666             0.170            0.120
Chain 1:   4100        -9114.456             0.153            0.091
Chain 1:   4200        -9896.154             0.113            0.088
Chain 1:   4300        -9608.314             0.104            0.079
Chain 1:   4400        -8192.874             0.099            0.079
Chain 1:   4500        -8645.480             0.095            0.068
Chain 1:   4600        -8336.438             0.056            0.052
Chain 1:   4700       -11774.540             0.077            0.052
Chain 1:   4800       -10392.213             0.089            0.068
Chain 1:   4900       -14078.125             0.114            0.079
Chain 1:   5000       -16100.250             0.125            0.126
Chain 1:   5100        -8271.058             0.213            0.133
Chain 1:   5200        -8461.972             0.207            0.133
Chain 1:   5300        -8972.534             0.210            0.133
Chain 1:   5400       -11959.710             0.218            0.133
Chain 1:   5500        -8713.305             0.250            0.250
Chain 1:   5600        -8210.822             0.252            0.250
Chain 1:   5700       -10039.567             0.241            0.182
Chain 1:   5800        -8525.949             0.246            0.182
Chain 1:   5900       -12835.121             0.253            0.182
Chain 1:   6000        -9349.850             0.278            0.250
Chain 1:   6100        -9547.244             0.185            0.182
Chain 1:   6200       -12975.263             0.209            0.250
Chain 1:   6300       -12298.264             0.209            0.250
Chain 1:   6400       -12123.389             0.186            0.182
Chain 1:   6500       -14664.399             0.166            0.178
Chain 1:   6600        -9136.146             0.220            0.182
Chain 1:   6700        -9820.303             0.209            0.178
Chain 1:   6800        -8140.586             0.212            0.206
Chain 1:   6900        -9510.830             0.193            0.173
Chain 1:   7000       -14336.092             0.189            0.173
Chain 1:   7100        -8434.217             0.257            0.206
Chain 1:   7200        -9787.860             0.244            0.173
Chain 1:   7300       -10801.047             0.248            0.173
Chain 1:   7400        -9005.589             0.267            0.199
Chain 1:   7500        -8204.039             0.259            0.199
Chain 1:   7600        -8682.376             0.204            0.144
Chain 1:   7700        -8808.842             0.199            0.144
Chain 1:   7800        -8182.782             0.186            0.138
Chain 1:   7900        -8067.682             0.173            0.098
Chain 1:   8000        -8437.887             0.143            0.094
Chain 1:   8100        -8168.208             0.077            0.077
Chain 1:   8200       -12385.661             0.097            0.077
Chain 1:   8300        -8617.678             0.131            0.077
Chain 1:   8400        -8236.029             0.116            0.055
Chain 1:   8500        -8084.774             0.108            0.046
Chain 1:   8600        -8693.217             0.109            0.046
Chain 1:   8700        -8246.278             0.113            0.054
Chain 1:   8800        -7959.416             0.109            0.046
Chain 1:   8900        -8284.337             0.112            0.046
Chain 1:   9000        -9665.306             0.122            0.054
Chain 1:   9100        -8053.832             0.139            0.070
Chain 1:   9200        -8691.508             0.112            0.070
Chain 1:   9300        -8346.342             0.072            0.054
Chain 1:   9400       -11281.732             0.094            0.070
Chain 1:   9500        -8017.540             0.132            0.073
Chain 1:   9600        -8047.745             0.126            0.073
Chain 1:   9700        -8194.218             0.122            0.073
Chain 1:   9800        -8494.340             0.122            0.073
Chain 1:   9900        -9706.143             0.131            0.125
Chain 1:   10000        -8377.378             0.132            0.125
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56755.481             1.000            1.000
Chain 1:    200       -17101.567             1.659            2.319
Chain 1:    300        -8554.654             1.439            1.000
Chain 1:    400        -7807.572             1.103            1.000
Chain 1:    500        -8452.891             0.898            0.999
Chain 1:    600        -7963.752             0.759            0.999
Chain 1:    700        -8045.430             0.652            0.096
Chain 1:    800        -8067.500             0.571            0.096
Chain 1:    900        -7722.878             0.512            0.076
Chain 1:   1000        -7752.809             0.461            0.076
Chain 1:   1100        -7568.353             0.364            0.061
Chain 1:   1200        -7592.850             0.132            0.045
Chain 1:   1300        -7670.122             0.033            0.024
Chain 1:   1400        -7759.379             0.025            0.012
Chain 1:   1500        -7537.007             0.020            0.012
Chain 1:   1600        -7496.798             0.015            0.010
Chain 1:   1700        -7435.648             0.014            0.010
Chain 1:   1800        -7508.018             0.015            0.010
Chain 1:   1900        -7495.716             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003261 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86241.617             1.000            1.000
Chain 1:    200       -13175.923             3.273            5.545
Chain 1:    300        -9618.878             2.305            1.000
Chain 1:    400       -10619.720             1.752            1.000
Chain 1:    500        -8537.443             1.451            0.370
Chain 1:    600        -8127.811             1.217            0.370
Chain 1:    700        -8214.554             1.045            0.244
Chain 1:    800        -8444.396             0.918            0.244
Chain 1:    900        -8453.937             0.816            0.094
Chain 1:   1000        -8195.313             0.737            0.094
Chain 1:   1100        -8494.059             0.641            0.050   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8321.591             0.088            0.035
Chain 1:   1300        -8388.261             0.052            0.032
Chain 1:   1400        -8346.709             0.043            0.027
Chain 1:   1500        -8232.909             0.020            0.021
Chain 1:   1600        -8332.155             0.017            0.014
Chain 1:   1700        -8415.431             0.016            0.014
Chain 1:   1800        -8024.723             0.019            0.014
Chain 1:   1900        -8127.114             0.020            0.014
Chain 1:   2000        -8097.320             0.017            0.013
Chain 1:   2100        -8224.636             0.015            0.013
Chain 1:   2200        -8010.850             0.016            0.013
Chain 1:   2300        -8155.960             0.017            0.014
Chain 1:   2400        -8171.153             0.016            0.014
Chain 1:   2500        -8137.666             0.015            0.013
Chain 1:   2600        -8139.344             0.014            0.013
Chain 1:   2700        -8046.412             0.014            0.013
Chain 1:   2800        -8019.943             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003459 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8443038.110             1.000            1.000
Chain 1:    200     -1591747.980             2.652            4.304
Chain 1:    300      -890697.012             2.030            1.000
Chain 1:    400      -456728.675             1.760            1.000
Chain 1:    500      -356597.945             1.464            0.950
Chain 1:    600      -231597.588             1.310            0.950
Chain 1:    700      -118296.968             1.260            0.950
Chain 1:    800       -85670.759             1.150            0.950
Chain 1:    900       -66117.571             1.055            0.787
Chain 1:   1000       -51002.940             0.979            0.787
Chain 1:   1100       -38564.948             0.912            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37745.789             0.483            0.381
Chain 1:   1300       -25789.417             0.451            0.381
Chain 1:   1400       -25515.050             0.357            0.323
Chain 1:   1500       -22126.090             0.344            0.323
Chain 1:   1600       -21349.156             0.294            0.296
Chain 1:   1700       -20233.607             0.204            0.296
Chain 1:   1800       -20179.981             0.166            0.153
Chain 1:   1900       -20505.754             0.138            0.055
Chain 1:   2000       -19023.284             0.116            0.055
Chain 1:   2100       -19261.240             0.085            0.036
Chain 1:   2200       -19486.653             0.084            0.036
Chain 1:   2300       -19104.871             0.040            0.020
Chain 1:   2400       -18877.209             0.040            0.020
Chain 1:   2500       -18678.974             0.025            0.016
Chain 1:   2600       -18309.933             0.024            0.016
Chain 1:   2700       -18267.079             0.019            0.012
Chain 1:   2800       -17984.093             0.020            0.016
Chain 1:   2900       -18264.938             0.020            0.015
Chain 1:   3000       -18251.239             0.012            0.012
Chain 1:   3100       -18336.186             0.011            0.012
Chain 1:   3200       -18027.210             0.012            0.015
Chain 1:   3300       -18231.631             0.011            0.012
Chain 1:   3400       -17707.131             0.013            0.015
Chain 1:   3500       -18318.068             0.015            0.016
Chain 1:   3600       -17625.852             0.017            0.016
Chain 1:   3700       -18011.797             0.019            0.017
Chain 1:   3800       -16973.248             0.023            0.021
Chain 1:   3900       -16969.372             0.022            0.021
Chain 1:   4000       -17086.711             0.022            0.021
Chain 1:   4100       -17000.605             0.023            0.021
Chain 1:   4200       -16817.178             0.022            0.021
Chain 1:   4300       -16955.365             0.022            0.021
Chain 1:   4400       -16912.480             0.019            0.011
Chain 1:   4500       -16815.024             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001236 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48895.349             1.000            1.000
Chain 1:    200       -16777.472             1.457            1.914
Chain 1:    300       -20887.421             1.037            1.000
Chain 1:    400       -16468.934             0.845            1.000
Chain 1:    500       -14312.634             0.706            0.268
Chain 1:    600       -13426.076             0.599            0.268
Chain 1:    700       -16260.932             0.539            0.197
Chain 1:    800       -13445.577             0.497            0.209
Chain 1:    900       -11768.730             0.458            0.197
Chain 1:   1000       -12630.612             0.419            0.197
Chain 1:   1100       -13094.919             0.323            0.174
Chain 1:   1200       -11314.518             0.147            0.157
Chain 1:   1300       -13193.733             0.141            0.151
Chain 1:   1400       -13422.894             0.116            0.142
Chain 1:   1500       -10979.817             0.124            0.142
Chain 1:   1600       -10461.616             0.122            0.142
Chain 1:   1700       -18814.222             0.149            0.142
Chain 1:   1800       -22606.413             0.145            0.142
Chain 1:   1900       -10418.863             0.247            0.157
Chain 1:   2000       -17813.127             0.282            0.168
Chain 1:   2100       -21137.599             0.294            0.168
Chain 1:   2200        -9556.418             0.400            0.223
Chain 1:   2300        -9705.959             0.387            0.223
Chain 1:   2400       -12776.525             0.409            0.240
Chain 1:   2500        -9826.638             0.417            0.300
Chain 1:   2600       -17123.611             0.455            0.415
Chain 1:   2700        -9404.683             0.492            0.415
Chain 1:   2800       -10739.121             0.488            0.415
Chain 1:   2900        -9939.594             0.379            0.300
Chain 1:   3000       -16564.850             0.378            0.300
Chain 1:   3100        -9697.244             0.433            0.400
Chain 1:   3200       -10362.602             0.318            0.300
Chain 1:   3300        -9295.646             0.328            0.300
Chain 1:   3400        -9743.491             0.308            0.300
Chain 1:   3500       -11239.476             0.292            0.133
Chain 1:   3600       -10286.157             0.258            0.124
Chain 1:   3700       -14462.558             0.205            0.124
Chain 1:   3800        -8812.921             0.257            0.133
Chain 1:   3900        -8927.477             0.250            0.133
Chain 1:   4000       -10743.425             0.227            0.133
Chain 1:   4100        -9054.764             0.175            0.133
Chain 1:   4200        -9909.043             0.177            0.133
Chain 1:   4300       -14203.778             0.196            0.169
Chain 1:   4400        -9379.080             0.243            0.186
Chain 1:   4500        -9044.134             0.233            0.186
Chain 1:   4600        -8602.178             0.229            0.186
Chain 1:   4700       -10644.826             0.219            0.186
Chain 1:   4800        -9096.841             0.172            0.170
Chain 1:   4900       -12230.611             0.197            0.186
Chain 1:   5000       -14883.714             0.197            0.186
Chain 1:   5100        -8709.807             0.250            0.192
Chain 1:   5200        -9046.275             0.245            0.192
Chain 1:   5300       -11632.555             0.237            0.192
Chain 1:   5400        -8461.728             0.223            0.192
Chain 1:   5500       -13484.582             0.256            0.222
Chain 1:   5600        -9704.879             0.290            0.256
Chain 1:   5700        -9209.980             0.276            0.256
Chain 1:   5800        -8744.125             0.265            0.256
Chain 1:   5900       -12953.986             0.272            0.325
Chain 1:   6000        -9071.263             0.297            0.372
Chain 1:   6100        -9019.077             0.226            0.325
Chain 1:   6200        -8352.091             0.230            0.325
Chain 1:   6300        -9320.088             0.219            0.325
Chain 1:   6400        -8582.148             0.190            0.104
Chain 1:   6500        -9066.486             0.158            0.086
Chain 1:   6600       -11329.049             0.139            0.086
Chain 1:   6700       -10563.870             0.141            0.086
Chain 1:   6800       -13938.675             0.160            0.104
Chain 1:   6900        -9872.943             0.168            0.104
Chain 1:   7000        -8263.238             0.145            0.104
Chain 1:   7100        -8939.090             0.152            0.104
Chain 1:   7200       -11275.918             0.165            0.195
Chain 1:   7300        -8359.234             0.189            0.200
Chain 1:   7400       -13959.010             0.221            0.207
Chain 1:   7500        -9401.184             0.264            0.242
Chain 1:   7600        -9183.251             0.246            0.242
Chain 1:   7700       -10975.699             0.255            0.242
Chain 1:   7800        -9430.290             0.248            0.207
Chain 1:   7900        -8420.973             0.218            0.195
Chain 1:   8000        -9236.119             0.208            0.164
Chain 1:   8100        -8533.169             0.208            0.164
Chain 1:   8200        -8355.429             0.190            0.163
Chain 1:   8300        -8382.410             0.155            0.120
Chain 1:   8400        -8496.676             0.116            0.088
Chain 1:   8500        -8338.751             0.070            0.082
Chain 1:   8600        -8784.444             0.073            0.082
Chain 1:   8700        -9245.340             0.061            0.051
Chain 1:   8800        -8647.890             0.052            0.051
Chain 1:   8900        -9283.618             0.047            0.051
Chain 1:   9000       -11361.413             0.056            0.051
Chain 1:   9100        -8474.733             0.082            0.051
Chain 1:   9200        -9634.020             0.092            0.068
Chain 1:   9300        -9510.380             0.093            0.068
Chain 1:   9400        -8332.073             0.106            0.069
Chain 1:   9500       -11345.009             0.130            0.120
Chain 1:   9600        -8536.547             0.158            0.141
Chain 1:   9700        -8814.373             0.156            0.141
Chain 1:   9800        -8507.323             0.153            0.141
Chain 1:   9900        -9176.122             0.153            0.141
Chain 1:   10000        -8238.325             0.146            0.120
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57284.696             1.000            1.000
Chain 1:    200       -17642.010             1.624            2.247
Chain 1:    300        -8786.794             1.418            1.008
Chain 1:    400        -8261.488             1.080            1.008
Chain 1:    500        -8521.222             0.870            1.000
Chain 1:    600        -8261.433             0.730            1.000
Chain 1:    700        -7940.112             0.632            0.064
Chain 1:    800        -8390.565             0.559            0.064
Chain 1:    900        -7703.948             0.507            0.064
Chain 1:   1000        -7809.093             0.458            0.064
Chain 1:   1100        -7709.980             0.359            0.054
Chain 1:   1200        -7777.338             0.135            0.040
Chain 1:   1300        -7637.943             0.036            0.031
Chain 1:   1400        -7813.778             0.032            0.030
Chain 1:   1500        -7592.796             0.032            0.029
Chain 1:   1600        -7718.431             0.030            0.023
Chain 1:   1700        -7568.214             0.028            0.020
Chain 1:   1800        -7630.678             0.024            0.018
Chain 1:   1900        -7581.154             0.016            0.016
Chain 1:   2000        -7581.530             0.014            0.016
Chain 1:   2100        -7514.515             0.014            0.016
Chain 1:   2200        -7724.953             0.016            0.018
Chain 1:   2300        -7521.729             0.017            0.020
Chain 1:   2400        -7612.533             0.016            0.016
Chain 1:   2500        -7617.981             0.013            0.012
Chain 1:   2600        -7540.341             0.012            0.010
Chain 1:   2700        -7570.133             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002882 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86554.124             1.000            1.000
Chain 1:    200       -13628.314             3.176            5.351
Chain 1:    300        -9963.248             2.240            1.000
Chain 1:    400       -10958.983             1.702            1.000
Chain 1:    500        -8873.311             1.409            0.368
Chain 1:    600        -8425.314             1.183            0.368
Chain 1:    700        -8547.443             1.016            0.235
Chain 1:    800        -8696.722             0.891            0.235
Chain 1:    900        -8715.287             0.792            0.091
Chain 1:   1000        -8686.418             0.713            0.091
Chain 1:   1100        -8648.662             0.614            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8384.572             0.082            0.031
Chain 1:   1300        -8711.903             0.049            0.031
Chain 1:   1400        -8655.309             0.041            0.017
Chain 1:   1500        -8496.724             0.019            0.017
Chain 1:   1600        -8610.878             0.015            0.014
Chain 1:   1700        -8687.190             0.014            0.013
Chain 1:   1800        -8259.910             0.018            0.013
Chain 1:   1900        -8362.640             0.019            0.013
Chain 1:   2000        -8337.552             0.019            0.013
Chain 1:   2100        -8464.621             0.020            0.015
Chain 1:   2200        -8263.509             0.019            0.015
Chain 1:   2300        -8358.011             0.016            0.013
Chain 1:   2400        -8425.845             0.017            0.013
Chain 1:   2500        -8372.034             0.015            0.012
Chain 1:   2600        -8374.525             0.014            0.011
Chain 1:   2700        -8290.710             0.014            0.011
Chain 1:   2800        -8249.191             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003618 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412129.449             1.000            1.000
Chain 1:    200     -1587420.747             2.650            4.299
Chain 1:    300      -891367.400             2.027            1.000
Chain 1:    400      -457815.730             1.757            1.000
Chain 1:    500      -357992.204             1.461            0.947
Chain 1:    600      -232865.771             1.307            0.947
Chain 1:    700      -119194.982             1.257            0.947
Chain 1:    800       -86464.089             1.147            0.947
Chain 1:    900       -66837.788             1.052            0.781
Chain 1:   1000       -51667.830             0.976            0.781
Chain 1:   1100       -39170.289             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38350.720             0.480            0.379
Chain 1:   1300       -26324.082             0.448            0.379
Chain 1:   1400       -26045.624             0.354            0.319
Chain 1:   1500       -22637.792             0.342            0.319
Chain 1:   1600       -21856.202             0.291            0.294
Chain 1:   1700       -20731.491             0.201            0.294
Chain 1:   1800       -20676.117             0.164            0.151
Chain 1:   1900       -21002.494             0.136            0.054
Chain 1:   2000       -19514.262             0.114            0.054
Chain 1:   2100       -19752.514             0.084            0.036
Chain 1:   2200       -19979.089             0.083            0.036
Chain 1:   2300       -19596.162             0.039            0.020
Chain 1:   2400       -19368.204             0.039            0.020
Chain 1:   2500       -19170.252             0.025            0.016
Chain 1:   2600       -18800.254             0.023            0.016
Chain 1:   2700       -18757.175             0.018            0.012
Chain 1:   2800       -18473.991             0.019            0.015
Chain 1:   2900       -18755.256             0.019            0.015
Chain 1:   3000       -18741.455             0.012            0.012
Chain 1:   3100       -18826.478             0.011            0.012
Chain 1:   3200       -18517.042             0.012            0.015
Chain 1:   3300       -18721.864             0.011            0.012
Chain 1:   3400       -18196.592             0.012            0.015
Chain 1:   3500       -18808.760             0.015            0.015
Chain 1:   3600       -18115.056             0.017            0.015
Chain 1:   3700       -18502.115             0.018            0.017
Chain 1:   3800       -17461.258             0.023            0.021
Chain 1:   3900       -17457.397             0.021            0.021
Chain 1:   4000       -17574.693             0.022            0.021
Chain 1:   4100       -17488.442             0.022            0.021
Chain 1:   4200       -17304.564             0.021            0.021
Chain 1:   4300       -17443.041             0.021            0.021
Chain 1:   4400       -17399.745             0.018            0.011
Chain 1:   4500       -17302.272             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001126 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13232.777             1.000            1.000
Chain 1:    200        -9684.130             0.683            1.000
Chain 1:    300        -8696.357             0.493            0.366
Chain 1:    400        -8122.988             0.388            0.366
Chain 1:    500        -8214.949             0.312            0.114
Chain 1:    600        -8037.261             0.264            0.114
Chain 1:    700        -8009.815             0.227            0.071
Chain 1:    800        -7955.184             0.199            0.071
Chain 1:    900        -8034.430             0.178            0.022
Chain 1:   1000        -7994.255             0.161            0.022
Chain 1:   1100        -8102.695             0.062            0.013
Chain 1:   1200        -7935.177             0.028            0.013
Chain 1:   1300        -7920.098             0.017            0.011
Chain 1:   1400        -7928.129             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58865.420             1.000            1.000
Chain 1:    200       -18218.296             1.616            2.231
Chain 1:    300        -8914.196             1.425            1.044
Chain 1:    400        -8040.729             1.096            1.044
Chain 1:    500        -8719.590             0.892            1.000
Chain 1:    600        -8270.909             0.753            1.000
Chain 1:    700        -7965.461             0.651            0.109
Chain 1:    800        -7978.756             0.569            0.109
Chain 1:    900        -7797.649             0.509            0.078
Chain 1:   1000        -7584.534             0.461            0.078
Chain 1:   1100        -7797.858             0.363            0.054
Chain 1:   1200        -7840.132             0.141            0.038
Chain 1:   1300        -7579.230             0.040            0.034
Chain 1:   1400        -7827.222             0.032            0.032
Chain 1:   1500        -7612.218             0.027            0.028
Chain 1:   1600        -7786.415             0.024            0.028
Chain 1:   1700        -7632.114             0.022            0.027
Chain 1:   1800        -7689.330             0.023            0.027
Chain 1:   1900        -7596.604             0.022            0.027
Chain 1:   2000        -7696.500             0.020            0.022
Chain 1:   2100        -7578.481             0.019            0.020
Chain 1:   2200        -7781.283             0.021            0.022
Chain 1:   2300        -7542.211             0.021            0.022
Chain 1:   2400        -7718.670             0.020            0.022
Chain 1:   2500        -7620.486             0.018            0.020
Chain 1:   2600        -7528.610             0.017            0.016
Chain 1:   2700        -7519.952             0.016            0.013
Chain 1:   2800        -7520.087             0.015            0.013
Chain 1:   2900        -7373.330             0.016            0.016
Chain 1:   3000        -7528.646             0.016            0.020
Chain 1:   3100        -7522.975             0.015            0.020
Chain 1:   3200        -7742.173             0.015            0.020
Chain 1:   3300        -7461.621             0.016            0.020
Chain 1:   3400        -7701.437             0.016            0.020
Chain 1:   3500        -7435.454             0.019            0.021
Chain 1:   3600        -7500.063             0.018            0.021
Chain 1:   3700        -7452.713             0.019            0.021
Chain 1:   3800        -7452.509             0.019            0.021
Chain 1:   3900        -7407.987             0.018            0.021
Chain 1:   4000        -7401.512             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003127 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86701.838             1.000            1.000
Chain 1:    200       -13944.808             3.109            5.217
Chain 1:    300       -10138.226             2.198            1.000
Chain 1:    400       -11938.996             1.686            1.000
Chain 1:    500        -8537.068             1.428            0.398
Chain 1:    600        -8402.845             1.193            0.398
Chain 1:    700        -8741.075             1.028            0.375
Chain 1:    800        -9273.760             0.907            0.375
Chain 1:    900        -8749.110             0.813            0.151
Chain 1:   1000        -8759.260             0.732            0.151
Chain 1:   1100        -8904.769             0.633            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8355.086             0.118            0.060
Chain 1:   1300        -8744.479             0.085            0.057
Chain 1:   1400        -8622.917             0.071            0.045
Chain 1:   1500        -8572.047             0.032            0.039
Chain 1:   1600        -8691.102             0.032            0.039
Chain 1:   1700        -8731.482             0.028            0.016
Chain 1:   1800        -8267.432             0.028            0.016
Chain 1:   1900        -8381.602             0.024            0.014
Chain 1:   2000        -8401.859             0.024            0.014
Chain 1:   2100        -8495.954             0.023            0.014
Chain 1:   2200        -8266.792             0.019            0.014
Chain 1:   2300        -8487.138             0.018            0.014
Chain 1:   2400        -8272.723             0.019            0.014
Chain 1:   2500        -8350.040             0.019            0.014
Chain 1:   2600        -8258.944             0.019            0.014
Chain 1:   2700        -8294.769             0.019            0.014
Chain 1:   2800        -8246.576             0.014            0.011
Chain 1:   2900        -8360.863             0.014            0.011
Chain 1:   3000        -8270.199             0.015            0.011
Chain 1:   3100        -8237.622             0.014            0.011
Chain 1:   3200        -8208.471             0.011            0.011
Chain 1:   3300        -8472.098             0.012            0.011
Chain 1:   3400        -8518.733             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003307 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418859.176             1.000            1.000
Chain 1:    200     -1585567.737             2.655            4.310
Chain 1:    300      -890660.872             2.030            1.000
Chain 1:    400      -458467.327             1.758            1.000
Chain 1:    500      -358646.015             1.462            0.943
Chain 1:    600      -233594.480             1.308            0.943
Chain 1:    700      -119726.706             1.257            0.943
Chain 1:    800       -86943.722             1.147            0.943
Chain 1:    900       -67274.797             1.052            0.780
Chain 1:   1000       -52082.521             0.976            0.780
Chain 1:   1100       -39564.279             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38747.712             0.479            0.377
Chain 1:   1300       -26694.765             0.446            0.377
Chain 1:   1400       -26417.300             0.353            0.316
Chain 1:   1500       -23001.734             0.340            0.316
Chain 1:   1600       -22219.060             0.290            0.292
Chain 1:   1700       -21090.375             0.200            0.292
Chain 1:   1800       -21034.727             0.162            0.148
Chain 1:   1900       -21361.709             0.135            0.054
Chain 1:   2000       -19870.176             0.113            0.054
Chain 1:   2100       -20108.806             0.083            0.035
Chain 1:   2200       -20335.999             0.082            0.035
Chain 1:   2300       -19952.297             0.038            0.019
Chain 1:   2400       -19724.003             0.038            0.019
Chain 1:   2500       -19526.102             0.025            0.015
Chain 1:   2600       -19155.349             0.023            0.015
Chain 1:   2700       -19112.029             0.018            0.012
Chain 1:   2800       -18828.479             0.019            0.015
Chain 1:   2900       -19110.186             0.019            0.015
Chain 1:   3000       -19096.253             0.012            0.012
Chain 1:   3100       -19181.413             0.011            0.012
Chain 1:   3200       -18871.499             0.011            0.015
Chain 1:   3300       -19076.687             0.011            0.012
Chain 1:   3400       -18550.586             0.012            0.015
Chain 1:   3500       -19164.067             0.014            0.015
Chain 1:   3600       -18468.579             0.016            0.015
Chain 1:   3700       -18856.975             0.018            0.016
Chain 1:   3800       -17813.424             0.022            0.021
Chain 1:   3900       -17809.462             0.021            0.021
Chain 1:   4000       -17926.768             0.022            0.021
Chain 1:   4100       -17840.382             0.022            0.021
Chain 1:   4200       -17655.873             0.021            0.021
Chain 1:   4300       -17794.792             0.021            0.021
Chain 1:   4400       -17751.025             0.018            0.010
Chain 1:   4500       -17653.428             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49551.342             1.000            1.000
Chain 1:    200       -18387.940             1.347            1.695
Chain 1:    300       -20739.758             0.936            1.000
Chain 1:    400       -29913.482             0.779            1.000
Chain 1:    500       -26855.113             0.646            0.307
Chain 1:    600       -23401.438             0.563            0.307
Chain 1:    700       -14924.121             0.563            0.307
Chain 1:    800       -12072.782             0.523            0.307
Chain 1:    900       -12042.240             0.465            0.236
Chain 1:   1000       -14141.609             0.433            0.236
Chain 1:   1100       -11537.659             0.356            0.226
Chain 1:   1200       -15291.303             0.211            0.226
Chain 1:   1300       -13620.971             0.212            0.226
Chain 1:   1400       -12190.238             0.193            0.148
Chain 1:   1500       -11388.146             0.188            0.148
Chain 1:   1600       -10350.914             0.184            0.148
Chain 1:   1700        -9934.487             0.131            0.123
Chain 1:   1800       -27386.743             0.171            0.123
Chain 1:   1900       -11172.931             0.316            0.148
Chain 1:   2000       -10733.701             0.305            0.123
Chain 1:   2100        -9946.530             0.291            0.117
Chain 1:   2200       -11494.520             0.280            0.117
Chain 1:   2300       -10024.286             0.282            0.117
Chain 1:   2400       -23749.866             0.328            0.135
Chain 1:   2500       -11107.922             0.435            0.147
Chain 1:   2600       -10299.368             0.433            0.147
Chain 1:   2700       -10518.596             0.431            0.147
Chain 1:   2800       -10525.224             0.367            0.135
Chain 1:   2900        -9732.090             0.230            0.081
Chain 1:   3000       -10020.406             0.229            0.081
Chain 1:   3100       -19615.830             0.270            0.135
Chain 1:   3200        -9824.672             0.356            0.147
Chain 1:   3300       -11025.377             0.352            0.109
Chain 1:   3400       -10423.287             0.300            0.081
Chain 1:   3500        -9583.241             0.195            0.081
Chain 1:   3600       -11969.966             0.207            0.088
Chain 1:   3700       -10180.784             0.223            0.109
Chain 1:   3800       -19809.720             0.271            0.176
Chain 1:   3900        -9229.426             0.378            0.199
Chain 1:   4000        -9685.932             0.379            0.199
Chain 1:   4100       -10383.171             0.337            0.176
Chain 1:   4200       -10594.253             0.240            0.109
Chain 1:   4300       -10488.336             0.230            0.088
Chain 1:   4400        -9076.350             0.240            0.156
Chain 1:   4500        -9289.850             0.233            0.156
Chain 1:   4600        -9033.705             0.216            0.067
Chain 1:   4700        -9202.748             0.200            0.047
Chain 1:   4800        -9424.062             0.154            0.028
Chain 1:   4900       -15580.758             0.079            0.028
Chain 1:   5000       -10000.262             0.130            0.028
Chain 1:   5100        -8964.794             0.135            0.028
Chain 1:   5200       -10309.188             0.146            0.116
Chain 1:   5300       -12800.499             0.164            0.130
Chain 1:   5400        -9840.800             0.179            0.130
Chain 1:   5500       -15091.531             0.211            0.195
Chain 1:   5600       -14486.636             0.213            0.195
Chain 1:   5700        -8829.909             0.275            0.301
Chain 1:   5800       -15128.821             0.314            0.348
Chain 1:   5900       -11481.914             0.306            0.318
Chain 1:   6000       -11098.986             0.254            0.301
Chain 1:   6100        -9183.577             0.263            0.301
Chain 1:   6200       -10208.188             0.260            0.301
Chain 1:   6300        -8876.978             0.256            0.301
Chain 1:   6400        -9077.656             0.228            0.209
Chain 1:   6500        -9574.953             0.198            0.150
Chain 1:   6600        -9636.369             0.195            0.150
Chain 1:   6700       -13537.891             0.160            0.150
Chain 1:   6800        -9600.033             0.159            0.150
Chain 1:   6900        -9983.030             0.131            0.100
Chain 1:   7000       -16144.930             0.166            0.150
Chain 1:   7100        -8620.272             0.232            0.150
Chain 1:   7200        -8929.196             0.226            0.150
Chain 1:   7300        -8541.370             0.215            0.052
Chain 1:   7400        -8948.334             0.218            0.052
Chain 1:   7500       -10748.720             0.229            0.167
Chain 1:   7600       -10001.663             0.236            0.167
Chain 1:   7700        -9380.736             0.214            0.075
Chain 1:   7800       -12664.571             0.199            0.075
Chain 1:   7900        -8827.200             0.238            0.167
Chain 1:   8000        -8693.202             0.202            0.075
Chain 1:   8100        -8958.043             0.117            0.066
Chain 1:   8200       -10147.919             0.126            0.075
Chain 1:   8300       -11869.228             0.136            0.117
Chain 1:   8400        -8809.033             0.166            0.145
Chain 1:   8500        -9713.509             0.158            0.117
Chain 1:   8600        -8754.655             0.162            0.117
Chain 1:   8700        -9211.157             0.160            0.117
Chain 1:   8800       -14561.000             0.171            0.117
Chain 1:   8900        -9593.035             0.179            0.117
Chain 1:   9000        -8486.192             0.191            0.130
Chain 1:   9100        -8415.872             0.189            0.130
Chain 1:   9200        -9199.648             0.185            0.130
Chain 1:   9300        -8939.742             0.174            0.110
Chain 1:   9400        -9592.409             0.146            0.093
Chain 1:   9500       -10313.830             0.144            0.085
Chain 1:   9600       -10267.581             0.133            0.070
Chain 1:   9700        -8536.052             0.148            0.085
Chain 1:   9800       -11928.260             0.140            0.085
Chain 1:   9900        -8560.937             0.128            0.085
Chain 1:   10000        -9342.425             0.123            0.084
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001438 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58681.284             1.000            1.000
Chain 1:    200       -18478.444             1.588            2.176
Chain 1:    300        -9053.851             1.406            1.041
Chain 1:    400        -8107.930             1.083            1.041
Chain 1:    500        -8933.110             0.885            1.000
Chain 1:    600        -8478.348             0.747            1.000
Chain 1:    700        -8526.707             0.641            0.117
Chain 1:    800        -8637.285             0.562            0.117
Chain 1:    900        -8188.062             0.506            0.092
Chain 1:   1000        -7738.747             0.461            0.092
Chain 1:   1100        -7828.563             0.362            0.058
Chain 1:   1200        -8007.007             0.147            0.055
Chain 1:   1300        -7911.061             0.044            0.054
Chain 1:   1400        -8035.975             0.034            0.022
Chain 1:   1500        -7520.281             0.032            0.022
Chain 1:   1600        -7741.768             0.029            0.022
Chain 1:   1700        -7455.654             0.032            0.029
Chain 1:   1800        -7584.669             0.033            0.029
Chain 1:   1900        -7603.732             0.027            0.022
Chain 1:   2000        -7732.358             0.023            0.017
Chain 1:   2100        -7582.098             0.024            0.020
Chain 1:   2200        -7657.333             0.023            0.017
Chain 1:   2300        -7480.100             0.024            0.020
Chain 1:   2400        -7626.082             0.024            0.020
Chain 1:   2500        -7505.344             0.019            0.019
Chain 1:   2600        -7540.819             0.017            0.017
Chain 1:   2700        -7512.255             0.013            0.017
Chain 1:   2800        -7550.781             0.012            0.016
Chain 1:   2900        -7376.178             0.014            0.017
Chain 1:   3000        -7531.896             0.015            0.019
Chain 1:   3100        -7521.820             0.013            0.016
Chain 1:   3200        -7731.810             0.015            0.019
Chain 1:   3300        -7394.647             0.017            0.019
Chain 1:   3400        -7656.277             0.018            0.021
Chain 1:   3500        -7467.482             0.019            0.024
Chain 1:   3600        -7463.555             0.019            0.024
Chain 1:   3700        -7439.601             0.019            0.024
Chain 1:   3800        -7412.021             0.019            0.024
Chain 1:   3900        -7429.067             0.016            0.021
Chain 1:   4000        -7386.631             0.015            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003249 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86973.307             1.000            1.000
Chain 1:    200       -14281.379             3.045            5.090
Chain 1:    300       -10441.563             2.153            1.000
Chain 1:    400       -12560.161             1.657            1.000
Chain 1:    500        -8836.584             1.410            0.421
Chain 1:    600        -8658.903             1.178            0.421
Chain 1:    700        -8753.510             1.011            0.368
Chain 1:    800        -9393.365             0.893            0.368
Chain 1:    900        -9014.842             0.799            0.169
Chain 1:   1000        -8901.332             0.720            0.169
Chain 1:   1100        -9183.663             0.623            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8650.566             0.120            0.062
Chain 1:   1300        -8965.732             0.087            0.042
Chain 1:   1400        -8891.156             0.071            0.035
Chain 1:   1500        -8868.584             0.029            0.031
Chain 1:   1600        -8925.885             0.028            0.031
Chain 1:   1700        -8988.126             0.027            0.031
Chain 1:   1800        -8521.268             0.026            0.031
Chain 1:   1900        -8631.021             0.023            0.013
Chain 1:   2000        -8648.259             0.022            0.013
Chain 1:   2100        -8753.118             0.020            0.012
Chain 1:   2200        -8507.439             0.017            0.012
Chain 1:   2300        -8614.158             0.015            0.012
Chain 1:   2400        -8683.145             0.015            0.012
Chain 1:   2500        -8622.836             0.015            0.012
Chain 1:   2600        -8662.094             0.015            0.012
Chain 1:   2700        -8551.208             0.016            0.012
Chain 1:   2800        -8497.661             0.011            0.012
Chain 1:   2900        -8604.470             0.011            0.012
Chain 1:   3000        -8518.819             0.011            0.012
Chain 1:   3100        -8484.124             0.011            0.010
Chain 1:   3200        -8450.340             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003257 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8359977.669             1.000            1.000
Chain 1:    200     -1576447.418             2.652            4.303
Chain 1:    300      -890235.841             2.025            1.000
Chain 1:    400      -458000.315             1.754            1.000
Chain 1:    500      -359374.288             1.458            0.944
Chain 1:    600      -234479.262             1.304            0.944
Chain 1:    700      -120472.598             1.253            0.944
Chain 1:    800       -87614.118             1.143            0.944
Chain 1:    900       -67886.975             1.049            0.771
Chain 1:   1000       -52630.905             0.973            0.771
Chain 1:   1100       -40046.321             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39223.657             0.476            0.375
Chain 1:   1300       -27094.280             0.444            0.375
Chain 1:   1400       -26809.669             0.350            0.314
Chain 1:   1500       -23374.136             0.337            0.314
Chain 1:   1600       -22585.769             0.288            0.291
Chain 1:   1700       -21447.994             0.198            0.290
Chain 1:   1800       -21390.164             0.161            0.147
Chain 1:   1900       -21717.448             0.134            0.053
Chain 1:   2000       -20220.560             0.112            0.053
Chain 1:   2100       -20459.381             0.082            0.035
Chain 1:   2200       -20687.626             0.081            0.035
Chain 1:   2300       -20302.935             0.038            0.019
Chain 1:   2400       -20074.522             0.038            0.019
Chain 1:   2500       -19876.883             0.024            0.015
Chain 1:   2600       -19505.629             0.023            0.015
Chain 1:   2700       -19462.127             0.018            0.012
Chain 1:   2800       -19178.712             0.019            0.015
Chain 1:   2900       -19460.546             0.019            0.014
Chain 1:   3000       -19446.522             0.011            0.012
Chain 1:   3100       -19531.747             0.011            0.011
Chain 1:   3200       -19221.596             0.011            0.014
Chain 1:   3300       -19426.963             0.010            0.011
Chain 1:   3400       -18900.572             0.012            0.014
Chain 1:   3500       -19514.597             0.014            0.015
Chain 1:   3600       -18818.444             0.016            0.015
Chain 1:   3700       -19207.470             0.018            0.016
Chain 1:   3800       -18162.901             0.022            0.020
Chain 1:   3900       -18158.983             0.021            0.020
Chain 1:   4000       -18276.226             0.021            0.020
Chain 1:   4100       -18189.845             0.021            0.020
Chain 1:   4200       -18005.117             0.021            0.020
Chain 1:   4300       -18144.173             0.020            0.020
Chain 1:   4400       -18100.243             0.018            0.010
Chain 1:   4500       -18002.652             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48623.484             1.000            1.000
Chain 1:    200       -19629.450             1.239            1.477
Chain 1:    300       -19150.391             0.834            1.000
Chain 1:    400       -15989.447             0.675            1.000
Chain 1:    500       -12611.078             0.594            0.268
Chain 1:    600       -17162.101             0.539            0.268
Chain 1:    700       -14517.697             0.488            0.265
Chain 1:    800       -21264.603             0.467            0.268
Chain 1:    900       -11013.065             0.518            0.268
Chain 1:   1000       -24743.282             0.522            0.317
Chain 1:   1100       -22656.814             0.431            0.268
Chain 1:   1200       -10767.440             0.394            0.268
Chain 1:   1300       -12833.740             0.407            0.268
Chain 1:   1400       -12391.901             0.391            0.268
Chain 1:   1500       -11874.670             0.369            0.265
Chain 1:   1600       -12159.667             0.345            0.182
Chain 1:   1700       -10913.730             0.338            0.161
Chain 1:   1800       -10010.886             0.315            0.114
Chain 1:   1900       -15924.279             0.259            0.114
Chain 1:   2000       -11112.553             0.247            0.114
Chain 1:   2100       -10561.665             0.243            0.114
Chain 1:   2200        -9484.056             0.144            0.114
Chain 1:   2300        -9007.419             0.133            0.090
Chain 1:   2400       -10490.502             0.144            0.114
Chain 1:   2500        -9054.491             0.155            0.114
Chain 1:   2600        -9901.327             0.161            0.114
Chain 1:   2700        -8990.972             0.160            0.114
Chain 1:   2800        -9245.313             0.154            0.114
Chain 1:   2900        -9410.347             0.118            0.101
Chain 1:   3000       -10001.052             0.081            0.086
Chain 1:   3100        -9022.367             0.087            0.101
Chain 1:   3200        -8911.312             0.076            0.086
Chain 1:   3300       -12645.480             0.101            0.101
Chain 1:   3400        -8652.847             0.133            0.101
Chain 1:   3500        -9094.239             0.122            0.086
Chain 1:   3600        -9619.882             0.119            0.059
Chain 1:   3700        -8718.920             0.119            0.059
Chain 1:   3800       -13720.404             0.153            0.103
Chain 1:   3900        -9086.158             0.202            0.108
Chain 1:   4000       -10656.038             0.211            0.147
Chain 1:   4100       -13504.176             0.221            0.211
Chain 1:   4200       -12812.590             0.225            0.211
Chain 1:   4300        -9815.354             0.226            0.211
Chain 1:   4400       -11373.754             0.194            0.147
Chain 1:   4500        -9350.715             0.210            0.211
Chain 1:   4600       -11917.251             0.226            0.215
Chain 1:   4700        -8606.667             0.255            0.216
Chain 1:   4800        -8407.784             0.220            0.215
Chain 1:   4900       -10912.197             0.192            0.215
Chain 1:   5000       -15509.530             0.207            0.216
Chain 1:   5100       -11999.110             0.215            0.230
Chain 1:   5200        -9956.584             0.231            0.230
Chain 1:   5300        -9494.954             0.205            0.216
Chain 1:   5400       -10084.247             0.197            0.216
Chain 1:   5500        -9013.731             0.187            0.215
Chain 1:   5600       -10651.246             0.181            0.205
Chain 1:   5700       -12743.393             0.159            0.164
Chain 1:   5800        -9552.148             0.190            0.205
Chain 1:   5900       -13090.173             0.194            0.205
Chain 1:   6000       -11865.934             0.175            0.164
Chain 1:   6100        -8563.493             0.184            0.164
Chain 1:   6200        -8272.860             0.167            0.154
Chain 1:   6300       -13608.209             0.202            0.164
Chain 1:   6400        -9750.359             0.235            0.270
Chain 1:   6500        -8376.617             0.240            0.270
Chain 1:   6600       -11652.974             0.253            0.281
Chain 1:   6700        -8329.868             0.276            0.334
Chain 1:   6800       -11211.036             0.268            0.281
Chain 1:   6900       -10491.364             0.248            0.281
Chain 1:   7000        -8502.269             0.261            0.281
Chain 1:   7100       -10630.375             0.243            0.257
Chain 1:   7200       -11220.545             0.244            0.257
Chain 1:   7300        -8548.922             0.236            0.257
Chain 1:   7400        -8312.577             0.200            0.234
Chain 1:   7500        -8169.089             0.185            0.234
Chain 1:   7600       -10368.120             0.178            0.212
Chain 1:   7700        -8219.964             0.164            0.212
Chain 1:   7800       -11655.628             0.168            0.212
Chain 1:   7900        -9185.985             0.188            0.234
Chain 1:   8000        -8178.206             0.177            0.212
Chain 1:   8100        -8094.432             0.158            0.212
Chain 1:   8200        -9897.910             0.171            0.212
Chain 1:   8300       -11182.804             0.151            0.182
Chain 1:   8400        -8120.058             0.186            0.212
Chain 1:   8500        -8232.818             0.186            0.212
Chain 1:   8600        -8206.561             0.165            0.182
Chain 1:   8700        -8562.647             0.143            0.123
Chain 1:   8800        -8065.271             0.120            0.115
Chain 1:   8900        -8951.011             0.103            0.099
Chain 1:   9000        -8325.847             0.098            0.075
Chain 1:   9100        -9833.925             0.112            0.099
Chain 1:   9200        -9590.404             0.097            0.075
Chain 1:   9300        -9609.844             0.085            0.062
Chain 1:   9400        -8197.044             0.065            0.062
Chain 1:   9500       -11689.356             0.093            0.075
Chain 1:   9600        -8217.492             0.135            0.099
Chain 1:   9700        -8117.243             0.132            0.099
Chain 1:   9800       -10332.352             0.148            0.153
Chain 1:   9900        -9708.070             0.144            0.153
Chain 1:   10000        -8693.154             0.148            0.153
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58040.846             1.000            1.000
Chain 1:    200       -17459.450             1.662            2.324
Chain 1:    300        -8580.856             1.453            1.035
Chain 1:    400        -8195.423             1.102            1.035
Chain 1:    500        -8229.975             0.882            1.000
Chain 1:    600        -8539.021             0.741            1.000
Chain 1:    700        -7901.911             0.647            0.081
Chain 1:    800        -7839.899             0.567            0.081
Chain 1:    900        -7925.509             0.505            0.047
Chain 1:   1000        -7813.419             0.456            0.047
Chain 1:   1100        -7699.200             0.357            0.036
Chain 1:   1200        -7713.335             0.125            0.015
Chain 1:   1300        -7760.858             0.022            0.014
Chain 1:   1400        -7664.854             0.019            0.013
Chain 1:   1500        -7597.087             0.019            0.013
Chain 1:   1600        -7573.167             0.016            0.011
Chain 1:   1700        -7533.043             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86015.660             1.000            1.000
Chain 1:    200       -13261.915             3.243            5.486
Chain 1:    300        -9701.971             2.284            1.000
Chain 1:    400       -10713.084             1.737            1.000
Chain 1:    500        -8626.344             1.438            0.367
Chain 1:    600        -8260.369             1.206            0.367
Chain 1:    700        -8457.071             1.037            0.242
Chain 1:    800        -8968.352             0.914            0.242
Chain 1:    900        -8521.441             0.818            0.094
Chain 1:   1000        -8304.892             0.739            0.094
Chain 1:   1100        -8582.090             0.642            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8215.909             0.098            0.052
Chain 1:   1300        -8273.575             0.062            0.045
Chain 1:   1400        -8267.164             0.053            0.044
Chain 1:   1500        -8302.276             0.029            0.032
Chain 1:   1600        -8308.611             0.025            0.026
Chain 1:   1700        -8239.276             0.023            0.026
Chain 1:   1800        -8119.957             0.019            0.015
Chain 1:   1900        -8238.473             0.015            0.014
Chain 1:   2000        -8198.150             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003188 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398274.109             1.000            1.000
Chain 1:    200     -1582335.625             2.654            4.308
Chain 1:    300      -891147.473             2.028            1.000
Chain 1:    400      -458559.538             1.757            1.000
Chain 1:    500      -358892.145             1.461            0.943
Chain 1:    600      -233547.765             1.307            0.943
Chain 1:    700      -119323.081             1.257            0.943
Chain 1:    800       -86458.570             1.147            0.943
Chain 1:    900       -66714.710             1.053            0.776
Chain 1:   1000       -51447.051             0.977            0.776
Chain 1:   1100       -38876.547             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38039.280             0.481            0.380
Chain 1:   1300       -25955.102             0.450            0.380
Chain 1:   1400       -25667.436             0.357            0.323
Chain 1:   1500       -22245.804             0.344            0.323
Chain 1:   1600       -21458.972             0.294            0.297
Chain 1:   1700       -20328.244             0.204            0.296
Chain 1:   1800       -20271.055             0.166            0.154
Chain 1:   1900       -20596.673             0.138            0.056
Chain 1:   2000       -19106.638             0.116            0.056
Chain 1:   2100       -19344.872             0.085            0.037
Chain 1:   2200       -19571.546             0.084            0.037
Chain 1:   2300       -19188.683             0.040            0.020
Chain 1:   2400       -18960.916             0.040            0.020
Chain 1:   2500       -18763.254             0.026            0.016
Chain 1:   2600       -18393.656             0.024            0.016
Chain 1:   2700       -18350.616             0.019            0.012
Chain 1:   2800       -18067.898             0.020            0.016
Chain 1:   2900       -18348.944             0.020            0.015
Chain 1:   3000       -18335.028             0.012            0.012
Chain 1:   3100       -18420.022             0.011            0.012
Chain 1:   3200       -18110.949             0.012            0.015
Chain 1:   3300       -18315.441             0.011            0.012
Chain 1:   3400       -17791.005             0.013            0.015
Chain 1:   3500       -18402.076             0.015            0.016
Chain 1:   3600       -17709.756             0.017            0.016
Chain 1:   3700       -18095.899             0.019            0.017
Chain 1:   3800       -17057.287             0.023            0.021
Chain 1:   3900       -17053.511             0.022            0.021
Chain 1:   4000       -17170.753             0.022            0.021
Chain 1:   4100       -17084.691             0.022            0.021
Chain 1:   4200       -16901.227             0.022            0.021
Chain 1:   4300       -17039.357             0.022            0.021
Chain 1:   4400       -16996.477             0.019            0.011
Chain 1:   4500       -16899.098             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -14191.915             1.000            1.000
Chain 1:    200       -10649.459             0.666            1.000
Chain 1:    300        -9377.400             0.489            0.333
Chain 1:    400        -8689.603             0.387            0.333
Chain 1:    500        -8617.187             0.311            0.136
Chain 1:    600        -8721.148             0.261            0.136
Chain 1:    700        -8733.452             0.224            0.079
Chain 1:    800        -8631.473             0.198            0.079
Chain 1:    900        -8508.966             0.177            0.014
Chain 1:   1000        -8610.580             0.161            0.014
Chain 1:   1100        -8636.707             0.061            0.012
Chain 1:   1200        -8531.425             0.029            0.012
Chain 1:   1300        -8492.741             0.016            0.012
Chain 1:   1400        -8511.725             0.008            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001473 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -64761.399             1.000            1.000
Chain 1:    200       -19500.068             1.661            2.321
Chain 1:    300        -9447.127             1.462            1.064
Chain 1:    400        -8840.994             1.113            1.064
Chain 1:    500        -9081.614             0.896            1.000
Chain 1:    600        -9752.238             0.758            1.000
Chain 1:    700        -8468.834             0.672            0.152
Chain 1:    800        -8528.743             0.588            0.152
Chain 1:    900        -8548.262             0.523            0.069
Chain 1:   1000        -7655.032             0.483            0.117
Chain 1:   1100        -7910.267             0.386            0.069
Chain 1:   1200        -7915.768             0.154            0.069
Chain 1:   1300        -7598.998             0.052            0.042
Chain 1:   1400        -8057.693             0.050            0.042
Chain 1:   1500        -7632.461             0.053            0.056
Chain 1:   1600        -7836.167             0.049            0.042
Chain 1:   1700        -7557.510             0.038            0.037
Chain 1:   1800        -7594.033             0.037            0.037
Chain 1:   1900        -7698.079             0.039            0.037
Chain 1:   2000        -7634.615             0.028            0.032
Chain 1:   2100        -7575.696             0.025            0.026
Chain 1:   2200        -7982.057             0.030            0.037
Chain 1:   2300        -7600.300             0.031            0.037
Chain 1:   2400        -7564.639             0.026            0.026
Chain 1:   2500        -7559.056             0.020            0.014
Chain 1:   2600        -7624.128             0.019            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002954 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87414.574             1.000            1.000
Chain 1:    200       -14693.902             2.975            4.949
Chain 1:    300       -10826.847             2.102            1.000
Chain 1:    400       -13077.261             1.620            1.000
Chain 1:    500        -9155.268             1.381            0.428
Chain 1:    600        -9225.136             1.152            0.428
Chain 1:    700        -9336.237             0.989            0.357
Chain 1:    800        -9294.295             0.866            0.357
Chain 1:    900        -9535.326             0.773            0.172
Chain 1:   1000        -9595.809             0.696            0.172
Chain 1:   1100        -9473.182             0.598            0.025   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8939.336             0.109            0.025
Chain 1:   1300        -9338.908             0.077            0.025
Chain 1:   1400        -9282.640             0.061            0.013
Chain 1:   1500        -9237.598             0.018            0.012
Chain 1:   1600        -9249.844             0.018            0.012
Chain 1:   1700        -9366.001             0.018            0.012
Chain 1:   1800        -8879.475             0.023            0.013
Chain 1:   1900        -9002.145             0.021            0.013
Chain 1:   2000        -9011.957             0.021            0.013
Chain 1:   2100        -9135.326             0.021            0.014
Chain 1:   2200        -8873.363             0.018            0.014
Chain 1:   2300        -8965.535             0.015            0.012
Chain 1:   2400        -9053.693             0.015            0.012
Chain 1:   2500        -8973.411             0.016            0.012
Chain 1:   2600        -8997.841             0.016            0.012
Chain 1:   2700        -8911.759             0.015            0.010
Chain 1:   2800        -8872.922             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003273 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426767.277             1.000            1.000
Chain 1:    200     -1585036.008             2.658            4.316
Chain 1:    300      -890984.758             2.032            1.000
Chain 1:    400      -458287.827             1.760            1.000
Chain 1:    500      -358446.856             1.464            0.944
Chain 1:    600      -233648.670             1.309            0.944
Chain 1:    700      -120183.983             1.257            0.944
Chain 1:    800       -87473.818             1.146            0.944
Chain 1:    900       -67883.380             1.051            0.779
Chain 1:   1000       -52743.946             0.975            0.779
Chain 1:   1100       -40266.911             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39460.798             0.476            0.374
Chain 1:   1300       -27441.751             0.442            0.374
Chain 1:   1400       -27169.570             0.348            0.310
Chain 1:   1500       -23761.928             0.335            0.310
Chain 1:   1600       -22982.257             0.285            0.289
Chain 1:   1700       -21857.358             0.196            0.287
Chain 1:   1800       -21802.835             0.159            0.143
Chain 1:   1900       -22130.172             0.131            0.051
Chain 1:   2000       -20639.909             0.110            0.051
Chain 1:   2100       -20878.493             0.080            0.034
Chain 1:   2200       -21105.583             0.079            0.034
Chain 1:   2300       -20721.953             0.037            0.019
Chain 1:   2400       -20493.619             0.037            0.019
Chain 1:   2500       -20295.480             0.024            0.015
Chain 1:   2600       -19924.574             0.022            0.015
Chain 1:   2700       -19881.265             0.017            0.011
Chain 1:   2800       -19597.477             0.018            0.014
Chain 1:   2900       -19879.288             0.018            0.014
Chain 1:   3000       -19865.412             0.011            0.011
Chain 1:   3100       -19950.561             0.010            0.011
Chain 1:   3200       -19640.470             0.011            0.014
Chain 1:   3300       -19845.835             0.010            0.011
Chain 1:   3400       -19319.287             0.012            0.014
Chain 1:   3500       -19933.282             0.014            0.014
Chain 1:   3600       -19237.194             0.016            0.014
Chain 1:   3700       -19626.000             0.017            0.016
Chain 1:   3800       -18581.370             0.022            0.020
Chain 1:   3900       -18577.369             0.020            0.020
Chain 1:   4000       -18694.713             0.021            0.020
Chain 1:   4100       -18608.221             0.021            0.020
Chain 1:   4200       -18423.531             0.020            0.020
Chain 1:   4300       -18562.613             0.020            0.020
Chain 1:   4400       -18518.662             0.017            0.010
Chain 1:   4500       -18421.015             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12397.789             1.000            1.000
Chain 1:    200        -9275.742             0.668            1.000
Chain 1:    300        -8185.208             0.490            0.337
Chain 1:    400        -8269.517             0.370            0.337
Chain 1:    500        -8136.701             0.299            0.133
Chain 1:    600        -8053.100             0.251            0.133
Chain 1:    700        -7976.394             0.217            0.016
Chain 1:    800        -8026.684             0.190            0.016
Chain 1:    900        -8100.890             0.170            0.010
Chain 1:   1000        -8043.733             0.154            0.010
Chain 1:   1100        -8120.728             0.055            0.010
Chain 1:   1200        -8001.433             0.023            0.010
Chain 1:   1300        -7944.641             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001674 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56755.408             1.000            1.000
Chain 1:    200       -17400.798             1.631            2.262
Chain 1:    300        -8727.240             1.419            1.000
Chain 1:    400        -8382.838             1.074            1.000
Chain 1:    500        -8644.321             0.865            0.994
Chain 1:    600        -9250.784             0.732            0.994
Chain 1:    700        -7907.746             0.652            0.170
Chain 1:    800        -8203.304             0.575            0.170
Chain 1:    900        -7922.730             0.515            0.066
Chain 1:   1000        -7764.404             0.465            0.066
Chain 1:   1100        -7780.841             0.366            0.041
Chain 1:   1200        -7708.348             0.140            0.036
Chain 1:   1300        -7672.855             0.041            0.035
Chain 1:   1400        -7737.162             0.038            0.030
Chain 1:   1500        -7636.036             0.036            0.020
Chain 1:   1600        -7692.660             0.031            0.013
Chain 1:   1700        -7541.789             0.016            0.013
Chain 1:   1800        -7560.517             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86989.818             1.000            1.000
Chain 1:    200       -13467.786             3.230            5.459
Chain 1:    300        -9880.051             2.274            1.000
Chain 1:    400       -10786.082             1.727            1.000
Chain 1:    500        -8831.186             1.426            0.363
Chain 1:    600        -8619.507             1.192            0.363
Chain 1:    700        -8446.475             1.025            0.221
Chain 1:    800        -9262.700             0.908            0.221
Chain 1:    900        -8699.671             0.814            0.088
Chain 1:   1000        -8486.163             0.735            0.088
Chain 1:   1100        -8773.430             0.638            0.084   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8303.557             0.098            0.065
Chain 1:   1300        -8511.708             0.064            0.057
Chain 1:   1400        -8626.591             0.057            0.033
Chain 1:   1500        -8490.942             0.037            0.025
Chain 1:   1600        -8600.355             0.035            0.025
Chain 1:   1700        -8682.938             0.034            0.025
Chain 1:   1800        -8286.336             0.030            0.025
Chain 1:   1900        -8388.395             0.025            0.024
Chain 1:   2000        -8358.845             0.023            0.016
Chain 1:   2100        -8481.329             0.021            0.014
Chain 1:   2200        -8262.865             0.018            0.014
Chain 1:   2300        -8416.945             0.017            0.014
Chain 1:   2400        -8430.958             0.016            0.014
Chain 1:   2500        -8400.245             0.015            0.013
Chain 1:   2600        -8402.826             0.014            0.012
Chain 1:   2700        -8309.093             0.014            0.012
Chain 1:   2800        -8280.347             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003018 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410951.295             1.000            1.000
Chain 1:    200     -1586437.769             2.651            4.302
Chain 1:    300      -890995.128             2.027            1.000
Chain 1:    400      -457381.087             1.758            1.000
Chain 1:    500      -357492.405             1.462            0.948
Chain 1:    600      -232468.325             1.308            0.948
Chain 1:    700      -118946.373             1.257            0.948
Chain 1:    800       -86192.233             1.148            0.948
Chain 1:    900       -66586.428             1.053            0.781
Chain 1:   1000       -51418.123             0.977            0.781
Chain 1:   1100       -38928.181             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38108.784             0.481            0.380
Chain 1:   1300       -26107.446             0.449            0.380
Chain 1:   1400       -25828.952             0.355            0.321
Chain 1:   1500       -22426.581             0.343            0.321
Chain 1:   1600       -21645.636             0.292            0.295
Chain 1:   1700       -20524.972             0.202            0.294
Chain 1:   1800       -20470.290             0.165            0.152
Chain 1:   1900       -20796.062             0.137            0.055
Chain 1:   2000       -19310.808             0.115            0.055
Chain 1:   2100       -19549.060             0.084            0.036
Chain 1:   2200       -19774.640             0.083            0.036
Chain 1:   2300       -19392.722             0.039            0.020
Chain 1:   2400       -19165.008             0.039            0.020
Chain 1:   2500       -18966.727             0.025            0.016
Chain 1:   2600       -18597.527             0.024            0.016
Chain 1:   2700       -18554.781             0.018            0.012
Chain 1:   2800       -18271.610             0.020            0.015
Chain 1:   2900       -18552.690             0.020            0.015
Chain 1:   3000       -18538.981             0.012            0.012
Chain 1:   3100       -18623.848             0.011            0.012
Chain 1:   3200       -18314.857             0.012            0.015
Chain 1:   3300       -18519.378             0.011            0.012
Chain 1:   3400       -17994.716             0.013            0.015
Chain 1:   3500       -18605.822             0.015            0.015
Chain 1:   3600       -17913.592             0.017            0.015
Chain 1:   3700       -18299.486             0.019            0.017
Chain 1:   3800       -17260.755             0.023            0.021
Chain 1:   3900       -17256.925             0.022            0.021
Chain 1:   4000       -17374.262             0.022            0.021
Chain 1:   4100       -17287.998             0.022            0.021
Chain 1:   4200       -17104.687             0.022            0.021
Chain 1:   4300       -17242.825             0.021            0.021
Chain 1:   4400       -17199.935             0.019            0.011
Chain 1:   4500       -17102.500             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001456 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49711.195             1.000            1.000
Chain 1:    200       -25659.642             0.969            1.000
Chain 1:    300       -20606.988             0.728            0.937
Chain 1:    400       -19920.910             0.554            0.937
Chain 1:    500       -17178.558             0.475            0.245
Chain 1:    600       -18267.541             0.406            0.245
Chain 1:    700       -15968.750             0.369            0.160
Chain 1:    800       -13076.252             0.350            0.221
Chain 1:    900       -16613.663             0.335            0.213
Chain 1:   1000       -11546.310             0.345            0.221
Chain 1:   1100       -10831.559             0.252            0.213
Chain 1:   1200       -12933.444             0.174            0.163
Chain 1:   1300       -17219.130             0.175            0.163
Chain 1:   1400       -11823.734             0.217            0.213
Chain 1:   1500       -10589.537             0.213            0.213
Chain 1:   1600       -13944.243             0.231            0.221
Chain 1:   1700       -10908.986             0.244            0.241
Chain 1:   1800       -10410.580             0.227            0.241
Chain 1:   1900       -10592.311             0.207            0.241
Chain 1:   2000       -10546.604             0.164            0.163
Chain 1:   2100       -10129.592             0.161            0.163
Chain 1:   2200       -10261.189             0.146            0.117
Chain 1:   2300       -10661.897             0.125            0.048
Chain 1:   2400        -9771.118             0.089            0.048
Chain 1:   2500       -10996.486             0.088            0.048
Chain 1:   2600       -11466.786             0.068            0.041
Chain 1:   2700       -10443.101             0.050            0.041
Chain 1:   2800       -19550.622             0.092            0.041
Chain 1:   2900       -10052.179             0.185            0.091
Chain 1:   3000       -18045.132             0.229            0.098
Chain 1:   3100       -17757.741             0.226            0.098
Chain 1:   3200       -10669.364             0.291            0.111
Chain 1:   3300       -10360.373             0.291            0.111
Chain 1:   3400       -16440.057             0.318            0.370
Chain 1:   3500       -10746.500             0.360            0.443
Chain 1:   3600       -10900.876             0.358            0.443
Chain 1:   3700       -10692.682             0.350            0.443
Chain 1:   3800       -12638.877             0.319            0.370
Chain 1:   3900       -10796.690             0.241            0.171
Chain 1:   4000        -9553.671             0.210            0.154
Chain 1:   4100       -10956.767             0.221            0.154
Chain 1:   4200       -16736.452             0.189            0.154
Chain 1:   4300        -9890.572             0.255            0.171
Chain 1:   4400        -9946.811             0.219            0.154
Chain 1:   4500       -10971.545             0.175            0.130
Chain 1:   4600       -10767.879             0.176            0.130
Chain 1:   4700       -16555.601             0.209            0.154
Chain 1:   4800        -9534.777             0.267            0.171
Chain 1:   4900       -16111.105             0.291            0.345
Chain 1:   5000        -9817.504             0.342            0.350
Chain 1:   5100       -10323.823             0.334            0.350
Chain 1:   5200       -10988.902             0.305            0.350
Chain 1:   5300       -13869.934             0.257            0.208
Chain 1:   5400        -9892.133             0.297            0.350
Chain 1:   5500       -11739.280             0.303            0.350
Chain 1:   5600        -9054.940             0.331            0.350
Chain 1:   5700       -12521.350             0.324            0.296
Chain 1:   5800       -11641.131             0.257            0.277
Chain 1:   5900       -10806.859             0.224            0.208
Chain 1:   6000        -9555.000             0.173            0.157
Chain 1:   6100       -10056.159             0.173            0.157
Chain 1:   6200       -13400.908             0.192            0.208
Chain 1:   6300        -9240.753             0.217            0.250
Chain 1:   6400       -10065.014             0.185            0.157
Chain 1:   6500       -13098.528             0.192            0.232
Chain 1:   6600        -9359.895             0.202            0.232
Chain 1:   6700        -9783.828             0.179            0.131
Chain 1:   6800       -13032.945             0.196            0.232
Chain 1:   6900       -11747.414             0.200            0.232
Chain 1:   7000        -8977.554             0.217            0.249
Chain 1:   7100        -9159.354             0.214            0.249
Chain 1:   7200        -9427.356             0.192            0.232
Chain 1:   7300       -10806.507             0.160            0.128
Chain 1:   7400        -9176.010             0.170            0.178
Chain 1:   7500        -9731.285             0.152            0.128
Chain 1:   7600        -9007.622             0.120            0.109
Chain 1:   7700       -11474.120             0.137            0.128
Chain 1:   7800       -13469.255             0.127            0.128
Chain 1:   7900        -9221.697             0.162            0.148
Chain 1:   8000        -8939.666             0.135            0.128
Chain 1:   8100        -9725.411             0.141            0.128
Chain 1:   8200        -9773.109             0.138            0.128
Chain 1:   8300        -9298.976             0.131            0.081
Chain 1:   8400        -8999.747             0.116            0.080
Chain 1:   8500       -13445.867             0.144            0.081
Chain 1:   8600        -8920.291             0.186            0.148
Chain 1:   8700       -11130.521             0.185            0.148
Chain 1:   8800        -9377.852             0.189            0.187
Chain 1:   8900       -10535.003             0.153            0.110
Chain 1:   9000       -10859.328             0.153            0.110
Chain 1:   9100        -9208.881             0.163            0.179
Chain 1:   9200        -9147.738             0.163            0.179
Chain 1:   9300       -10852.429             0.174            0.179
Chain 1:   9400       -11372.268             0.175            0.179
Chain 1:   9500       -11505.369             0.143            0.157
Chain 1:   9600        -9153.490             0.118            0.157
Chain 1:   9700        -8770.843             0.103            0.110
Chain 1:   9800       -10731.637             0.102            0.110
Chain 1:   9900        -8777.545             0.114            0.157
Chain 1:   10000       -11247.542             0.133            0.179
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58865.249             1.000            1.000
Chain 1:    200       -18465.015             1.594            2.188
Chain 1:    300        -9027.527             1.411            1.045
Chain 1:    400        -8073.394             1.088            1.045
Chain 1:    500        -9139.828             0.894            1.000
Chain 1:    600        -9138.914             0.745            1.000
Chain 1:    700        -7949.592             0.660            0.150
Chain 1:    800        -8513.767             0.586            0.150
Chain 1:    900        -8463.128             0.521            0.118
Chain 1:   1000        -7734.179             0.478            0.118
Chain 1:   1100        -7648.109             0.380            0.117
Chain 1:   1200        -7951.740             0.165            0.094
Chain 1:   1300        -8054.875             0.061            0.066
Chain 1:   1400        -7788.149             0.053            0.038
Chain 1:   1500        -7481.270             0.045            0.038
Chain 1:   1600        -7756.345             0.049            0.038
Chain 1:   1700        -7424.878             0.038            0.038
Chain 1:   1800        -7595.911             0.034            0.035
Chain 1:   1900        -7710.867             0.035            0.035
Chain 1:   2000        -7614.087             0.027            0.034
Chain 1:   2100        -7544.002             0.027            0.034
Chain 1:   2200        -7843.857             0.027            0.034
Chain 1:   2300        -7633.438             0.028            0.034
Chain 1:   2400        -7642.489             0.025            0.028
Chain 1:   2500        -7381.929             0.024            0.028
Chain 1:   2600        -7512.918             0.022            0.023
Chain 1:   2700        -7493.910             0.018            0.017
Chain 1:   2800        -7472.423             0.016            0.015
Chain 1:   2900        -7342.250             0.016            0.017
Chain 1:   3000        -7535.779             0.018            0.018
Chain 1:   3100        -7508.051             0.017            0.018
Chain 1:   3200        -7706.280             0.016            0.018
Chain 1:   3300        -7392.985             0.017            0.018
Chain 1:   3400        -7657.407             0.021            0.026
Chain 1:   3500        -7438.921             0.020            0.026
Chain 1:   3600        -7447.387             0.019            0.026
Chain 1:   3700        -7383.075             0.019            0.026
Chain 1:   3800        -7420.599             0.019            0.026
Chain 1:   3900        -7370.355             0.018            0.026
Chain 1:   4000        -7370.896             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00358 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86477.727             1.000            1.000
Chain 1:    200       -14275.378             3.029            5.058
Chain 1:    300       -10610.183             2.134            1.000
Chain 1:    400       -11629.698             1.623            1.000
Chain 1:    500        -9601.557             1.340            0.345
Chain 1:    600        -9042.505             1.127            0.345
Chain 1:    700        -9190.344             0.969            0.211
Chain 1:    800        -9720.009             0.854            0.211
Chain 1:    900        -9346.772             0.764            0.088
Chain 1:   1000        -9332.008             0.688            0.088
Chain 1:   1100        -9475.126             0.589            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9024.439             0.088            0.054
Chain 1:   1300        -9280.770             0.057            0.050
Chain 1:   1400        -9287.489             0.048            0.040
Chain 1:   1500        -9137.659             0.028            0.028
Chain 1:   1600        -9252.915             0.023            0.016
Chain 1:   1700        -9323.621             0.023            0.016
Chain 1:   1800        -8893.521             0.022            0.016
Chain 1:   1900        -8997.156             0.019            0.015
Chain 1:   2000        -8972.541             0.019            0.015
Chain 1:   2100        -9105.355             0.019            0.015
Chain 1:   2200        -8900.820             0.016            0.015
Chain 1:   2300        -8995.958             0.015            0.012
Chain 1:   2400        -9060.874             0.015            0.012
Chain 1:   2500        -9005.990             0.014            0.012
Chain 1:   2600        -9010.003             0.013            0.011
Chain 1:   2700        -8925.292             0.013            0.011
Chain 1:   2800        -8882.332             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003486 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398030.358             1.000            1.000
Chain 1:    200     -1580951.547             2.656            4.312
Chain 1:    300      -890513.004             2.029            1.000
Chain 1:    400      -458477.725             1.757            1.000
Chain 1:    500      -358889.121             1.461            0.942
Chain 1:    600      -233964.963             1.307            0.942
Chain 1:    700      -120104.914             1.256            0.942
Chain 1:    800       -87326.698             1.146            0.942
Chain 1:    900       -67648.379             1.051            0.775
Chain 1:   1000       -52432.972             0.975            0.775
Chain 1:   1100       -39899.692             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39075.690             0.477            0.375
Chain 1:   1300       -27010.682             0.444            0.375
Chain 1:   1400       -26730.105             0.351            0.314
Chain 1:   1500       -23311.972             0.338            0.314
Chain 1:   1600       -22527.770             0.288            0.291
Chain 1:   1700       -21398.171             0.198            0.290
Chain 1:   1800       -21341.904             0.161            0.147
Chain 1:   1900       -21668.325             0.133            0.053
Chain 1:   2000       -20177.510             0.112            0.053
Chain 1:   2100       -20415.866             0.082            0.035
Chain 1:   2200       -20642.899             0.081            0.035
Chain 1:   2300       -20259.567             0.038            0.019
Chain 1:   2400       -20031.497             0.038            0.019
Chain 1:   2500       -19833.773             0.024            0.015
Chain 1:   2600       -19463.443             0.023            0.015
Chain 1:   2700       -19420.294             0.018            0.012
Chain 1:   2800       -19137.152             0.019            0.015
Chain 1:   2900       -19418.547             0.019            0.014
Chain 1:   3000       -19404.667             0.011            0.012
Chain 1:   3100       -19489.698             0.011            0.011
Chain 1:   3200       -19180.178             0.011            0.014
Chain 1:   3300       -19385.071             0.010            0.011
Chain 1:   3400       -18859.699             0.012            0.014
Chain 1:   3500       -19472.084             0.014            0.015
Chain 1:   3600       -18778.105             0.016            0.015
Chain 1:   3700       -19165.413             0.018            0.016
Chain 1:   3800       -18124.158             0.022            0.020
Chain 1:   3900       -18120.312             0.021            0.020
Chain 1:   4000       -18237.593             0.021            0.020
Chain 1:   4100       -18151.315             0.021            0.020
Chain 1:   4200       -17967.347             0.021            0.020
Chain 1:   4300       -18105.867             0.020            0.020
Chain 1:   4400       -18062.499             0.018            0.010
Chain 1:   4500       -17965.028             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48398.951             1.000            1.000
Chain 1:    200       -18167.584             1.332            1.664
Chain 1:    300       -38616.913             1.065            1.000
Chain 1:    400       -13814.314             1.247            1.664
Chain 1:    500       -11708.395             1.034            1.000
Chain 1:    600       -24006.759             0.947            1.000
Chain 1:    700       -18127.573             0.858            0.530
Chain 1:    800       -11111.086             0.830            0.631
Chain 1:    900       -19995.820             0.787            0.530
Chain 1:   1000        -9878.869             0.811            0.631
Chain 1:   1100       -16980.024             0.752            0.530   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -19565.891             0.599            0.512   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -12019.293             0.609            0.512   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -13590.690             0.441            0.444
Chain 1:   1500       -10598.348             0.451            0.444
Chain 1:   1600       -25894.140             0.459            0.444
Chain 1:   1700        -9729.146             0.593            0.591   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800       -10002.923             0.532            0.444   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900        -9955.796             0.488            0.418
Chain 1:   2000        -9972.109             0.386            0.282
Chain 1:   2100        -9432.597             0.350            0.132
Chain 1:   2200        -9302.833             0.338            0.116
Chain 1:   2300        -9160.017             0.277            0.057
Chain 1:   2400        -9696.076             0.271            0.055
Chain 1:   2500       -10310.433             0.249            0.055
Chain 1:   2600       -11158.229             0.197            0.055
Chain 1:   2700       -10044.315             0.042            0.055
Chain 1:   2800       -14267.692             0.069            0.057
Chain 1:   2900        -9027.139             0.127            0.060
Chain 1:   3000       -10528.218             0.141            0.076
Chain 1:   3100        -9670.916             0.144            0.089
Chain 1:   3200       -13510.689             0.171            0.111
Chain 1:   3300       -10224.493             0.202            0.143
Chain 1:   3400        -9245.846             0.207            0.143
Chain 1:   3500        -9181.415             0.201            0.143
Chain 1:   3600       -10071.877             0.203            0.143
Chain 1:   3700        -8846.058             0.205            0.143
Chain 1:   3800       -10972.565             0.195            0.143
Chain 1:   3900        -9103.004             0.158            0.143
Chain 1:   4000        -8603.004             0.149            0.139
Chain 1:   4100        -9312.350             0.148            0.139
Chain 1:   4200        -8990.529             0.123            0.106
Chain 1:   4300        -8989.292             0.091            0.088
Chain 1:   4400        -9222.874             0.083            0.076
Chain 1:   4500        -8965.763             0.085            0.076
Chain 1:   4600       -12498.609             0.104            0.076
Chain 1:   4700       -15690.252             0.111            0.076
Chain 1:   4800        -8696.129             0.172            0.076
Chain 1:   4900        -8537.860             0.153            0.058
Chain 1:   5000        -9178.854             0.154            0.070
Chain 1:   5100       -13383.353             0.178            0.070
Chain 1:   5200        -8610.897             0.230            0.203
Chain 1:   5300       -12069.731             0.259            0.283
Chain 1:   5400        -9694.550             0.281            0.283
Chain 1:   5500        -9192.061             0.283            0.283
Chain 1:   5600       -13530.687             0.287            0.287
Chain 1:   5700       -11214.529             0.287            0.287
Chain 1:   5800       -10995.960             0.209            0.245
Chain 1:   5900        -9312.920             0.225            0.245
Chain 1:   6000        -8152.997             0.232            0.245
Chain 1:   6100        -9118.129             0.212            0.207
Chain 1:   6200        -8996.282             0.158            0.181
Chain 1:   6300        -8439.842             0.136            0.142
Chain 1:   6400        -9738.424             0.124            0.133
Chain 1:   6500       -14463.533             0.152            0.142
Chain 1:   6600        -8358.190             0.193            0.142
Chain 1:   6700        -8157.274             0.174            0.133
Chain 1:   6800       -11138.164             0.199            0.142
Chain 1:   6900       -11791.042             0.187            0.133
Chain 1:   7000       -12382.914             0.177            0.106
Chain 1:   7100        -8710.726             0.209            0.133
Chain 1:   7200       -11117.622             0.229            0.216
Chain 1:   7300        -8206.803             0.258            0.268
Chain 1:   7400        -8411.492             0.247            0.268
Chain 1:   7500        -8342.574             0.215            0.216
Chain 1:   7600        -8843.705             0.148            0.057
Chain 1:   7700        -8362.577             0.151            0.058
Chain 1:   7800       -13278.387             0.161            0.058
Chain 1:   7900        -8079.825             0.220            0.216
Chain 1:   8000       -10432.343             0.238            0.226
Chain 1:   8100        -8287.565             0.222            0.226
Chain 1:   8200        -8132.801             0.202            0.226
Chain 1:   8300        -8692.949             0.173            0.064
Chain 1:   8400        -8447.860             0.173            0.064
Chain 1:   8500        -8566.048             0.174            0.064
Chain 1:   8600       -11032.538             0.191            0.224
Chain 1:   8700        -8170.000             0.220            0.226
Chain 1:   8800       -10360.278             0.204            0.224
Chain 1:   8900       -11671.842             0.151            0.211
Chain 1:   9000       -10580.332             0.139            0.112
Chain 1:   9100        -8405.037             0.139            0.112
Chain 1:   9200        -8865.945             0.142            0.112
Chain 1:   9300       -10835.302             0.154            0.182
Chain 1:   9400        -8159.791             0.184            0.211
Chain 1:   9500       -10470.792             0.204            0.221
Chain 1:   9600        -8242.334             0.209            0.221
Chain 1:   9700        -8003.218             0.177            0.211
Chain 1:   9800        -9850.290             0.174            0.188
Chain 1:   9900        -8112.642             0.185            0.214
Chain 1:   10000        -8139.893             0.175            0.214
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001632 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57710.526             1.000            1.000
Chain 1:    200       -17377.021             1.661            2.321
Chain 1:    300        -8577.952             1.449            1.026
Chain 1:    400        -8182.543             1.099            1.026
Chain 1:    500        -8432.374             0.885            1.000
Chain 1:    600        -8832.612             0.745            1.000
Chain 1:    700        -8234.128             0.649            0.073
Chain 1:    800        -8162.806             0.569            0.073
Chain 1:    900        -7874.418             0.510            0.048
Chain 1:   1000        -7820.185             0.460            0.048
Chain 1:   1100        -7737.403             0.361            0.045
Chain 1:   1200        -7804.883             0.129            0.037
Chain 1:   1300        -7693.285             0.028            0.030
Chain 1:   1400        -7671.002             0.024            0.015
Chain 1:   1500        -7631.730             0.021            0.011
Chain 1:   1600        -7583.812             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85374.789             1.000            1.000
Chain 1:    200       -13226.283             3.227            5.455
Chain 1:    300        -9682.366             2.274            1.000
Chain 1:    400       -10396.694             1.722            1.000
Chain 1:    500        -8585.524             1.420            0.366
Chain 1:    600        -8387.725             1.187            0.366
Chain 1:    700        -8525.389             1.020            0.211
Chain 1:    800        -8541.076             0.893            0.211
Chain 1:    900        -8456.515             0.795            0.069
Chain 1:   1000        -8266.308             0.718            0.069
Chain 1:   1100        -8569.500             0.621            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8324.174             0.079            0.029
Chain 1:   1300        -8273.834             0.043            0.024
Chain 1:   1400        -8446.804             0.038            0.023
Chain 1:   1500        -8287.097             0.019            0.020
Chain 1:   1600        -8397.133             0.017            0.019
Chain 1:   1700        -8479.877             0.017            0.019
Chain 1:   1800        -8090.132             0.021            0.020
Chain 1:   1900        -8192.871             0.022            0.020
Chain 1:   2000        -8162.715             0.020            0.019
Chain 1:   2100        -8291.381             0.018            0.016
Chain 1:   2200        -8077.911             0.018            0.016
Chain 1:   2300        -8221.531             0.019            0.017
Chain 1:   2400        -8235.753             0.017            0.016
Chain 1:   2500        -8202.813             0.015            0.013
Chain 1:   2600        -8204.065             0.014            0.013
Chain 1:   2700        -8111.416             0.014            0.013
Chain 1:   2800        -8085.751             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003398 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388786.357             1.000            1.000
Chain 1:    200     -1581275.559             2.653            4.305
Chain 1:    300      -890717.792             2.027            1.000
Chain 1:    400      -458065.821             1.756            1.000
Chain 1:    500      -358404.349             1.461            0.945
Chain 1:    600      -233269.658             1.307            0.945
Chain 1:    700      -119217.020             1.257            0.945
Chain 1:    800       -86366.190             1.147            0.945
Chain 1:    900       -66649.520             1.052            0.775
Chain 1:   1000       -51398.013             0.977            0.775
Chain 1:   1100       -38835.178             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38002.146             0.481            0.380
Chain 1:   1300       -25924.092             0.450            0.380
Chain 1:   1400       -25637.391             0.357            0.323
Chain 1:   1500       -22217.093             0.344            0.323
Chain 1:   1600       -21430.867             0.294            0.297
Chain 1:   1700       -20301.031             0.204            0.296
Chain 1:   1800       -20244.101             0.166            0.154
Chain 1:   1900       -20569.802             0.138            0.056
Chain 1:   2000       -19080.225             0.117            0.056
Chain 1:   2100       -19318.312             0.085            0.037
Chain 1:   2200       -19544.935             0.084            0.037
Chain 1:   2300       -19162.171             0.040            0.020
Chain 1:   2400       -18934.446             0.040            0.020
Chain 1:   2500       -18736.699             0.026            0.016
Chain 1:   2600       -18367.020             0.024            0.016
Chain 1:   2700       -18324.084             0.019            0.012
Chain 1:   2800       -18041.267             0.020            0.016
Chain 1:   2900       -18322.387             0.020            0.015
Chain 1:   3000       -18308.490             0.012            0.012
Chain 1:   3100       -18393.416             0.011            0.012
Chain 1:   3200       -18084.334             0.012            0.015
Chain 1:   3300       -18288.899             0.011            0.012
Chain 1:   3400       -17764.330             0.013            0.015
Chain 1:   3500       -18375.472             0.015            0.016
Chain 1:   3600       -17683.198             0.017            0.016
Chain 1:   3700       -18069.267             0.019            0.017
Chain 1:   3800       -17030.562             0.023            0.021
Chain 1:   3900       -17026.827             0.022            0.021
Chain 1:   4000       -17144.076             0.022            0.021
Chain 1:   4100       -17057.925             0.022            0.021
Chain 1:   4200       -16874.559             0.022            0.021
Chain 1:   4300       -17012.637             0.022            0.021
Chain 1:   4400       -16969.744             0.019            0.011
Chain 1:   4500       -16872.413             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001456 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48736.523             1.000            1.000
Chain 1:    200       -19228.415             1.267            1.535
Chain 1:    300       -20057.140             0.859            1.000
Chain 1:    400       -23348.762             0.679            1.000
Chain 1:    500       -13803.398             0.682            0.692
Chain 1:    600       -14035.697             0.571            0.692
Chain 1:    700       -15211.196             0.500            0.141
Chain 1:    800       -15447.277             0.440            0.141
Chain 1:    900       -11619.002             0.427            0.141
Chain 1:   1000       -14834.810             0.406            0.217
Chain 1:   1100       -11699.298             0.333            0.217
Chain 1:   1200       -10713.272             0.189            0.141
Chain 1:   1300       -11152.235             0.189            0.141
Chain 1:   1400       -10646.761             0.179            0.092
Chain 1:   1500       -11704.103             0.119            0.090
Chain 1:   1600       -12234.144             0.122            0.090
Chain 1:   1700       -15785.536             0.137            0.092
Chain 1:   1800       -10928.609             0.180            0.217
Chain 1:   1900        -9745.494             0.159            0.121
Chain 1:   2000       -23313.455             0.195            0.121
Chain 1:   2100        -9399.749             0.317            0.121
Chain 1:   2200       -10664.542             0.319            0.121
Chain 1:   2300       -13982.914             0.339            0.225
Chain 1:   2400        -8985.016             0.390            0.237
Chain 1:   2500       -16946.483             0.428            0.444
Chain 1:   2600        -9580.356             0.500            0.470   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2700        -9070.035             0.484            0.470
Chain 1:   2800        -9227.147             0.441            0.470
Chain 1:   2900        -9651.139             0.433            0.470
Chain 1:   3000       -11580.433             0.391            0.237
Chain 1:   3100        -9809.305             0.262            0.181
Chain 1:   3200        -9308.814             0.255            0.181
Chain 1:   3300        -9017.978             0.235            0.167
Chain 1:   3400        -9738.749             0.186            0.074
Chain 1:   3500       -12651.059             0.162            0.074
Chain 1:   3600       -10335.329             0.108            0.074
Chain 1:   3700        -9424.188             0.112            0.097
Chain 1:   3800       -10518.934             0.121            0.104
Chain 1:   3900       -15403.267             0.148            0.167
Chain 1:   4000        -9312.463             0.197            0.181
Chain 1:   4100        -9521.222             0.181            0.104
Chain 1:   4200        -9409.984             0.177            0.104
Chain 1:   4300       -13044.245             0.201            0.224
Chain 1:   4400       -12166.547             0.201            0.224
Chain 1:   4500        -9582.508             0.205            0.224
Chain 1:   4600       -12622.410             0.207            0.241
Chain 1:   4700        -8583.544             0.244            0.270
Chain 1:   4800        -8773.218             0.236            0.270
Chain 1:   4900       -13770.081             0.240            0.270
Chain 1:   5000        -9048.296             0.227            0.270
Chain 1:   5100       -10281.861             0.237            0.270
Chain 1:   5200       -11351.927             0.245            0.270
Chain 1:   5300       -12333.292             0.225            0.241
Chain 1:   5400       -12308.303             0.218            0.241
Chain 1:   5500        -8249.539             0.241            0.241
Chain 1:   5600        -9068.138             0.225            0.120
Chain 1:   5700       -15488.827             0.220            0.120
Chain 1:   5800        -9927.745             0.274            0.363
Chain 1:   5900        -9341.516             0.244            0.120
Chain 1:   6000        -9145.783             0.194            0.094
Chain 1:   6100       -15223.549             0.222            0.094
Chain 1:   6200        -8938.883             0.283            0.399
Chain 1:   6300        -8572.491             0.279            0.399
Chain 1:   6400        -8603.408             0.279            0.399
Chain 1:   6500        -8357.189             0.233            0.090
Chain 1:   6600        -9441.191             0.235            0.115
Chain 1:   6700        -9387.709             0.194            0.063
Chain 1:   6800        -8415.635             0.150            0.063
Chain 1:   6900       -13035.416             0.179            0.115
Chain 1:   7000        -9142.392             0.219            0.116
Chain 1:   7100       -11251.556             0.198            0.116
Chain 1:   7200        -8370.053             0.162            0.116
Chain 1:   7300        -8671.999             0.162            0.116
Chain 1:   7400       -11003.815             0.182            0.187
Chain 1:   7500        -9898.665             0.191            0.187
Chain 1:   7600       -11696.442             0.195            0.187
Chain 1:   7700        -8898.421             0.225            0.212
Chain 1:   7800        -8880.610             0.214            0.212
Chain 1:   7900        -8194.326             0.187            0.187
Chain 1:   8000        -8604.771             0.149            0.154
Chain 1:   8100        -8442.703             0.132            0.112
Chain 1:   8200       -10699.280             0.119            0.112
Chain 1:   8300       -10532.319             0.117            0.112
Chain 1:   8400        -9998.672             0.101            0.084
Chain 1:   8500        -8569.856             0.107            0.084
Chain 1:   8600        -8043.244             0.098            0.065
Chain 1:   8700        -9893.562             0.085            0.065
Chain 1:   8800        -8268.432             0.105            0.084
Chain 1:   8900        -9068.651             0.105            0.088
Chain 1:   9000       -11717.178             0.123            0.167
Chain 1:   9100        -8302.280             0.162            0.187
Chain 1:   9200        -8566.344             0.144            0.167
Chain 1:   9300        -9405.069             0.151            0.167
Chain 1:   9400        -9991.804             0.152            0.167
Chain 1:   9500        -8326.574             0.155            0.187
Chain 1:   9600        -8324.613             0.149            0.187
Chain 1:   9700        -8011.732             0.134            0.089
Chain 1:   9800        -8127.020             0.116            0.088
Chain 1:   9900        -9605.777             0.122            0.089
Chain 1:   10000        -9160.946             0.105            0.059
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001543 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56885.888             1.000            1.000
Chain 1:    200       -17436.841             1.631            2.262
Chain 1:    300        -8711.879             1.421            1.002
Chain 1:    400        -8352.026             1.077            1.002
Chain 1:    500        -8221.741             0.865            1.000
Chain 1:    600        -8664.951             0.729            1.000
Chain 1:    700        -8739.628             0.626            0.051
Chain 1:    800        -8032.707             0.559            0.088
Chain 1:    900        -8031.925             0.497            0.051
Chain 1:   1000        -7822.342             0.450            0.051
Chain 1:   1100        -7537.995             0.354            0.043
Chain 1:   1200        -7678.053             0.129            0.038
Chain 1:   1300        -7531.252             0.031            0.027
Chain 1:   1400        -7594.927             0.027            0.019
Chain 1:   1500        -7569.283             0.026            0.019
Chain 1:   1600        -7713.897             0.023            0.019
Chain 1:   1700        -7467.354             0.025            0.019
Chain 1:   1800        -7559.314             0.018            0.019
Chain 1:   1900        -7543.679             0.018            0.019
Chain 1:   2000        -7597.792             0.016            0.018
Chain 1:   2100        -7544.770             0.013            0.012
Chain 1:   2200        -7660.389             0.013            0.012
Chain 1:   2300        -7554.671             0.012            0.012
Chain 1:   2400        -7608.963             0.012            0.012
Chain 1:   2500        -7518.068             0.013            0.012
Chain 1:   2600        -7487.874             0.011            0.012
Chain 1:   2700        -7499.911             0.008            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002975 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86979.583             1.000            1.000
Chain 1:    200       -13495.326             3.223            5.445
Chain 1:    300        -9847.183             2.272            1.000
Chain 1:    400       -10728.924             1.724            1.000
Chain 1:    500        -8808.005             1.423            0.370
Chain 1:    600        -8322.294             1.196            0.370
Chain 1:    700        -8386.231             1.026            0.218
Chain 1:    800        -8985.657             0.906            0.218
Chain 1:    900        -8682.454             0.809            0.082
Chain 1:   1000        -8495.578             0.731            0.082
Chain 1:   1100        -8661.425             0.632            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8411.168             0.091            0.058
Chain 1:   1300        -8514.593             0.055            0.035
Chain 1:   1400        -8529.906             0.047            0.030
Chain 1:   1500        -8405.530             0.027            0.022
Chain 1:   1600        -8522.542             0.022            0.019
Chain 1:   1700        -8607.924             0.022            0.019
Chain 1:   1800        -8193.947             0.021            0.019
Chain 1:   1900        -8290.022             0.019            0.015
Chain 1:   2000        -8263.474             0.017            0.014
Chain 1:   2100        -8386.197             0.016            0.014
Chain 1:   2200        -8206.247             0.015            0.014
Chain 1:   2300        -8284.990             0.015            0.014
Chain 1:   2400        -8354.709             0.016            0.014
Chain 1:   2500        -8300.172             0.015            0.012
Chain 1:   2600        -8299.687             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00347 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412788.092             1.000            1.000
Chain 1:    200     -1585298.652             2.653            4.307
Chain 1:    300      -892139.258             2.028            1.000
Chain 1:    400      -458141.369             1.758            1.000
Chain 1:    500      -358573.522             1.462            0.947
Chain 1:    600      -233354.684             1.308            0.947
Chain 1:    700      -119422.279             1.257            0.947
Chain 1:    800       -86558.237             1.147            0.947
Chain 1:    900       -66863.942             1.053            0.777
Chain 1:   1000       -51629.741             0.977            0.777
Chain 1:   1100       -39080.612             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38251.517             0.480            0.380
Chain 1:   1300       -26188.912             0.449            0.380
Chain 1:   1400       -25904.249             0.355            0.321
Chain 1:   1500       -22486.546             0.343            0.321
Chain 1:   1600       -21701.100             0.293            0.295
Chain 1:   1700       -20573.348             0.203            0.295
Chain 1:   1800       -20516.817             0.165            0.152
Chain 1:   1900       -20842.921             0.137            0.055
Chain 1:   2000       -19353.086             0.115            0.055
Chain 1:   2100       -19591.592             0.084            0.036
Chain 1:   2200       -19818.096             0.083            0.036
Chain 1:   2300       -19435.245             0.039            0.020
Chain 1:   2400       -19207.359             0.039            0.020
Chain 1:   2500       -19009.296             0.025            0.016
Chain 1:   2600       -18639.624             0.024            0.016
Chain 1:   2700       -18596.571             0.018            0.012
Chain 1:   2800       -18313.473             0.020            0.015
Chain 1:   2900       -18594.696             0.020            0.015
Chain 1:   3000       -18580.879             0.012            0.012
Chain 1:   3100       -18665.894             0.011            0.012
Chain 1:   3200       -18356.588             0.012            0.015
Chain 1:   3300       -18561.269             0.011            0.012
Chain 1:   3400       -18036.238             0.013            0.015
Chain 1:   3500       -18648.030             0.015            0.015
Chain 1:   3600       -17954.808             0.017            0.015
Chain 1:   3700       -18341.602             0.019            0.017
Chain 1:   3800       -17301.415             0.023            0.021
Chain 1:   3900       -17297.546             0.022            0.021
Chain 1:   4000       -17414.863             0.022            0.021
Chain 1:   4100       -17328.645             0.022            0.021
Chain 1:   4200       -17144.888             0.022            0.021
Chain 1:   4300       -17283.297             0.021            0.021
Chain 1:   4400       -17240.175             0.019            0.011
Chain 1:   4500       -17142.676             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001306 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48649.731             1.000            1.000
Chain 1:    200       -21080.044             1.154            1.308
Chain 1:    300       -17821.496             0.830            1.000
Chain 1:    400       -20016.633             0.650            1.000
Chain 1:    500       -11098.100             0.681            0.804
Chain 1:    600       -12186.900             0.582            0.804
Chain 1:    700       -15735.138             0.531            0.225
Chain 1:    800       -12776.700             0.494            0.232
Chain 1:    900       -10983.653             0.457            0.225
Chain 1:   1000       -11757.221             0.418            0.225
Chain 1:   1100       -19700.530             0.358            0.225
Chain 1:   1200       -16627.123             0.246            0.185
Chain 1:   1300       -12770.221             0.258            0.225
Chain 1:   1400        -9582.335             0.280            0.232
Chain 1:   1500       -10776.632             0.211            0.225
Chain 1:   1600        -9329.047             0.217            0.225
Chain 1:   1700       -12184.268             0.218            0.232
Chain 1:   1800       -12152.768             0.195            0.185
Chain 1:   1900       -11762.201             0.182            0.185
Chain 1:   2000       -10366.299             0.189            0.185
Chain 1:   2100       -10241.335             0.150            0.155
Chain 1:   2200       -12213.343             0.148            0.155
Chain 1:   2300        -9713.767             0.143            0.155
Chain 1:   2400        -9022.414             0.118            0.135
Chain 1:   2500       -13296.292             0.139            0.155
Chain 1:   2600        -9145.027             0.169            0.161
Chain 1:   2700       -13979.807             0.180            0.161
Chain 1:   2800       -10076.265             0.218            0.257
Chain 1:   2900       -10699.615             0.221            0.257
Chain 1:   3000        -9682.084             0.218            0.257
Chain 1:   3100        -9643.799             0.217            0.257
Chain 1:   3200       -10887.042             0.212            0.257
Chain 1:   3300       -14895.478             0.214            0.269
Chain 1:   3400       -11804.240             0.232            0.269
Chain 1:   3500        -8701.296             0.236            0.269
Chain 1:   3600        -9919.737             0.203            0.262
Chain 1:   3700        -9121.219             0.177            0.123
Chain 1:   3800       -15393.504             0.179            0.123
Chain 1:   3900        -8934.410             0.245            0.262
Chain 1:   4000        -8434.952             0.241            0.262
Chain 1:   4100        -8439.683             0.240            0.262
Chain 1:   4200        -8344.461             0.230            0.262
Chain 1:   4300        -9685.031             0.217            0.138
Chain 1:   4400       -12478.821             0.213            0.138
Chain 1:   4500        -9913.029             0.203            0.138
Chain 1:   4600       -13141.633             0.216            0.224
Chain 1:   4700        -8261.091             0.266            0.246
Chain 1:   4800        -8255.748             0.225            0.224
Chain 1:   4900        -8525.771             0.156            0.138
Chain 1:   5000       -10648.916             0.170            0.199
Chain 1:   5100        -8405.853             0.197            0.224
Chain 1:   5200        -8812.043             0.200            0.224
Chain 1:   5300        -9419.386             0.193            0.224
Chain 1:   5400        -8115.935             0.187            0.199
Chain 1:   5500        -8588.452             0.166            0.161
Chain 1:   5600       -14951.165             0.184            0.161
Chain 1:   5700        -9484.519             0.183            0.161
Chain 1:   5800        -8793.782             0.190            0.161
Chain 1:   5900       -13342.826             0.221            0.199
Chain 1:   6000        -8317.047             0.262            0.267
Chain 1:   6100        -8922.871             0.242            0.161
Chain 1:   6200        -9885.153             0.247            0.161
Chain 1:   6300       -12314.388             0.260            0.197
Chain 1:   6400        -9989.957             0.268            0.233
Chain 1:   6500        -8525.986             0.279            0.233
Chain 1:   6600       -11778.961             0.264            0.233
Chain 1:   6700       -13313.534             0.218            0.197
Chain 1:   6800       -12112.748             0.220            0.197
Chain 1:   6900       -12297.910             0.188            0.172
Chain 1:   7000        -8450.935             0.173            0.172
Chain 1:   7100        -8160.012             0.170            0.172
Chain 1:   7200        -8180.944             0.160            0.172
Chain 1:   7300        -9491.951             0.154            0.138
Chain 1:   7400        -8294.570             0.145            0.138
Chain 1:   7500        -8096.998             0.131            0.115
Chain 1:   7600        -8310.801             0.106            0.099
Chain 1:   7700        -8343.227             0.094            0.036
Chain 1:   7800        -8755.782             0.089            0.036
Chain 1:   7900        -8140.512             0.095            0.047
Chain 1:   8000        -8101.516             0.050            0.036
Chain 1:   8100        -8254.782             0.049            0.026
Chain 1:   8200        -8459.774             0.051            0.026
Chain 1:   8300        -8056.983             0.042            0.026
Chain 1:   8400        -8450.139             0.032            0.026
Chain 1:   8500        -8526.527             0.031            0.026
Chain 1:   8600        -7931.798             0.035            0.047
Chain 1:   8700        -9435.640             0.051            0.047
Chain 1:   8800        -9079.795             0.050            0.047
Chain 1:   8900        -8461.388             0.050            0.047
Chain 1:   9000        -8220.632             0.052            0.047
Chain 1:   9100        -9524.254             0.064            0.050
Chain 1:   9200        -8249.626             0.077            0.073
Chain 1:   9300       -11223.764             0.099            0.075
Chain 1:   9400       -10570.444             0.100            0.075
Chain 1:   9500        -7988.188             0.132            0.137
Chain 1:   9600        -8095.119             0.126            0.137
Chain 1:   9700       -11091.350             0.137            0.137
Chain 1:   9800        -8133.663             0.169            0.155
Chain 1:   9900        -8529.079             0.166            0.155
Chain 1:   10000        -9584.949             0.174            0.155
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001426 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62916.745             1.000            1.000
Chain 1:    200       -17808.866             1.766            2.533
Chain 1:    300        -8612.194             1.534            1.068
Chain 1:    400        -8168.055             1.164            1.068
Chain 1:    500        -8245.612             0.933            1.000
Chain 1:    600        -8824.621             0.788            1.000
Chain 1:    700        -7835.150             0.694            0.126
Chain 1:    800        -8071.479             0.611            0.126
Chain 1:    900        -7932.604             0.545            0.066
Chain 1:   1000        -7848.639             0.491            0.066
Chain 1:   1100        -7707.312             0.393            0.054
Chain 1:   1200        -7581.448             0.142            0.029
Chain 1:   1300        -7726.531             0.037            0.019
Chain 1:   1400        -7702.871             0.032            0.018
Chain 1:   1500        -7612.471             0.032            0.018
Chain 1:   1600        -7537.683             0.026            0.018
Chain 1:   1700        -7519.419             0.014            0.017
Chain 1:   1800        -7526.988             0.011            0.012
Chain 1:   1900        -7613.297             0.010            0.011
Chain 1:   2000        -7611.326             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003273 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86403.563             1.000            1.000
Chain 1:    200       -13108.966             3.296            5.591
Chain 1:    300        -9582.747             2.320            1.000
Chain 1:    400       -10481.038             1.761            1.000
Chain 1:    500        -8473.252             1.456            0.368
Chain 1:    600        -8296.450             1.217            0.368
Chain 1:    700        -8459.794             1.046            0.237
Chain 1:    800        -8702.486             0.919            0.237
Chain 1:    900        -8432.453             0.820            0.086
Chain 1:   1000        -8166.914             0.741            0.086
Chain 1:   1100        -8398.810             0.644            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8130.774             0.088            0.033
Chain 1:   1300        -8166.695             0.052            0.032
Chain 1:   1400        -8270.433             0.045            0.028
Chain 1:   1500        -8201.329             0.022            0.028
Chain 1:   1600        -8207.861             0.020            0.028
Chain 1:   1700        -8144.905             0.019            0.028
Chain 1:   1800        -8025.527             0.017            0.015
Chain 1:   1900        -8141.456             0.016            0.014
Chain 1:   2000        -8101.452             0.013            0.013
Chain 1:   2100        -8239.239             0.012            0.013
Chain 1:   2200        -8025.699             0.011            0.013
Chain 1:   2300        -8167.054             0.012            0.014
Chain 1:   2400        -8175.109             0.011            0.014
Chain 1:   2500        -8144.443             0.011            0.014
Chain 1:   2600        -8139.277             0.011            0.014
Chain 1:   2700        -8049.844             0.011            0.014
Chain 1:   2800        -8030.542             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003359 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8435595.773             1.000            1.000
Chain 1:    200     -1591708.083             2.650            4.300
Chain 1:    300      -890916.478             2.029            1.000
Chain 1:    400      -456815.273             1.759            1.000
Chain 1:    500      -356796.463             1.463            0.950
Chain 1:    600      -231747.611             1.309            0.950
Chain 1:    700      -118376.360             1.259            0.950
Chain 1:    800       -85672.134             1.149            0.950
Chain 1:    900       -66100.916             1.055            0.787
Chain 1:   1000       -50962.768             0.979            0.787
Chain 1:   1100       -38502.514             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37680.184             0.483            0.382
Chain 1:   1300       -25714.736             0.451            0.382
Chain 1:   1400       -25437.120             0.357            0.324
Chain 1:   1500       -22045.246             0.345            0.324
Chain 1:   1600       -21266.625             0.294            0.297
Chain 1:   1700       -20150.769             0.204            0.296
Chain 1:   1800       -20096.768             0.166            0.154
Chain 1:   1900       -20422.275             0.138            0.055
Chain 1:   2000       -18939.983             0.116            0.055
Chain 1:   2100       -19177.986             0.085            0.037
Chain 1:   2200       -19403.103             0.084            0.037
Chain 1:   2300       -19021.650             0.040            0.020
Chain 1:   2400       -18794.107             0.040            0.020
Chain 1:   2500       -18595.735             0.026            0.016
Chain 1:   2600       -18227.066             0.024            0.016
Chain 1:   2700       -18184.350             0.019            0.012
Chain 1:   2800       -17901.365             0.020            0.016
Chain 1:   2900       -18182.172             0.020            0.015
Chain 1:   3000       -18168.508             0.012            0.012
Chain 1:   3100       -18253.372             0.011            0.012
Chain 1:   3200       -17944.616             0.012            0.015
Chain 1:   3300       -18148.884             0.011            0.012
Chain 1:   3400       -17624.671             0.013            0.015
Chain 1:   3500       -18235.126             0.015            0.016
Chain 1:   3600       -17543.636             0.017            0.016
Chain 1:   3700       -17929.025             0.019            0.017
Chain 1:   3800       -16891.482             0.023            0.021
Chain 1:   3900       -16887.644             0.022            0.021
Chain 1:   4000       -17004.991             0.023            0.021
Chain 1:   4100       -16918.879             0.023            0.021
Chain 1:   4200       -16735.723             0.022            0.021
Chain 1:   4300       -16873.742             0.022            0.021
Chain 1:   4400       -16831.067             0.019            0.011
Chain 1:   4500       -16733.643             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12350.934             1.000            1.000
Chain 1:    200        -9291.762             0.665            1.000
Chain 1:    300        -8054.807             0.494            0.329
Chain 1:    400        -8272.256             0.377            0.329
Chain 1:    500        -8145.030             0.305            0.154
Chain 1:    600        -7997.829             0.257            0.154
Chain 1:    700        -7937.052             0.222            0.026
Chain 1:    800        -7925.787             0.194            0.026
Chain 1:    900        -8012.160             0.174            0.018
Chain 1:   1000        -7984.018             0.157            0.018
Chain 1:   1100        -8036.784             0.057            0.016
Chain 1:   1200        -7940.053             0.026            0.012
Chain 1:   1300        -7907.590             0.011            0.011
Chain 1:   1400        -7931.040             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61892.378             1.000            1.000
Chain 1:    200       -17926.354             1.726            2.453
Chain 1:    300        -8865.306             1.492            1.022
Chain 1:    400        -9437.682             1.134            1.022
Chain 1:    500        -8740.068             0.923            1.000
Chain 1:    600        -8784.249             0.770            1.000
Chain 1:    700        -8235.821             0.670            0.080
Chain 1:    800        -8263.247             0.586            0.080
Chain 1:    900        -7960.301             0.525            0.067
Chain 1:   1000        -7625.808             0.477            0.067
Chain 1:   1100        -7626.825             0.377            0.061
Chain 1:   1200        -7636.184             0.132            0.044
Chain 1:   1300        -7687.514             0.031            0.038
Chain 1:   1400        -7928.829             0.028            0.030
Chain 1:   1500        -7613.495             0.024            0.030
Chain 1:   1600        -7773.223             0.025            0.030
Chain 1:   1700        -7503.012             0.022            0.030
Chain 1:   1800        -7568.688             0.023            0.030
Chain 1:   1900        -7593.611             0.019            0.021
Chain 1:   2000        -7631.901             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00314 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86238.468             1.000            1.000
Chain 1:    200       -13503.848             3.193            5.386
Chain 1:    300        -9885.307             2.251            1.000
Chain 1:    400       -10797.085             1.709            1.000
Chain 1:    500        -8854.285             1.411            0.366
Chain 1:    600        -8339.113             1.186            0.366
Chain 1:    700        -8428.284             1.018            0.219
Chain 1:    800        -9176.612             0.901            0.219
Chain 1:    900        -8744.398             0.807            0.084
Chain 1:   1000        -8367.511             0.730            0.084
Chain 1:   1100        -8731.772             0.635            0.082   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8383.281             0.100            0.062
Chain 1:   1300        -8596.732             0.066            0.049
Chain 1:   1400        -8603.023             0.058            0.045
Chain 1:   1500        -8461.345             0.037            0.042
Chain 1:   1600        -8573.327             0.033            0.042
Chain 1:   1700        -8660.134             0.032            0.042
Chain 1:   1800        -8253.336             0.029            0.042
Chain 1:   1900        -8349.521             0.025            0.025
Chain 1:   2000        -8321.814             0.021            0.017
Chain 1:   2100        -8442.560             0.019            0.014
Chain 1:   2200        -8261.744             0.017            0.014
Chain 1:   2300        -8388.894             0.016            0.014
Chain 1:   2400        -8399.399             0.016            0.014
Chain 1:   2500        -8361.430             0.014            0.013
Chain 1:   2600        -8360.257             0.013            0.012
Chain 1:   2700        -8275.107             0.013            0.012
Chain 1:   2800        -8239.974             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003221 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403619.041             1.000            1.000
Chain 1:    200     -1584749.415             2.651            4.303
Chain 1:    300      -892145.973             2.026            1.000
Chain 1:    400      -458735.803             1.756            1.000
Chain 1:    500      -359099.309             1.460            0.945
Chain 1:    600      -233768.599             1.306            0.945
Chain 1:    700      -119581.326             1.256            0.945
Chain 1:    800       -86695.587             1.146            0.945
Chain 1:    900       -66962.420             1.052            0.776
Chain 1:   1000       -51700.949             0.976            0.776
Chain 1:   1100       -39130.287             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38296.039             0.480            0.379
Chain 1:   1300       -26211.787             0.449            0.379
Chain 1:   1400       -25925.212             0.355            0.321
Chain 1:   1500       -22502.918             0.343            0.321
Chain 1:   1600       -21715.964             0.293            0.295
Chain 1:   1700       -20585.374             0.203            0.295
Chain 1:   1800       -20528.219             0.165            0.152
Chain 1:   1900       -20854.152             0.137            0.055
Chain 1:   2000       -19363.349             0.115            0.055
Chain 1:   2100       -19601.750             0.084            0.036
Chain 1:   2200       -19828.559             0.083            0.036
Chain 1:   2300       -19445.505             0.039            0.020
Chain 1:   2400       -19217.619             0.039            0.020
Chain 1:   2500       -19019.808             0.025            0.016
Chain 1:   2600       -18650.039             0.024            0.016
Chain 1:   2700       -18606.912             0.018            0.012
Chain 1:   2800       -18323.990             0.020            0.015
Chain 1:   2900       -18605.181             0.020            0.015
Chain 1:   3000       -18591.313             0.012            0.012
Chain 1:   3100       -18676.322             0.011            0.012
Chain 1:   3200       -18367.052             0.012            0.015
Chain 1:   3300       -18571.691             0.011            0.012
Chain 1:   3400       -18046.828             0.013            0.015
Chain 1:   3500       -18658.479             0.015            0.015
Chain 1:   3600       -17965.427             0.017            0.015
Chain 1:   3700       -18352.103             0.019            0.017
Chain 1:   3800       -17312.287             0.023            0.021
Chain 1:   3900       -17308.453             0.021            0.021
Chain 1:   4000       -17425.728             0.022            0.021
Chain 1:   4100       -17339.588             0.022            0.021
Chain 1:   4200       -17155.855             0.022            0.021
Chain 1:   4300       -17294.196             0.021            0.021
Chain 1:   4400       -17251.107             0.019            0.011
Chain 1:   4500       -17153.671             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00131 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48436.886             1.000            1.000
Chain 1:    200       -14863.210             1.629            2.259
Chain 1:    300       -15572.379             1.101            1.000
Chain 1:    400       -11288.231             0.921            1.000
Chain 1:    500       -12118.972             0.750            0.380
Chain 1:    600       -14646.623             0.654            0.380
Chain 1:    700       -12001.302             0.592            0.220
Chain 1:    800       -14285.291             0.538            0.220
Chain 1:    900       -12430.590             0.495            0.173
Chain 1:   1000       -12849.041             0.449            0.173
Chain 1:   1100       -17372.430             0.375            0.173
Chain 1:   1200       -23556.728             0.175            0.173
Chain 1:   1300       -12644.979             0.257            0.220
Chain 1:   1400       -12672.805             0.219            0.173
Chain 1:   1500       -16110.339             0.234            0.213
Chain 1:   1600       -11720.161             0.254            0.220
Chain 1:   1700       -11496.881             0.234            0.213
Chain 1:   1800       -10016.026             0.233            0.213
Chain 1:   1900       -10437.526             0.222            0.213
Chain 1:   2000       -11537.533             0.228            0.213
Chain 1:   2100        -9423.482             0.224            0.213
Chain 1:   2200        -9281.983             0.200            0.148
Chain 1:   2300        -9169.058             0.115            0.095
Chain 1:   2400        -9358.267             0.116            0.095
Chain 1:   2500       -11686.838             0.115            0.095
Chain 1:   2600        -9028.731             0.107            0.095
Chain 1:   2700        -9743.068             0.112            0.095
Chain 1:   2800       -10901.585             0.108            0.095
Chain 1:   2900        -9121.566             0.124            0.106
Chain 1:   3000        -8774.949             0.118            0.106
Chain 1:   3100        -8999.927             0.098            0.073
Chain 1:   3200        -8635.371             0.101            0.073
Chain 1:   3300       -11293.069             0.123            0.106
Chain 1:   3400       -10725.236             0.126            0.106
Chain 1:   3500       -10087.670             0.113            0.073
Chain 1:   3600        -9106.817             0.094            0.073
Chain 1:   3700        -8649.263             0.092            0.063
Chain 1:   3800        -8575.207             0.082            0.053
Chain 1:   3900       -10349.646             0.080            0.053
Chain 1:   4000        -8950.265             0.092            0.063
Chain 1:   4100       -11225.337             0.109            0.108
Chain 1:   4200        -8567.852             0.136            0.156
Chain 1:   4300        -9458.508             0.122            0.108
Chain 1:   4400       -10014.702             0.122            0.108
Chain 1:   4500        -8694.946             0.131            0.152
Chain 1:   4600        -9976.171             0.133            0.152
Chain 1:   4700        -8493.540             0.145            0.156
Chain 1:   4800       -11720.649             0.172            0.171
Chain 1:   4900       -12515.400             0.161            0.156
Chain 1:   5000        -9397.394             0.179            0.175
Chain 1:   5100        -8659.368             0.167            0.152
Chain 1:   5200        -8878.002             0.138            0.128
Chain 1:   5300       -10666.123             0.146            0.152
Chain 1:   5400       -15410.262             0.171            0.168
Chain 1:   5500        -9784.649             0.213            0.175
Chain 1:   5600        -8924.522             0.210            0.175
Chain 1:   5700       -11822.506             0.217            0.245
Chain 1:   5800        -8827.091             0.224            0.245
Chain 1:   5900       -11090.014             0.238            0.245
Chain 1:   6000        -9258.508             0.224            0.204
Chain 1:   6100       -11828.407             0.238            0.217
Chain 1:   6200        -8089.112             0.281            0.245
Chain 1:   6300        -9995.336             0.284            0.245
Chain 1:   6400        -8810.166             0.266            0.217
Chain 1:   6500        -8562.307             0.212            0.204
Chain 1:   6600       -10195.406             0.218            0.204
Chain 1:   6700        -8233.651             0.217            0.204
Chain 1:   6800        -9286.326             0.195            0.198
Chain 1:   6900       -11813.996             0.196            0.198
Chain 1:   7000       -12265.085             0.180            0.191
Chain 1:   7100       -13888.137             0.170            0.160
Chain 1:   7200        -8329.576             0.190            0.160
Chain 1:   7300        -8139.559             0.173            0.135
Chain 1:   7400       -11610.720             0.190            0.160
Chain 1:   7500       -11133.369             0.191            0.160
Chain 1:   7600        -8641.238             0.204            0.214
Chain 1:   7700        -8945.093             0.184            0.117
Chain 1:   7800        -8595.687             0.176            0.117
Chain 1:   7900        -8037.136             0.162            0.069
Chain 1:   8000       -11647.374             0.189            0.117
Chain 1:   8100        -8019.903             0.223            0.288
Chain 1:   8200        -8647.644             0.163            0.073
Chain 1:   8300        -8103.298             0.168            0.073
Chain 1:   8400       -10795.225             0.163            0.073
Chain 1:   8500       -11334.517             0.163            0.073
Chain 1:   8600        -8151.386             0.173            0.073
Chain 1:   8700        -8013.656             0.172            0.073
Chain 1:   8800        -8884.822             0.177            0.098
Chain 1:   8900        -8270.742             0.178            0.098
Chain 1:   9000       -10013.086             0.164            0.098
Chain 1:   9100        -8149.792             0.142            0.098
Chain 1:   9200        -9526.437             0.149            0.145
Chain 1:   9300        -9486.906             0.143            0.145
Chain 1:   9400        -9335.417             0.120            0.098
Chain 1:   9500        -8119.455             0.130            0.145
Chain 1:   9600        -8318.339             0.093            0.098
Chain 1:   9700       -10476.176             0.112            0.145
Chain 1:   9800       -10311.638             0.104            0.145
Chain 1:   9900        -8213.888             0.122            0.150
Chain 1:   10000        -8217.968             0.105            0.145
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00143 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45802.495             1.000            1.000
Chain 1:    200       -15274.252             1.499            1.999
Chain 1:    300        -8550.256             1.262            1.000
Chain 1:    400        -8530.689             0.947            1.000
Chain 1:    500        -8262.794             0.764            0.786
Chain 1:    600        -8691.551             0.645            0.786
Chain 1:    700        -7927.399             0.567            0.096
Chain 1:    800        -8010.427             0.497            0.096
Chain 1:    900        -8149.328             0.444            0.049
Chain 1:   1000        -7700.432             0.405            0.058
Chain 1:   1100        -7664.804             0.306            0.049
Chain 1:   1200        -7519.674             0.108            0.032
Chain 1:   1300        -7666.937             0.031            0.019
Chain 1:   1400        -7739.592             0.032            0.019
Chain 1:   1500        -7553.072             0.031            0.019
Chain 1:   1600        -7709.074             0.028            0.019
Chain 1:   1700        -7439.733             0.022            0.019
Chain 1:   1800        -7539.512             0.022            0.019
Chain 1:   1900        -7561.697             0.021            0.019
Chain 1:   2000        -7534.037             0.015            0.019
Chain 1:   2100        -7585.556             0.016            0.019
Chain 1:   2200        -7610.925             0.014            0.013
Chain 1:   2300        -7516.028             0.013            0.013
Chain 1:   2400        -7570.544             0.013            0.013
Chain 1:   2500        -7410.140             0.013            0.013
Chain 1:   2600        -7477.560             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003092 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86150.487             1.000            1.000
Chain 1:    200       -13255.340             3.250            5.499
Chain 1:    300        -9639.204             2.291            1.000
Chain 1:    400       -10336.804             1.735            1.000
Chain 1:    500        -8602.553             1.429            0.375
Chain 1:    600        -8139.824             1.200            0.375
Chain 1:    700        -8440.410             1.034            0.202
Chain 1:    800        -9019.965             0.913            0.202
Chain 1:    900        -8423.043             0.819            0.071
Chain 1:   1000        -8191.671             0.740            0.071
Chain 1:   1100        -8324.270             0.642            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8122.250             0.094            0.064
Chain 1:   1300        -8327.367             0.059            0.057
Chain 1:   1400        -8329.607             0.052            0.036
Chain 1:   1500        -8224.150             0.033            0.028
Chain 1:   1600        -8327.123             0.029            0.025
Chain 1:   1700        -8415.231             0.026            0.025
Chain 1:   1800        -8007.288             0.025            0.025
Chain 1:   1900        -8103.928             0.019            0.016
Chain 1:   2000        -8076.090             0.017            0.013
Chain 1:   2100        -8196.898             0.017            0.013
Chain 1:   2200        -8014.387             0.016            0.013
Chain 1:   2300        -8143.502             0.016            0.013
Chain 1:   2400        -8153.590             0.016            0.013
Chain 1:   2500        -8115.811             0.015            0.012
Chain 1:   2600        -8114.559             0.014            0.012
Chain 1:   2700        -8029.459             0.014            0.012
Chain 1:   2800        -7994.279             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003419 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386132.733             1.000            1.000
Chain 1:    200     -1583558.541             2.648            4.296
Chain 1:    300      -891319.954             2.024            1.000
Chain 1:    400      -457121.137             1.756            1.000
Chain 1:    500      -357589.937             1.460            0.950
Chain 1:    600      -232717.563             1.306            0.950
Chain 1:    700      -119048.403             1.256            0.950
Chain 1:    800       -86195.492             1.147            0.950
Chain 1:    900       -66555.366             1.052            0.777
Chain 1:   1000       -51344.860             0.976            0.777
Chain 1:   1100       -38809.915             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37986.496             0.481            0.381
Chain 1:   1300       -25940.866             0.450            0.381
Chain 1:   1400       -25657.995             0.356            0.323
Chain 1:   1500       -22243.348             0.344            0.323
Chain 1:   1600       -21458.543             0.294            0.296
Chain 1:   1700       -20333.005             0.204            0.295
Chain 1:   1800       -20277.070             0.166            0.154
Chain 1:   1900       -20603.055             0.138            0.055
Chain 1:   2000       -19114.329             0.116            0.055
Chain 1:   2100       -19352.944             0.085            0.037
Chain 1:   2200       -19579.023             0.084            0.037
Chain 1:   2300       -19196.574             0.040            0.020
Chain 1:   2400       -18968.755             0.040            0.020
Chain 1:   2500       -18770.469             0.025            0.016
Chain 1:   2600       -18401.110             0.024            0.016
Chain 1:   2700       -18358.212             0.019            0.012
Chain 1:   2800       -18075.002             0.020            0.016
Chain 1:   2900       -18356.180             0.020            0.015
Chain 1:   3000       -18342.498             0.012            0.012
Chain 1:   3100       -18427.418             0.011            0.012
Chain 1:   3200       -18118.259             0.012            0.015
Chain 1:   3300       -18322.861             0.011            0.012
Chain 1:   3400       -17797.934             0.013            0.015
Chain 1:   3500       -18409.490             0.015            0.016
Chain 1:   3600       -17716.660             0.017            0.016
Chain 1:   3700       -18103.089             0.019            0.017
Chain 1:   3800       -17063.416             0.023            0.021
Chain 1:   3900       -17059.560             0.022            0.021
Chain 1:   4000       -17176.901             0.022            0.021
Chain 1:   4100       -17090.623             0.022            0.021
Chain 1:   4200       -16907.070             0.022            0.021
Chain 1:   4300       -17045.388             0.022            0.021
Chain 1:   4400       -17002.363             0.019            0.011
Chain 1:   4500       -16904.875             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001162 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12237.843             1.000            1.000
Chain 1:    200        -9078.553             0.674            1.000
Chain 1:    300        -7913.618             0.498            0.348
Chain 1:    400        -8108.293             0.380            0.348
Chain 1:    500        -8145.983             0.305            0.147
Chain 1:    600        -7987.452             0.257            0.147
Chain 1:    700        -7794.156             0.224            0.025
Chain 1:    800        -7799.036             0.196            0.025
Chain 1:    900        -7759.497             0.175            0.024
Chain 1:   1000        -7806.603             0.158            0.024
Chain 1:   1100        -8026.693             0.061            0.024
Chain 1:   1200        -7801.055             0.029            0.024
Chain 1:   1300        -7742.890             0.015            0.020
Chain 1:   1400        -7772.350             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56746.849             1.000            1.000
Chain 1:    200       -17329.290             1.637            2.275
Chain 1:    300        -8641.133             1.427            1.005
Chain 1:    400        -8421.846             1.077            1.005
Chain 1:    500        -8773.519             0.869            1.000
Chain 1:    600        -8475.977             0.730            1.000
Chain 1:    700        -7753.566             0.639            0.093
Chain 1:    800        -7974.884             0.563            0.093
Chain 1:    900        -7940.080             0.501            0.040
Chain 1:   1000        -8099.155             0.453            0.040
Chain 1:   1100        -7590.837             0.359            0.040
Chain 1:   1200        -7593.480             0.132            0.035
Chain 1:   1300        -7640.884             0.032            0.028
Chain 1:   1400        -7796.773             0.031            0.028
Chain 1:   1500        -7620.846             0.030            0.023
Chain 1:   1600        -7772.445             0.028            0.020
Chain 1:   1700        -7452.780             0.023            0.020
Chain 1:   1800        -7564.420             0.022            0.020
Chain 1:   1900        -7624.789             0.022            0.020
Chain 1:   2000        -7621.867             0.020            0.020
Chain 1:   2100        -7579.556             0.014            0.015
Chain 1:   2200        -7689.942             0.015            0.015
Chain 1:   2300        -7590.724             0.016            0.015
Chain 1:   2400        -7629.995             0.015            0.014
Chain 1:   2500        -7569.145             0.013            0.013
Chain 1:   2600        -7509.210             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002743 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85337.842             1.000            1.000
Chain 1:    200       -13388.912             3.187            5.374
Chain 1:    300        -9765.424             2.248            1.000
Chain 1:    400       -10624.877             1.706            1.000
Chain 1:    500        -8581.885             1.413            0.371
Chain 1:    600        -8207.557             1.185            0.371
Chain 1:    700        -8672.942             1.023            0.238
Chain 1:    800        -8798.975             0.897            0.238
Chain 1:    900        -8599.054             0.800            0.081
Chain 1:   1000        -8234.768             0.724            0.081
Chain 1:   1100        -8575.329             0.628            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8319.170             0.094            0.046
Chain 1:   1300        -8449.431             0.059            0.044
Chain 1:   1400        -8487.585             0.051            0.040
Chain 1:   1500        -8320.387             0.029            0.031
Chain 1:   1600        -8439.831             0.026            0.023
Chain 1:   1700        -8520.633             0.022            0.020
Chain 1:   1800        -8107.898             0.025            0.023
Chain 1:   1900        -8203.682             0.024            0.020
Chain 1:   2000        -8176.962             0.020            0.015
Chain 1:   2100        -8299.652             0.018            0.015
Chain 1:   2200        -8119.658             0.017            0.015
Chain 1:   2300        -8198.784             0.016            0.014
Chain 1:   2400        -8268.426             0.016            0.014
Chain 1:   2500        -8213.790             0.015            0.012
Chain 1:   2600        -8213.178             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003532 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8364924.017             1.000            1.000
Chain 1:    200     -1579127.500             2.649            4.297
Chain 1:    300      -890821.386             2.023            1.000
Chain 1:    400      -457995.555             1.754            1.000
Chain 1:    500      -358722.965             1.458            0.945
Chain 1:    600      -233702.959             1.304            0.945
Chain 1:    700      -119540.515             1.255            0.945
Chain 1:    800       -86655.462             1.145            0.945
Chain 1:    900       -66917.838             1.051            0.773
Chain 1:   1000       -51650.709             0.975            0.773
Chain 1:   1100       -39065.166             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38236.081             0.480            0.379
Chain 1:   1300       -26126.329             0.449            0.379
Chain 1:   1400       -25839.886             0.356            0.322
Chain 1:   1500       -22409.979             0.343            0.322
Chain 1:   1600       -21621.492             0.293            0.296
Chain 1:   1700       -20487.095             0.203            0.295
Chain 1:   1800       -20429.519             0.166            0.153
Chain 1:   1900       -20755.610             0.138            0.055
Chain 1:   2000       -19262.766             0.116            0.055
Chain 1:   2100       -19501.286             0.085            0.036
Chain 1:   2200       -19728.407             0.084            0.036
Chain 1:   2300       -19345.066             0.040            0.020
Chain 1:   2400       -19117.070             0.040            0.020
Chain 1:   2500       -18919.445             0.025            0.016
Chain 1:   2600       -18549.285             0.024            0.016
Chain 1:   2700       -18506.224             0.018            0.012
Chain 1:   2800       -18223.184             0.020            0.016
Chain 1:   2900       -18504.593             0.020            0.015
Chain 1:   3000       -18490.682             0.012            0.012
Chain 1:   3100       -18575.637             0.011            0.012
Chain 1:   3200       -18266.294             0.012            0.015
Chain 1:   3300       -18471.093             0.011            0.012
Chain 1:   3400       -17946.027             0.013            0.015
Chain 1:   3500       -18557.971             0.015            0.016
Chain 1:   3600       -17864.683             0.017            0.016
Chain 1:   3700       -18251.468             0.019            0.017
Chain 1:   3800       -17211.243             0.023            0.021
Chain 1:   3900       -17207.468             0.022            0.021
Chain 1:   4000       -17324.714             0.022            0.021
Chain 1:   4100       -17238.434             0.022            0.021
Chain 1:   4200       -17054.762             0.022            0.021
Chain 1:   4300       -17193.064             0.021            0.021
Chain 1:   4400       -17149.890             0.019            0.011
Chain 1:   4500       -17052.493             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12683.616             1.000            1.000
Chain 1:    200        -9572.323             0.663            1.000
Chain 1:    300        -8179.246             0.498            0.325
Chain 1:    400        -8285.553             0.377            0.325
Chain 1:    500        -8254.799             0.302            0.170
Chain 1:    600        -8101.957             0.255            0.170
Chain 1:    700        -8083.011             0.219            0.019
Chain 1:    800        -8036.873             0.192            0.019
Chain 1:    900        -8002.409             0.171            0.013
Chain 1:   1000        -8078.959             0.155            0.013
Chain 1:   1100        -8068.219             0.055            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001433 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46220.201             1.000            1.000
Chain 1:    200       -15715.730             1.471            1.941
Chain 1:    300        -8820.046             1.241            1.000
Chain 1:    400        -8774.663             0.932            1.000
Chain 1:    500        -8369.218             0.755            0.782
Chain 1:    600        -8733.356             0.636            0.782
Chain 1:    700        -7970.711             0.559            0.096
Chain 1:    800        -7738.264             0.493            0.096
Chain 1:    900        -8093.185             0.443            0.048
Chain 1:   1000        -7964.431             0.400            0.048
Chain 1:   1100        -7954.385             0.301            0.044
Chain 1:   1200        -7842.567             0.108            0.042
Chain 1:   1300        -7846.000             0.030            0.030
Chain 1:   1400        -7893.521             0.030            0.030
Chain 1:   1500        -7677.396             0.028            0.028
Chain 1:   1600        -7740.078             0.024            0.016
Chain 1:   1700        -7629.492             0.016            0.014
Chain 1:   1800        -7683.633             0.014            0.014
Chain 1:   1900        -7703.374             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003219 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86324.364             1.000            1.000
Chain 1:    200       -13660.662             3.160            5.319
Chain 1:    300       -10013.734             2.228            1.000
Chain 1:    400       -10722.439             1.687            1.000
Chain 1:    500        -8995.719             1.388            0.364
Chain 1:    600        -8434.855             1.168            0.364
Chain 1:    700        -8570.489             1.003            0.192
Chain 1:    800        -9487.864             0.890            0.192
Chain 1:    900        -8813.488             0.800            0.097
Chain 1:   1000        -8571.907             0.723            0.097
Chain 1:   1100        -8838.239             0.626            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8471.489             0.098            0.066
Chain 1:   1300        -8596.015             0.063            0.066
Chain 1:   1400        -8682.222             0.057            0.043
Chain 1:   1500        -8572.029             0.039            0.030
Chain 1:   1600        -8676.473             0.034            0.028
Chain 1:   1700        -8764.978             0.033            0.028
Chain 1:   1800        -8347.611             0.029            0.028
Chain 1:   1900        -8445.618             0.022            0.014
Chain 1:   2000        -8419.277             0.020            0.013
Chain 1:   2100        -8542.844             0.018            0.013
Chain 1:   2200        -8359.657             0.016            0.013
Chain 1:   2300        -8440.122             0.016            0.012
Chain 1:   2400        -8509.769             0.015            0.012
Chain 1:   2500        -8455.606             0.015            0.012
Chain 1:   2600        -8455.656             0.014            0.010
Chain 1:   2700        -8372.880             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003249 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8376683.139             1.000            1.000
Chain 1:    200     -1577882.727             2.654            4.309
Chain 1:    300      -890838.029             2.027            1.000
Chain 1:    400      -457919.859             1.756            1.000
Chain 1:    500      -358917.851             1.460            0.945
Chain 1:    600      -233796.907             1.306            0.945
Chain 1:    700      -119775.303             1.255            0.945
Chain 1:    800       -86888.751             1.146            0.945
Chain 1:    900       -67158.690             1.051            0.771
Chain 1:   1000       -51895.907             0.975            0.771
Chain 1:   1100       -39317.716             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38486.452             0.479            0.378
Chain 1:   1300       -26388.244             0.447            0.378
Chain 1:   1400       -26100.682             0.354            0.320
Chain 1:   1500       -22673.751             0.342            0.320
Chain 1:   1600       -21885.903             0.292            0.294
Chain 1:   1700       -20753.408             0.202            0.294
Chain 1:   1800       -20696.050             0.164            0.151
Chain 1:   1900       -21022.177             0.137            0.055
Chain 1:   2000       -19529.996             0.115            0.055
Chain 1:   2100       -19768.520             0.084            0.036
Chain 1:   2200       -19995.495             0.083            0.036
Chain 1:   2300       -19612.244             0.039            0.020
Chain 1:   2400       -19384.283             0.039            0.020
Chain 1:   2500       -19186.448             0.025            0.016
Chain 1:   2600       -18816.443             0.023            0.016
Chain 1:   2700       -18773.378             0.018            0.012
Chain 1:   2800       -18490.321             0.019            0.015
Chain 1:   2900       -18771.631             0.019            0.015
Chain 1:   3000       -18757.739             0.012            0.012
Chain 1:   3100       -18842.762             0.011            0.012
Chain 1:   3200       -18533.389             0.012            0.015
Chain 1:   3300       -18738.160             0.011            0.012
Chain 1:   3400       -18213.062             0.012            0.015
Chain 1:   3500       -18825.037             0.015            0.015
Chain 1:   3600       -18131.618             0.017            0.015
Chain 1:   3700       -18518.552             0.018            0.017
Chain 1:   3800       -17478.126             0.023            0.021
Chain 1:   3900       -17474.316             0.021            0.021
Chain 1:   4000       -17591.570             0.022            0.021
Chain 1:   4100       -17505.328             0.022            0.021
Chain 1:   4200       -17321.574             0.021            0.021
Chain 1:   4300       -17459.969             0.021            0.021
Chain 1:   4400       -17416.791             0.018            0.011
Chain 1:   4500       -17319.325             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001583 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12662.972             1.000            1.000
Chain 1:    200        -9590.488             0.660            1.000
Chain 1:    300        -8297.858             0.492            0.320
Chain 1:    400        -8437.352             0.373            0.320
Chain 1:    500        -8368.360             0.300            0.156
Chain 1:    600        -8211.338             0.253            0.156
Chain 1:    700        -8140.330             0.218            0.019
Chain 1:    800        -8100.580             0.192            0.019
Chain 1:    900        -8062.559             0.171            0.017
Chain 1:   1000        -8236.482             0.156            0.019
Chain 1:   1100        -8245.938             0.056            0.017
Chain 1:   1200        -8127.823             0.025            0.015
Chain 1:   1300        -8099.874             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001464 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58305.827             1.000            1.000
Chain 1:    200       -18027.627             1.617            2.234
Chain 1:    300        -8812.521             1.427            1.046
Chain 1:    400        -8154.098             1.090            1.046
Chain 1:    500        -8820.528             0.887            1.000
Chain 1:    600        -8742.392             0.741            1.000
Chain 1:    700        -7986.552             0.649            0.095
Chain 1:    800        -8078.658             0.569            0.095
Chain 1:    900        -7827.879             0.509            0.081
Chain 1:   1000        -7720.550             0.460            0.081
Chain 1:   1100        -7858.093             0.361            0.076
Chain 1:   1200        -7575.816             0.142            0.037
Chain 1:   1300        -7555.209             0.037            0.032
Chain 1:   1400        -8140.282             0.037            0.032
Chain 1:   1500        -7606.374             0.036            0.032
Chain 1:   1600        -7781.172             0.037            0.032
Chain 1:   1700        -7515.966             0.031            0.032
Chain 1:   1800        -7654.508             0.032            0.032
Chain 1:   1900        -7673.978             0.029            0.022
Chain 1:   2000        -7686.417             0.028            0.022
Chain 1:   2100        -7506.511             0.029            0.024
Chain 1:   2200        -7797.490             0.029            0.024
Chain 1:   2300        -7528.368             0.032            0.035
Chain 1:   2400        -7550.587             0.025            0.024
Chain 1:   2500        -7584.989             0.018            0.022
Chain 1:   2600        -7541.961             0.017            0.018
Chain 1:   2700        -7542.453             0.013            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002772 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86795.293             1.000            1.000
Chain 1:    200       -13796.141             3.146            5.291
Chain 1:    300       -10139.803             2.217            1.000
Chain 1:    400       -11069.637             1.684            1.000
Chain 1:    500        -9121.242             1.390            0.361
Chain 1:    600        -9036.753             1.160            0.361
Chain 1:    700        -8698.468             1.000            0.214
Chain 1:    800        -8894.249             0.877            0.214
Chain 1:    900        -8915.848             0.780            0.084
Chain 1:   1000        -8590.778             0.706            0.084
Chain 1:   1100        -8934.963             0.610            0.039   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8550.782             0.085            0.039
Chain 1:   1300        -8833.841             0.052            0.039
Chain 1:   1400        -8812.582             0.044            0.038
Chain 1:   1500        -8670.054             0.024            0.032
Chain 1:   1600        -8791.939             0.025            0.032
Chain 1:   1700        -8869.827             0.022            0.022
Chain 1:   1800        -8445.375             0.025            0.032
Chain 1:   1900        -8546.588             0.026            0.032
Chain 1:   2000        -8521.141             0.022            0.016
Chain 1:   2100        -8646.675             0.020            0.015
Chain 1:   2200        -8449.608             0.018            0.015
Chain 1:   2300        -8541.400             0.016            0.014
Chain 1:   2400        -8610.193             0.016            0.014
Chain 1:   2500        -8556.446             0.015            0.012
Chain 1:   2600        -8557.816             0.014            0.011
Chain 1:   2700        -8474.526             0.014            0.011
Chain 1:   2800        -8434.403             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003164 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416980.466             1.000            1.000
Chain 1:    200     -1586550.662             2.653            4.305
Chain 1:    300      -890821.338             2.029            1.000
Chain 1:    400      -458098.882             1.758            1.000
Chain 1:    500      -358416.395             1.462            0.945
Chain 1:    600      -233410.549             1.307            0.945
Chain 1:    700      -119569.530             1.257            0.945
Chain 1:    800       -86783.552             1.147            0.945
Chain 1:    900       -67114.468             1.052            0.781
Chain 1:   1000       -51902.169             0.976            0.781
Chain 1:   1100       -39373.938             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38548.675             0.479            0.378
Chain 1:   1300       -26497.864             0.447            0.378
Chain 1:   1400       -26216.489             0.353            0.318
Chain 1:   1500       -22802.795             0.341            0.318
Chain 1:   1600       -22018.996             0.291            0.293
Chain 1:   1700       -20891.721             0.201            0.293
Chain 1:   1800       -20835.620             0.163            0.150
Chain 1:   1900       -21161.931             0.136            0.054
Chain 1:   2000       -19672.391             0.114            0.054
Chain 1:   2100       -19910.751             0.083            0.036
Chain 1:   2200       -20137.509             0.082            0.036
Chain 1:   2300       -19754.402             0.039            0.019
Chain 1:   2400       -19526.426             0.039            0.019
Chain 1:   2500       -19328.544             0.025            0.015
Chain 1:   2600       -18958.583             0.023            0.015
Chain 1:   2700       -18915.402             0.018            0.012
Chain 1:   2800       -18632.313             0.019            0.015
Chain 1:   2900       -18913.610             0.019            0.015
Chain 1:   3000       -18899.730             0.012            0.012
Chain 1:   3100       -18984.783             0.011            0.012
Chain 1:   3200       -18675.348             0.011            0.015
Chain 1:   3300       -18880.126             0.011            0.012
Chain 1:   3400       -18354.937             0.012            0.015
Chain 1:   3500       -18967.028             0.015            0.015
Chain 1:   3600       -18273.364             0.016            0.015
Chain 1:   3700       -18660.492             0.018            0.017
Chain 1:   3800       -17619.698             0.023            0.021
Chain 1:   3900       -17615.813             0.021            0.021
Chain 1:   4000       -17733.119             0.022            0.021
Chain 1:   4100       -17646.919             0.022            0.021
Chain 1:   4200       -17462.968             0.021            0.021
Chain 1:   4300       -17601.476             0.021            0.021
Chain 1:   4400       -17558.219             0.018            0.011
Chain 1:   4500       -17460.721             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001252 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49741.638             1.000            1.000
Chain 1:    200       -17928.826             1.387            1.774
Chain 1:    300       -18079.906             0.928            1.000
Chain 1:    400       -19489.795             0.714            1.000
Chain 1:    500       -12768.953             0.676            0.526
Chain 1:    600       -23172.326             0.638            0.526
Chain 1:    700       -17637.001             0.592            0.449
Chain 1:    800       -16225.412             0.529            0.449
Chain 1:    900       -14374.187             0.484            0.314
Chain 1:   1000       -12176.863             0.454            0.314
Chain 1:   1100       -23550.884             0.402            0.314
Chain 1:   1200       -14660.946             0.286            0.314
Chain 1:   1300       -15090.611             0.288            0.314
Chain 1:   1400       -14264.539             0.286            0.314
Chain 1:   1500       -12097.617             0.251            0.180
Chain 1:   1600       -12203.270             0.207            0.179
Chain 1:   1700       -10437.400             0.193            0.169
Chain 1:   1800       -10596.330             0.186            0.169
Chain 1:   1900       -11263.727             0.179            0.169
Chain 1:   2000       -11303.014             0.161            0.059
Chain 1:   2100       -11380.977             0.113            0.058
Chain 1:   2200       -10672.790             0.059            0.058
Chain 1:   2300       -10277.831             0.060            0.058
Chain 1:   2400       -10318.839             0.055            0.038
Chain 1:   2500       -17377.489             0.078            0.038
Chain 1:   2600       -10396.965             0.144            0.059
Chain 1:   2700       -11417.476             0.136            0.059
Chain 1:   2800       -10656.450             0.142            0.066
Chain 1:   2900       -10373.308             0.138            0.066
Chain 1:   3000       -12334.322             0.154            0.071
Chain 1:   3100        -9436.030             0.184            0.089
Chain 1:   3200        -9795.979             0.181            0.089
Chain 1:   3300        -9676.389             0.178            0.089
Chain 1:   3400       -10388.635             0.185            0.089
Chain 1:   3500       -10518.427             0.146            0.071
Chain 1:   3600       -10701.775             0.080            0.069
Chain 1:   3700        -9715.202             0.081            0.069
Chain 1:   3800       -12995.824             0.099            0.069
Chain 1:   3900       -10695.391             0.118            0.102
Chain 1:   4000       -15398.142             0.133            0.102
Chain 1:   4100        -9655.908             0.162            0.102
Chain 1:   4200        -9566.727             0.159            0.102
Chain 1:   4300       -10626.845             0.168            0.102
Chain 1:   4400        -9977.087             0.167            0.102
Chain 1:   4500       -14752.739             0.198            0.215
Chain 1:   4600        -9577.309             0.251            0.252
Chain 1:   4700       -12235.467             0.262            0.252
Chain 1:   4800       -10611.844             0.252            0.217
Chain 1:   4900        -9659.899             0.241            0.217
Chain 1:   5000       -13204.273             0.237            0.217
Chain 1:   5100        -9444.318             0.217            0.217
Chain 1:   5200       -14132.946             0.250            0.268
Chain 1:   5300       -14694.715             0.243            0.268
Chain 1:   5400       -10661.800             0.275            0.324
Chain 1:   5500        -9595.363             0.254            0.268
Chain 1:   5600       -15894.384             0.239            0.268
Chain 1:   5700        -9318.900             0.288            0.332
Chain 1:   5800        -9138.651             0.275            0.332
Chain 1:   5900        -9443.145             0.268            0.332
Chain 1:   6000       -10123.082             0.248            0.332
Chain 1:   6100       -14203.744             0.237            0.287
Chain 1:   6200        -9097.774             0.260            0.287
Chain 1:   6300        -9316.860             0.258            0.287
Chain 1:   6400        -9877.853             0.226            0.111
Chain 1:   6500        -9555.529             0.218            0.067
Chain 1:   6600       -12316.368             0.201            0.067
Chain 1:   6700        -9054.174             0.167            0.067
Chain 1:   6800        -9132.199             0.165            0.067
Chain 1:   6900       -13597.885             0.195            0.224
Chain 1:   7000        -9142.824             0.237            0.287
Chain 1:   7100        -9159.685             0.209            0.224
Chain 1:   7200       -10068.972             0.161            0.090
Chain 1:   7300       -12465.404             0.178            0.192
Chain 1:   7400        -8998.435             0.211            0.224
Chain 1:   7500       -12371.424             0.235            0.273
Chain 1:   7600        -9257.256             0.246            0.328
Chain 1:   7700       -12269.997             0.235            0.273
Chain 1:   7800        -8830.579             0.273            0.328
Chain 1:   7900        -8827.118             0.240            0.273
Chain 1:   8000        -8727.376             0.193            0.246
Chain 1:   8100        -9046.700             0.196            0.246
Chain 1:   8200       -11038.083             0.205            0.246
Chain 1:   8300        -9127.129             0.207            0.246
Chain 1:   8400        -9466.066             0.172            0.209
Chain 1:   8500        -8811.049             0.152            0.180
Chain 1:   8600       -10831.938             0.137            0.180
Chain 1:   8700       -12679.561             0.127            0.146
Chain 1:   8800       -11230.523             0.101            0.129
Chain 1:   8900       -10797.642             0.105            0.129
Chain 1:   9000       -11129.684             0.107            0.129
Chain 1:   9100        -8671.822             0.131            0.146
Chain 1:   9200        -8974.794             0.117            0.129
Chain 1:   9300        -9614.416             0.103            0.074
Chain 1:   9400        -8642.945             0.110            0.112
Chain 1:   9500       -11716.363             0.129            0.129
Chain 1:   9600        -8925.671             0.142            0.129
Chain 1:   9700        -8606.381             0.131            0.112
Chain 1:   9800        -8855.535             0.121            0.067
Chain 1:   9900        -8801.721             0.117            0.067
Chain 1:   10000        -9029.027             0.117            0.067
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001524 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57905.350             1.000            1.000
Chain 1:    200       -18312.228             1.581            2.162
Chain 1:    300        -9177.376             1.386            1.000
Chain 1:    400        -8257.775             1.067            1.000
Chain 1:    500        -8584.510             0.861            0.995
Chain 1:    600        -8513.471             0.719            0.995
Chain 1:    700        -7595.899             0.634            0.121
Chain 1:    800        -8532.629             0.568            0.121
Chain 1:    900        -7854.496             0.515            0.111
Chain 1:   1000        -8177.636             0.467            0.111
Chain 1:   1100        -7769.025             0.372            0.110
Chain 1:   1200        -7686.467             0.157            0.086
Chain 1:   1300        -7791.333             0.059            0.053
Chain 1:   1400        -7728.704             0.049            0.040
Chain 1:   1500        -7683.257             0.046            0.040
Chain 1:   1600        -7743.305             0.046            0.040
Chain 1:   1700        -7723.815             0.034            0.013
Chain 1:   1800        -7701.172             0.023            0.011
Chain 1:   1900        -7616.390             0.015            0.011
Chain 1:   2000        -7835.668             0.014            0.011
Chain 1:   2100        -7679.959             0.011            0.011
Chain 1:   2200        -7877.154             0.013            0.011
Chain 1:   2300        -7657.596             0.014            0.011
Chain 1:   2400        -7658.611             0.013            0.011
Chain 1:   2500        -7670.190             0.013            0.011
Chain 1:   2600        -7593.081             0.013            0.011
Chain 1:   2700        -7594.169             0.013            0.011
Chain 1:   2800        -7559.485             0.013            0.011
Chain 1:   2900        -7431.326             0.014            0.017
Chain 1:   3000        -7614.282             0.013            0.017
Chain 1:   3100        -7587.112             0.012            0.010
Chain 1:   3200        -7791.942             0.012            0.010
Chain 1:   3300        -7471.877             0.013            0.010
Chain 1:   3400        -7730.065             0.016            0.017
Chain 1:   3500        -7522.586             0.019            0.024
Chain 1:   3600        -7551.624             0.018            0.024
Chain 1:   3700        -7522.756             0.019            0.024
Chain 1:   3800        -7478.854             0.019            0.024
Chain 1:   3900        -7462.461             0.017            0.024
Chain 1:   4000        -7452.298             0.015            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002537 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86661.743             1.000            1.000
Chain 1:    200       -14345.846             3.020            5.041
Chain 1:    300       -10565.200             2.133            1.000
Chain 1:    400       -12372.766             1.636            1.000
Chain 1:    500        -9113.489             1.380            0.358
Chain 1:    600        -8866.235             1.155            0.358
Chain 1:    700        -9495.021             1.000            0.358
Chain 1:    800       -10182.420             0.883            0.358
Chain 1:    900        -9362.822             0.795            0.146
Chain 1:   1000        -9356.324             0.715            0.146
Chain 1:   1100        -9294.388             0.616            0.088   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8838.043             0.117            0.068
Chain 1:   1300        -9088.947             0.084            0.066
Chain 1:   1400        -9112.460             0.070            0.052
Chain 1:   1500        -9023.505             0.035            0.028
Chain 1:   1600        -9100.330             0.033            0.028
Chain 1:   1700        -9182.287             0.027            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00267 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387580.984             1.000            1.000
Chain 1:    200     -1585580.683             2.645            4.290
Chain 1:    300      -892933.562             2.022            1.000
Chain 1:    400      -459501.457             1.752            1.000
Chain 1:    500      -359811.033             1.457            0.943
Chain 1:    600      -234514.966             1.303            0.943
Chain 1:    700      -120407.983             1.253            0.943
Chain 1:    800       -87565.436             1.143            0.943
Chain 1:    900       -67852.441             1.048            0.776
Chain 1:   1000       -52616.756             0.972            0.776
Chain 1:   1100       -40054.154             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39233.587             0.477            0.375
Chain 1:   1300       -27130.177             0.444            0.375
Chain 1:   1400       -26848.098             0.351            0.314
Chain 1:   1500       -23419.388             0.337            0.314
Chain 1:   1600       -22632.727             0.288            0.291
Chain 1:   1700       -21498.080             0.198            0.290
Chain 1:   1800       -21440.928             0.161            0.146
Chain 1:   1900       -21767.878             0.133            0.053
Chain 1:   2000       -20273.333             0.112            0.053
Chain 1:   2100       -20511.993             0.081            0.035
Chain 1:   2200       -20739.784             0.080            0.035
Chain 1:   2300       -20355.603             0.038            0.019
Chain 1:   2400       -20127.327             0.038            0.019
Chain 1:   2500       -19929.697             0.024            0.015
Chain 1:   2600       -19558.737             0.023            0.015
Chain 1:   2700       -19515.378             0.018            0.012
Chain 1:   2800       -19232.017             0.019            0.015
Chain 1:   2900       -19513.727             0.019            0.014
Chain 1:   3000       -19499.783             0.011            0.012
Chain 1:   3100       -19584.917             0.011            0.011
Chain 1:   3200       -19274.987             0.011            0.014
Chain 1:   3300       -19480.211             0.010            0.011
Chain 1:   3400       -18954.163             0.012            0.014
Chain 1:   3500       -19567.607             0.014            0.015
Chain 1:   3600       -18872.289             0.016            0.015
Chain 1:   3700       -19260.606             0.018            0.016
Chain 1:   3800       -18217.288             0.022            0.020
Chain 1:   3900       -18213.412             0.021            0.020
Chain 1:   4000       -18330.668             0.021            0.020
Chain 1:   4100       -18244.297             0.021            0.020
Chain 1:   4200       -18059.890             0.021            0.020
Chain 1:   4300       -18198.703             0.020            0.020
Chain 1:   4400       -18154.975             0.018            0.010
Chain 1:   4500       -18057.464             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001441 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12753.761             1.000            1.000
Chain 1:    200        -9544.981             0.668            1.000
Chain 1:    300        -8336.727             0.494            0.336
Chain 1:    400        -8504.995             0.375            0.336
Chain 1:    500        -8402.178             0.303            0.145
Chain 1:    600        -8235.530             0.256            0.145
Chain 1:    700        -8117.690             0.221            0.020
Chain 1:    800        -8120.379             0.194            0.020
Chain 1:    900        -8106.206             0.172            0.020
Chain 1:   1000        -8193.510             0.156            0.020
Chain 1:   1100        -8171.634             0.056            0.015
Chain 1:   1200        -8145.701             0.023            0.012
Chain 1:   1300        -8093.671             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57158.592             1.000            1.000
Chain 1:    200       -17886.087             1.598            2.196
Chain 1:    300        -8958.918             1.397            1.000
Chain 1:    400        -8257.970             1.069            1.000
Chain 1:    500        -8513.357             0.861            0.996
Chain 1:    600        -8812.525             0.723            0.996
Chain 1:    700        -8694.113             0.622            0.085
Chain 1:    800        -8238.310             0.551            0.085
Chain 1:    900        -8196.552             0.491            0.055
Chain 1:   1000        -7883.487             0.445            0.055
Chain 1:   1100        -7917.855             0.346            0.040
Chain 1:   1200        -8045.227             0.128            0.034
Chain 1:   1300        -7964.806             0.029            0.030
Chain 1:   1400        -8096.676             0.022            0.016
Chain 1:   1500        -7670.801             0.025            0.016
Chain 1:   1600        -7823.102             0.024            0.016
Chain 1:   1700        -7677.516             0.024            0.019
Chain 1:   1800        -7763.052             0.020            0.016
Chain 1:   1900        -7680.279             0.020            0.016
Chain 1:   2000        -7720.346             0.017            0.016
Chain 1:   2100        -7673.061             0.017            0.016
Chain 1:   2200        -7820.650             0.017            0.016
Chain 1:   2300        -7677.207             0.018            0.019
Chain 1:   2400        -7705.441             0.017            0.019
Chain 1:   2500        -7720.364             0.011            0.011
Chain 1:   2600        -7617.413             0.011            0.011
Chain 1:   2700        -7618.536             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002744 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86324.192             1.000            1.000
Chain 1:    200       -13944.483             3.095            5.191
Chain 1:    300       -10222.018             2.185            1.000
Chain 1:    400       -11581.554             1.668            1.000
Chain 1:    500        -9232.575             1.385            0.364
Chain 1:    600        -8651.862             1.166            0.364
Chain 1:    700        -8911.992             1.003            0.254
Chain 1:    800        -9411.061             0.884            0.254
Chain 1:    900        -8945.443             0.792            0.117
Chain 1:   1000        -8932.546             0.713            0.117
Chain 1:   1100        -8960.621             0.613            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8595.578             0.098            0.053
Chain 1:   1300        -8869.114             0.065            0.052
Chain 1:   1400        -8854.953             0.054            0.042
Chain 1:   1500        -8725.526             0.030            0.031
Chain 1:   1600        -8840.288             0.024            0.029
Chain 1:   1700        -8899.887             0.022            0.015
Chain 1:   1800        -8462.157             0.022            0.015
Chain 1:   1900        -8566.074             0.018            0.013
Chain 1:   2000        -8544.410             0.018            0.013
Chain 1:   2100        -8520.913             0.018            0.013
Chain 1:   2200        -8484.611             0.014            0.012
Chain 1:   2300        -8619.362             0.013            0.012
Chain 1:   2400        -8464.256             0.014            0.013
Chain 1:   2500        -8535.467             0.014            0.012
Chain 1:   2600        -8448.291             0.013            0.010
Chain 1:   2700        -8485.571             0.013            0.010
Chain 1:   2800        -8443.639             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003046 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386207.188             1.000            1.000
Chain 1:    200     -1581962.442             2.651            4.301
Chain 1:    300      -891571.767             2.025            1.000
Chain 1:    400      -458630.713             1.755            1.000
Chain 1:    500      -359333.178             1.459            0.944
Chain 1:    600      -234103.229             1.305            0.944
Chain 1:    700      -120004.463             1.255            0.944
Chain 1:    800       -87158.580             1.145            0.944
Chain 1:    900       -67436.479             1.050            0.774
Chain 1:   1000       -52188.570             0.974            0.774
Chain 1:   1100       -39622.544             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38796.903             0.478            0.377
Chain 1:   1300       -26698.461             0.446            0.377
Chain 1:   1400       -26414.710             0.353            0.317
Chain 1:   1500       -22988.171             0.340            0.317
Chain 1:   1600       -22201.408             0.290            0.292
Chain 1:   1700       -21067.932             0.200            0.292
Chain 1:   1800       -21010.797             0.163            0.149
Chain 1:   1900       -21337.375             0.135            0.054
Chain 1:   2000       -19844.144             0.113            0.054
Chain 1:   2100       -20082.662             0.083            0.035
Chain 1:   2200       -20310.141             0.082            0.035
Chain 1:   2300       -19926.342             0.038            0.019
Chain 1:   2400       -19698.192             0.039            0.019
Chain 1:   2500       -19500.528             0.025            0.015
Chain 1:   2600       -19129.921             0.023            0.015
Chain 1:   2700       -19086.672             0.018            0.012
Chain 1:   2800       -18803.469             0.019            0.015
Chain 1:   2900       -19084.997             0.019            0.015
Chain 1:   3000       -19071.054             0.012            0.012
Chain 1:   3100       -19156.140             0.011            0.012
Chain 1:   3200       -18846.451             0.011            0.015
Chain 1:   3300       -19051.474             0.011            0.012
Chain 1:   3400       -18525.846             0.012            0.015
Chain 1:   3500       -19138.671             0.014            0.015
Chain 1:   3600       -18444.129             0.016            0.015
Chain 1:   3700       -18831.867             0.018            0.016
Chain 1:   3800       -17789.774             0.022            0.021
Chain 1:   3900       -17785.920             0.021            0.021
Chain 1:   4000       -17903.169             0.022            0.021
Chain 1:   4100       -17816.875             0.022            0.021
Chain 1:   4200       -17632.714             0.021            0.021
Chain 1:   4300       -17771.348             0.021            0.021
Chain 1:   4400       -17727.834             0.018            0.010
Chain 1:   4500       -17630.353             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12728.997             1.000            1.000
Chain 1:    200        -9520.715             0.668            1.000
Chain 1:    300        -8306.566             0.494            0.337
Chain 1:    400        -8477.185             0.376            0.337
Chain 1:    500        -8430.023             0.302            0.146
Chain 1:    600        -8236.783             0.255            0.146
Chain 1:    700        -8165.143             0.220            0.023
Chain 1:    800        -8191.580             0.193            0.023
Chain 1:    900        -8232.867             0.172            0.020
Chain 1:   1000        -8173.206             0.156            0.020
Chain 1:   1100        -8256.189             0.057            0.010
Chain 1:   1200        -8179.220             0.024            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002458 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57266.176             1.000            1.000
Chain 1:    200       -17771.874             1.611            2.222
Chain 1:    300        -8941.523             1.403            1.000
Chain 1:    400        -8234.334             1.074            1.000
Chain 1:    500        -8796.165             0.872            0.988
Chain 1:    600        -8835.979             0.727            0.988
Chain 1:    700        -8522.502             0.629            0.086
Chain 1:    800        -8234.319             0.554            0.086
Chain 1:    900        -8059.075             0.495            0.064
Chain 1:   1000        -8029.081             0.446            0.064
Chain 1:   1100        -7737.327             0.350            0.038
Chain 1:   1200        -7766.414             0.128            0.037
Chain 1:   1300        -7866.695             0.031            0.035
Chain 1:   1400        -7902.717             0.022            0.022
Chain 1:   1500        -7623.696             0.020            0.022
Chain 1:   1600        -7772.250             0.021            0.022
Chain 1:   1700        -7760.968             0.018            0.019
Chain 1:   1800        -7710.491             0.015            0.013
Chain 1:   1900        -7644.151             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86545.678             1.000            1.000
Chain 1:    200       -13855.060             3.123            5.247
Chain 1:    300       -10187.311             2.202            1.000
Chain 1:    400       -11272.499             1.676            1.000
Chain 1:    500        -9171.739             1.386            0.360
Chain 1:    600        -8583.436             1.167            0.360
Chain 1:    700        -9147.437             1.009            0.229
Chain 1:    800        -9495.240             0.887            0.229
Chain 1:    900        -8991.708             0.795            0.096
Chain 1:   1000        -8952.181             0.716            0.096
Chain 1:   1100        -8809.186             0.618            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8576.871             0.096            0.062
Chain 1:   1300        -8862.426             0.063            0.056
Chain 1:   1400        -8818.888             0.054            0.037
Chain 1:   1500        -8708.193             0.032            0.032
Chain 1:   1600        -8815.663             0.026            0.027
Chain 1:   1700        -8892.185             0.021            0.016
Chain 1:   1800        -8461.865             0.023            0.016
Chain 1:   1900        -8565.651             0.018            0.013
Chain 1:   2000        -8540.893             0.018            0.013
Chain 1:   2100        -8675.175             0.018            0.013
Chain 1:   2200        -8469.704             0.018            0.013
Chain 1:   2300        -8565.191             0.016            0.012
Chain 1:   2400        -8629.459             0.016            0.012
Chain 1:   2500        -8574.289             0.015            0.012
Chain 1:   2600        -8578.378             0.014            0.011
Chain 1:   2700        -8493.671             0.014            0.011
Chain 1:   2800        -8450.517             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002728 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413954.481             1.000            1.000
Chain 1:    200     -1588537.453             2.648            4.297
Chain 1:    300      -892484.432             2.026            1.000
Chain 1:    400      -458572.932             1.756            1.000
Chain 1:    500      -358395.410             1.460            0.946
Chain 1:    600      -233227.333             1.306            0.946
Chain 1:    700      -119490.249             1.256            0.946
Chain 1:    800       -86715.944             1.146            0.946
Chain 1:    900       -67082.197             1.051            0.780
Chain 1:   1000       -51903.397             0.975            0.780
Chain 1:   1100       -39401.869             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38583.374             0.480            0.378
Chain 1:   1300       -26557.537             0.447            0.378
Chain 1:   1400       -26279.899             0.353            0.317
Chain 1:   1500       -22870.943             0.340            0.317
Chain 1:   1600       -22089.185             0.290            0.293
Chain 1:   1700       -20964.703             0.200            0.292
Chain 1:   1800       -20909.518             0.163            0.149
Chain 1:   1900       -21235.817             0.135            0.054
Chain 1:   2000       -19747.509             0.113            0.054
Chain 1:   2100       -19985.966             0.083            0.035
Chain 1:   2200       -20212.353             0.082            0.035
Chain 1:   2300       -19829.538             0.038            0.019
Chain 1:   2400       -19601.551             0.039            0.019
Chain 1:   2500       -19403.470             0.025            0.015
Chain 1:   2600       -19033.566             0.023            0.015
Chain 1:   2700       -18990.521             0.018            0.012
Chain 1:   2800       -18707.212             0.019            0.015
Chain 1:   2900       -18988.516             0.019            0.015
Chain 1:   3000       -18974.772             0.012            0.012
Chain 1:   3100       -19059.772             0.011            0.012
Chain 1:   3200       -18750.345             0.011            0.015
Chain 1:   3300       -18955.155             0.011            0.012
Chain 1:   3400       -18429.823             0.012            0.015
Chain 1:   3500       -19042.065             0.014            0.015
Chain 1:   3600       -18348.254             0.016            0.015
Chain 1:   3700       -18735.351             0.018            0.017
Chain 1:   3800       -17694.342             0.023            0.021
Chain 1:   3900       -17690.443             0.021            0.021
Chain 1:   4000       -17807.761             0.022            0.021
Chain 1:   4100       -17721.467             0.022            0.021
Chain 1:   4200       -17537.567             0.021            0.021
Chain 1:   4300       -17676.089             0.021            0.021
Chain 1:   4400       -17632.770             0.018            0.010
Chain 1:   4500       -17535.262             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49982.824             1.000            1.000
Chain 1:    200       -17059.946             1.465            1.930
Chain 1:    300       -18682.299             1.006            1.000
Chain 1:    400       -15248.982             0.810            1.000
Chain 1:    500       -19816.998             0.694            0.231
Chain 1:    600       -18426.129             0.591            0.231
Chain 1:    700       -13019.188             0.566            0.231
Chain 1:    800       -12161.041             0.504            0.231
Chain 1:    900       -12520.314             0.451            0.225
Chain 1:   1000       -24926.445             0.456            0.231
Chain 1:   1100       -21666.174             0.371            0.225
Chain 1:   1200       -12550.726             0.251            0.225
Chain 1:   1300       -13285.921             0.248            0.225
Chain 1:   1400       -11863.612             0.237            0.150
Chain 1:   1500       -11398.166             0.218            0.120
Chain 1:   1600       -10738.629             0.217            0.120
Chain 1:   1700       -13104.637             0.193            0.120
Chain 1:   1800       -13534.047             0.189            0.120
Chain 1:   1900       -11916.603             0.200            0.136
Chain 1:   2000       -18321.048             0.185            0.136
Chain 1:   2100       -11363.116             0.231            0.136
Chain 1:   2200       -12390.733             0.167            0.120
Chain 1:   2300       -10891.075             0.175            0.136
Chain 1:   2400        -9726.643             0.175            0.136
Chain 1:   2500       -10496.293             0.178            0.136
Chain 1:   2600       -10481.592             0.172            0.136
Chain 1:   2700       -11329.699             0.162            0.120
Chain 1:   2800       -11371.591             0.159            0.120
Chain 1:   2900       -11581.322             0.147            0.083
Chain 1:   3000        -9681.257             0.132            0.083
Chain 1:   3100       -10460.241             0.078            0.075
Chain 1:   3200       -10359.320             0.071            0.074
Chain 1:   3300       -16808.799             0.096            0.074
Chain 1:   3400        -9999.277             0.152            0.074
Chain 1:   3500       -11875.492             0.160            0.075
Chain 1:   3600       -16697.447             0.189            0.158
Chain 1:   3700       -17077.722             0.184            0.158
Chain 1:   3800       -10406.736             0.247            0.196
Chain 1:   3900       -12169.726             0.260            0.196
Chain 1:   4000       -10503.800             0.256            0.159
Chain 1:   4100       -10395.595             0.250            0.159
Chain 1:   4200       -10944.770             0.254            0.159
Chain 1:   4300       -10795.209             0.217            0.158
Chain 1:   4400       -10416.764             0.152            0.145
Chain 1:   4500       -10010.907             0.141            0.050
Chain 1:   4600       -10334.786             0.115            0.041
Chain 1:   4700        -9354.552             0.123            0.050
Chain 1:   4800        -9749.477             0.063            0.041
Chain 1:   4900        -9837.104             0.050            0.041
Chain 1:   5000        -9889.956             0.034            0.036
Chain 1:   5100        -9745.952             0.035            0.036
Chain 1:   5200       -11485.724             0.045            0.036
Chain 1:   5300       -10326.056             0.055            0.041
Chain 1:   5400        -9249.516             0.063            0.041
Chain 1:   5500        -9531.704             0.062            0.041
Chain 1:   5600        -9543.747             0.059            0.041
Chain 1:   5700        -9936.120             0.052            0.039
Chain 1:   5800        -9628.546             0.051            0.032
Chain 1:   5900       -13073.939             0.077            0.039
Chain 1:   6000        -9475.241             0.114            0.112
Chain 1:   6100       -15871.571             0.153            0.116
Chain 1:   6200       -15021.289             0.143            0.112
Chain 1:   6300        -9424.883             0.192            0.116
Chain 1:   6400        -9216.113             0.182            0.057
Chain 1:   6500       -13776.569             0.212            0.264
Chain 1:   6600       -10014.717             0.250            0.331
Chain 1:   6700       -14037.753             0.274            0.331
Chain 1:   6800        -9972.569             0.312            0.376
Chain 1:   6900        -9452.653             0.291            0.376
Chain 1:   7000        -9622.268             0.255            0.331
Chain 1:   7100       -11063.102             0.228            0.287
Chain 1:   7200        -9099.091             0.244            0.287
Chain 1:   7300       -11157.749             0.203            0.216
Chain 1:   7400        -8709.690             0.229            0.281
Chain 1:   7500       -11568.533             0.220            0.247
Chain 1:   7600       -10402.290             0.194            0.216
Chain 1:   7700        -9017.148             0.180            0.185
Chain 1:   7800       -13306.856             0.172            0.185
Chain 1:   7900       -12572.605             0.172            0.185
Chain 1:   8000        -8944.234             0.211            0.216
Chain 1:   8100       -14091.382             0.235            0.247
Chain 1:   8200        -9149.984             0.267            0.281
Chain 1:   8300        -9179.025             0.249            0.281
Chain 1:   8400       -11475.220             0.241            0.247
Chain 1:   8500        -9179.696             0.241            0.250
Chain 1:   8600        -9662.233             0.235            0.250
Chain 1:   8700        -9140.832             0.225            0.250
Chain 1:   8800        -8694.490             0.198            0.200
Chain 1:   8900        -9548.189             0.201            0.200
Chain 1:   9000       -11220.851             0.176            0.149
Chain 1:   9100        -9083.103             0.163            0.149
Chain 1:   9200        -8837.210             0.111            0.089
Chain 1:   9300        -8988.000             0.113            0.089
Chain 1:   9400        -8812.198             0.095            0.057
Chain 1:   9500       -11612.543             0.094            0.057
Chain 1:   9600        -8999.774             0.118            0.089
Chain 1:   9700        -8832.973             0.114            0.089
Chain 1:   9800       -11096.466             0.129            0.149
Chain 1:   9900        -8851.293             0.146            0.204
Chain 1:   10000        -9420.791             0.137            0.204
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001374 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58016.562             1.000            1.000
Chain 1:    200       -18628.230             1.557            2.114
Chain 1:    300        -9406.656             1.365            1.000
Chain 1:    400        -8384.394             1.054            1.000
Chain 1:    500        -8991.632             0.857            0.980
Chain 1:    600        -9734.021             0.727            0.980
Chain 1:    700        -8400.043             0.646            0.159
Chain 1:    800        -8609.479             0.568            0.159
Chain 1:    900        -8164.271             0.511            0.122
Chain 1:   1000        -7904.025             0.463            0.122
Chain 1:   1100        -7885.008             0.363            0.076
Chain 1:   1200        -8054.476             0.154            0.068
Chain 1:   1300        -7944.886             0.057            0.055
Chain 1:   1400        -7988.098             0.046            0.033
Chain 1:   1500        -7859.833             0.041            0.024
Chain 1:   1600        -7948.250             0.034            0.021
Chain 1:   1700        -7701.656             0.021            0.021
Chain 1:   1800        -7744.838             0.020            0.016
Chain 1:   1900        -7770.887             0.014            0.014
Chain 1:   2000        -7923.511             0.013            0.014
Chain 1:   2100        -7803.875             0.014            0.015
Chain 1:   2200        -8214.422             0.017            0.015
Chain 1:   2300        -7781.853             0.021            0.016
Chain 1:   2400        -7751.678             0.021            0.016
Chain 1:   2500        -7883.314             0.021            0.017
Chain 1:   2600        -7716.508             0.022            0.019
Chain 1:   2700        -7715.666             0.019            0.017
Chain 1:   2800        -7789.398             0.020            0.017
Chain 1:   2900        -7540.067             0.023            0.019
Chain 1:   3000        -7711.850             0.023            0.022
Chain 1:   3100        -7688.763             0.022            0.022
Chain 1:   3200        -7975.184             0.020            0.022
Chain 1:   3300        -7590.709             0.020            0.022
Chain 1:   3400        -7808.737             0.022            0.022
Chain 1:   3500        -7601.570             0.023            0.027
Chain 1:   3600        -7653.922             0.022            0.027
Chain 1:   3700        -7561.041             0.023            0.027
Chain 1:   3800        -7581.463             0.022            0.027
Chain 1:   3900        -7586.574             0.019            0.022
Chain 1:   4000        -7552.893             0.017            0.012
Chain 1:   4100        -7565.421             0.017            0.012
Chain 1:   4200        -7746.515             0.016            0.012
Chain 1:   4300        -7548.114             0.013            0.012
Chain 1:   4400        -7603.243             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004983 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87813.602             1.000            1.000
Chain 1:    200       -14648.132             2.997            4.995
Chain 1:    300       -10733.436             2.120            1.000
Chain 1:    400       -13126.940             1.635            1.000
Chain 1:    500        -9140.799             1.396            0.436
Chain 1:    600        -9733.241             1.173            0.436
Chain 1:    700        -8798.665             1.021            0.365
Chain 1:    800        -9302.453             0.900            0.365
Chain 1:    900        -9343.927             0.800            0.182
Chain 1:   1000        -8950.395             0.725            0.182
Chain 1:   1100        -9219.545             0.628            0.106   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9013.189             0.130            0.061
Chain 1:   1300        -9215.826             0.096            0.054
Chain 1:   1400        -9307.817             0.079            0.044
Chain 1:   1500        -9103.391             0.038            0.029
Chain 1:   1600        -9176.502             0.032            0.023
Chain 1:   1700        -9247.104             0.022            0.022
Chain 1:   1800        -8755.739             0.023            0.022
Chain 1:   1900        -8877.936             0.024            0.022
Chain 1:   2000        -8883.444             0.019            0.022
Chain 1:   2100        -9030.369             0.018            0.016
Chain 1:   2200        -8745.017             0.019            0.016
Chain 1:   2300        -8831.070             0.018            0.014
Chain 1:   2400        -8925.036             0.018            0.014
Chain 1:   2500        -8821.880             0.017            0.012
Chain 1:   2600        -8872.875             0.016            0.012
Chain 1:   2700        -8779.209             0.017            0.012
Chain 1:   2800        -8747.628             0.012            0.011
Chain 1:   2900        -8833.373             0.011            0.011
Chain 1:   3000        -8764.332             0.012            0.011
Chain 1:   3100        -8719.189             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003065 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392860.474             1.000            1.000
Chain 1:    200     -1580637.054             2.655            4.310
Chain 1:    300      -892280.589             2.027            1.000
Chain 1:    400      -459820.811             1.755            1.000
Chain 1:    500      -360518.445             1.459            0.940
Chain 1:    600      -235246.922             1.305            0.940
Chain 1:    700      -120989.361             1.253            0.940
Chain 1:    800       -88079.728             1.143            0.940
Chain 1:    900       -68318.530             1.049            0.771
Chain 1:   1000       -53044.686             0.972            0.771
Chain 1:   1100       -40445.877             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39623.889             0.475            0.374
Chain 1:   1300       -27480.066             0.442            0.374
Chain 1:   1400       -27195.351             0.349            0.311
Chain 1:   1500       -23755.958             0.336            0.311
Chain 1:   1600       -22966.773             0.286            0.289
Chain 1:   1700       -21827.219             0.197            0.288
Chain 1:   1800       -21769.103             0.160            0.145
Chain 1:   1900       -22096.617             0.132            0.052
Chain 1:   2000       -20598.306             0.111            0.052
Chain 1:   2100       -20837.248             0.081            0.034
Chain 1:   2200       -21065.770             0.080            0.034
Chain 1:   2300       -20680.766             0.037            0.019
Chain 1:   2400       -20452.259             0.037            0.019
Chain 1:   2500       -20254.643             0.024            0.015
Chain 1:   2600       -19883.044             0.022            0.015
Chain 1:   2700       -19839.445             0.017            0.011
Chain 1:   2800       -19555.896             0.018            0.014
Chain 1:   2900       -19837.929             0.018            0.014
Chain 1:   3000       -19823.836             0.011            0.011
Chain 1:   3100       -19909.114             0.010            0.011
Chain 1:   3200       -19598.750             0.011            0.014
Chain 1:   3300       -19804.291             0.010            0.011
Chain 1:   3400       -19277.538             0.012            0.014
Chain 1:   3500       -19892.059             0.014            0.014
Chain 1:   3600       -19195.290             0.016            0.014
Chain 1:   3700       -19584.800             0.017            0.016
Chain 1:   3800       -18539.196             0.022            0.020
Chain 1:   3900       -18535.253             0.020            0.020
Chain 1:   4000       -18652.507             0.021            0.020
Chain 1:   4100       -18566.056             0.021            0.020
Chain 1:   4200       -18381.105             0.020            0.020
Chain 1:   4300       -18520.300             0.020            0.020
Chain 1:   4400       -18476.201             0.017            0.010
Chain 1:   4500       -18378.570             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13987.024             1.000            1.000
Chain 1:    200       -10616.719             0.659            1.000
Chain 1:    300        -9336.128             0.485            0.317
Chain 1:    400        -8957.274             0.374            0.317
Chain 1:    500        -8895.709             0.301            0.137
Chain 1:    600        -8581.290             0.257            0.137
Chain 1:    700        -8733.415             0.223            0.042
Chain 1:    800        -8587.949             0.197            0.042
Chain 1:    900        -8588.526             0.175            0.037
Chain 1:   1000        -8604.701             0.158            0.037
Chain 1:   1100        -8603.827             0.058            0.017
Chain 1:   1200        -8526.086             0.027            0.017
Chain 1:   1300        -8472.292             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001366 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -64702.744             1.000            1.000
Chain 1:    200       -19391.424             1.668            2.337
Chain 1:    300        -9397.792             1.467            1.063
Chain 1:    400        -9104.566             1.108            1.063
Chain 1:    500        -7784.053             0.920            1.000
Chain 1:    600        -8495.419             0.781            1.000
Chain 1:    700        -8786.701             0.674            0.170
Chain 1:    800        -8647.185             0.592            0.170
Chain 1:    900        -8641.555             0.526            0.084
Chain 1:   1000        -7838.124             0.484            0.103
Chain 1:   1100        -7864.251             0.384            0.084
Chain 1:   1200        -7922.534             0.151            0.033
Chain 1:   1300        -7913.342             0.045            0.032
Chain 1:   1400        -7985.499             0.043            0.016
Chain 1:   1500        -7605.628             0.031            0.016
Chain 1:   1600        -7626.964             0.023            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003325 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87066.713             1.000            1.000
Chain 1:    200       -14574.216             2.987            4.974
Chain 1:    300       -10740.702             2.110            1.000
Chain 1:    400       -12842.443             1.624            1.000
Chain 1:    500        -9326.567             1.374            0.377
Chain 1:    600        -9546.849             1.149            0.377
Chain 1:    700        -8965.495             0.994            0.357
Chain 1:    800        -9496.943             0.877            0.357
Chain 1:    900        -9311.826             0.782            0.164
Chain 1:   1000        -9293.834             0.704            0.164
Chain 1:   1100        -9494.131             0.606            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9093.745             0.113            0.056
Chain 1:   1300        -9318.758             0.080            0.044
Chain 1:   1400        -9261.754             0.064            0.024
Chain 1:   1500        -9189.066             0.027            0.023
Chain 1:   1600        -9264.484             0.025            0.021
Chain 1:   1700        -9326.225             0.020            0.020
Chain 1:   1800        -8877.643             0.019            0.020
Chain 1:   1900        -8973.738             0.018            0.011
Chain 1:   2000        -8995.294             0.018            0.011
Chain 1:   2100        -9090.521             0.017            0.010
Chain 1:   2200        -8854.182             0.015            0.010
Chain 1:   2300        -9027.659             0.015            0.010
Chain 1:   2400        -8882.590             0.016            0.011
Chain 1:   2500        -8944.579             0.016            0.011
Chain 1:   2600        -8851.750             0.016            0.011
Chain 1:   2700        -8887.032             0.016            0.011
Chain 1:   2800        -8843.491             0.011            0.010
Chain 1:   2900        -8953.523             0.011            0.010
Chain 1:   3000        -8861.625             0.012            0.010
Chain 1:   3100        -8829.166             0.011            0.010
Chain 1:   3200        -8798.560             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004109 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422054.838             1.000            1.000
Chain 1:    200     -1587606.573             2.652            4.305
Chain 1:    300      -892274.504             2.028            1.000
Chain 1:    400      -459155.924             1.757            1.000
Chain 1:    500      -359109.594             1.461            0.943
Chain 1:    600      -233968.004             1.307            0.943
Chain 1:    700      -120282.777             1.255            0.943
Chain 1:    800       -87492.082             1.145            0.943
Chain 1:    900       -67857.274             1.050            0.779
Chain 1:   1000       -52687.083             0.974            0.779
Chain 1:   1100       -40180.436             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39369.372             0.477            0.375
Chain 1:   1300       -27327.510             0.443            0.375
Chain 1:   1400       -27051.457             0.349            0.311
Chain 1:   1500       -23638.093             0.336            0.311
Chain 1:   1600       -22856.095             0.286            0.289
Chain 1:   1700       -21728.894             0.197            0.288
Chain 1:   1800       -21673.654             0.159            0.144
Chain 1:   1900       -22000.765             0.132            0.052
Chain 1:   2000       -20509.685             0.110            0.052
Chain 1:   2100       -20748.195             0.080            0.034
Chain 1:   2200       -20975.397             0.079            0.034
Chain 1:   2300       -20591.748             0.037            0.019
Chain 1:   2400       -20363.488             0.037            0.019
Chain 1:   2500       -20165.435             0.024            0.015
Chain 1:   2600       -19794.617             0.022            0.015
Chain 1:   2700       -19751.339             0.017            0.011
Chain 1:   2800       -19467.645             0.019            0.015
Chain 1:   2900       -19749.428             0.018            0.014
Chain 1:   3000       -19735.565             0.011            0.011
Chain 1:   3100       -19820.684             0.011            0.011
Chain 1:   3200       -19510.711             0.011            0.014
Chain 1:   3300       -19715.982             0.010            0.011
Chain 1:   3400       -19189.644             0.012            0.014
Chain 1:   3500       -19803.333             0.014            0.015
Chain 1:   3600       -19107.724             0.016            0.015
Chain 1:   3700       -19496.185             0.017            0.016
Chain 1:   3800       -18452.260             0.022            0.020
Chain 1:   3900       -18448.323             0.020            0.020
Chain 1:   4000       -18565.658             0.021            0.020
Chain 1:   4100       -18479.177             0.021            0.020
Chain 1:   4200       -18294.680             0.020            0.020
Chain 1:   4300       -18433.621             0.020            0.020
Chain 1:   4400       -18389.797             0.018            0.010
Chain 1:   4500       -18292.222             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48937.176             1.000            1.000
Chain 1:    200       -22187.787             1.103            1.206
Chain 1:    300       -20731.101             0.759            1.000
Chain 1:    400       -17798.659             0.610            1.000
Chain 1:    500       -12010.672             0.585            0.482
Chain 1:    600       -14836.833             0.519            0.482
Chain 1:    700       -11907.443             0.480            0.246
Chain 1:    800       -14100.862             0.439            0.246
Chain 1:    900       -10693.998             0.426            0.246
Chain 1:   1000       -24475.873             0.440            0.319
Chain 1:   1100       -34583.788             0.369            0.292
Chain 1:   1200       -16706.656             0.355            0.292
Chain 1:   1300       -11706.221             0.391            0.319
Chain 1:   1400       -20646.587             0.418            0.427
Chain 1:   1500       -10684.555             0.463            0.427
Chain 1:   1600       -24272.703             0.500            0.433
Chain 1:   1700       -20231.490             0.495            0.433
Chain 1:   1800        -9372.817             0.595            0.560   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -10993.122             0.578            0.560   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000        -9692.677             0.535            0.433   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100        -9533.923             0.508            0.433   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200       -13080.231             0.428            0.427
Chain 1:   2300       -15805.510             0.403            0.271
Chain 1:   2400        -9618.786             0.424            0.271
Chain 1:   2500       -10252.837             0.336            0.200
Chain 1:   2600        -9006.173             0.294            0.172
Chain 1:   2700       -10197.591             0.286            0.147
Chain 1:   2800        -9371.774             0.179            0.138
Chain 1:   2900        -9766.726             0.168            0.134
Chain 1:   3000       -13431.372             0.182            0.138
Chain 1:   3100        -9745.691             0.218            0.172
Chain 1:   3200        -9053.920             0.199            0.138
Chain 1:   3300        -9290.994             0.184            0.117
Chain 1:   3400        -9606.439             0.123            0.088
Chain 1:   3500        -9356.305             0.120            0.088
Chain 1:   3600       -10165.554             0.114            0.080
Chain 1:   3700        -9664.237             0.107            0.076
Chain 1:   3800        -9457.092             0.101            0.052
Chain 1:   3900       -10028.635             0.102            0.057
Chain 1:   4000       -10132.801             0.076            0.052
Chain 1:   4100       -10228.318             0.039            0.033
Chain 1:   4200        -9756.980             0.036            0.033
Chain 1:   4300        -9586.028             0.036            0.033
Chain 1:   4400       -10489.220             0.041            0.048
Chain 1:   4500        -8508.106             0.062            0.052
Chain 1:   4600       -12533.304             0.086            0.052
Chain 1:   4700        -8642.606             0.125            0.057
Chain 1:   4800        -8607.694             0.124            0.057
Chain 1:   4900        -9227.992             0.125            0.067
Chain 1:   5000       -16305.140             0.167            0.086
Chain 1:   5100        -9409.324             0.239            0.233
Chain 1:   5200        -9800.856             0.239            0.233
Chain 1:   5300       -12909.470             0.261            0.241
Chain 1:   5400        -9334.474             0.291            0.321
Chain 1:   5500       -13035.569             0.296            0.321
Chain 1:   5600        -8518.686             0.317            0.383
Chain 1:   5700        -8516.226             0.272            0.284
Chain 1:   5800        -8461.414             0.272            0.284
Chain 1:   5900        -8782.635             0.269            0.284
Chain 1:   6000        -9658.482             0.234            0.241
Chain 1:   6100        -8860.321             0.170            0.091
Chain 1:   6200        -8398.221             0.172            0.091
Chain 1:   6300        -8567.443             0.150            0.090
Chain 1:   6400       -12375.363             0.142            0.090
Chain 1:   6500       -13958.311             0.125            0.090
Chain 1:   6600       -12723.608             0.082            0.090
Chain 1:   6700        -8424.729             0.133            0.091
Chain 1:   6800        -8893.638             0.137            0.091
Chain 1:   6900       -10055.295             0.145            0.097
Chain 1:   7000       -10008.769             0.137            0.097
Chain 1:   7100        -8420.046             0.146            0.113
Chain 1:   7200        -9116.730             0.149            0.113
Chain 1:   7300        -8471.948             0.154            0.113
Chain 1:   7400        -8248.025             0.126            0.097
Chain 1:   7500        -8379.629             0.116            0.076
Chain 1:   7600        -9518.417             0.119            0.076
Chain 1:   7700        -8419.324             0.081            0.076
Chain 1:   7800       -11957.607             0.105            0.116
Chain 1:   7900        -9627.696             0.118            0.120
Chain 1:   8000        -8242.388             0.134            0.131
Chain 1:   8100        -8195.018             0.116            0.120
Chain 1:   8200        -9654.052             0.123            0.131
Chain 1:   8300        -9435.402             0.118            0.131
Chain 1:   8400        -8123.138             0.131            0.151
Chain 1:   8500        -8347.771             0.132            0.151
Chain 1:   8600       -10895.658             0.144            0.162
Chain 1:   8700       -10573.505             0.134            0.162
Chain 1:   8800        -8667.424             0.126            0.162
Chain 1:   8900        -9928.222             0.115            0.151
Chain 1:   9000        -9667.045             0.101            0.127
Chain 1:   9100        -8252.984             0.117            0.151
Chain 1:   9200        -8852.387             0.109            0.127
Chain 1:   9300        -8279.273             0.113            0.127
Chain 1:   9400        -8391.779             0.099            0.069
Chain 1:   9500        -8019.423             0.101            0.069
Chain 1:   9600        -9117.831             0.089            0.069
Chain 1:   9700       -10119.892             0.096            0.099
Chain 1:   9800       -10196.700             0.075            0.069
Chain 1:   9900        -8330.833             0.085            0.069
Chain 1:   10000        -8994.553             0.089            0.074
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001449 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46502.211             1.000            1.000
Chain 1:    200       -15603.140             1.490            1.980
Chain 1:    300        -8705.216             1.258            1.000
Chain 1:    400        -8680.130             0.944            1.000
Chain 1:    500        -8239.879             0.766            0.792
Chain 1:    600        -8064.426             0.642            0.792
Chain 1:    700        -8434.341             0.556            0.053
Chain 1:    800        -8603.844             0.489            0.053
Chain 1:    900        -7958.278             0.444            0.053
Chain 1:   1000        -7822.166             0.401            0.053
Chain 1:   1100        -7737.336             0.302            0.044
Chain 1:   1200        -7606.241             0.106            0.022
Chain 1:   1300        -7750.177             0.029            0.020
Chain 1:   1400        -7913.793             0.030            0.021
Chain 1:   1500        -7611.110             0.029            0.021
Chain 1:   1600        -7717.180             0.028            0.020
Chain 1:   1700        -7544.734             0.026            0.020
Chain 1:   1800        -7607.568             0.025            0.019
Chain 1:   1900        -7608.884             0.017            0.017
Chain 1:   2000        -7719.501             0.017            0.017
Chain 1:   2100        -7661.145             0.016            0.017
Chain 1:   2200        -7711.886             0.015            0.014
Chain 1:   2300        -7623.988             0.015            0.014
Chain 1:   2400        -7675.973             0.013            0.012
Chain 1:   2500        -7554.202             0.011            0.012
Chain 1:   2600        -7541.583             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00362 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86341.264             1.000            1.000
Chain 1:    200       -13484.259             3.202            5.403
Chain 1:    300        -9874.040             2.256            1.000
Chain 1:    400       -10645.973             1.710            1.000
Chain 1:    500        -8846.818             1.409            0.366
Chain 1:    600        -8366.390             1.184            0.366
Chain 1:    700        -8454.479             1.016            0.203
Chain 1:    800        -9217.073             0.899            0.203
Chain 1:    900        -8684.691             0.806            0.083
Chain 1:   1000        -8502.447             0.728            0.083
Chain 1:   1100        -8701.952             0.630            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8348.192             0.094            0.061
Chain 1:   1300        -8567.024             0.060            0.057
Chain 1:   1400        -8569.985             0.053            0.042
Chain 1:   1500        -8458.894             0.034            0.026
Chain 1:   1600        -8563.106             0.029            0.023
Chain 1:   1700        -8651.611             0.029            0.023
Chain 1:   1800        -8244.026             0.026            0.023
Chain 1:   1900        -8340.876             0.021            0.021
Chain 1:   2000        -8312.994             0.019            0.013
Chain 1:   2100        -8433.527             0.018            0.013
Chain 1:   2200        -8245.034             0.016            0.013
Chain 1:   2300        -8380.754             0.015            0.013
Chain 1:   2400        -8388.269             0.015            0.013
Chain 1:   2500        -8354.073             0.015            0.012
Chain 1:   2600        -8352.050             0.013            0.012
Chain 1:   2700        -8266.225             0.013            0.012
Chain 1:   2800        -8231.359             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401792.667             1.000            1.000
Chain 1:    200     -1586635.364             2.648            4.295
Chain 1:    300      -891640.756             2.025            1.000
Chain 1:    400      -458090.787             1.755            1.000
Chain 1:    500      -358148.195             1.460            0.946
Chain 1:    600      -233060.447             1.306            0.946
Chain 1:    700      -119230.664             1.256            0.946
Chain 1:    800       -86416.385             1.146            0.946
Chain 1:    900       -66755.729             1.052            0.779
Chain 1:   1000       -51550.681             0.976            0.779
Chain 1:   1100       -39027.108             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38200.920             0.481            0.380
Chain 1:   1300       -26165.338             0.449            0.380
Chain 1:   1400       -25883.746             0.355            0.321
Chain 1:   1500       -22473.276             0.343            0.321
Chain 1:   1600       -21689.873             0.293            0.295
Chain 1:   1700       -20565.155             0.203            0.295
Chain 1:   1800       -20509.441             0.165            0.152
Chain 1:   1900       -20835.403             0.137            0.055
Chain 1:   2000       -19347.405             0.115            0.055
Chain 1:   2100       -19585.836             0.084            0.036
Chain 1:   2200       -19812.021             0.083            0.036
Chain 1:   2300       -19429.496             0.039            0.020
Chain 1:   2400       -19201.641             0.039            0.020
Chain 1:   2500       -19003.541             0.025            0.016
Chain 1:   2600       -18634.064             0.024            0.016
Chain 1:   2700       -18591.059             0.018            0.012
Chain 1:   2800       -18307.966             0.020            0.015
Chain 1:   2900       -18589.114             0.020            0.015
Chain 1:   3000       -18575.347             0.012            0.012
Chain 1:   3100       -18660.320             0.011            0.012
Chain 1:   3200       -18351.131             0.012            0.015
Chain 1:   3300       -18555.721             0.011            0.012
Chain 1:   3400       -18030.860             0.013            0.015
Chain 1:   3500       -18642.408             0.015            0.015
Chain 1:   3600       -17949.481             0.017            0.015
Chain 1:   3700       -18335.995             0.019            0.017
Chain 1:   3800       -17296.314             0.023            0.021
Chain 1:   3900       -17292.443             0.022            0.021
Chain 1:   4000       -17409.774             0.022            0.021
Chain 1:   4100       -17323.581             0.022            0.021
Chain 1:   4200       -17139.911             0.022            0.021
Chain 1:   4300       -17278.256             0.021            0.021
Chain 1:   4400       -17235.197             0.019            0.011
Chain 1:   4500       -17137.719             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12838.615             1.000            1.000
Chain 1:    200        -9563.478             0.671            1.000
Chain 1:    300        -8443.430             0.492            0.342
Chain 1:    400        -8571.408             0.373            0.342
Chain 1:    500        -8520.021             0.299            0.133
Chain 1:    600        -8343.339             0.253            0.133
Chain 1:    700        -8258.918             0.218            0.021
Chain 1:    800        -8270.616             0.191            0.021
Chain 1:    900        -8198.751             0.171            0.015
Chain 1:   1000        -8380.388             0.156            0.021
Chain 1:   1100        -8400.076             0.056            0.015
Chain 1:   1200        -8275.238             0.023            0.015
Chain 1:   1300        -8244.853             0.011            0.010
Chain 1:   1400        -8255.171             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002827 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57596.368             1.000            1.000
Chain 1:    200       -17881.655             1.610            2.221
Chain 1:    300        -8943.654             1.407            1.000
Chain 1:    400        -8226.766             1.077            1.000
Chain 1:    500        -9119.290             0.881            0.999
Chain 1:    600        -8547.122             0.745            0.999
Chain 1:    700        -8929.022             0.645            0.098
Chain 1:    800        -8189.379             0.576            0.098
Chain 1:    900        -8013.616             0.514            0.090
Chain 1:   1000        -7865.628             0.465            0.090
Chain 1:   1100        -7687.406             0.367            0.087
Chain 1:   1200        -7970.428             0.148            0.067
Chain 1:   1300        -7856.344             0.050            0.043
Chain 1:   1400        -7908.964             0.042            0.036
Chain 1:   1500        -7614.808             0.036            0.036
Chain 1:   1600        -7761.725             0.031            0.023
Chain 1:   1700        -7730.703             0.027            0.022
Chain 1:   1800        -7752.814             0.019            0.019
Chain 1:   1900        -7620.025             0.018            0.019
Chain 1:   2000        -7735.955             0.018            0.017
Chain 1:   2100        -7501.413             0.018            0.017
Chain 1:   2200        -7755.431             0.018            0.017
Chain 1:   2300        -7607.137             0.019            0.019
Chain 1:   2400        -7677.900             0.019            0.019
Chain 1:   2500        -7621.832             0.016            0.017
Chain 1:   2600        -7542.634             0.015            0.015
Chain 1:   2700        -7528.688             0.015            0.015
Chain 1:   2800        -7510.811             0.015            0.015
Chain 1:   2900        -7400.171             0.014            0.015
Chain 1:   3000        -7547.871             0.015            0.015
Chain 1:   3100        -7547.559             0.012            0.011
Chain 1:   3200        -7768.232             0.011            0.011
Chain 1:   3300        -7471.079             0.013            0.011
Chain 1:   3400        -7714.083             0.016            0.015
Chain 1:   3500        -7460.414             0.018            0.020
Chain 1:   3600        -7526.873             0.018            0.020
Chain 1:   3700        -7475.458             0.019            0.020
Chain 1:   3800        -7476.102             0.018            0.020
Chain 1:   3900        -7435.338             0.017            0.020
Chain 1:   4000        -7427.441             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003594 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86593.087             1.000            1.000
Chain 1:    200       -13933.741             3.107            5.215
Chain 1:    300       -10289.877             2.190            1.000
Chain 1:    400       -11271.598             1.664            1.000
Chain 1:    500        -9079.144             1.379            0.354
Chain 1:    600        -8701.366             1.157            0.354
Chain 1:    700        -8805.147             0.993            0.241
Chain 1:    800        -9374.945             0.877            0.241
Chain 1:    900        -9132.548             0.782            0.087
Chain 1:   1000        -8745.987             0.708            0.087
Chain 1:   1100        -9093.954             0.612            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8693.458             0.095            0.046
Chain 1:   1300        -8917.370             0.062            0.044
Chain 1:   1400        -8934.435             0.054            0.043
Chain 1:   1500        -8829.784             0.031            0.038
Chain 1:   1600        -8938.161             0.028            0.027
Chain 1:   1700        -9018.063             0.028            0.027
Chain 1:   1800        -8593.535             0.026            0.027
Chain 1:   1900        -8695.008             0.025            0.025
Chain 1:   2000        -8669.612             0.021            0.012
Chain 1:   2100        -8795.537             0.018            0.012
Chain 1:   2200        -8597.080             0.016            0.012
Chain 1:   2300        -8689.919             0.015            0.012
Chain 1:   2400        -8758.507             0.015            0.012
Chain 1:   2500        -8704.809             0.015            0.012
Chain 1:   2600        -8706.459             0.014            0.011
Chain 1:   2700        -8623.009             0.014            0.011
Chain 1:   2800        -8582.519             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0035 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417568.224             1.000            1.000
Chain 1:    200     -1585334.467             2.655            4.310
Chain 1:    300      -890134.584             2.030            1.000
Chain 1:    400      -457882.159             1.759            1.000
Chain 1:    500      -357942.676             1.463            0.944
Chain 1:    600      -232946.770             1.308            0.944
Chain 1:    700      -119381.866             1.257            0.944
Chain 1:    800       -86711.345             1.147            0.944
Chain 1:    900       -67095.353             1.052            0.781
Chain 1:   1000       -51934.512             0.976            0.781
Chain 1:   1100       -39455.719             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38636.340             0.479            0.377
Chain 1:   1300       -26626.117             0.446            0.377
Chain 1:   1400       -26349.982             0.353            0.316
Chain 1:   1500       -22947.037             0.340            0.316
Chain 1:   1600       -22167.371             0.289            0.292
Chain 1:   1700       -21044.261             0.200            0.292
Chain 1:   1800       -20989.577             0.162            0.148
Chain 1:   1900       -21315.852             0.135            0.053
Chain 1:   2000       -19828.854             0.113            0.053
Chain 1:   2100       -20066.937             0.082            0.035
Chain 1:   2200       -20293.400             0.081            0.035
Chain 1:   2300       -19910.592             0.038            0.019
Chain 1:   2400       -19682.630             0.038            0.019
Chain 1:   2500       -19484.761             0.025            0.015
Chain 1:   2600       -19114.685             0.023            0.015
Chain 1:   2700       -19071.628             0.018            0.012
Chain 1:   2800       -18788.473             0.019            0.015
Chain 1:   2900       -19069.725             0.019            0.015
Chain 1:   3000       -19055.851             0.012            0.012
Chain 1:   3100       -19140.889             0.011            0.012
Chain 1:   3200       -18831.474             0.011            0.015
Chain 1:   3300       -19036.293             0.011            0.012
Chain 1:   3400       -18511.071             0.012            0.015
Chain 1:   3500       -19123.177             0.014            0.015
Chain 1:   3600       -18429.496             0.016            0.015
Chain 1:   3700       -18816.521             0.018            0.016
Chain 1:   3800       -17775.766             0.022            0.021
Chain 1:   3900       -17771.908             0.021            0.021
Chain 1:   4000       -17889.197             0.022            0.021
Chain 1:   4100       -17802.945             0.022            0.021
Chain 1:   4200       -17619.090             0.021            0.021
Chain 1:   4300       -17757.541             0.021            0.021
Chain 1:   4400       -17714.239             0.018            0.010
Chain 1:   4500       -17616.770             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002305 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49381.017             1.000            1.000
Chain 1:    200       -26585.495             0.929            1.000
Chain 1:    300       -21157.269             0.705            0.857
Chain 1:    400       -28020.652             0.590            0.857
Chain 1:    500       -23838.518             0.507            0.257
Chain 1:    600       -11233.041             0.609            0.857
Chain 1:    700       -16279.073             0.567            0.310
Chain 1:    800       -15090.845             0.506            0.310
Chain 1:    900       -14814.441             0.452            0.257
Chain 1:   1000       -12552.299             0.424            0.257
Chain 1:   1100       -12190.574             0.327            0.245
Chain 1:   1200       -11936.020             0.244            0.180
Chain 1:   1300       -12187.565             0.220            0.175
Chain 1:   1400       -11262.006             0.204            0.082
Chain 1:   1500       -10992.728             0.189            0.079
Chain 1:   1600        -9840.932             0.088            0.079
Chain 1:   1700       -11159.305             0.069            0.079
Chain 1:   1800       -10657.901             0.066            0.047
Chain 1:   1900       -11364.758             0.070            0.062
Chain 1:   2000       -18518.029             0.091            0.062
Chain 1:   2100       -11336.724             0.151            0.082
Chain 1:   2200        -9837.711             0.164            0.117
Chain 1:   2300        -9599.909             0.165            0.117
Chain 1:   2400        -9373.913             0.159            0.117
Chain 1:   2500       -16596.708             0.200            0.118
Chain 1:   2600        -9615.581             0.261            0.152
Chain 1:   2700       -19159.270             0.299            0.386
Chain 1:   2800        -9044.839             0.406            0.435
Chain 1:   2900       -10207.771             0.411            0.435
Chain 1:   3000       -10052.800             0.374            0.435
Chain 1:   3100        -9576.375             0.316            0.152
Chain 1:   3200       -15115.971             0.337            0.366
Chain 1:   3300       -14446.020             0.339            0.366
Chain 1:   3400        -9897.868             0.383            0.435
Chain 1:   3500        -9165.465             0.347            0.366
Chain 1:   3600       -12627.682             0.302            0.274
Chain 1:   3700        -8820.239             0.296            0.274
Chain 1:   3800       -15057.150             0.225            0.274
Chain 1:   3900        -8831.773             0.284            0.366
Chain 1:   4000       -10732.095             0.300            0.366
Chain 1:   4100        -9266.413             0.311            0.366
Chain 1:   4200        -9253.378             0.275            0.274
Chain 1:   4300       -11016.873             0.286            0.274
Chain 1:   4400        -9500.046             0.256            0.177
Chain 1:   4500        -9414.698             0.249            0.177
Chain 1:   4600       -13125.409             0.250            0.177
Chain 1:   4700       -13511.146             0.210            0.160
Chain 1:   4800        -8806.081             0.222            0.160
Chain 1:   4900        -9080.385             0.154            0.160
Chain 1:   5000       -11663.962             0.159            0.160
Chain 1:   5100       -12249.620             0.148            0.160
Chain 1:   5200        -9892.557             0.171            0.160
Chain 1:   5300       -11127.310             0.166            0.160
Chain 1:   5400        -9720.677             0.165            0.145
Chain 1:   5500        -9270.550             0.169            0.145
Chain 1:   5600       -10010.792             0.148            0.111
Chain 1:   5700       -12627.726             0.166            0.145
Chain 1:   5800        -9527.149             0.145            0.145
Chain 1:   5900        -9314.458             0.144            0.145
Chain 1:   6000        -8629.744             0.130            0.111
Chain 1:   6100       -11142.392             0.148            0.145
Chain 1:   6200        -8395.920             0.157            0.145
Chain 1:   6300       -11737.312             0.174            0.207
Chain 1:   6400       -11301.175             0.163            0.207
Chain 1:   6500        -9480.069             0.178            0.207
Chain 1:   6600        -9691.660             0.172            0.207
Chain 1:   6700       -14611.684             0.185            0.226
Chain 1:   6800       -13528.393             0.161            0.192
Chain 1:   6900       -12087.622             0.171            0.192
Chain 1:   7000        -9953.316             0.184            0.214
Chain 1:   7100        -8971.180             0.172            0.192
Chain 1:   7200        -8957.932             0.140            0.119
Chain 1:   7300       -10216.165             0.124            0.119
Chain 1:   7400       -11696.944             0.133            0.123
Chain 1:   7500        -9071.185             0.142            0.123
Chain 1:   7600        -8585.296             0.146            0.123
Chain 1:   7700        -8692.041             0.113            0.119
Chain 1:   7800       -15444.861             0.149            0.123
Chain 1:   7900        -9263.685             0.204            0.127
Chain 1:   8000        -9395.584             0.184            0.123
Chain 1:   8100        -8780.062             0.180            0.123
Chain 1:   8200        -8338.745             0.185            0.123
Chain 1:   8300        -8273.900             0.173            0.070
Chain 1:   8400        -9030.138             0.169            0.070
Chain 1:   8500        -8316.863             0.149            0.070
Chain 1:   8600        -9170.312             0.152            0.084
Chain 1:   8700        -9867.515             0.158            0.084
Chain 1:   8800        -8935.044             0.125            0.084
Chain 1:   8900        -9456.519             0.064            0.071
Chain 1:   9000        -8399.480             0.075            0.084
Chain 1:   9100        -8835.508             0.073            0.084
Chain 1:   9200        -8426.408             0.072            0.084
Chain 1:   9300       -12164.181             0.102            0.086
Chain 1:   9400       -11892.498             0.096            0.086
Chain 1:   9500        -9992.851             0.107            0.093
Chain 1:   9600        -8509.558             0.115            0.104
Chain 1:   9700        -8177.349             0.112            0.104
Chain 1:   9800       -11434.729             0.130            0.126
Chain 1:   9900       -10102.208             0.138            0.132
Chain 1:   10000        -8440.015             0.145            0.174
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001584 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59342.657             1.000            1.000
Chain 1:    200       -17940.687             1.654            2.308
Chain 1:    300        -8960.734             1.437            1.002
Chain 1:    400        -8426.048             1.093            1.002
Chain 1:    500        -8323.752             0.877            1.000
Chain 1:    600        -9487.120             0.751            1.000
Chain 1:    700        -8107.533             0.668            0.170
Chain 1:    800        -7808.274             0.590            0.170
Chain 1:    900        -7979.742             0.526            0.123
Chain 1:   1000        -7872.809             0.475            0.123
Chain 1:   1100        -7827.756             0.376            0.063
Chain 1:   1200        -7611.291             0.148            0.038
Chain 1:   1300        -7724.693             0.049            0.028
Chain 1:   1400        -7789.361             0.044            0.021
Chain 1:   1500        -7595.293             0.045            0.026
Chain 1:   1600        -7737.903             0.034            0.021
Chain 1:   1700        -7615.377             0.019            0.018
Chain 1:   1800        -7671.332             0.016            0.016
Chain 1:   1900        -7751.840             0.015            0.015
Chain 1:   2000        -7684.121             0.014            0.015
Chain 1:   2100        -7604.039             0.015            0.015
Chain 1:   2200        -7733.964             0.014            0.015
Chain 1:   2300        -7577.367             0.014            0.016
Chain 1:   2400        -7547.312             0.014            0.016
Chain 1:   2500        -7539.584             0.011            0.011
Chain 1:   2600        -7534.559             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004057 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85768.293             1.000            1.000
Chain 1:    200       -13814.700             3.104            5.208
Chain 1:    300       -10090.752             2.193            1.000
Chain 1:    400       -11604.706             1.677            1.000
Chain 1:    500        -8926.154             1.402            0.369
Chain 1:    600        -9294.558             1.175            0.369
Chain 1:    700        -8724.304             1.016            0.300
Chain 1:    800        -8656.032             0.890            0.300
Chain 1:    900        -8843.602             0.794            0.130
Chain 1:   1000        -8663.101             0.716            0.130
Chain 1:   1100        -8857.801             0.618            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8405.519             0.103            0.054
Chain 1:   1300        -8707.812             0.070            0.040
Chain 1:   1400        -8578.359             0.058            0.035
Chain 1:   1500        -8636.007             0.029            0.022
Chain 1:   1600        -8684.756             0.025            0.021
Chain 1:   1700        -8743.141             0.019            0.021
Chain 1:   1800        -8318.874             0.024            0.021
Chain 1:   1900        -8416.369             0.023            0.021
Chain 1:   2000        -8403.221             0.021            0.015
Chain 1:   2100        -8524.993             0.020            0.014
Chain 1:   2200        -8316.330             0.017            0.014
Chain 1:   2300        -8410.909             0.015            0.012
Chain 1:   2400        -8478.065             0.014            0.011
Chain 1:   2500        -8426.583             0.014            0.011
Chain 1:   2600        -8439.460             0.014            0.011
Chain 1:   2700        -8347.502             0.014            0.011
Chain 1:   2800        -8295.592             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003879 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388754.643             1.000            1.000
Chain 1:    200     -1580088.641             2.655            4.309
Chain 1:    300      -890412.052             2.028            1.000
Chain 1:    400      -457683.107             1.757            1.000
Chain 1:    500      -358499.816             1.461            0.945
Chain 1:    600      -233465.598             1.307            0.945
Chain 1:    700      -119669.828             1.256            0.945
Chain 1:    800       -86875.455             1.146            0.945
Chain 1:    900       -67201.537             1.051            0.775
Chain 1:   1000       -51993.251             0.975            0.775
Chain 1:   1100       -39456.251             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38636.155             0.478            0.377
Chain 1:   1300       -26562.541             0.446            0.377
Chain 1:   1400       -26281.504             0.353            0.318
Chain 1:   1500       -22860.799             0.340            0.318
Chain 1:   1600       -22076.095             0.290            0.293
Chain 1:   1700       -20945.457             0.201            0.293
Chain 1:   1800       -20889.120             0.163            0.150
Chain 1:   1900       -21215.829             0.135            0.054
Chain 1:   2000       -19723.828             0.114            0.054
Chain 1:   2100       -19962.270             0.083            0.036
Chain 1:   2200       -20189.557             0.082            0.036
Chain 1:   2300       -19805.931             0.039            0.019
Chain 1:   2400       -19577.819             0.039            0.019
Chain 1:   2500       -19380.012             0.025            0.015
Chain 1:   2600       -19009.444             0.023            0.015
Chain 1:   2700       -18966.254             0.018            0.012
Chain 1:   2800       -18682.944             0.019            0.015
Chain 1:   2900       -18964.463             0.019            0.015
Chain 1:   3000       -18950.577             0.012            0.012
Chain 1:   3100       -19035.656             0.011            0.012
Chain 1:   3200       -18725.946             0.011            0.015
Chain 1:   3300       -18930.999             0.011            0.012
Chain 1:   3400       -18405.264             0.012            0.015
Chain 1:   3500       -19018.177             0.015            0.015
Chain 1:   3600       -18323.550             0.016            0.015
Chain 1:   3700       -18711.337             0.018            0.017
Chain 1:   3800       -17669.045             0.023            0.021
Chain 1:   3900       -17665.193             0.021            0.021
Chain 1:   4000       -17782.449             0.022            0.021
Chain 1:   4100       -17696.123             0.022            0.021
Chain 1:   4200       -17511.975             0.021            0.021
Chain 1:   4300       -17650.629             0.021            0.021
Chain 1:   4400       -17607.089             0.018            0.011
Chain 1:   4500       -17509.613             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001275 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12890.777             1.000            1.000
Chain 1:    200        -9840.131             0.655            1.000
Chain 1:    300        -8352.375             0.496            0.310
Chain 1:    400        -8584.118             0.379            0.310
Chain 1:    500        -8453.786             0.306            0.178
Chain 1:    600        -8309.907             0.258            0.178
Chain 1:    700        -8221.747             0.223            0.027
Chain 1:    800        -8169.183             0.196            0.027
Chain 1:    900        -8189.592             0.174            0.017
Chain 1:   1000        -8290.881             0.158            0.017
Chain 1:   1100        -8316.159             0.058            0.015
Chain 1:   1200        -8221.405             0.028            0.012
Chain 1:   1300        -8143.940             0.012            0.012
Chain 1:   1400        -8157.392             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59110.403             1.000            1.000
Chain 1:    200       -18216.915             1.622            2.245
Chain 1:    300        -9160.933             1.411            1.000
Chain 1:    400        -8589.469             1.075            1.000
Chain 1:    500        -8945.175             0.868            0.989
Chain 1:    600        -8685.949             0.728            0.989
Chain 1:    700        -8036.745             0.636            0.081
Chain 1:    800        -8563.002             0.564            0.081
Chain 1:    900        -8228.481             0.506            0.067
Chain 1:   1000        -8101.605             0.457            0.067
Chain 1:   1100        -7925.469             0.359            0.061
Chain 1:   1200        -7916.372             0.135            0.041
Chain 1:   1300        -7953.936             0.036            0.040
Chain 1:   1400        -7921.256             0.030            0.030
Chain 1:   1500        -7680.049             0.029            0.030
Chain 1:   1600        -7940.761             0.030            0.031
Chain 1:   1700        -7716.529             0.024            0.029
Chain 1:   1800        -7756.065             0.019            0.022
Chain 1:   1900        -7822.140             0.015            0.016
Chain 1:   2000        -7895.947             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003875 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86445.670             1.000            1.000
Chain 1:    200       -14106.131             3.064            5.128
Chain 1:    300       -10351.589             2.164            1.000
Chain 1:    400       -12022.154             1.657            1.000
Chain 1:    500        -8954.833             1.394            0.363
Chain 1:    600        -9090.319             1.165            0.363
Chain 1:    700        -9493.595             1.004            0.343
Chain 1:    800        -8589.067             0.892            0.343
Chain 1:    900        -8730.558             0.795            0.139
Chain 1:   1000        -8965.050             0.718            0.139
Chain 1:   1100        -9127.221             0.620            0.105   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8639.902             0.112            0.056
Chain 1:   1300        -8998.640             0.080            0.042
Chain 1:   1400        -8750.499             0.069            0.040
Chain 1:   1500        -8826.153             0.036            0.028
Chain 1:   1600        -8929.067             0.035            0.028
Chain 1:   1700        -8985.820             0.032            0.026
Chain 1:   1800        -8536.163             0.026            0.026
Chain 1:   1900        -8644.168             0.026            0.026
Chain 1:   2000        -8640.422             0.023            0.018
Chain 1:   2100        -8795.076             0.023            0.018
Chain 1:   2200        -8541.458             0.021            0.018
Chain 1:   2300        -8716.553             0.019            0.018
Chain 1:   2400        -8539.359             0.018            0.018
Chain 1:   2500        -8617.306             0.018            0.018
Chain 1:   2600        -8650.044             0.017            0.018
Chain 1:   2700        -8570.100             0.018            0.018
Chain 1:   2800        -8520.866             0.013            0.012
Chain 1:   2900        -8630.343             0.013            0.013
Chain 1:   3000        -8560.988             0.014            0.013
Chain 1:   3100        -8505.534             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003215 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386024.956             1.000            1.000
Chain 1:    200     -1577671.844             2.658            4.315
Chain 1:    300      -891441.740             2.028            1.000
Chain 1:    400      -458880.189             1.757            1.000
Chain 1:    500      -359865.444             1.461            0.943
Chain 1:    600      -234676.323             1.306            0.943
Chain 1:    700      -120432.451             1.255            0.943
Chain 1:    800       -87517.106             1.145            0.943
Chain 1:    900       -67745.501             1.050            0.770
Chain 1:   1000       -52463.740             0.974            0.770
Chain 1:   1100       -39860.666             0.906            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39033.399             0.477            0.376
Chain 1:   1300       -26894.498             0.445            0.376
Chain 1:   1400       -26607.284             0.352            0.316
Chain 1:   1500       -23169.787             0.339            0.316
Chain 1:   1600       -22380.173             0.289            0.292
Chain 1:   1700       -21241.728             0.200            0.291
Chain 1:   1800       -21183.550             0.162            0.148
Chain 1:   1900       -21510.328             0.135            0.054
Chain 1:   2000       -20014.143             0.113            0.054
Chain 1:   2100       -20252.822             0.083            0.035
Chain 1:   2200       -20480.781             0.081            0.035
Chain 1:   2300       -20096.526             0.038            0.019
Chain 1:   2400       -19868.281             0.038            0.019
Chain 1:   2500       -19670.710             0.025            0.015
Chain 1:   2600       -19299.721             0.023            0.015
Chain 1:   2700       -19256.412             0.018            0.012
Chain 1:   2800       -18973.138             0.019            0.015
Chain 1:   2900       -19254.862             0.019            0.015
Chain 1:   3000       -19240.819             0.012            0.012
Chain 1:   3100       -19325.955             0.011            0.011
Chain 1:   3200       -19016.064             0.011            0.015
Chain 1:   3300       -19221.274             0.010            0.011
Chain 1:   3400       -18695.353             0.012            0.015
Chain 1:   3500       -19308.602             0.014            0.015
Chain 1:   3600       -18613.563             0.016            0.015
Chain 1:   3700       -19001.733             0.018            0.016
Chain 1:   3800       -17958.801             0.022            0.020
Chain 1:   3900       -17954.969             0.021            0.020
Chain 1:   4000       -18072.188             0.021            0.020
Chain 1:   4100       -17985.821             0.021            0.020
Chain 1:   4200       -17801.543             0.021            0.020
Chain 1:   4300       -17940.264             0.021            0.020
Chain 1:   4400       -17896.626             0.018            0.010
Chain 1:   4500       -17799.132             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49749.136             1.000            1.000
Chain 1:    200       -18483.543             1.346            1.692
Chain 1:    300       -33020.862             1.044            1.000
Chain 1:    400       -22950.117             0.893            1.000
Chain 1:    500       -24535.763             0.727            0.440
Chain 1:    600       -21197.288             0.632            0.440
Chain 1:    700       -13820.283             0.618            0.440
Chain 1:    800       -16072.554             0.558            0.440
Chain 1:    900       -14482.143             0.508            0.439
Chain 1:   1000       -12955.675             0.469            0.439
Chain 1:   1100       -18513.521             0.399            0.300
Chain 1:   1200       -14198.780             0.261            0.300
Chain 1:   1300       -13100.412             0.225            0.157
Chain 1:   1400       -11172.492             0.198            0.157
Chain 1:   1500       -10747.874             0.196            0.157
Chain 1:   1600       -23505.340             0.234            0.173
Chain 1:   1700       -11247.482             0.290            0.173
Chain 1:   1800       -10452.889             0.284            0.173
Chain 1:   1900       -12303.137             0.288            0.173
Chain 1:   2000       -11966.954             0.279            0.173
Chain 1:   2100       -10959.768             0.258            0.150
Chain 1:   2200       -10156.478             0.235            0.092
Chain 1:   2300        -9988.139             0.229            0.092
Chain 1:   2400       -10700.516             0.218            0.079
Chain 1:   2500        -9831.258             0.223            0.088
Chain 1:   2600       -13368.507             0.195            0.088
Chain 1:   2700       -11757.521             0.100            0.088
Chain 1:   2800       -10161.617             0.108            0.092
Chain 1:   2900        -9708.711             0.098            0.088
Chain 1:   3000        -9346.603             0.099            0.088
Chain 1:   3100       -11376.891             0.107            0.088
Chain 1:   3200        -9924.736             0.114            0.137
Chain 1:   3300       -18850.340             0.160            0.146
Chain 1:   3400        -9322.617             0.255            0.157
Chain 1:   3500       -10729.474             0.260            0.157
Chain 1:   3600       -10244.616             0.238            0.146
Chain 1:   3700        -9435.628             0.233            0.146
Chain 1:   3800        -9097.428             0.221            0.131
Chain 1:   3900       -13861.843             0.250            0.146
Chain 1:   4000       -10128.603             0.283            0.178
Chain 1:   4100        -9963.028             0.267            0.146
Chain 1:   4200       -12939.507             0.276            0.230
Chain 1:   4300        -9884.818             0.259            0.230
Chain 1:   4400       -18404.573             0.203            0.230
Chain 1:   4500        -9485.441             0.284            0.309
Chain 1:   4600        -9201.638             0.282            0.309
Chain 1:   4700        -9363.179             0.276            0.309
Chain 1:   4800        -9147.103             0.274            0.309
Chain 1:   4900        -9045.624             0.241            0.230
Chain 1:   5000       -14894.985             0.243            0.230
Chain 1:   5100       -11656.590             0.270            0.278
Chain 1:   5200       -10173.455             0.261            0.278
Chain 1:   5300        -9571.193             0.237            0.146
Chain 1:   5400       -11284.903             0.205            0.146
Chain 1:   5500        -9365.884             0.132            0.146
Chain 1:   5600        -9966.731             0.135            0.146
Chain 1:   5700        -9674.050             0.136            0.146
Chain 1:   5800        -8991.401             0.141            0.146
Chain 1:   5900       -10118.482             0.151            0.146
Chain 1:   6000        -9427.483             0.119            0.111
Chain 1:   6100        -9105.322             0.095            0.076
Chain 1:   6200        -8828.298             0.084            0.073
Chain 1:   6300        -9022.139             0.080            0.073
Chain 1:   6400       -11741.829             0.088            0.073
Chain 1:   6500        -9164.210             0.095            0.073
Chain 1:   6600       -10795.596             0.104            0.076
Chain 1:   6700       -13170.192             0.119            0.111
Chain 1:   6800        -8852.706             0.160            0.151
Chain 1:   6900       -11839.665             0.175            0.180
Chain 1:   7000        -8768.789             0.202            0.232
Chain 1:   7100       -11417.827             0.222            0.232
Chain 1:   7200       -13698.311             0.235            0.232
Chain 1:   7300        -8909.645             0.287            0.252
Chain 1:   7400        -8924.873             0.264            0.252
Chain 1:   7500        -8706.517             0.238            0.232
Chain 1:   7600        -9074.295             0.227            0.232
Chain 1:   7700       -11581.049             0.231            0.232
Chain 1:   7800        -9231.616             0.208            0.232
Chain 1:   7900        -9229.010             0.182            0.216
Chain 1:   8000        -8653.755             0.154            0.166
Chain 1:   8100        -8877.240             0.133            0.066
Chain 1:   8200        -8970.891             0.118            0.041
Chain 1:   8300       -12389.163             0.092            0.041
Chain 1:   8400        -9249.775             0.125            0.066
Chain 1:   8500        -9015.748             0.126            0.066
Chain 1:   8600       -12880.294             0.151            0.216
Chain 1:   8700       -11295.491             0.144            0.140
Chain 1:   8800       -10471.601             0.126            0.079
Chain 1:   8900        -9666.232             0.135            0.083
Chain 1:   9000        -8954.064             0.136            0.083
Chain 1:   9100        -9309.773             0.137            0.083
Chain 1:   9200        -9387.895             0.137            0.083
Chain 1:   9300        -8820.018             0.116            0.080
Chain 1:   9400       -12839.029             0.113            0.080
Chain 1:   9500        -9225.088             0.150            0.083
Chain 1:   9600        -8832.421             0.124            0.080
Chain 1:   9700        -8533.060             0.114            0.079
Chain 1:   9800        -8883.218             0.110            0.064
Chain 1:   9900       -10572.631             0.117            0.064
Chain 1:   10000        -8527.763             0.133            0.064
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001651 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59528.887             1.000            1.000
Chain 1:    200       -18428.949             1.615            2.230
Chain 1:    300        -9237.937             1.408            1.000
Chain 1:    400        -8404.609             1.081            1.000
Chain 1:    500        -8405.003             0.865            0.995
Chain 1:    600        -9266.447             0.736            0.995
Chain 1:    700        -8404.996             0.646            0.102
Chain 1:    800        -7982.697             0.572            0.102
Chain 1:    900        -8278.379             0.512            0.099
Chain 1:   1000        -7719.334             0.468            0.099
Chain 1:   1100        -7863.910             0.370            0.093
Chain 1:   1200        -7953.549             0.148            0.072
Chain 1:   1300        -7680.386             0.052            0.053
Chain 1:   1400        -7988.032             0.046            0.039
Chain 1:   1500        -7729.785             0.049            0.039
Chain 1:   1600        -7846.662             0.042            0.036
Chain 1:   1700        -7679.524             0.033            0.036
Chain 1:   1800        -7782.195             0.030            0.033
Chain 1:   1900        -7601.686             0.028            0.024
Chain 1:   2000        -7751.725             0.023            0.022
Chain 1:   2100        -7612.530             0.023            0.022
Chain 1:   2200        -7935.537             0.026            0.024
Chain 1:   2300        -7676.756             0.026            0.024
Chain 1:   2400        -7779.423             0.023            0.022
Chain 1:   2500        -7736.329             0.020            0.019
Chain 1:   2600        -7639.613             0.020            0.019
Chain 1:   2700        -7659.853             0.018            0.018
Chain 1:   2800        -7728.682             0.018            0.018
Chain 1:   2900        -7481.955             0.019            0.018
Chain 1:   3000        -7642.865             0.019            0.018
Chain 1:   3100        -7640.368             0.017            0.013
Chain 1:   3200        -7858.826             0.016            0.013
Chain 1:   3300        -7582.253             0.016            0.013
Chain 1:   3400        -7827.702             0.018            0.021
Chain 1:   3500        -7550.282             0.021            0.028
Chain 1:   3600        -7615.285             0.021            0.028
Chain 1:   3700        -7563.962             0.021            0.028
Chain 1:   3800        -7564.581             0.020            0.028
Chain 1:   3900        -7518.423             0.018            0.021
Chain 1:   4000        -7513.358             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003033 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86304.301             1.000            1.000
Chain 1:    200       -14147.130             3.050            5.100
Chain 1:    300       -10401.405             2.154            1.000
Chain 1:    400       -12131.558             1.651            1.000
Chain 1:    500        -9039.821             1.389            0.360
Chain 1:    600        -8967.394             1.159            0.360
Chain 1:    700        -9397.318             1.000            0.342
Chain 1:    800        -9790.446             0.880            0.342
Chain 1:    900        -9157.990             0.790            0.143
Chain 1:   1000        -8738.356             0.716            0.143
Chain 1:   1100        -8953.477             0.618            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8725.985             0.111            0.048
Chain 1:   1300        -9008.386             0.078            0.046
Chain 1:   1400        -8832.445             0.065            0.040
Chain 1:   1500        -8891.122             0.032            0.031
Chain 1:   1600        -8997.122             0.032            0.031
Chain 1:   1700        -9052.099             0.028            0.026
Chain 1:   1800        -8604.575             0.029            0.026
Chain 1:   1900        -8712.902             0.024            0.024
Chain 1:   2000        -8689.755             0.019            0.020
Chain 1:   2100        -8830.769             0.018            0.016
Chain 1:   2200        -8607.508             0.018            0.016
Chain 1:   2300        -8717.056             0.017            0.013
Chain 1:   2400        -8773.106             0.015            0.012
Chain 1:   2500        -8721.278             0.015            0.012
Chain 1:   2600        -8736.257             0.014            0.012
Chain 1:   2700        -8643.091             0.015            0.012
Chain 1:   2800        -8588.624             0.010            0.011
Chain 1:   2900        -8689.798             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004367 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8427704.871             1.000            1.000
Chain 1:    200     -1585498.829             2.658            4.315
Chain 1:    300      -891348.965             2.031            1.000
Chain 1:    400      -458275.983             1.760            1.000
Chain 1:    500      -358459.012             1.464            0.945
Chain 1:    600      -233371.934             1.309            0.945
Chain 1:    700      -119737.985             1.258            0.945
Chain 1:    800       -87017.277             1.147            0.945
Chain 1:    900       -67386.851             1.052            0.779
Chain 1:   1000       -52221.016             0.976            0.779
Chain 1:   1100       -39725.521             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38909.780             0.478            0.376
Chain 1:   1300       -26875.087             0.445            0.376
Chain 1:   1400       -26598.739             0.351            0.315
Chain 1:   1500       -23188.684             0.338            0.315
Chain 1:   1600       -22407.455             0.288            0.291
Chain 1:   1700       -21281.144             0.199            0.290
Chain 1:   1800       -21225.935             0.161            0.147
Chain 1:   1900       -21552.711             0.134            0.053
Chain 1:   2000       -20063.019             0.112            0.053
Chain 1:   2100       -20301.252             0.082            0.035
Chain 1:   2200       -20528.302             0.081            0.035
Chain 1:   2300       -20144.900             0.038            0.019
Chain 1:   2400       -19916.780             0.038            0.019
Chain 1:   2500       -19718.892             0.024            0.015
Chain 1:   2600       -19348.305             0.023            0.015
Chain 1:   2700       -19305.112             0.018            0.012
Chain 1:   2800       -19021.720             0.019            0.015
Chain 1:   2900       -19303.237             0.019            0.015
Chain 1:   3000       -19289.368             0.011            0.012
Chain 1:   3100       -19374.454             0.011            0.011
Chain 1:   3200       -19064.695             0.011            0.015
Chain 1:   3300       -19269.784             0.010            0.011
Chain 1:   3400       -18743.940             0.012            0.015
Chain 1:   3500       -19356.951             0.014            0.015
Chain 1:   3600       -18662.161             0.016            0.015
Chain 1:   3700       -19050.033             0.018            0.016
Chain 1:   3800       -18007.476             0.022            0.020
Chain 1:   3900       -18003.583             0.021            0.020
Chain 1:   4000       -18120.881             0.021            0.020
Chain 1:   4100       -18034.536             0.021            0.020
Chain 1:   4200       -17850.302             0.021            0.020
Chain 1:   4300       -17989.017             0.020            0.020
Chain 1:   4400       -17945.409             0.018            0.010
Chain 1:   4500       -17847.904             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001343 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49005.676             1.000            1.000
Chain 1:    200       -20112.965             1.218            1.437
Chain 1:    300       -18589.119             0.839            1.000
Chain 1:    400       -16800.714             0.656            1.000
Chain 1:    500       -17249.225             0.530            0.106
Chain 1:    600       -18350.931             0.452            0.106
Chain 1:    700       -15060.256             0.418            0.106
Chain 1:    800       -11308.816             0.408            0.219
Chain 1:    900       -11797.421             0.367            0.106
Chain 1:   1000       -12828.905             0.338            0.106
Chain 1:   1100       -16719.187             0.262            0.106
Chain 1:   1200       -10586.123             0.176            0.106
Chain 1:   1300       -12548.288             0.183            0.156
Chain 1:   1400       -10712.856             0.190            0.171
Chain 1:   1500       -13385.121             0.207            0.200
Chain 1:   1600       -11844.048             0.214            0.200
Chain 1:   1700       -17724.773             0.225            0.200
Chain 1:   1800       -10120.241             0.267            0.200
Chain 1:   1900       -10860.837             0.270            0.200
Chain 1:   2000       -18632.696             0.304            0.233
Chain 1:   2100       -10953.895             0.351            0.332
Chain 1:   2200       -10476.273             0.297            0.200
Chain 1:   2300       -11873.197             0.293            0.200
Chain 1:   2400        -9594.363             0.300            0.238
Chain 1:   2500       -16079.168             0.320            0.332
Chain 1:   2600        -9451.653             0.377            0.403
Chain 1:   2700       -10742.046             0.356            0.403
Chain 1:   2800       -16467.838             0.316            0.348
Chain 1:   2900        -9168.111             0.389            0.403
Chain 1:   3000       -16590.850             0.392            0.403
Chain 1:   3100       -10225.118             0.384            0.403
Chain 1:   3200       -14966.915             0.411            0.403
Chain 1:   3300        -9596.124             0.455            0.447
Chain 1:   3400       -12151.811             0.453            0.447
Chain 1:   3500        -9957.277             0.434            0.447
Chain 1:   3600        -9818.987             0.366            0.348
Chain 1:   3700        -9190.180             0.360            0.348
Chain 1:   3800       -11267.050             0.344            0.317
Chain 1:   3900        -9985.768             0.277            0.220
Chain 1:   4000       -16605.683             0.272            0.220
Chain 1:   4100       -11195.330             0.258            0.220
Chain 1:   4200        -9763.627             0.241            0.210
Chain 1:   4300        -9402.394             0.189            0.184
Chain 1:   4400        -8755.387             0.176            0.147
Chain 1:   4500        -9098.948             0.157            0.128
Chain 1:   4600       -14496.097             0.193            0.147
Chain 1:   4700       -13131.779             0.197            0.147
Chain 1:   4800        -9146.352             0.222            0.147
Chain 1:   4900        -8725.421             0.214            0.147
Chain 1:   5000       -10032.662             0.187            0.130
Chain 1:   5100        -8624.794             0.155            0.130
Chain 1:   5200       -11009.876             0.162            0.130
Chain 1:   5300        -8876.265             0.182            0.163
Chain 1:   5400       -13481.463             0.209            0.217
Chain 1:   5500       -12759.814             0.211            0.217
Chain 1:   5600       -12432.574             0.176            0.163
Chain 1:   5700       -14596.921             0.181            0.163
Chain 1:   5800       -11523.005             0.164            0.163
Chain 1:   5900       -10798.197             0.166            0.163
Chain 1:   6000       -12342.894             0.165            0.163
Chain 1:   6100        -8846.545             0.188            0.217
Chain 1:   6200        -8753.975             0.168            0.148
Chain 1:   6300       -12275.089             0.172            0.148
Chain 1:   6400       -13178.133             0.145            0.125
Chain 1:   6500        -9908.926             0.172            0.148
Chain 1:   6600        -8496.341             0.186            0.166
Chain 1:   6700        -9043.060             0.178            0.166
Chain 1:   6800        -9851.043             0.159            0.125
Chain 1:   6900        -9760.755             0.153            0.125
Chain 1:   7000        -8699.988             0.153            0.122
Chain 1:   7100        -8346.206             0.118            0.082
Chain 1:   7200        -8385.794             0.117            0.082
Chain 1:   7300        -8989.017             0.095            0.069
Chain 1:   7400        -8998.744             0.089            0.067
Chain 1:   7500        -9752.663             0.063            0.067
Chain 1:   7600        -8423.519             0.062            0.067
Chain 1:   7700       -12195.799             0.087            0.077
Chain 1:   7800       -10734.171             0.093            0.077
Chain 1:   7900       -13063.941             0.110            0.122
Chain 1:   8000        -9689.801             0.132            0.136
Chain 1:   8100        -9897.467             0.130            0.136
Chain 1:   8200       -12065.458             0.148            0.158
Chain 1:   8300        -8526.024             0.182            0.178
Chain 1:   8400       -11151.319             0.206            0.180
Chain 1:   8500        -8150.977             0.235            0.235
Chain 1:   8600        -8824.034             0.227            0.235
Chain 1:   8700       -10189.636             0.209            0.180
Chain 1:   8800        -8678.011             0.213            0.180
Chain 1:   8900        -8727.983             0.196            0.180
Chain 1:   9000        -8883.254             0.163            0.174
Chain 1:   9100       -10707.996             0.178            0.174
Chain 1:   9200       -10039.721             0.166            0.170
Chain 1:   9300       -10105.057             0.125            0.134
Chain 1:   9400        -9638.083             0.107            0.076
Chain 1:   9500        -8948.371             0.078            0.076
Chain 1:   9600       -10318.927             0.083            0.077
Chain 1:   9700        -8311.751             0.094            0.077
Chain 1:   9800       -10866.445             0.100            0.077
Chain 1:   9900       -11225.467             0.103            0.077
Chain 1:   10000        -8995.837             0.126            0.133
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001529 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58225.024             1.000            1.000
Chain 1:    200       -17854.051             1.631            2.261
Chain 1:    300        -8774.864             1.432            1.035
Chain 1:    400        -8076.463             1.096            1.035
Chain 1:    500        -8782.424             0.893            1.000
Chain 1:    600        -8827.640             0.745            1.000
Chain 1:    700        -7947.168             0.654            0.111
Chain 1:    800        -8091.033             0.575            0.111
Chain 1:    900        -8168.058             0.512            0.086
Chain 1:   1000        -7947.259             0.463            0.086
Chain 1:   1100        -7712.324             0.366            0.080
Chain 1:   1200        -7583.604             0.142            0.030
Chain 1:   1300        -7789.996             0.041            0.028
Chain 1:   1400        -7925.816             0.034            0.026
Chain 1:   1500        -7583.430             0.031            0.026
Chain 1:   1600        -7607.934             0.031            0.026
Chain 1:   1700        -7561.005             0.020            0.018
Chain 1:   1800        -7612.630             0.019            0.017
Chain 1:   1900        -7611.245             0.018            0.017
Chain 1:   2000        -7665.135             0.016            0.017
Chain 1:   2100        -7534.772             0.015            0.017
Chain 1:   2200        -7690.268             0.015            0.017
Chain 1:   2300        -7561.455             0.014            0.017
Chain 1:   2400        -7665.599             0.014            0.014
Chain 1:   2500        -7655.842             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003621 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85987.632             1.000            1.000
Chain 1:    200       -13731.182             3.131            5.262
Chain 1:    300       -10054.683             2.209            1.000
Chain 1:    400       -11070.542             1.680            1.000
Chain 1:    500        -9038.348             1.389            0.366
Chain 1:    600        -8486.411             1.168            0.366
Chain 1:    700        -8678.230             1.005            0.225
Chain 1:    800        -9315.922             0.888            0.225
Chain 1:    900        -8895.881             0.794            0.092
Chain 1:   1000        -8739.475             0.717            0.092
Chain 1:   1100        -8832.812             0.618            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8345.381             0.097            0.065
Chain 1:   1300        -8704.357             0.065            0.058
Chain 1:   1400        -8672.120             0.056            0.047
Chain 1:   1500        -8597.338             0.034            0.041
Chain 1:   1600        -8699.971             0.029            0.022
Chain 1:   1700        -8768.897             0.028            0.018
Chain 1:   1800        -8337.898             0.026            0.018
Chain 1:   1900        -8441.955             0.022            0.012
Chain 1:   2000        -8417.191             0.021            0.012
Chain 1:   2100        -8550.328             0.021            0.012
Chain 1:   2200        -8345.627             0.018            0.012
Chain 1:   2300        -8440.813             0.015            0.012
Chain 1:   2400        -8505.642             0.015            0.012
Chain 1:   2500        -8450.680             0.015            0.012
Chain 1:   2600        -8454.720             0.014            0.011
Chain 1:   2700        -8370.071             0.014            0.011
Chain 1:   2800        -8327.021             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8365938.485             1.000            1.000
Chain 1:    200     -1579916.962             2.648            4.295
Chain 1:    300      -891262.168             2.023            1.000
Chain 1:    400      -458570.350             1.753            1.000
Chain 1:    500      -359290.438             1.458            0.944
Chain 1:    600      -234092.750             1.304            0.944
Chain 1:    700      -119908.903             1.254            0.944
Chain 1:    800       -87008.039             1.144            0.944
Chain 1:    900       -67270.281             1.050            0.773
Chain 1:   1000       -52005.645             0.974            0.773
Chain 1:   1100       -39422.899             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38593.609             0.479            0.378
Chain 1:   1300       -26485.162             0.447            0.378
Chain 1:   1400       -26199.417             0.354            0.319
Chain 1:   1500       -22769.243             0.341            0.319
Chain 1:   1600       -21980.956             0.291            0.294
Chain 1:   1700       -20846.657             0.201            0.293
Chain 1:   1800       -20789.086             0.164            0.151
Chain 1:   1900       -21115.490             0.136            0.054
Chain 1:   2000       -19621.790             0.114            0.054
Chain 1:   2100       -19860.505             0.084            0.036
Chain 1:   2200       -20087.839             0.083            0.036
Chain 1:   2300       -19704.145             0.039            0.019
Chain 1:   2400       -19476.034             0.039            0.019
Chain 1:   2500       -19278.329             0.025            0.015
Chain 1:   2600       -18908.069             0.023            0.015
Chain 1:   2700       -18864.818             0.018            0.012
Chain 1:   2800       -18581.710             0.019            0.015
Chain 1:   2900       -18863.121             0.019            0.015
Chain 1:   3000       -18849.273             0.012            0.012
Chain 1:   3100       -18934.336             0.011            0.012
Chain 1:   3200       -18624.794             0.012            0.015
Chain 1:   3300       -18829.644             0.011            0.012
Chain 1:   3400       -18304.298             0.012            0.015
Chain 1:   3500       -18916.762             0.015            0.015
Chain 1:   3600       -18222.640             0.016            0.015
Chain 1:   3700       -18610.108             0.018            0.017
Chain 1:   3800       -17568.704             0.023            0.021
Chain 1:   3900       -17564.839             0.021            0.021
Chain 1:   4000       -17682.105             0.022            0.021
Chain 1:   4100       -17595.881             0.022            0.021
Chain 1:   4200       -17411.819             0.021            0.021
Chain 1:   4300       -17550.404             0.021            0.021
Chain 1:   4400       -17507.032             0.018            0.011
Chain 1:   4500       -17409.540             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001201 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49353.853             1.000            1.000
Chain 1:    200       -17012.158             1.451            1.901
Chain 1:    300       -24731.516             1.071            1.000
Chain 1:    400       -14861.813             0.969            1.000
Chain 1:    500       -15162.199             0.779            0.664
Chain 1:    600       -17248.387             0.670            0.664
Chain 1:    700       -17768.500             0.578            0.312
Chain 1:    800       -12728.970             0.555            0.396
Chain 1:    900       -16503.296             0.519            0.312
Chain 1:   1000       -12704.051             0.497            0.312
Chain 1:   1100       -12377.747             0.400            0.299
Chain 1:   1200       -16978.872             0.237            0.271
Chain 1:   1300       -12248.321             0.244            0.271
Chain 1:   1400       -10760.070             0.192            0.229
Chain 1:   1500       -11952.980             0.200            0.229
Chain 1:   1600       -11623.903             0.190            0.229
Chain 1:   1700        -9993.935             0.204            0.229
Chain 1:   1800       -11129.052             0.174            0.163
Chain 1:   1900       -11534.933             0.155            0.138
Chain 1:   2000       -16225.023             0.154            0.138
Chain 1:   2100       -11050.831             0.198            0.163
Chain 1:   2200       -12714.710             0.184            0.138
Chain 1:   2300       -12360.756             0.148            0.131
Chain 1:   2400       -10446.958             0.153            0.131
Chain 1:   2500        -9490.062             0.153            0.131
Chain 1:   2600       -10952.788             0.163            0.134
Chain 1:   2700        -9812.585             0.159            0.131
Chain 1:   2800       -11680.584             0.165            0.134
Chain 1:   2900        -9690.619             0.182            0.160
Chain 1:   3000        -9812.165             0.154            0.134
Chain 1:   3100       -10229.562             0.111            0.131
Chain 1:   3200        -9415.748             0.107            0.116
Chain 1:   3300       -10110.158             0.111            0.116
Chain 1:   3400        -9556.583             0.098            0.101
Chain 1:   3500        -9471.654             0.089            0.086
Chain 1:   3600       -10547.397             0.086            0.086
Chain 1:   3700        -9062.564             0.091            0.086
Chain 1:   3800       -13931.685             0.110            0.086
Chain 1:   3900        -9886.576             0.130            0.086
Chain 1:   4000       -10775.899             0.137            0.086
Chain 1:   4100        -9675.471             0.144            0.102
Chain 1:   4200       -12841.684             0.160            0.114
Chain 1:   4300       -10876.786             0.171            0.164
Chain 1:   4400       -12753.202             0.180            0.164
Chain 1:   4500       -10013.554             0.207            0.181
Chain 1:   4600       -14796.721             0.229            0.247
Chain 1:   4700       -13841.276             0.220            0.247
Chain 1:   4800        -9291.333             0.234            0.247
Chain 1:   4900       -15783.770             0.234            0.247
Chain 1:   5000       -14157.334             0.237            0.247
Chain 1:   5100       -15269.403             0.233            0.247
Chain 1:   5200        -9686.486             0.266            0.274
Chain 1:   5300       -11064.400             0.260            0.274
Chain 1:   5400        -8875.443             0.270            0.274
Chain 1:   5500        -9617.232             0.251            0.247
Chain 1:   5600        -8865.957             0.227            0.125
Chain 1:   5700        -9722.074             0.229            0.125
Chain 1:   5800       -11101.076             0.192            0.124
Chain 1:   5900       -14617.957             0.175            0.124
Chain 1:   6000        -9486.865             0.218            0.125
Chain 1:   6100        -8838.854             0.218            0.125
Chain 1:   6200        -9568.693             0.168            0.124
Chain 1:   6300       -10259.799             0.162            0.088
Chain 1:   6400        -9578.559             0.144            0.085
Chain 1:   6500        -8975.962             0.143            0.085
Chain 1:   6600        -8904.861             0.136            0.076
Chain 1:   6700        -9098.309             0.129            0.073
Chain 1:   6800        -9260.838             0.118            0.071
Chain 1:   6900       -13387.568             0.125            0.071
Chain 1:   7000       -10497.316             0.099            0.071
Chain 1:   7100        -8608.699             0.113            0.071
Chain 1:   7200       -11828.938             0.133            0.071
Chain 1:   7300       -10751.312             0.136            0.100
Chain 1:   7400       -13607.648             0.150            0.210
Chain 1:   7500       -11276.663             0.164            0.210
Chain 1:   7600        -8879.687             0.190            0.219
Chain 1:   7700        -9726.992             0.197            0.219
Chain 1:   7800        -8454.883             0.210            0.219
Chain 1:   7900        -8599.157             0.181            0.210
Chain 1:   8000       -10237.178             0.169            0.207
Chain 1:   8100        -9184.609             0.159            0.160
Chain 1:   8200        -8788.598             0.136            0.150
Chain 1:   8300        -9451.184             0.133            0.150
Chain 1:   8400        -9147.292             0.115            0.115
Chain 1:   8500        -8699.974             0.100            0.087
Chain 1:   8600        -9429.137             0.081            0.077
Chain 1:   8700        -8645.921             0.081            0.077
Chain 1:   8800       -11142.823             0.088            0.077
Chain 1:   8900        -9717.655             0.101            0.091
Chain 1:   9000       -10181.528             0.090            0.077
Chain 1:   9100        -9179.786             0.089            0.077
Chain 1:   9200        -9141.328             0.085            0.077
Chain 1:   9300        -9468.660             0.082            0.077
Chain 1:   9400       -11373.263             0.095            0.091
Chain 1:   9500        -9380.466             0.111            0.109
Chain 1:   9600       -11898.856             0.125            0.147
Chain 1:   9700       -12736.576             0.122            0.147
Chain 1:   9800       -11229.099             0.113            0.134
Chain 1:   9900        -8635.943             0.129            0.134
Chain 1:   10000        -8708.980             0.125            0.134
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001451 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57504.271             1.000            1.000
Chain 1:    200       -18061.087             1.592            2.184
Chain 1:    300        -9015.895             1.396            1.003
Chain 1:    400        -8214.760             1.071            1.003
Chain 1:    500        -8571.041             0.865            1.000
Chain 1:    600        -7945.965             0.734            1.000
Chain 1:    700        -7900.367             0.630            0.098
Chain 1:    800        -8468.833             0.560            0.098
Chain 1:    900        -8420.754             0.498            0.079
Chain 1:   1000        -8041.412             0.453            0.079
Chain 1:   1100        -7785.955             0.356            0.067
Chain 1:   1200        -7796.623             0.138            0.047
Chain 1:   1300        -7780.802             0.038            0.042
Chain 1:   1400        -7698.087             0.029            0.033
Chain 1:   1500        -7568.552             0.027            0.017
Chain 1:   1600        -7822.420             0.022            0.017
Chain 1:   1700        -7637.260             0.024            0.024
Chain 1:   1800        -7643.849             0.017            0.017
Chain 1:   1900        -7605.230             0.017            0.017
Chain 1:   2000        -7635.767             0.013            0.011
Chain 1:   2100        -7601.559             0.010            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002952 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86544.364             1.000            1.000
Chain 1:    200       -14074.153             3.075            5.149
Chain 1:    300       -10319.018             2.171            1.000
Chain 1:    400       -11664.319             1.657            1.000
Chain 1:    500        -9339.403             1.375            0.364
Chain 1:    600        -9127.635             1.150            0.364
Chain 1:    700        -9214.046             0.987            0.249
Chain 1:    800        -8577.620             0.873            0.249
Chain 1:    900        -8606.225             0.776            0.115
Chain 1:   1000        -9368.308             0.707            0.115
Chain 1:   1100        -8821.895             0.613            0.081   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9230.266             0.103            0.074
Chain 1:   1300        -8634.074             0.073            0.069
Chain 1:   1400        -8811.494             0.064            0.062
Chain 1:   1500        -8703.292             0.040            0.044
Chain 1:   1600        -8708.421             0.038            0.044
Chain 1:   1700        -8590.441             0.038            0.044
Chain 1:   1800        -8645.802             0.031            0.020
Chain 1:   1900        -8525.395             0.032            0.020
Chain 1:   2000        -8596.362             0.025            0.014
Chain 1:   2100        -8582.634             0.019            0.014
Chain 1:   2200        -8537.675             0.015            0.012
Chain 1:   2300        -8696.304             0.010            0.012
Chain 1:   2400        -8515.003             0.010            0.012
Chain 1:   2500        -8589.696             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003194 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8362315.821             1.000            1.000
Chain 1:    200     -1577996.343             2.650            4.299
Chain 1:    300      -889782.151             2.024            1.000
Chain 1:    400      -457793.355             1.754            1.000
Chain 1:    500      -358699.072             1.459            0.944
Chain 1:    600      -233913.705             1.304            0.944
Chain 1:    700      -120036.236             1.254            0.944
Chain 1:    800       -87222.269             1.144            0.944
Chain 1:    900       -67537.659             1.049            0.773
Chain 1:   1000       -52309.632             0.973            0.773
Chain 1:   1100       -39753.693             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38934.600             0.477            0.376
Chain 1:   1300       -26841.764             0.445            0.376
Chain 1:   1400       -26560.214             0.352            0.316
Chain 1:   1500       -23133.888             0.339            0.316
Chain 1:   1600       -22347.657             0.289            0.291
Chain 1:   1700       -21214.423             0.199            0.291
Chain 1:   1800       -21157.611             0.162            0.148
Chain 1:   1900       -21484.464             0.134            0.053
Chain 1:   2000       -19990.744             0.113            0.053
Chain 1:   2100       -20229.472             0.082            0.035
Chain 1:   2200       -20456.960             0.081            0.035
Chain 1:   2300       -20073.069             0.038            0.019
Chain 1:   2400       -19844.825             0.038            0.019
Chain 1:   2500       -19647.079             0.024            0.015
Chain 1:   2600       -19276.357             0.023            0.015
Chain 1:   2700       -19233.078             0.018            0.012
Chain 1:   2800       -18949.703             0.019            0.015
Chain 1:   2900       -19231.388             0.019            0.015
Chain 1:   3000       -19217.452             0.012            0.012
Chain 1:   3100       -19302.542             0.011            0.012
Chain 1:   3200       -18992.745             0.011            0.015
Chain 1:   3300       -19197.881             0.010            0.012
Chain 1:   3400       -18671.986             0.012            0.015
Chain 1:   3500       -19285.166             0.014            0.015
Chain 1:   3600       -18590.194             0.016            0.015
Chain 1:   3700       -18978.255             0.018            0.016
Chain 1:   3800       -17935.423             0.022            0.020
Chain 1:   3900       -17931.541             0.021            0.020
Chain 1:   4000       -18048.820             0.021            0.020
Chain 1:   4100       -17962.437             0.021            0.020
Chain 1:   4200       -17778.149             0.021            0.020
Chain 1:   4300       -17916.907             0.021            0.020
Chain 1:   4400       -17873.278             0.018            0.010
Chain 1:   4500       -17775.743             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12361.863             1.000            1.000
Chain 1:    200        -9299.107             0.665            1.000
Chain 1:    300        -8033.654             0.496            0.329
Chain 1:    400        -8184.518             0.376            0.329
Chain 1:    500        -8117.032             0.303            0.158
Chain 1:    600        -8016.514             0.254            0.158
Chain 1:    700        -7927.925             0.220            0.018
Chain 1:    800        -7966.268             0.193            0.018
Chain 1:    900        -8090.198             0.173            0.015
Chain 1:   1000        -8019.220             0.157            0.015
Chain 1:   1100        -8023.730             0.057            0.013
Chain 1:   1200        -7965.431             0.024            0.011
Chain 1:   1300        -7905.722             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001764 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63404.045             1.000            1.000
Chain 1:    200       -18173.978             1.744            2.489
Chain 1:    300        -8772.656             1.520            1.072
Chain 1:    400        -9213.677             1.152            1.072
Chain 1:    500        -8492.868             0.939            1.000
Chain 1:    600        -9235.565             0.796            1.000
Chain 1:    700        -8135.251             0.701            0.135
Chain 1:    800        -8103.659             0.614            0.135
Chain 1:    900        -8090.217             0.546            0.085
Chain 1:   1000        -7773.179             0.496            0.085
Chain 1:   1100        -7690.508             0.397            0.080
Chain 1:   1200        -7608.313             0.149            0.048
Chain 1:   1300        -7775.500             0.044            0.041
Chain 1:   1400        -7675.338             0.040            0.022
Chain 1:   1500        -7611.350             0.033            0.013
Chain 1:   1600        -7654.385             0.025            0.011
Chain 1:   1700        -7571.356             0.013            0.011
Chain 1:   1800        -7611.381             0.013            0.011
Chain 1:   1900        -7606.155             0.013            0.011
Chain 1:   2000        -7579.551             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003819 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86566.581             1.000            1.000
Chain 1:    200       -13460.231             3.216            5.431
Chain 1:    300        -9859.276             2.266            1.000
Chain 1:    400       -10704.354             1.719            1.000
Chain 1:    500        -8817.808             1.418            0.365
Chain 1:    600        -8682.826             1.184            0.365
Chain 1:    700        -8622.364             1.016            0.214
Chain 1:    800        -9318.872             0.898            0.214
Chain 1:    900        -8644.299             0.807            0.079
Chain 1:   1000        -8517.977             0.728            0.079
Chain 1:   1100        -8745.647             0.631            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8306.694             0.093            0.075
Chain 1:   1300        -8588.531             0.059            0.053
Chain 1:   1400        -8580.735             0.052            0.033
Chain 1:   1500        -8452.965             0.032            0.026
Chain 1:   1600        -8558.716             0.031            0.026
Chain 1:   1700        -8645.821             0.032            0.026
Chain 1:   1800        -8244.608             0.029            0.026
Chain 1:   1900        -8343.821             0.023            0.015
Chain 1:   2000        -8315.207             0.021            0.015
Chain 1:   2100        -8435.023             0.020            0.014
Chain 1:   2200        -8225.730             0.017            0.014
Chain 1:   2300        -8376.206             0.016            0.014
Chain 1:   2400        -8255.886             0.017            0.015
Chain 1:   2500        -8319.348             0.017            0.014
Chain 1:   2600        -8341.333             0.016            0.014
Chain 1:   2700        -8260.199             0.016            0.014
Chain 1:   2800        -8233.837             0.011            0.012
Chain 1:   2900        -8289.227             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8423959.157             1.000            1.000
Chain 1:    200     -1585879.762             2.656            4.312
Chain 1:    300      -890361.620             2.031            1.000
Chain 1:    400      -457486.963             1.760            1.000
Chain 1:    500      -357560.901             1.464            0.946
Chain 1:    600      -232556.363             1.309            0.946
Chain 1:    700      -118936.250             1.259            0.946
Chain 1:    800       -86212.778             1.149            0.946
Chain 1:    900       -66589.591             1.054            0.781
Chain 1:   1000       -51418.584             0.978            0.781
Chain 1:   1100       -38931.202             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38109.731             0.481            0.380
Chain 1:   1300       -26106.784             0.449            0.380
Chain 1:   1400       -25828.648             0.355            0.321
Chain 1:   1500       -22427.274             0.343            0.321
Chain 1:   1600       -21647.249             0.293            0.295
Chain 1:   1700       -20525.836             0.202            0.295
Chain 1:   1800       -20471.148             0.165            0.152
Chain 1:   1900       -20796.995             0.137            0.055
Chain 1:   2000       -19311.440             0.115            0.055
Chain 1:   2100       -19549.582             0.084            0.036
Chain 1:   2200       -19775.472             0.083            0.036
Chain 1:   2300       -19393.251             0.039            0.020
Chain 1:   2400       -19165.475             0.039            0.020
Chain 1:   2500       -18967.409             0.025            0.016
Chain 1:   2600       -18597.942             0.024            0.016
Chain 1:   2700       -18555.024             0.018            0.012
Chain 1:   2800       -18271.949             0.020            0.015
Chain 1:   2900       -18553.055             0.020            0.015
Chain 1:   3000       -18539.214             0.012            0.012
Chain 1:   3100       -18624.195             0.011            0.012
Chain 1:   3200       -18315.054             0.012            0.015
Chain 1:   3300       -18519.652             0.011            0.012
Chain 1:   3400       -17994.913             0.013            0.015
Chain 1:   3500       -18606.235             0.015            0.015
Chain 1:   3600       -17913.579             0.017            0.015
Chain 1:   3700       -18299.858             0.019            0.017
Chain 1:   3800       -17260.620             0.023            0.021
Chain 1:   3900       -17256.769             0.022            0.021
Chain 1:   4000       -17374.082             0.022            0.021
Chain 1:   4100       -17287.895             0.022            0.021
Chain 1:   4200       -17104.364             0.022            0.021
Chain 1:   4300       -17242.607             0.021            0.021
Chain 1:   4400       -17199.612             0.019            0.011
Chain 1:   4500       -17102.159             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001184 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12165.772             1.000            1.000
Chain 1:    200        -9144.524             0.665            1.000
Chain 1:    300        -8133.607             0.485            0.330
Chain 1:    400        -8181.877             0.365            0.330
Chain 1:    500        -8222.269             0.293            0.124
Chain 1:    600        -8118.832             0.246            0.124
Chain 1:    700        -7882.495             0.215            0.030
Chain 1:    800        -7911.754             0.189            0.030
Chain 1:    900        -7859.769             0.169            0.013
Chain 1:   1000        -7940.227             0.153            0.013
Chain 1:   1100        -7994.262             0.054            0.010
Chain 1:   1200        -7899.880             0.022            0.010
Chain 1:   1300        -7927.793             0.010            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001449 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55708.786             1.000            1.000
Chain 1:    200       -16960.817             1.642            2.285
Chain 1:    300        -8603.430             1.419            1.000
Chain 1:    400        -9164.423             1.079            1.000
Chain 1:    500        -8626.322             0.876            0.971
Chain 1:    600        -8839.105             0.734            0.971
Chain 1:    700        -7824.771             0.648            0.130
Chain 1:    800        -8302.588             0.574            0.130
Chain 1:    900        -7989.969             0.514            0.062
Chain 1:   1000        -7772.233             0.466            0.062
Chain 1:   1100        -7730.547             0.366            0.061
Chain 1:   1200        -7657.972             0.139            0.058
Chain 1:   1300        -7745.214             0.043            0.039
Chain 1:   1400        -7886.260             0.038            0.028
Chain 1:   1500        -7625.508             0.036            0.028
Chain 1:   1600        -7635.380             0.033            0.028
Chain 1:   1700        -7544.751             0.022            0.018
Chain 1:   1800        -7606.614             0.017            0.012
Chain 1:   1900        -7658.971             0.013            0.011
Chain 1:   2000        -7642.506             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85833.198             1.000            1.000
Chain 1:    200       -13276.827             3.232            5.465
Chain 1:    300        -9751.871             2.275            1.000
Chain 1:    400       -10550.978             1.726            1.000
Chain 1:    500        -8671.363             1.424            0.361
Chain 1:    600        -8537.400             1.189            0.361
Chain 1:    700        -8538.634             1.019            0.217
Chain 1:    800        -8816.907             0.896            0.217
Chain 1:    900        -8635.742             0.799            0.076
Chain 1:   1000        -8370.795             0.722            0.076
Chain 1:   1100        -8664.567             0.625            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8235.387             0.084            0.034
Chain 1:   1300        -8518.515             0.051            0.033
Chain 1:   1400        -8497.106             0.044            0.032
Chain 1:   1500        -8395.046             0.023            0.032
Chain 1:   1600        -8488.831             0.023            0.032
Chain 1:   1700        -8589.723             0.024            0.032
Chain 1:   1800        -8197.433             0.026            0.032
Chain 1:   1900        -8298.670             0.025            0.032
Chain 1:   2000        -8268.709             0.022            0.012
Chain 1:   2100        -8407.233             0.020            0.012
Chain 1:   2200        -8188.881             0.018            0.012
Chain 1:   2300        -8330.964             0.016            0.012
Chain 1:   2400        -8341.189             0.016            0.012
Chain 1:   2500        -8307.169             0.015            0.012
Chain 1:   2600        -8303.692             0.014            0.012
Chain 1:   2700        -8214.046             0.014            0.012
Chain 1:   2800        -8194.009             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003196 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400002.482             1.000            1.000
Chain 1:    200     -1584605.196             2.651            4.301
Chain 1:    300      -891052.393             2.026            1.000
Chain 1:    400      -457736.126             1.757            1.000
Chain 1:    500      -358054.940             1.461            0.947
Chain 1:    600      -232864.258             1.307            0.947
Chain 1:    700      -119009.303             1.257            0.947
Chain 1:    800       -86225.585             1.147            0.947
Chain 1:    900       -66548.840             1.053            0.778
Chain 1:   1000       -51330.530             0.977            0.778
Chain 1:   1100       -38802.813             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37972.661             0.481            0.380
Chain 1:   1300       -25932.721             0.450            0.380
Chain 1:   1400       -25649.540             0.357            0.323
Chain 1:   1500       -22239.112             0.344            0.323
Chain 1:   1600       -21455.524             0.294            0.296
Chain 1:   1700       -20330.188             0.204            0.296
Chain 1:   1800       -20274.231             0.166            0.153
Chain 1:   1900       -20599.760             0.138            0.055
Chain 1:   2000       -19112.721             0.116            0.055
Chain 1:   2100       -19350.834             0.085            0.037
Chain 1:   2200       -19576.945             0.084            0.037
Chain 1:   2300       -19194.633             0.040            0.020
Chain 1:   2400       -18966.959             0.040            0.020
Chain 1:   2500       -18769.101             0.025            0.016
Chain 1:   2600       -18399.852             0.024            0.016
Chain 1:   2700       -18356.962             0.019            0.012
Chain 1:   2800       -18074.186             0.020            0.016
Chain 1:   2900       -18355.120             0.020            0.015
Chain 1:   3000       -18341.292             0.012            0.012
Chain 1:   3100       -18426.215             0.011            0.012
Chain 1:   3200       -18117.302             0.012            0.015
Chain 1:   3300       -18321.697             0.011            0.012
Chain 1:   3400       -17797.422             0.013            0.015
Chain 1:   3500       -18408.146             0.015            0.016
Chain 1:   3600       -17716.304             0.017            0.016
Chain 1:   3700       -18102.042             0.019            0.017
Chain 1:   3800       -17064.087             0.023            0.021
Chain 1:   3900       -17060.303             0.022            0.021
Chain 1:   4000       -17177.579             0.022            0.021
Chain 1:   4100       -17091.506             0.022            0.021
Chain 1:   4200       -16908.224             0.022            0.021
Chain 1:   4300       -17046.246             0.021            0.021
Chain 1:   4400       -17003.481             0.019            0.011
Chain 1:   4500       -16906.114             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12463.278             1.000            1.000
Chain 1:    200        -9312.598             0.669            1.000
Chain 1:    300        -8031.434             0.499            0.338
Chain 1:    400        -8197.269             0.380            0.338
Chain 1:    500        -8075.637             0.307            0.160
Chain 1:    600        -7983.526             0.257            0.160
Chain 1:    700        -7881.742             0.223            0.020
Chain 1:    800        -7890.680             0.195            0.020
Chain 1:    900        -7782.936             0.175            0.015
Chain 1:   1000        -7949.099             0.159            0.020
Chain 1:   1100        -7975.888             0.060            0.015
Chain 1:   1200        -7896.753             0.027            0.014
Chain 1:   1300        -7852.581             0.011            0.013
Chain 1:   1400        -7880.900             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001483 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58026.200             1.000            1.000
Chain 1:    200       -17668.950             1.642            2.284
Chain 1:    300        -8679.188             1.440            1.036
Chain 1:    400        -8149.227             1.096            1.036
Chain 1:    500        -8356.549             0.882            1.000
Chain 1:    600        -9067.071             0.748            1.000
Chain 1:    700        -8248.558             0.655            0.099
Chain 1:    800        -8027.328             0.577            0.099
Chain 1:    900        -7639.505             0.518            0.078
Chain 1:   1000        -7715.373             0.468            0.078
Chain 1:   1100        -7682.668             0.368            0.065
Chain 1:   1200        -7579.772             0.141            0.051
Chain 1:   1300        -7593.349             0.038            0.028
Chain 1:   1400        -7720.844             0.033            0.025
Chain 1:   1500        -7611.193             0.032            0.017
Chain 1:   1600        -7673.732             0.025            0.014
Chain 1:   1700        -7515.394             0.017            0.014
Chain 1:   1800        -7537.905             0.014            0.014
Chain 1:   1900        -7573.586             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86245.271             1.000            1.000
Chain 1:    200       -13495.211             3.195            5.391
Chain 1:    300        -9862.949             2.253            1.000
Chain 1:    400       -10766.049             1.711            1.000
Chain 1:    500        -8840.242             1.412            0.368
Chain 1:    600        -8756.459             1.178            0.368
Chain 1:    700        -8436.665             1.015            0.218
Chain 1:    800        -9058.072             0.897            0.218
Chain 1:    900        -8676.565             0.802            0.084
Chain 1:   1000        -8488.765             0.724            0.084
Chain 1:   1100        -8684.330             0.627            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8221.744             0.093            0.056
Chain 1:   1300        -8541.379             0.060            0.044
Chain 1:   1400        -8554.921             0.052            0.038
Chain 1:   1500        -8428.341             0.031            0.037
Chain 1:   1600        -8536.514             0.032            0.037
Chain 1:   1700        -8620.574             0.029            0.023
Chain 1:   1800        -8206.838             0.027            0.023
Chain 1:   1900        -8303.050             0.024            0.022
Chain 1:   2000        -8276.453             0.022            0.015
Chain 1:   2100        -8399.266             0.021            0.015
Chain 1:   2200        -8219.243             0.018            0.015
Chain 1:   2300        -8297.924             0.015            0.013
Chain 1:   2400        -8367.651             0.016            0.013
Chain 1:   2500        -8313.145             0.015            0.012
Chain 1:   2600        -8312.698             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411224.314             1.000            1.000
Chain 1:    200     -1584936.826             2.653            4.307
Chain 1:    300      -890603.339             2.029            1.000
Chain 1:    400      -457543.339             1.758            1.000
Chain 1:    500      -357799.998             1.462            0.946
Chain 1:    600      -232823.780             1.308            0.946
Chain 1:    700      -119115.569             1.258            0.946
Chain 1:    800       -86358.672             1.148            0.946
Chain 1:    900       -66717.266             1.053            0.780
Chain 1:   1000       -51532.203             0.977            0.780
Chain 1:   1100       -39027.458             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38204.910             0.481            0.379
Chain 1:   1300       -26178.926             0.449            0.379
Chain 1:   1400       -25899.513             0.355            0.320
Chain 1:   1500       -22491.494             0.342            0.320
Chain 1:   1600       -21709.537             0.292            0.295
Chain 1:   1700       -20585.269             0.202            0.294
Chain 1:   1800       -20529.886             0.165            0.152
Chain 1:   1900       -20855.998             0.137            0.055
Chain 1:   2000       -19368.296             0.115            0.055
Chain 1:   2100       -19606.625             0.084            0.036
Chain 1:   2200       -19832.915             0.083            0.036
Chain 1:   2300       -19450.241             0.039            0.020
Chain 1:   2400       -19222.358             0.039            0.020
Chain 1:   2500       -19024.328             0.025            0.016
Chain 1:   2600       -18654.639             0.024            0.016
Chain 1:   2700       -18611.608             0.018            0.012
Chain 1:   2800       -18328.506             0.020            0.015
Chain 1:   2900       -18609.668             0.019            0.015
Chain 1:   3000       -18595.888             0.012            0.012
Chain 1:   3100       -18680.891             0.011            0.012
Chain 1:   3200       -18371.591             0.012            0.015
Chain 1:   3300       -18576.278             0.011            0.012
Chain 1:   3400       -18051.284             0.013            0.015
Chain 1:   3500       -18663.035             0.015            0.015
Chain 1:   3600       -17969.810             0.017            0.015
Chain 1:   3700       -18356.547             0.019            0.017
Chain 1:   3800       -17316.434             0.023            0.021
Chain 1:   3900       -17312.555             0.021            0.021
Chain 1:   4000       -17429.875             0.022            0.021
Chain 1:   4100       -17343.669             0.022            0.021
Chain 1:   4200       -17159.922             0.022            0.021
Chain 1:   4300       -17298.326             0.021            0.021
Chain 1:   4400       -17255.178             0.019            0.011
Chain 1:   4500       -17157.689             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001263 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12505.422             1.000            1.000
Chain 1:    200        -9258.595             0.675            1.000
Chain 1:    300        -8071.303             0.499            0.351
Chain 1:    400        -8236.589             0.379            0.351
Chain 1:    500        -7864.011             0.313            0.147
Chain 1:    600        -7952.603             0.263            0.147
Chain 1:    700        -7880.775             0.226            0.047
Chain 1:    800        -7822.175             0.199            0.047
Chain 1:    900        -7845.481             0.177            0.020
Chain 1:   1000        -7878.039             0.160            0.020
Chain 1:   1100        -7901.298             0.060            0.011
Chain 1:   1200        -7888.662             0.025            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001598 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59017.755             1.000            1.000
Chain 1:    200       -17962.570             1.643            2.286
Chain 1:    300        -8991.841             1.428            1.000
Chain 1:    400        -8394.451             1.089            1.000
Chain 1:    500        -8540.478             0.874            0.998
Chain 1:    600        -9104.928             0.739            0.998
Chain 1:    700        -8298.323             0.647            0.097
Chain 1:    800        -8202.408             0.568            0.097
Chain 1:    900        -8490.222             0.508            0.071
Chain 1:   1000        -8050.634             0.463            0.071
Chain 1:   1100        -7762.508             0.367            0.062
Chain 1:   1200        -7766.539             0.138            0.055
Chain 1:   1300        -7807.372             0.039            0.037
Chain 1:   1400        -8016.351             0.035            0.034
Chain 1:   1500        -7647.024             0.038            0.037
Chain 1:   1600        -7929.445             0.035            0.036
Chain 1:   1700        -7542.305             0.030            0.036
Chain 1:   1800        -7681.961             0.031            0.036
Chain 1:   1900        -7632.710             0.028            0.036
Chain 1:   2000        -7691.327             0.024            0.026
Chain 1:   2100        -7597.474             0.021            0.018
Chain 1:   2200        -7744.760             0.023            0.019
Chain 1:   2300        -7594.008             0.024            0.020
Chain 1:   2400        -7748.184             0.024            0.020
Chain 1:   2500        -7678.476             0.020            0.019
Chain 1:   2600        -7586.734             0.018            0.018
Chain 1:   2700        -7506.496             0.014            0.012
Chain 1:   2800        -7545.142             0.012            0.012
Chain 1:   2900        -7431.435             0.013            0.012
Chain 1:   3000        -7572.005             0.014            0.015
Chain 1:   3100        -7568.049             0.013            0.015
Chain 1:   3200        -7774.877             0.014            0.015
Chain 1:   3300        -7498.565             0.015            0.015
Chain 1:   3400        -7715.775             0.016            0.015
Chain 1:   3500        -7482.004             0.019            0.019
Chain 1:   3600        -7548.941             0.018            0.019
Chain 1:   3700        -7497.053             0.018            0.019
Chain 1:   3800        -7496.966             0.017            0.019
Chain 1:   3900        -7464.125             0.016            0.019
Chain 1:   4000        -7458.376             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00326 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86039.157             1.000            1.000
Chain 1:    200       -13739.867             3.131            5.262
Chain 1:    300        -9992.651             2.212            1.000
Chain 1:    400       -11629.834             1.694            1.000
Chain 1:    500        -8755.885             1.421            0.375
Chain 1:    600        -8429.965             1.191            0.375
Chain 1:    700        -8507.807             1.022            0.328
Chain 1:    800        -9296.577             0.905            0.328
Chain 1:    900        -8844.978             0.810            0.141
Chain 1:   1000        -8655.907             0.731            0.141
Chain 1:   1100        -8840.682             0.633            0.085   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8327.252             0.113            0.062
Chain 1:   1300        -8638.983             0.079            0.051
Chain 1:   1400        -8472.295             0.067            0.039
Chain 1:   1500        -8493.528             0.035            0.036
Chain 1:   1600        -8594.215             0.032            0.022
Chain 1:   1700        -8651.993             0.032            0.022
Chain 1:   1800        -8202.251             0.029            0.022
Chain 1:   1900        -8311.592             0.025            0.021
Chain 1:   2000        -8287.215             0.023            0.020
Chain 1:   2100        -8416.648             0.022            0.015
Chain 1:   2200        -8205.850             0.019            0.015
Chain 1:   2300        -8306.969             0.016            0.013
Chain 1:   2400        -8368.710             0.015            0.012
Chain 1:   2500        -8319.712             0.016            0.012
Chain 1:   2600        -8335.588             0.015            0.012
Chain 1:   2700        -8241.660             0.015            0.012
Chain 1:   2800        -8186.478             0.010            0.011
Chain 1:   2900        -8287.710             0.010            0.011
Chain 1:   3000        -8135.925             0.012            0.012
Chain 1:   3100        -8272.364             0.012            0.012
Chain 1:   3200        -8140.951             0.011            0.012
Chain 1:   3300        -8378.296             0.013            0.012
Chain 1:   3400        -8381.761             0.012            0.012
Chain 1:   3500        -8249.346             0.013            0.016
Chain 1:   3600        -8098.101             0.015            0.016
Chain 1:   3700        -8245.756             0.015            0.016
Chain 1:   3800        -8100.244             0.016            0.018
Chain 1:   3900        -8032.270             0.016            0.018
Chain 1:   4000        -8148.164             0.015            0.016
Chain 1:   4100        -8107.896             0.014            0.016
Chain 1:   4200        -8093.737             0.013            0.016
Chain 1:   4300        -8127.273             0.010            0.014
Chain 1:   4400        -8084.269             0.011            0.014
Chain 1:   4500        -8182.409             0.011            0.012
Chain 1:   4600        -8073.456             0.010            0.012
Chain 1:   4700        -8280.756             0.011            0.012
Chain 1:   4800        -8162.408             0.010            0.012
Chain 1:   4900        -8172.406             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003469 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408373.298             1.000            1.000
Chain 1:    200     -1585558.089             2.652            4.303
Chain 1:    300      -891846.539             2.027            1.000
Chain 1:    400      -458392.401             1.757            1.000
Chain 1:    500      -358698.149             1.461            0.946
Chain 1:    600      -233380.123             1.307            0.946
Chain 1:    700      -119537.671             1.256            0.946
Chain 1:    800       -86762.988             1.146            0.946
Chain 1:    900       -67091.663             1.052            0.778
Chain 1:   1000       -51894.011             0.976            0.778
Chain 1:   1100       -39370.597             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38552.754             0.479            0.378
Chain 1:   1300       -26493.000             0.447            0.378
Chain 1:   1400       -26213.682             0.354            0.318
Chain 1:   1500       -22797.131             0.341            0.318
Chain 1:   1600       -22013.896             0.291            0.293
Chain 1:   1700       -20884.688             0.201            0.293
Chain 1:   1800       -20828.835             0.163            0.150
Chain 1:   1900       -21155.598             0.136            0.054
Chain 1:   2000       -19664.472             0.114            0.054
Chain 1:   2100       -19902.752             0.083            0.036
Chain 1:   2200       -20130.021             0.082            0.036
Chain 1:   2300       -19746.421             0.039            0.019
Chain 1:   2400       -19518.285             0.039            0.019
Chain 1:   2500       -19320.475             0.025            0.015
Chain 1:   2600       -18949.759             0.023            0.015
Chain 1:   2700       -18906.569             0.018            0.012
Chain 1:   2800       -18623.172             0.019            0.015
Chain 1:   2900       -18904.795             0.019            0.015
Chain 1:   3000       -18890.834             0.012            0.012
Chain 1:   3100       -18975.915             0.011            0.012
Chain 1:   3200       -18666.144             0.012            0.015
Chain 1:   3300       -18871.288             0.011            0.012
Chain 1:   3400       -18345.409             0.012            0.015
Chain 1:   3500       -18958.485             0.015            0.015
Chain 1:   3600       -18263.680             0.016            0.015
Chain 1:   3700       -18651.558             0.018            0.017
Chain 1:   3800       -17608.949             0.023            0.021
Chain 1:   3900       -17605.094             0.021            0.021
Chain 1:   4000       -17722.364             0.022            0.021
Chain 1:   4100       -17635.989             0.022            0.021
Chain 1:   4200       -17451.786             0.021            0.021
Chain 1:   4300       -17590.464             0.021            0.021
Chain 1:   4400       -17546.858             0.018            0.011
Chain 1:   4500       -17449.383             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001573 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12704.555             1.000            1.000
Chain 1:    200        -9611.983             0.661            1.000
Chain 1:    300        -8310.309             0.493            0.322
Chain 1:    400        -8492.972             0.375            0.322
Chain 1:    500        -8401.863             0.302            0.157
Chain 1:    600        -8252.785             0.255            0.157
Chain 1:    700        -8229.784             0.219            0.022
Chain 1:    800        -8179.230             0.192            0.022
Chain 1:    900        -8237.492             0.172            0.018
Chain 1:   1000        -8237.679             0.154            0.018
Chain 1:   1100        -8257.133             0.055            0.011
Chain 1:   1200        -8170.569             0.024            0.011
Chain 1:   1300        -8129.611             0.008            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001443 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58481.042             1.000            1.000
Chain 1:    200       -17974.845             1.627            2.253
Chain 1:    300        -8856.182             1.428            1.030
Chain 1:    400        -8245.985             1.089            1.030
Chain 1:    500        -8779.949             0.884            1.000
Chain 1:    600        -8906.250             0.739            1.000
Chain 1:    700        -8376.219             0.642            0.074
Chain 1:    800        -8314.526             0.563            0.074
Chain 1:    900        -8063.934             0.504            0.063
Chain 1:   1000        -8073.280             0.454            0.063
Chain 1:   1100        -7928.905             0.355            0.061
Chain 1:   1200        -7660.413             0.133            0.035
Chain 1:   1300        -7723.139             0.031            0.031
Chain 1:   1400        -7968.875             0.027            0.031
Chain 1:   1500        -7700.277             0.024            0.031
Chain 1:   1600        -7912.940             0.026            0.031
Chain 1:   1700        -7544.261             0.024            0.031
Chain 1:   1800        -7731.770             0.026            0.031
Chain 1:   1900        -7647.135             0.024            0.027
Chain 1:   2000        -7742.605             0.025            0.027
Chain 1:   2100        -7675.370             0.024            0.027
Chain 1:   2200        -7800.085             0.022            0.024
Chain 1:   2300        -7709.060             0.023            0.024
Chain 1:   2400        -7646.550             0.020            0.016
Chain 1:   2500        -7697.493             0.017            0.012
Chain 1:   2600        -7617.361             0.016            0.012
Chain 1:   2700        -7564.924             0.012            0.011
Chain 1:   2800        -7531.752             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86794.535             1.000            1.000
Chain 1:    200       -13791.564             3.147            5.293
Chain 1:    300       -10149.207             2.217            1.000
Chain 1:    400       -11069.035             1.684            1.000
Chain 1:    500        -9115.816             1.390            0.359
Chain 1:    600        -8616.916             1.168            0.359
Chain 1:    700        -8646.660             1.002            0.214
Chain 1:    800        -9403.554             0.886            0.214
Chain 1:    900        -8886.774             0.794            0.083
Chain 1:   1000        -8776.866             0.716            0.083
Chain 1:   1100        -9010.073             0.619            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8507.797             0.095            0.059
Chain 1:   1300        -8838.817             0.063            0.058
Chain 1:   1400        -8844.092             0.055            0.058
Chain 1:   1500        -8715.224             0.035            0.037
Chain 1:   1600        -8823.428             0.030            0.026
Chain 1:   1700        -8904.480             0.031            0.026
Chain 1:   1800        -8488.115             0.028            0.026
Chain 1:   1900        -8585.225             0.023            0.015
Chain 1:   2000        -8558.928             0.022            0.015
Chain 1:   2100        -8682.308             0.021            0.014
Chain 1:   2200        -8500.156             0.017            0.014
Chain 1:   2300        -8579.845             0.015            0.012
Chain 1:   2400        -8649.541             0.015            0.012
Chain 1:   2500        -8595.342             0.014            0.011
Chain 1:   2600        -8595.280             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004949 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426745.839             1.000            1.000
Chain 1:    200     -1589714.112             2.650            4.301
Chain 1:    300      -890990.626             2.028            1.000
Chain 1:    400      -457701.989             1.758            1.000
Chain 1:    500      -357525.589             1.462            0.947
Chain 1:    600      -232527.574             1.308            0.947
Chain 1:    700      -119095.719             1.257            0.947
Chain 1:    800       -86412.328             1.148            0.947
Chain 1:    900       -66835.937             1.053            0.784
Chain 1:   1000       -51699.574             0.977            0.784
Chain 1:   1100       -39240.177             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38424.608             0.480            0.378
Chain 1:   1300       -26445.648             0.447            0.378
Chain 1:   1400       -26171.173             0.354            0.318
Chain 1:   1500       -22775.388             0.341            0.318
Chain 1:   1600       -21997.229             0.290            0.293
Chain 1:   1700       -20878.522             0.200            0.293
Chain 1:   1800       -20824.495             0.163            0.149
Chain 1:   1900       -21150.670             0.135            0.054
Chain 1:   2000       -19665.689             0.113            0.054
Chain 1:   2100       -19903.973             0.083            0.035
Chain 1:   2200       -20129.820             0.082            0.035
Chain 1:   2300       -19747.511             0.038            0.019
Chain 1:   2400       -19519.639             0.039            0.019
Chain 1:   2500       -19321.424             0.025            0.015
Chain 1:   2600       -18951.931             0.023            0.015
Chain 1:   2700       -18908.917             0.018            0.012
Chain 1:   2800       -18625.697             0.019            0.015
Chain 1:   2900       -18906.840             0.019            0.015
Chain 1:   3000       -18893.068             0.012            0.012
Chain 1:   3100       -18978.095             0.011            0.012
Chain 1:   3200       -18668.819             0.011            0.015
Chain 1:   3300       -18873.488             0.011            0.012
Chain 1:   3400       -18348.449             0.012            0.015
Chain 1:   3500       -18960.206             0.015            0.015
Chain 1:   3600       -18266.921             0.016            0.015
Chain 1:   3700       -18653.674             0.018            0.017
Chain 1:   3800       -17613.469             0.023            0.021
Chain 1:   3900       -17609.545             0.021            0.021
Chain 1:   4000       -17726.899             0.022            0.021
Chain 1:   4100       -17640.687             0.022            0.021
Chain 1:   4200       -17456.890             0.021            0.021
Chain 1:   4300       -17595.342             0.021            0.021
Chain 1:   4400       -17552.186             0.018            0.011
Chain 1:   4500       -17454.646             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001404 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12450.274             1.000            1.000
Chain 1:    200        -9372.294             0.664            1.000
Chain 1:    300        -8099.091             0.495            0.328
Chain 1:    400        -8303.158             0.378            0.328
Chain 1:    500        -8246.972             0.303            0.157
Chain 1:    600        -8066.146             0.257            0.157
Chain 1:    700        -7976.082             0.222            0.025
Chain 1:    800        -7984.902             0.194            0.025
Chain 1:    900        -7876.377             0.174            0.022
Chain 1:   1000        -8030.026             0.158            0.022
Chain 1:   1100        -8127.230             0.060            0.019
Chain 1:   1200        -7997.664             0.028            0.016
Chain 1:   1300        -7943.103             0.013            0.014
Chain 1:   1400        -7972.435             0.011            0.012
Chain 1:   1500        -8069.829             0.012            0.012
Chain 1:   1600        -8020.027             0.010            0.012
Chain 1:   1700        -7947.063             0.010            0.012
Chain 1:   1800        -7926.218             0.010            0.012
Chain 1:   1900        -7926.204             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63626.508             1.000            1.000
Chain 1:    200       -18355.524             1.733            2.466
Chain 1:    300        -8863.776             1.512            1.071
Chain 1:    400        -8164.606             1.156            1.071
Chain 1:    500        -8456.404             0.931            1.000
Chain 1:    600        -9055.984             0.787            1.000
Chain 1:    700        -7997.366             0.694            0.132
Chain 1:    800        -8372.243             0.613            0.132
Chain 1:    900        -8208.034             0.547            0.086
Chain 1:   1000        -7789.731             0.497            0.086
Chain 1:   1100        -7765.971             0.398            0.066
Chain 1:   1200        -7676.955             0.152            0.054
Chain 1:   1300        -7829.478             0.047            0.045
Chain 1:   1400        -7915.656             0.040            0.035
Chain 1:   1500        -7686.555             0.039            0.030
Chain 1:   1600        -7866.508             0.035            0.023
Chain 1:   1700        -7605.679             0.025            0.023
Chain 1:   1800        -7663.318             0.021            0.020
Chain 1:   1900        -7675.490             0.019            0.019
Chain 1:   2000        -7683.374             0.014            0.012
Chain 1:   2100        -7670.654             0.014            0.012
Chain 1:   2200        -7772.900             0.014            0.013
Chain 1:   2300        -7670.337             0.014            0.013
Chain 1:   2400        -7725.100             0.013            0.013
Chain 1:   2500        -7634.440             0.011            0.012
Chain 1:   2600        -7603.919             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003197 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86535.010             1.000            1.000
Chain 1:    200       -13581.581             3.186            5.371
Chain 1:    300        -9951.087             2.245            1.000
Chain 1:    400       -10832.534             1.704            1.000
Chain 1:    500        -8923.332             1.406            0.365
Chain 1:    600        -8620.356             1.178            0.365
Chain 1:    700        -8663.264             1.010            0.214
Chain 1:    800        -9128.117             0.890            0.214
Chain 1:    900        -8824.436             0.795            0.081
Chain 1:   1000        -8596.465             0.718            0.081
Chain 1:   1100        -8803.447             0.621            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8445.482             0.088            0.042
Chain 1:   1300        -8640.689             0.054            0.035
Chain 1:   1400        -8653.685             0.046            0.034
Chain 1:   1500        -8517.749             0.026            0.027
Chain 1:   1600        -8629.348             0.024            0.024
Chain 1:   1700        -8713.307             0.024            0.024
Chain 1:   1800        -8301.465             0.024            0.024
Chain 1:   1900        -8397.398             0.022            0.023
Chain 1:   2000        -8370.544             0.019            0.016
Chain 1:   2100        -8492.855             0.018            0.014
Chain 1:   2200        -8312.540             0.016            0.014
Chain 1:   2300        -8392.541             0.015            0.013
Chain 1:   2400        -8462.125             0.016            0.013
Chain 1:   2500        -8407.428             0.015            0.011
Chain 1:   2600        -8406.678             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003464 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431499.442             1.000            1.000
Chain 1:    200     -1590329.309             2.651            4.302
Chain 1:    300      -892568.231             2.028            1.000
Chain 1:    400      -458035.887             1.758            1.000
Chain 1:    500      -357772.593             1.462            0.949
Chain 1:    600      -232605.610             1.308            0.949
Chain 1:    700      -119046.751             1.258            0.949
Chain 1:    800       -86285.516             1.148            0.949
Chain 1:    900       -66685.120             1.053            0.782
Chain 1:   1000       -51531.516             0.977            0.782
Chain 1:   1100       -39052.026             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38236.313             0.481            0.380
Chain 1:   1300       -26241.532             0.449            0.380
Chain 1:   1400       -25964.783             0.355            0.320
Chain 1:   1500       -22564.216             0.342            0.320
Chain 1:   1600       -21784.422             0.292            0.294
Chain 1:   1700       -20664.247             0.202            0.294
Chain 1:   1800       -20609.875             0.164            0.151
Chain 1:   1900       -20935.955             0.136            0.054
Chain 1:   2000       -19450.497             0.114            0.054
Chain 1:   2100       -19688.649             0.084            0.036
Chain 1:   2200       -19914.453             0.083            0.036
Chain 1:   2300       -19532.308             0.039            0.020
Chain 1:   2400       -19304.528             0.039            0.020
Chain 1:   2500       -19106.221             0.025            0.016
Chain 1:   2600       -18736.678             0.023            0.016
Chain 1:   2700       -18693.847             0.018            0.012
Chain 1:   2800       -18410.506             0.019            0.015
Chain 1:   2900       -18691.761             0.019            0.015
Chain 1:   3000       -18678.019             0.012            0.012
Chain 1:   3100       -18762.920             0.011            0.012
Chain 1:   3200       -18453.713             0.012            0.015
Chain 1:   3300       -18658.426             0.011            0.012
Chain 1:   3400       -18133.338             0.012            0.015
Chain 1:   3500       -18745.011             0.015            0.015
Chain 1:   3600       -18052.091             0.017            0.015
Chain 1:   3700       -18438.487             0.018            0.017
Chain 1:   3800       -17398.619             0.023            0.021
Chain 1:   3900       -17394.772             0.021            0.021
Chain 1:   4000       -17512.126             0.022            0.021
Chain 1:   4100       -17425.787             0.022            0.021
Chain 1:   4200       -17242.244             0.021            0.021
Chain 1:   4300       -17380.537             0.021            0.021
Chain 1:   4400       -17337.447             0.018            0.011
Chain 1:   4500       -17240.004             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001422 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12854.567             1.000            1.000
Chain 1:    200        -9754.506             0.659            1.000
Chain 1:    300        -8322.125             0.497            0.318
Chain 1:    400        -8545.387             0.379            0.318
Chain 1:    500        -8417.300             0.306            0.172
Chain 1:    600        -8268.341             0.258            0.172
Chain 1:    700        -8354.311             0.223            0.026
Chain 1:    800        -8223.209             0.197            0.026
Chain 1:    900        -8293.130             0.176            0.018
Chain 1:   1000        -8233.026             0.159            0.018
Chain 1:   1100        -8311.300             0.060            0.016
Chain 1:   1200        -8180.467             0.030            0.016
Chain 1:   1300        -8124.962             0.013            0.015
Chain 1:   1400        -8159.610             0.011            0.010
Chain 1:   1500        -8249.082             0.011            0.010
Chain 1:   1600        -8181.810             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57517.657             1.000            1.000
Chain 1:    200       -17922.003             1.605            2.209
Chain 1:    300        -9010.198             1.399            1.000
Chain 1:    400        -8381.807             1.068            1.000
Chain 1:    500        -9340.377             0.875            0.989
Chain 1:    600        -8634.177             0.743            0.989
Chain 1:    700        -8068.426             0.647            0.103
Chain 1:    800        -8447.693             0.572            0.103
Chain 1:    900        -8001.571             0.514            0.082
Chain 1:   1000        -8180.777             0.465            0.082
Chain 1:   1100        -7873.381             0.369            0.075
Chain 1:   1200        -7739.271             0.150            0.070
Chain 1:   1300        -7806.712             0.052            0.056
Chain 1:   1400        -7900.817             0.045            0.045
Chain 1:   1500        -7689.251             0.038            0.039
Chain 1:   1600        -7840.756             0.032            0.028
Chain 1:   1700        -7656.493             0.027            0.024
Chain 1:   1800        -7681.007             0.023            0.022
Chain 1:   1900        -7678.774             0.017            0.019
Chain 1:   2000        -7834.509             0.017            0.019
Chain 1:   2100        -7689.284             0.015            0.019
Chain 1:   2200        -7824.146             0.015            0.019
Chain 1:   2300        -7651.772             0.016            0.019
Chain 1:   2400        -7741.530             0.016            0.019
Chain 1:   2500        -7494.672             0.017            0.019
Chain 1:   2600        -7614.773             0.017            0.019
Chain 1:   2700        -7608.300             0.014            0.017
Chain 1:   2800        -7591.183             0.014            0.017
Chain 1:   2900        -7459.660             0.016            0.018
Chain 1:   3000        -7619.306             0.016            0.018
Chain 1:   3100        -7608.328             0.014            0.017
Chain 1:   3200        -7815.877             0.015            0.018
Chain 1:   3300        -7527.733             0.017            0.018
Chain 1:   3400        -7767.441             0.019            0.021
Chain 1:   3500        -7514.055             0.019            0.021
Chain 1:   3600        -7581.517             0.018            0.021
Chain 1:   3700        -7530.739             0.019            0.021
Chain 1:   3800        -7530.881             0.019            0.021
Chain 1:   3900        -7492.426             0.017            0.021
Chain 1:   4000        -7484.066             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002995 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87478.036             1.000            1.000
Chain 1:    200       -13956.744             3.134            5.268
Chain 1:    300       -10241.431             2.210            1.000
Chain 1:    400       -11602.373             1.687            1.000
Chain 1:    500        -9205.899             1.402            0.363
Chain 1:    600        -8633.070             1.179            0.363
Chain 1:    700        -9048.448             1.017            0.260
Chain 1:    800        -9351.288             0.894            0.260
Chain 1:    900        -9062.948             0.798            0.117
Chain 1:   1000        -8929.637             0.720            0.117
Chain 1:   1100        -8991.447             0.621            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8514.782             0.099            0.056
Chain 1:   1300        -8827.295             0.067            0.046
Chain 1:   1400        -8833.783             0.055            0.035
Chain 1:   1500        -8768.464             0.030            0.032
Chain 1:   1600        -8876.551             0.024            0.032
Chain 1:   1700        -8937.612             0.020            0.015
Chain 1:   1800        -8502.965             0.022            0.015
Chain 1:   1900        -8606.763             0.020            0.012
Chain 1:   2000        -8582.226             0.019            0.012
Chain 1:   2100        -8719.360             0.020            0.012
Chain 1:   2200        -8513.469             0.017            0.012
Chain 1:   2300        -8671.476             0.015            0.012
Chain 1:   2400        -8510.428             0.017            0.016
Chain 1:   2500        -8580.813             0.017            0.016
Chain 1:   2600        -8493.070             0.017            0.016
Chain 1:   2700        -8526.730             0.017            0.016
Chain 1:   2800        -8487.092             0.012            0.012
Chain 1:   2900        -8580.047             0.012            0.011
Chain 1:   3000        -8411.275             0.014            0.016
Chain 1:   3100        -8569.434             0.014            0.018
Chain 1:   3200        -8441.732             0.013            0.015
Chain 1:   3300        -8449.514             0.011            0.011
Chain 1:   3400        -8607.107             0.011            0.011
Chain 1:   3500        -8611.212             0.010            0.011
Chain 1:   3600        -8398.406             0.012            0.015
Chain 1:   3700        -8543.581             0.013            0.017
Chain 1:   3800        -8405.034             0.014            0.017
Chain 1:   3900        -8339.773             0.014            0.017
Chain 1:   4000        -8414.671             0.013            0.016
Chain 1:   4100        -8405.645             0.011            0.015
Chain 1:   4200        -8395.191             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8425596.264             1.000            1.000
Chain 1:    200     -1588602.763             2.652            4.304
Chain 1:    300      -891630.848             2.028            1.000
Chain 1:    400      -458011.011             1.758            1.000
Chain 1:    500      -357920.246             1.462            0.947
Chain 1:    600      -232935.865             1.308            0.947
Chain 1:    700      -119413.762             1.257            0.947
Chain 1:    800       -86690.744             1.147            0.947
Chain 1:    900       -67094.362             1.052            0.782
Chain 1:   1000       -51942.517             0.976            0.782
Chain 1:   1100       -39463.284             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38649.878             0.479            0.377
Chain 1:   1300       -26643.686             0.446            0.377
Chain 1:   1400       -26368.647             0.353            0.316
Chain 1:   1500       -22964.859             0.340            0.316
Chain 1:   1600       -22184.827             0.289            0.292
Chain 1:   1700       -21062.473             0.200            0.292
Chain 1:   1800       -21007.860             0.162            0.148
Chain 1:   1900       -21334.404             0.134            0.053
Chain 1:   2000       -19846.875             0.113            0.053
Chain 1:   2100       -20085.304             0.082            0.035
Chain 1:   2200       -20311.625             0.081            0.035
Chain 1:   2300       -19928.823             0.038            0.019
Chain 1:   2400       -19700.809             0.038            0.019
Chain 1:   2500       -19502.621             0.025            0.015
Chain 1:   2600       -19132.633             0.023            0.015
Chain 1:   2700       -19089.555             0.018            0.012
Chain 1:   2800       -18806.147             0.019            0.015
Chain 1:   2900       -19087.519             0.019            0.015
Chain 1:   3000       -19073.765             0.012            0.012
Chain 1:   3100       -19158.793             0.011            0.012
Chain 1:   3200       -18849.255             0.011            0.015
Chain 1:   3300       -19054.162             0.011            0.012
Chain 1:   3400       -18528.623             0.012            0.015
Chain 1:   3500       -19141.093             0.014            0.015
Chain 1:   3600       -18446.979             0.016            0.015
Chain 1:   3700       -18834.328             0.018            0.016
Chain 1:   3800       -17792.749             0.022            0.021
Chain 1:   3900       -17788.814             0.021            0.021
Chain 1:   4000       -17906.172             0.022            0.021
Chain 1:   4100       -17819.834             0.022            0.021
Chain 1:   4200       -17635.806             0.021            0.021
Chain 1:   4300       -17774.434             0.021            0.021
Chain 1:   4400       -17731.031             0.018            0.010
Chain 1:   4500       -17633.474             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49574.137             1.000            1.000
Chain 1:    200       -20270.649             1.223            1.446
Chain 1:    300       -16342.016             0.895            1.000
Chain 1:    400       -18632.662             0.702            1.000
Chain 1:    500       -13566.245             0.636            0.373
Chain 1:    600       -15279.525             0.549            0.373
Chain 1:    700       -14886.565             0.474            0.240
Chain 1:    800       -13541.448             0.428            0.240
Chain 1:    900       -21314.638             0.421            0.240
Chain 1:   1000       -10934.859             0.473            0.365
Chain 1:   1100       -11359.772             0.377            0.240
Chain 1:   1200       -10831.194             0.237            0.123
Chain 1:   1300       -12294.193             0.225            0.119
Chain 1:   1400       -18133.683             0.245            0.119
Chain 1:   1500       -10086.613             0.288            0.119
Chain 1:   1600        -9698.239             0.280            0.119
Chain 1:   1700       -10009.903             0.281            0.119
Chain 1:   1800       -10802.718             0.278            0.119
Chain 1:   1900       -18169.797             0.282            0.119
Chain 1:   2000       -10372.929             0.263            0.119
Chain 1:   2100       -10051.935             0.262            0.119
Chain 1:   2200       -11181.225             0.267            0.119
Chain 1:   2300       -11859.755             0.261            0.101
Chain 1:   2400        -9624.349             0.252            0.101
Chain 1:   2500        -9280.273             0.176            0.073
Chain 1:   2600       -11180.896             0.189            0.101
Chain 1:   2700       -12072.337             0.193            0.101
Chain 1:   2800        -9229.306             0.217            0.170
Chain 1:   2900        -9455.938             0.179            0.101
Chain 1:   3000       -11711.891             0.123            0.101
Chain 1:   3100       -10537.914             0.131            0.111
Chain 1:   3200        -9695.749             0.129            0.111
Chain 1:   3300       -10841.766             0.134            0.111
Chain 1:   3400        -9483.206             0.125            0.111
Chain 1:   3500        -9228.512             0.124            0.111
Chain 1:   3600       -17657.703             0.155            0.111
Chain 1:   3700       -10542.743             0.215            0.143
Chain 1:   3800        -8872.642             0.203            0.143
Chain 1:   3900       -13080.779             0.233            0.188
Chain 1:   4000        -9581.848             0.250            0.188
Chain 1:   4100        -9453.133             0.240            0.188
Chain 1:   4200        -8843.075             0.239            0.188
Chain 1:   4300       -10144.092             0.241            0.188
Chain 1:   4400       -14833.688             0.258            0.316
Chain 1:   4500        -9141.660             0.318            0.322
Chain 1:   4600       -13848.588             0.304            0.322
Chain 1:   4700        -9594.389             0.281            0.322
Chain 1:   4800        -8640.790             0.273            0.322
Chain 1:   4900        -9471.766             0.250            0.316
Chain 1:   5000        -9818.791             0.217            0.128
Chain 1:   5100        -8851.233             0.226            0.128
Chain 1:   5200        -8582.561             0.222            0.128
Chain 1:   5300        -8600.410             0.210            0.110
Chain 1:   5400        -8880.558             0.181            0.109
Chain 1:   5500       -12682.567             0.149            0.109
Chain 1:   5600        -9237.221             0.152            0.109
Chain 1:   5700       -14203.595             0.143            0.109
Chain 1:   5800        -9168.139             0.187            0.109
Chain 1:   5900        -8751.865             0.183            0.109
Chain 1:   6000        -9898.858             0.191            0.116
Chain 1:   6100        -8816.201             0.192            0.123
Chain 1:   6200        -8853.061             0.190            0.123
Chain 1:   6300        -8625.562             0.192            0.123
Chain 1:   6400        -9008.053             0.193            0.123
Chain 1:   6500       -10479.335             0.177            0.123
Chain 1:   6600        -9105.514             0.155            0.123
Chain 1:   6700        -8720.870             0.124            0.116
Chain 1:   6800       -12361.295             0.099            0.116
Chain 1:   6900       -12698.758             0.097            0.116
Chain 1:   7000       -11800.228             0.093            0.076
Chain 1:   7100        -9593.001             0.104            0.076
Chain 1:   7200        -9277.921             0.107            0.076
Chain 1:   7300       -11587.073             0.124            0.140
Chain 1:   7400       -12064.295             0.124            0.140
Chain 1:   7500       -11676.441             0.113            0.076
Chain 1:   7600        -8934.364             0.128            0.076
Chain 1:   7700       -14825.138             0.164            0.199
Chain 1:   7800       -10919.564             0.170            0.199
Chain 1:   7900        -9523.937             0.182            0.199
Chain 1:   8000        -9762.903             0.177            0.199
Chain 1:   8100        -8554.156             0.168            0.147
Chain 1:   8200        -8668.687             0.166            0.147
Chain 1:   8300        -8388.538             0.149            0.141
Chain 1:   8400        -8713.388             0.149            0.141
Chain 1:   8500        -8206.375             0.152            0.141
Chain 1:   8600        -8766.648             0.128            0.064
Chain 1:   8700        -8291.451             0.094            0.062
Chain 1:   8800        -9095.124             0.067            0.062
Chain 1:   8900       -10753.420             0.068            0.062
Chain 1:   9000       -10515.088             0.067            0.062
Chain 1:   9100        -8431.845             0.078            0.062
Chain 1:   9200        -8863.506             0.081            0.062
Chain 1:   9300       -11604.029             0.102            0.064
Chain 1:   9400        -8815.205             0.130            0.088
Chain 1:   9500       -10098.523             0.136            0.127
Chain 1:   9600        -8563.586             0.148            0.154
Chain 1:   9700        -9149.486             0.148            0.154
Chain 1:   9800        -8256.437             0.150            0.154
Chain 1:   9900        -9755.654             0.150            0.154
Chain 1:   10000        -8503.544             0.163            0.154
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002167 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 21.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57235.231             1.000            1.000
Chain 1:    200       -17805.607             1.607            2.214
Chain 1:    300        -8768.702             1.415            1.031
Chain 1:    400        -7975.950             1.086            1.031
Chain 1:    500        -8691.246             0.885            1.000
Chain 1:    600        -7896.112             0.755            1.000
Chain 1:    700        -8612.673             0.659            0.101
Chain 1:    800        -8065.328             0.585            0.101
Chain 1:    900        -7908.639             0.522            0.099
Chain 1:   1000        -7870.263             0.470            0.099
Chain 1:   1100        -7653.505             0.373            0.083
Chain 1:   1200        -7559.995             0.153            0.082
Chain 1:   1300        -7739.045             0.052            0.068
Chain 1:   1400        -7749.213             0.042            0.028
Chain 1:   1500        -7514.957             0.037            0.028
Chain 1:   1600        -7733.079             0.030            0.028
Chain 1:   1700        -7534.754             0.024            0.026
Chain 1:   1800        -7624.843             0.019            0.023
Chain 1:   1900        -7574.534             0.017            0.023
Chain 1:   2000        -7606.025             0.017            0.023
Chain 1:   2100        -7512.200             0.016            0.012
Chain 1:   2200        -7678.360             0.017            0.022
Chain 1:   2300        -7504.149             0.017            0.022
Chain 1:   2400        -7623.607             0.018            0.022
Chain 1:   2500        -7576.335             0.016            0.016
Chain 1:   2600        -7475.546             0.014            0.013
Chain 1:   2700        -7464.735             0.012            0.012
Chain 1:   2800        -7461.297             0.011            0.012
Chain 1:   2900        -7318.390             0.012            0.013
Chain 1:   3000        -7468.566             0.013            0.016
Chain 1:   3100        -7468.341             0.012            0.016
Chain 1:   3200        -7688.327             0.013            0.016
Chain 1:   3300        -7401.384             0.014            0.016
Chain 1:   3400        -7641.669             0.016            0.020
Chain 1:   3500        -7379.640             0.019            0.020
Chain 1:   3600        -7446.364             0.018            0.020
Chain 1:   3700        -7396.124             0.019            0.020
Chain 1:   3800        -7397.720             0.019            0.020
Chain 1:   3900        -7354.316             0.018            0.020
Chain 1:   4000        -7347.074             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85332.876             1.000            1.000
Chain 1:    200       -13855.045             3.079            5.159
Chain 1:    300       -10140.226             2.175            1.000
Chain 1:    400       -11271.280             1.656            1.000
Chain 1:    500        -9072.376             1.374            0.366
Chain 1:    600        -9494.819             1.152            0.366
Chain 1:    700        -9068.248             0.994            0.242
Chain 1:    800        -8503.870             0.878            0.242
Chain 1:    900        -8461.063             0.781            0.100
Chain 1:   1000        -8683.411             0.706            0.100
Chain 1:   1100        -8797.247             0.607            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8482.015             0.095            0.047
Chain 1:   1300        -8786.815             0.062            0.044
Chain 1:   1400        -8691.528             0.053            0.037
Chain 1:   1500        -8623.500             0.029            0.035
Chain 1:   1600        -8732.289             0.026            0.026
Chain 1:   1700        -8788.533             0.022            0.013
Chain 1:   1800        -8344.238             0.021            0.013
Chain 1:   1900        -8449.690             0.021            0.013
Chain 1:   2000        -8434.028             0.019            0.012
Chain 1:   2100        -8556.522             0.019            0.012
Chain 1:   2200        -8345.202             0.018            0.012
Chain 1:   2300        -8444.918             0.016            0.012
Chain 1:   2400        -8508.768             0.015            0.012
Chain 1:   2500        -8458.645             0.015            0.012
Chain 1:   2600        -8472.002             0.014            0.012
Chain 1:   2700        -8378.959             0.015            0.012
Chain 1:   2800        -8325.129             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003244 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8369137.740             1.000            1.000
Chain 1:    200     -1578823.485             2.650            4.301
Chain 1:    300      -890001.552             2.025            1.000
Chain 1:    400      -457694.089             1.755            1.000
Chain 1:    500      -358625.877             1.459            0.945
Chain 1:    600      -233796.366             1.305            0.945
Chain 1:    700      -119864.189             1.254            0.945
Chain 1:    800       -87050.570             1.145            0.945
Chain 1:    900       -67346.426             1.050            0.774
Chain 1:   1000       -52115.650             0.974            0.774
Chain 1:   1100       -39554.325             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38731.943             0.478            0.377
Chain 1:   1300       -26634.386             0.446            0.377
Chain 1:   1400       -26351.903             0.353            0.318
Chain 1:   1500       -22924.584             0.340            0.318
Chain 1:   1600       -22138.102             0.290            0.293
Chain 1:   1700       -21004.276             0.200            0.292
Chain 1:   1800       -20947.350             0.163            0.150
Chain 1:   1900       -21274.047             0.135            0.054
Chain 1:   2000       -19780.313             0.114            0.054
Chain 1:   2100       -20018.981             0.083            0.036
Chain 1:   2200       -20246.474             0.082            0.036
Chain 1:   2300       -19862.637             0.039            0.019
Chain 1:   2400       -19634.414             0.039            0.019
Chain 1:   2500       -19436.743             0.025            0.015
Chain 1:   2600       -19066.067             0.023            0.015
Chain 1:   2700       -19022.786             0.018            0.012
Chain 1:   2800       -18739.462             0.019            0.015
Chain 1:   2900       -19021.089             0.019            0.015
Chain 1:   3000       -19007.180             0.012            0.012
Chain 1:   3100       -19092.273             0.011            0.012
Chain 1:   3200       -18782.508             0.011            0.015
Chain 1:   3300       -18987.601             0.011            0.012
Chain 1:   3400       -18461.818             0.012            0.015
Chain 1:   3500       -19074.869             0.014            0.015
Chain 1:   3600       -18380.045             0.016            0.015
Chain 1:   3700       -18767.977             0.018            0.016
Chain 1:   3800       -17725.464             0.023            0.021
Chain 1:   3900       -17721.602             0.021            0.021
Chain 1:   4000       -17838.859             0.022            0.021
Chain 1:   4100       -17752.506             0.022            0.021
Chain 1:   4200       -17568.293             0.021            0.021
Chain 1:   4300       -17706.997             0.021            0.021
Chain 1:   4400       -17663.399             0.018            0.010
Chain 1:   4500       -17565.905             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001339 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12611.206             1.000            1.000
Chain 1:    200        -9447.306             0.667            1.000
Chain 1:    300        -8237.118             0.494            0.335
Chain 1:    400        -8421.987             0.376            0.335
Chain 1:    500        -8438.037             0.301            0.147
Chain 1:    600        -8147.074             0.257            0.147
Chain 1:    700        -8076.112             0.221            0.036
Chain 1:    800        -8086.379             0.194            0.036
Chain 1:    900        -8016.696             0.173            0.022
Chain 1:   1000        -8196.150             0.158            0.022
Chain 1:   1100        -8214.177             0.058            0.022
Chain 1:   1200        -8096.290             0.026            0.015
Chain 1:   1300        -8055.354             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001917 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57872.100             1.000            1.000
Chain 1:    200       -17641.480             1.640            2.280
Chain 1:    300        -8829.302             1.426            1.000
Chain 1:    400        -8211.176             1.088            1.000
Chain 1:    500        -8124.815             0.873            0.998
Chain 1:    600        -8623.598             0.737            0.998
Chain 1:    700        -8334.829             0.637            0.075
Chain 1:    800        -8127.619             0.560            0.075
Chain 1:    900        -8042.027             0.499            0.058
Chain 1:   1000        -7870.806             0.451            0.058
Chain 1:   1100        -7665.852             0.354            0.035
Chain 1:   1200        -7675.692             0.126            0.027
Chain 1:   1300        -7606.123             0.027            0.025
Chain 1:   1400        -7852.763             0.023            0.025
Chain 1:   1500        -7665.955             0.024            0.025
Chain 1:   1600        -7846.185             0.021            0.024
Chain 1:   1700        -7586.973             0.021            0.024
Chain 1:   1800        -7677.431             0.019            0.023
Chain 1:   1900        -7610.529             0.019            0.023
Chain 1:   2000        -7661.952             0.018            0.023
Chain 1:   2100        -7588.494             0.016            0.012
Chain 1:   2200        -7723.528             0.018            0.017
Chain 1:   2300        -7571.367             0.019            0.020
Chain 1:   2400        -7602.323             0.016            0.017
Chain 1:   2500        -7611.366             0.014            0.012
Chain 1:   2600        -7532.034             0.012            0.011
Chain 1:   2700        -7549.186             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003233 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86022.062             1.000            1.000
Chain 1:    200       -13738.606             3.131            5.261
Chain 1:    300       -10095.713             2.207            1.000
Chain 1:    400       -11081.762             1.678            1.000
Chain 1:    500        -8841.662             1.393            0.361
Chain 1:    600        -8994.518             1.164            0.361
Chain 1:    700        -8976.381             0.998            0.253
Chain 1:    800        -8489.705             0.880            0.253
Chain 1:    900        -8542.699             0.783            0.089
Chain 1:   1000        -8719.948             0.707            0.089
Chain 1:   1100        -8915.848             0.609            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8523.344             0.087            0.046
Chain 1:   1300        -8853.356             0.055            0.037
Chain 1:   1400        -8738.882             0.047            0.022
Chain 1:   1500        -8641.780             0.023            0.020
Chain 1:   1600        -8744.738             0.023            0.020
Chain 1:   1700        -8831.650             0.024            0.020
Chain 1:   1800        -8408.738             0.023            0.020
Chain 1:   1900        -8508.990             0.023            0.020
Chain 1:   2000        -8483.142             0.022            0.013
Chain 1:   2100        -8607.938             0.021            0.013
Chain 1:   2200        -8414.471             0.019            0.013
Chain 1:   2300        -8503.514             0.016            0.012
Chain 1:   2400        -8572.648             0.015            0.012
Chain 1:   2500        -8518.820             0.015            0.012
Chain 1:   2600        -8519.736             0.014            0.010
Chain 1:   2700        -8436.651             0.014            0.010
Chain 1:   2800        -8397.209             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004608 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 46.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386589.940             1.000            1.000
Chain 1:    200     -1583824.564             2.648            4.295
Chain 1:    300      -890828.489             2.024            1.000
Chain 1:    400      -457612.793             1.755            1.000
Chain 1:    500      -358261.910             1.459            0.947
Chain 1:    600      -233228.579             1.306            0.947
Chain 1:    700      -119466.238             1.255            0.947
Chain 1:    800       -86697.246             1.145            0.947
Chain 1:    900       -67039.717             1.051            0.778
Chain 1:   1000       -51842.272             0.975            0.778
Chain 1:   1100       -39318.578             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38496.688             0.479            0.378
Chain 1:   1300       -26446.894             0.447            0.378
Chain 1:   1400       -26166.151             0.354            0.319
Chain 1:   1500       -22751.863             0.341            0.319
Chain 1:   1600       -21968.231             0.291            0.293
Chain 1:   1700       -20840.934             0.201            0.293
Chain 1:   1800       -20785.095             0.164            0.150
Chain 1:   1900       -21111.346             0.136            0.054
Chain 1:   2000       -19621.820             0.114            0.054
Chain 1:   2100       -19860.224             0.083            0.036
Chain 1:   2200       -20086.868             0.082            0.036
Chain 1:   2300       -19703.871             0.039            0.019
Chain 1:   2400       -19475.916             0.039            0.019
Chain 1:   2500       -19278.006             0.025            0.015
Chain 1:   2600       -18908.073             0.023            0.015
Chain 1:   2700       -18864.992             0.018            0.012
Chain 1:   2800       -18581.836             0.019            0.015
Chain 1:   2900       -18863.137             0.019            0.015
Chain 1:   3000       -18849.289             0.012            0.012
Chain 1:   3100       -18934.313             0.011            0.012
Chain 1:   3200       -18624.927             0.012            0.015
Chain 1:   3300       -18829.704             0.011            0.012
Chain 1:   3400       -18304.536             0.012            0.015
Chain 1:   3500       -18916.604             0.015            0.015
Chain 1:   3600       -18223.021             0.016            0.015
Chain 1:   3700       -18610.022             0.018            0.017
Chain 1:   3800       -17569.384             0.023            0.021
Chain 1:   3900       -17565.530             0.021            0.021
Chain 1:   4000       -17682.813             0.022            0.021
Chain 1:   4100       -17596.577             0.022            0.021
Chain 1:   4200       -17412.748             0.021            0.021
Chain 1:   4300       -17551.188             0.021            0.021
Chain 1:   4400       -17507.935             0.018            0.011
Chain 1:   4500       -17410.480             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49002.576             1.000            1.000
Chain 1:    200       -21370.796             1.146            1.293
Chain 1:    300       -31313.837             0.870            1.000
Chain 1:    400       -13575.964             0.979            1.293
Chain 1:    500       -17043.910             0.824            1.000
Chain 1:    600       -13508.656             0.730            1.000
Chain 1:    700       -13906.142             0.630            0.318
Chain 1:    800       -11872.914             0.573            0.318
Chain 1:    900       -14714.211             0.531            0.262
Chain 1:   1000       -14127.712             0.482            0.262
Chain 1:   1100       -12142.236             0.398            0.203
Chain 1:   1200       -11997.630             0.270            0.193
Chain 1:   1300       -10249.386             0.255            0.171
Chain 1:   1400       -15892.581             0.160            0.171
Chain 1:   1500       -12049.312             0.172            0.171
Chain 1:   1600       -12813.668             0.151            0.171
Chain 1:   1700       -10595.794             0.170            0.171
Chain 1:   1800       -13098.493             0.171            0.191
Chain 1:   1900       -10276.290             0.180            0.191
Chain 1:   2000       -10205.486             0.176            0.191
Chain 1:   2100       -10042.252             0.161            0.191
Chain 1:   2200       -11176.642             0.170            0.191
Chain 1:   2300       -10438.891             0.160            0.191
Chain 1:   2400        -9587.794             0.134            0.101
Chain 1:   2500        -9426.744             0.104            0.089
Chain 1:   2600       -11032.806             0.112            0.101
Chain 1:   2700        -9537.478             0.107            0.101
Chain 1:   2800        -9295.762             0.090            0.089
Chain 1:   2900        -9960.672             0.070            0.071
Chain 1:   3000        -8931.829             0.080            0.089
Chain 1:   3100        -9173.002             0.081            0.089
Chain 1:   3200        -8932.974             0.074            0.071
Chain 1:   3300        -9331.943             0.071            0.067
Chain 1:   3400       -16071.591             0.104            0.067
Chain 1:   3500        -9947.944             0.164            0.115
Chain 1:   3600        -9017.095             0.160            0.103
Chain 1:   3700        -9261.537             0.147            0.067
Chain 1:   3800        -9365.273             0.145            0.067
Chain 1:   3900       -12573.558             0.164            0.103
Chain 1:   4000        -9549.708             0.184            0.103
Chain 1:   4100        -9007.750             0.188            0.103
Chain 1:   4200        -9216.208             0.187            0.103
Chain 1:   4300        -9936.889             0.190            0.103
Chain 1:   4400        -9106.823             0.157            0.091
Chain 1:   4500        -9087.392             0.096            0.073
Chain 1:   4600        -9985.223             0.095            0.073
Chain 1:   4700       -11056.220             0.102            0.090
Chain 1:   4800       -10369.447             0.107            0.090
Chain 1:   4900        -8841.053             0.099            0.090
Chain 1:   5000       -10370.611             0.082            0.090
Chain 1:   5100        -8778.159             0.094            0.091
Chain 1:   5200        -9036.925             0.095            0.091
Chain 1:   5300       -11599.313             0.110            0.097
Chain 1:   5400        -9249.739             0.126            0.147
Chain 1:   5500        -8458.240             0.135            0.147
Chain 1:   5600        -8566.611             0.127            0.147
Chain 1:   5700        -9887.983             0.131            0.147
Chain 1:   5800        -9256.103             0.131            0.147
Chain 1:   5900        -9211.534             0.115            0.134
Chain 1:   6000       -12060.636             0.123            0.134
Chain 1:   6100       -12050.336             0.105            0.094
Chain 1:   6200       -10570.464             0.116            0.134
Chain 1:   6300        -8833.635             0.114            0.134
Chain 1:   6400       -10347.586             0.103            0.134
Chain 1:   6500       -12613.862             0.112            0.140
Chain 1:   6600        -8943.797             0.152            0.146
Chain 1:   6700       -12253.046             0.165            0.180
Chain 1:   6800        -9282.922             0.190            0.197
Chain 1:   6900       -10675.444             0.203            0.197
Chain 1:   7000        -8608.820             0.203            0.197
Chain 1:   7100        -8474.302             0.205            0.197
Chain 1:   7200       -12132.122             0.221            0.240
Chain 1:   7300        -9531.900             0.229            0.270
Chain 1:   7400       -10637.973             0.224            0.270
Chain 1:   7500        -8448.096             0.232            0.270
Chain 1:   7600        -8936.448             0.197            0.259
Chain 1:   7700        -8594.715             0.174            0.240
Chain 1:   7800       -11345.608             0.166            0.240
Chain 1:   7900        -8810.814             0.182            0.242
Chain 1:   8000       -10473.085             0.174            0.242
Chain 1:   8100       -11308.840             0.179            0.242
Chain 1:   8200       -10021.313             0.162            0.159
Chain 1:   8300        -8917.257             0.147            0.128
Chain 1:   8400       -12557.031             0.166            0.159
Chain 1:   8500        -8453.436             0.188            0.159
Chain 1:   8600        -8342.570             0.184            0.159
Chain 1:   8700       -11416.167             0.207            0.242
Chain 1:   8800        -8382.478             0.219            0.269
Chain 1:   8900        -8806.770             0.195            0.159
Chain 1:   9000        -9155.443             0.183            0.128
Chain 1:   9100        -9176.675             0.176            0.128
Chain 1:   9200       -10308.892             0.174            0.124
Chain 1:   9300        -8478.833             0.183            0.216
Chain 1:   9400        -9491.601             0.165            0.110
Chain 1:   9500        -8742.064             0.125            0.107
Chain 1:   9600       -10734.950             0.142            0.110
Chain 1:   9700        -8378.329             0.144            0.110
Chain 1:   9800        -8536.196             0.109            0.107
Chain 1:   9900        -8234.501             0.108            0.107
Chain 1:   10000        -8292.504             0.105            0.107
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001931 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57060.414             1.000            1.000
Chain 1:    200       -17619.216             1.619            2.239
Chain 1:    300        -8810.055             1.413            1.000
Chain 1:    400        -8344.800             1.074            1.000
Chain 1:    500        -8467.814             0.862            1.000
Chain 1:    600        -9099.922             0.730            1.000
Chain 1:    700        -7884.705             0.647            0.154
Chain 1:    800        -8100.037             0.570            0.154
Chain 1:    900        -8029.903             0.508            0.069
Chain 1:   1000        -7907.427             0.458            0.069
Chain 1:   1100        -7640.103             0.362            0.056
Chain 1:   1200        -7704.642             0.139            0.035
Chain 1:   1300        -7600.543             0.040            0.027
Chain 1:   1400        -7598.757             0.035            0.015
Chain 1:   1500        -7581.266             0.033            0.015
Chain 1:   1600        -7769.536             0.029            0.015
Chain 1:   1700        -7535.924             0.017            0.015
Chain 1:   1800        -7691.043             0.016            0.015
Chain 1:   1900        -7562.891             0.017            0.017
Chain 1:   2000        -7613.298             0.016            0.017
Chain 1:   2100        -7479.097             0.014            0.017
Chain 1:   2200        -7801.999             0.017            0.018
Chain 1:   2300        -7556.257             0.019            0.020
Chain 1:   2400        -7631.699             0.020            0.020
Chain 1:   2500        -7534.802             0.021            0.020
Chain 1:   2600        -7499.467             0.019            0.018
Chain 1:   2700        -7500.875             0.016            0.017
Chain 1:   2800        -7479.126             0.015            0.013
Chain 1:   2900        -7374.175             0.014            0.013
Chain 1:   3000        -7515.799             0.016            0.014
Chain 1:   3100        -7508.218             0.014            0.013
Chain 1:   3200        -7705.006             0.012            0.013
Chain 1:   3300        -7436.929             0.013            0.013
Chain 1:   3400        -7649.165             0.014            0.014
Chain 1:   3500        -7416.937             0.016            0.019
Chain 1:   3600        -7482.240             0.017            0.019
Chain 1:   3700        -7431.106             0.017            0.019
Chain 1:   3800        -7432.686             0.017            0.019
Chain 1:   3900        -7398.716             0.016            0.019
Chain 1:   4000        -7393.847             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003246 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87102.427             1.000            1.000
Chain 1:    200       -13697.812             3.179            5.359
Chain 1:    300       -10046.515             2.241            1.000
Chain 1:    400       -10958.859             1.701            1.000
Chain 1:    500        -8935.008             1.406            0.363
Chain 1:    600        -8577.325             1.179            0.363
Chain 1:    700        -8487.778             1.012            0.227
Chain 1:    800        -8751.634             0.889            0.227
Chain 1:    900        -8795.012             0.791            0.083
Chain 1:   1000        -8703.998             0.713            0.083
Chain 1:   1100        -8839.549             0.615            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8372.853             0.084            0.042
Chain 1:   1300        -8731.097             0.052            0.041
Chain 1:   1400        -8702.472             0.044            0.030
Chain 1:   1500        -8606.211             0.022            0.015
Chain 1:   1600        -8709.173             0.019            0.012
Chain 1:   1700        -8787.730             0.019            0.012
Chain 1:   1800        -8368.749             0.021            0.012
Chain 1:   1900        -8467.028             0.022            0.012
Chain 1:   2000        -8441.069             0.021            0.012
Chain 1:   2100        -8565.303             0.021            0.012
Chain 1:   2200        -8377.013             0.018            0.012
Chain 1:   2300        -8461.677             0.015            0.012
Chain 1:   2400        -8531.122             0.015            0.012
Chain 1:   2500        -8477.134             0.015            0.012
Chain 1:   2600        -8477.592             0.014            0.010
Chain 1:   2700        -8394.691             0.014            0.010
Chain 1:   2800        -8356.025             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003403 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411992.356             1.000            1.000
Chain 1:    200     -1586216.987             2.652            4.303
Chain 1:    300      -891627.354             2.027            1.000
Chain 1:    400      -458176.931             1.757            1.000
Chain 1:    500      -358315.866             1.461            0.946
Chain 1:    600      -233116.550             1.307            0.946
Chain 1:    700      -119355.483             1.257            0.946
Chain 1:    800       -86578.016             1.147            0.946
Chain 1:    900       -66931.647             1.052            0.779
Chain 1:   1000       -51741.121             0.976            0.779
Chain 1:   1100       -39233.014             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38409.756             0.480            0.379
Chain 1:   1300       -26379.700             0.448            0.379
Chain 1:   1400       -26099.891             0.354            0.319
Chain 1:   1500       -22690.449             0.341            0.319
Chain 1:   1600       -21908.171             0.291            0.294
Chain 1:   1700       -20783.412             0.201            0.294
Chain 1:   1800       -20727.885             0.164            0.150
Chain 1:   1900       -21054.106             0.136            0.054
Chain 1:   2000       -19565.878             0.114            0.054
Chain 1:   2100       -19804.258             0.083            0.036
Chain 1:   2200       -20030.659             0.082            0.036
Chain 1:   2300       -19647.848             0.039            0.019
Chain 1:   2400       -19419.922             0.039            0.019
Chain 1:   2500       -19221.868             0.025            0.015
Chain 1:   2600       -18852.102             0.023            0.015
Chain 1:   2700       -18809.065             0.018            0.012
Chain 1:   2800       -18525.909             0.019            0.015
Chain 1:   2900       -18807.107             0.019            0.015
Chain 1:   3000       -18793.310             0.012            0.012
Chain 1:   3100       -18878.327             0.011            0.012
Chain 1:   3200       -18569.002             0.012            0.015
Chain 1:   3300       -18773.708             0.011            0.012
Chain 1:   3400       -18248.609             0.012            0.015
Chain 1:   3500       -18860.564             0.015            0.015
Chain 1:   3600       -18167.082             0.016            0.015
Chain 1:   3700       -18553.994             0.018            0.017
Chain 1:   3800       -17513.515             0.023            0.021
Chain 1:   3900       -17509.624             0.021            0.021
Chain 1:   4000       -17626.930             0.022            0.021
Chain 1:   4100       -17540.726             0.022            0.021
Chain 1:   4200       -17356.879             0.021            0.021
Chain 1:   4300       -17495.346             0.021            0.021
Chain 1:   4400       -17452.143             0.018            0.011
Chain 1:   4500       -17354.627             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12183.499             1.000            1.000
Chain 1:    200        -9044.733             0.674            1.000
Chain 1:    300        -7855.142             0.499            0.347
Chain 1:    400        -8073.934             0.381            0.347
Chain 1:    500        -7920.276             0.309            0.151
Chain 1:    600        -7799.987             0.260            0.151
Chain 1:    700        -7720.624             0.224            0.027
Chain 1:    800        -7728.894             0.196            0.027
Chain 1:    900        -7632.280             0.176            0.019
Chain 1:   1000        -7748.384             0.160            0.019
Chain 1:   1100        -7838.466             0.061            0.015
Chain 1:   1200        -7756.478             0.027            0.015
Chain 1:   1300        -7697.817             0.013            0.013
Chain 1:   1400        -7728.500             0.011            0.011
Chain 1:   1500        -7808.704             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001421 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63078.501             1.000            1.000
Chain 1:    200       -17918.836             1.760            2.520
Chain 1:    300        -8658.291             1.530            1.070
Chain 1:    400        -8348.999             1.157            1.070
Chain 1:    500        -8074.441             0.932            1.000
Chain 1:    600        -8496.202             0.785            1.000
Chain 1:    700        -8573.634             0.674            0.050
Chain 1:    800        -8014.354             0.599            0.070
Chain 1:    900        -7942.703             0.533            0.050
Chain 1:   1000        -7791.841             0.482            0.050
Chain 1:   1100        -7678.668             0.383            0.037
Chain 1:   1200        -7541.857             0.133            0.034
Chain 1:   1300        -7683.709             0.028            0.019
Chain 1:   1400        -7822.503             0.026            0.018
Chain 1:   1500        -7575.800             0.026            0.018
Chain 1:   1600        -7508.902             0.022            0.018
Chain 1:   1700        -7479.512             0.021            0.018
Chain 1:   1800        -7561.465             0.015            0.018
Chain 1:   1900        -7553.686             0.015            0.018
Chain 1:   2000        -7563.016             0.013            0.015
Chain 1:   2100        -7551.446             0.011            0.011
Chain 1:   2200        -7648.616             0.011            0.011
Chain 1:   2300        -7729.174             0.010            0.010
Chain 1:   2400        -7604.187             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003655 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86291.366             1.000            1.000
Chain 1:    200       -13254.784             3.255            5.510
Chain 1:    300        -9668.591             2.294            1.000
Chain 1:    400       -10500.562             1.740            1.000
Chain 1:    500        -8610.488             1.436            0.371
Chain 1:    600        -8308.756             1.203            0.371
Chain 1:    700        -8518.757             1.034            0.220
Chain 1:    800        -9141.188             0.914            0.220
Chain 1:    900        -8531.394             0.820            0.079
Chain 1:   1000        -8253.725             0.741            0.079
Chain 1:   1100        -8532.464             0.645            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8196.501             0.098            0.068
Chain 1:   1300        -8375.241             0.063            0.041
Chain 1:   1400        -8382.051             0.055            0.036
Chain 1:   1500        -8245.970             0.035            0.034
Chain 1:   1600        -8356.936             0.032            0.033
Chain 1:   1700        -8443.296             0.031            0.033
Chain 1:   1800        -8040.618             0.029            0.033
Chain 1:   1900        -8138.503             0.023            0.021
Chain 1:   2000        -8110.102             0.020            0.017
Chain 1:   2100        -8229.978             0.018            0.015
Chain 1:   2200        -8039.123             0.017            0.015
Chain 1:   2300        -8174.165             0.016            0.015
Chain 1:   2400        -8049.032             0.018            0.016
Chain 1:   2500        -8113.658             0.017            0.015
Chain 1:   2600        -8137.470             0.016            0.015
Chain 1:   2700        -8055.725             0.016            0.015
Chain 1:   2800        -8028.234             0.011            0.012
Chain 1:   2900        -8083.663             0.011            0.010
Chain 1:   3000        -7966.917             0.012            0.015
Chain 1:   3100        -8105.944             0.012            0.015
Chain 1:   3200        -7985.210             0.011            0.015
Chain 1:   3300        -8007.503             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8440860.020             1.000            1.000
Chain 1:    200     -1590325.601             2.654            4.308
Chain 1:    300      -890086.547             2.031            1.000
Chain 1:    400      -456563.204             1.761            1.000
Chain 1:    500      -356167.741             1.465            0.950
Chain 1:    600      -231355.947             1.311            0.950
Chain 1:    700      -118232.219             1.260            0.950
Chain 1:    800       -85640.076             1.150            0.950
Chain 1:    900       -66125.472             1.055            0.787
Chain 1:   1000       -51036.490             0.979            0.787
Chain 1:   1100       -38621.271             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37810.681             0.483            0.381
Chain 1:   1300       -25872.704             0.450            0.381
Chain 1:   1400       -25602.808             0.356            0.321
Chain 1:   1500       -22217.588             0.343            0.321
Chain 1:   1600       -21442.621             0.293            0.296
Chain 1:   1700       -20328.615             0.203            0.295
Chain 1:   1800       -20275.784             0.165            0.152
Chain 1:   1900       -20601.744             0.137            0.055
Chain 1:   2000       -19119.840             0.115            0.055
Chain 1:   2100       -19357.868             0.084            0.036
Chain 1:   2200       -19583.153             0.083            0.036
Chain 1:   2300       -19201.433             0.039            0.020
Chain 1:   2400       -18973.684             0.039            0.020
Chain 1:   2500       -18775.383             0.025            0.016
Chain 1:   2600       -18406.178             0.024            0.016
Chain 1:   2700       -18363.353             0.018            0.012
Chain 1:   2800       -18080.150             0.020            0.016
Chain 1:   2900       -18361.159             0.020            0.015
Chain 1:   3000       -18347.471             0.012            0.012
Chain 1:   3100       -18432.395             0.011            0.012
Chain 1:   3200       -18123.316             0.012            0.015
Chain 1:   3300       -18327.878             0.011            0.012
Chain 1:   3400       -17803.099             0.013            0.015
Chain 1:   3500       -18414.354             0.015            0.016
Chain 1:   3600       -17721.768             0.017            0.016
Chain 1:   3700       -18107.934             0.019            0.017
Chain 1:   3800       -17068.735             0.023            0.021
Chain 1:   3900       -17064.834             0.022            0.021
Chain 1:   4000       -17182.210             0.022            0.021
Chain 1:   4100       -17095.981             0.022            0.021
Chain 1:   4200       -16912.479             0.022            0.021
Chain 1:   4300       -17050.741             0.022            0.021
Chain 1:   4400       -17007.743             0.019            0.011
Chain 1:   4500       -16910.253             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001326 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12291.020             1.000            1.000
Chain 1:    200        -9213.695             0.667            1.000
Chain 1:    300        -8099.806             0.491            0.334
Chain 1:    400        -8243.829             0.372            0.334
Chain 1:    500        -8169.858             0.300            0.138
Chain 1:    600        -8065.126             0.252            0.138
Chain 1:    700        -7930.905             0.218            0.017
Chain 1:    800        -7942.601             0.191            0.017
Chain 1:    900        -7897.360             0.171            0.017
Chain 1:   1000        -7980.834             0.155            0.017
Chain 1:   1100        -8048.953             0.055            0.013
Chain 1:   1200        -7932.203             0.023            0.013
Chain 1:   1300        -7907.864             0.010            0.010
Chain 1:   1400        -7915.313             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001486 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61461.284             1.000            1.000
Chain 1:    200       -17737.140             1.733            2.465
Chain 1:    300        -8803.538             1.493            1.015
Chain 1:    400        -9128.716             1.129            1.015
Chain 1:    500        -7973.376             0.932            1.000
Chain 1:    600        -8308.610             0.783            1.000
Chain 1:    700        -8100.713             0.675            0.145
Chain 1:    800        -8149.825             0.592            0.145
Chain 1:    900        -7952.538             0.529            0.040
Chain 1:   1000        -7921.832             0.476            0.040
Chain 1:   1100        -7698.471             0.379            0.036
Chain 1:   1200        -7789.556             0.134            0.029
Chain 1:   1300        -7759.364             0.033            0.026
Chain 1:   1400        -7675.440             0.030            0.025
Chain 1:   1500        -7589.027             0.017            0.012
Chain 1:   1600        -7845.626             0.016            0.012
Chain 1:   1700        -7526.360             0.018            0.012
Chain 1:   1800        -7620.156             0.018            0.012
Chain 1:   1900        -7477.110             0.018            0.012
Chain 1:   2000        -7604.176             0.019            0.017
Chain 1:   2100        -7659.810             0.017            0.012
Chain 1:   2200        -7690.702             0.016            0.012
Chain 1:   2300        -7595.183             0.017            0.013
Chain 1:   2400        -7645.060             0.017            0.013
Chain 1:   2500        -7586.611             0.016            0.013
Chain 1:   2600        -7527.824             0.014            0.012
Chain 1:   2700        -7566.969             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003724 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85875.843             1.000            1.000
Chain 1:    200       -13424.825             3.198            5.397
Chain 1:    300        -9841.362             2.254            1.000
Chain 1:    400       -10723.488             1.711            1.000
Chain 1:    500        -8776.731             1.413            0.364
Chain 1:    600        -8367.224             1.186            0.364
Chain 1:    700        -8482.580             1.018            0.222
Chain 1:    800        -9128.869             0.900            0.222
Chain 1:    900        -8676.307             0.806            0.082
Chain 1:   1000        -8449.551             0.728            0.082
Chain 1:   1100        -8746.417             0.631            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8267.830             0.097            0.058
Chain 1:   1300        -8532.752             0.064            0.052
Chain 1:   1400        -8544.629             0.056            0.049
Chain 1:   1500        -8461.955             0.035            0.034
Chain 1:   1600        -8555.165             0.031            0.031
Chain 1:   1700        -8633.534             0.030            0.031
Chain 1:   1800        -8239.364             0.028            0.031
Chain 1:   1900        -8340.149             0.024            0.027
Chain 1:   2000        -8311.187             0.022            0.012
Chain 1:   2100        -8432.365             0.020            0.012
Chain 1:   2200        -8210.307             0.017            0.012
Chain 1:   2300        -8369.281             0.015            0.012
Chain 1:   2400        -8380.882             0.015            0.012
Chain 1:   2500        -8353.229             0.015            0.012
Chain 1:   2600        -8355.763             0.014            0.012
Chain 1:   2700        -8261.895             0.014            0.012
Chain 1:   2800        -8232.201             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004724 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 47.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8377765.094             1.000            1.000
Chain 1:    200     -1581772.391             2.648            4.296
Chain 1:    300      -891023.673             2.024            1.000
Chain 1:    400      -457382.107             1.755            1.000
Chain 1:    500      -357999.358             1.459            0.948
Chain 1:    600      -233043.109             1.306            0.948
Chain 1:    700      -119249.952             1.255            0.948
Chain 1:    800       -86409.780             1.146            0.948
Chain 1:    900       -66746.013             1.051            0.775
Chain 1:   1000       -51526.169             0.976            0.775
Chain 1:   1100       -38985.142             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38161.167             0.480            0.380
Chain 1:   1300       -26106.486             0.449            0.380
Chain 1:   1400       -25823.970             0.355            0.322
Chain 1:   1500       -22407.153             0.343            0.322
Chain 1:   1600       -21621.987             0.293            0.295
Chain 1:   1700       -20494.737             0.203            0.295
Chain 1:   1800       -20438.641             0.165            0.152
Chain 1:   1900       -20764.423             0.137            0.055
Chain 1:   2000       -19275.526             0.116            0.055
Chain 1:   2100       -19514.066             0.085            0.036
Chain 1:   2200       -19740.204             0.084            0.036
Chain 1:   2300       -19357.778             0.039            0.020
Chain 1:   2400       -19129.951             0.039            0.020
Chain 1:   2500       -18931.920             0.025            0.016
Chain 1:   2600       -18562.522             0.024            0.016
Chain 1:   2700       -18519.658             0.018            0.012
Chain 1:   2800       -18236.554             0.020            0.016
Chain 1:   2900       -18517.734             0.020            0.015
Chain 1:   3000       -18503.989             0.012            0.012
Chain 1:   3100       -18588.873             0.011            0.012
Chain 1:   3200       -18279.820             0.012            0.015
Chain 1:   3300       -18484.376             0.011            0.012
Chain 1:   3400       -17959.678             0.013            0.015
Chain 1:   3500       -18570.966             0.015            0.016
Chain 1:   3600       -17878.502             0.017            0.016
Chain 1:   3700       -18264.617             0.019            0.017
Chain 1:   3800       -17225.623             0.023            0.021
Chain 1:   3900       -17221.809             0.022            0.021
Chain 1:   4000       -17339.108             0.022            0.021
Chain 1:   4100       -17252.855             0.022            0.021
Chain 1:   4200       -17069.464             0.022            0.021
Chain 1:   4300       -17207.633             0.021            0.021
Chain 1:   4400       -17164.696             0.019            0.011
Chain 1:   4500       -17067.275             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001818 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13355.653             1.000            1.000
Chain 1:    200        -9828.968             0.679            1.000
Chain 1:    300        -8485.440             0.506            0.359
Chain 1:    400        -8365.009             0.383            0.359
Chain 1:    500        -8194.975             0.310            0.158
Chain 1:    600        -8036.956             0.262            0.158
Chain 1:    700        -8175.692             0.227            0.021
Chain 1:    800        -7877.719             0.203            0.038
Chain 1:    900        -7899.872             0.181            0.021
Chain 1:   1000        -7981.535             0.164            0.021
Chain 1:   1100        -8078.135             0.065            0.020
Chain 1:   1200        -7942.488             0.031            0.017
Chain 1:   1300        -7930.180             0.015            0.017
Chain 1:   1400        -7899.213             0.014            0.017
Chain 1:   1500        -8005.866             0.014            0.013
Chain 1:   1600        -7935.427             0.012            0.012
Chain 1:   1700        -7889.635             0.011            0.010
Chain 1:   1800        -7862.392             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001496 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58389.051             1.000            1.000
Chain 1:    200       -18023.957             1.620            2.240
Chain 1:    300        -8866.361             1.424            1.033
Chain 1:    400        -8081.448             1.092            1.033
Chain 1:    500        -8684.419             0.888            1.000
Chain 1:    600        -8924.966             0.744            1.000
Chain 1:    700        -8630.400             0.643            0.097
Chain 1:    800        -7866.475             0.575            0.097
Chain 1:    900        -7927.886             0.512            0.097
Chain 1:   1000        -8183.249             0.464            0.097
Chain 1:   1100        -8168.187             0.364            0.069
Chain 1:   1200        -7878.254             0.144            0.037
Chain 1:   1300        -7840.502             0.041            0.034
Chain 1:   1400        -7921.242             0.032            0.031
Chain 1:   1500        -7611.546             0.029            0.031
Chain 1:   1600        -7763.873             0.028            0.031
Chain 1:   1700        -7584.006             0.027            0.024
Chain 1:   1800        -7658.495             0.019            0.020
Chain 1:   1900        -7714.982             0.019            0.020
Chain 1:   2000        -7709.275             0.016            0.010
Chain 1:   2100        -7635.311             0.016            0.010
Chain 1:   2200        -7766.401             0.014            0.010
Chain 1:   2300        -7605.695             0.016            0.017
Chain 1:   2400        -7703.438             0.016            0.017
Chain 1:   2500        -7474.718             0.015            0.017
Chain 1:   2600        -7567.637             0.014            0.013
Chain 1:   2700        -7562.330             0.012            0.012
Chain 1:   2800        -7520.839             0.012            0.012
Chain 1:   2900        -7416.290             0.012            0.013
Chain 1:   3000        -7560.052             0.014            0.014
Chain 1:   3100        -7560.813             0.013            0.014
Chain 1:   3200        -7774.843             0.014            0.014
Chain 1:   3300        -7486.337             0.016            0.014
Chain 1:   3400        -7720.294             0.018            0.019
Chain 1:   3500        -7472.199             0.018            0.019
Chain 1:   3600        -7540.097             0.018            0.019
Chain 1:   3700        -7488.766             0.018            0.019
Chain 1:   3800        -7487.086             0.018            0.019
Chain 1:   3900        -7451.870             0.017            0.019
Chain 1:   4000        -7444.584             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003302 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86937.509             1.000            1.000
Chain 1:    200       -13846.007             3.139            5.279
Chain 1:    300       -10089.471             2.217            1.000
Chain 1:    400       -11640.689             1.696            1.000
Chain 1:    500        -8957.690             1.417            0.372
Chain 1:    600        -8609.514             1.187            0.372
Chain 1:    700        -8702.457             1.019            0.300
Chain 1:    800        -8916.919             0.895            0.300
Chain 1:    900        -8872.015             0.796            0.133
Chain 1:   1000        -8910.257             0.717            0.133
Chain 1:   1100        -8922.054             0.617            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8402.304             0.095            0.040
Chain 1:   1300        -8712.342             0.062            0.036
Chain 1:   1400        -8647.352             0.049            0.024
Chain 1:   1500        -8571.272             0.020            0.011
Chain 1:   1600        -8667.965             0.017            0.011
Chain 1:   1700        -8725.769             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003159 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8441198.916             1.000            1.000
Chain 1:    200     -1589662.263             2.655            4.310
Chain 1:    300      -890457.774             2.032            1.000
Chain 1:    400      -457503.380             1.760            1.000
Chain 1:    500      -357363.891             1.464            0.946
Chain 1:    600      -232462.618             1.310            0.946
Chain 1:    700      -119132.941             1.259            0.946
Chain 1:    800       -86462.794             1.149            0.946
Chain 1:    900       -66903.327             1.053            0.785
Chain 1:   1000       -51781.127             0.977            0.785
Chain 1:   1100       -39332.929             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38525.193             0.480            0.378
Chain 1:   1300       -26542.836             0.447            0.378
Chain 1:   1400       -26272.072             0.353            0.316
Chain 1:   1500       -22874.111             0.340            0.316
Chain 1:   1600       -22096.575             0.290            0.292
Chain 1:   1700       -20976.555             0.200            0.292
Chain 1:   1800       -20922.840             0.162            0.149
Chain 1:   1900       -21249.718             0.135            0.053
Chain 1:   2000       -19762.969             0.113            0.053
Chain 1:   2100       -20001.343             0.082            0.035
Chain 1:   2200       -20227.651             0.082            0.035
Chain 1:   2300       -19844.780             0.038            0.019
Chain 1:   2400       -19616.656             0.038            0.019
Chain 1:   2500       -19418.421             0.025            0.015
Chain 1:   2600       -19048.139             0.023            0.015
Chain 1:   2700       -19005.060             0.018            0.012
Chain 1:   2800       -18721.425             0.019            0.015
Chain 1:   2900       -19002.946             0.019            0.015
Chain 1:   3000       -18989.177             0.012            0.012
Chain 1:   3100       -19074.222             0.011            0.012
Chain 1:   3200       -18764.544             0.011            0.015
Chain 1:   3300       -18969.595             0.011            0.012
Chain 1:   3400       -18443.670             0.012            0.015
Chain 1:   3500       -19056.686             0.014            0.015
Chain 1:   3600       -18361.866             0.016            0.015
Chain 1:   3700       -18749.665             0.018            0.017
Chain 1:   3800       -17706.999             0.023            0.021
Chain 1:   3900       -17703.031             0.021            0.021
Chain 1:   4000       -17820.398             0.022            0.021
Chain 1:   4100       -17733.962             0.022            0.021
Chain 1:   4200       -17549.733             0.021            0.021
Chain 1:   4300       -17688.514             0.021            0.021
Chain 1:   4400       -17644.904             0.018            0.010
Chain 1:   4500       -17547.311             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001655 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12721.529             1.000            1.000
Chain 1:    200        -9480.904             0.671            1.000
Chain 1:    300        -8145.238             0.502            0.342
Chain 1:    400        -8385.947             0.384            0.342
Chain 1:    500        -8411.817             0.308            0.164
Chain 1:    600        -8128.454             0.262            0.164
Chain 1:    700        -8017.337             0.227            0.035
Chain 1:    800        -8004.256             0.198            0.035
Chain 1:    900        -8007.839             0.176            0.029
Chain 1:   1000        -8164.705             0.161            0.029
Chain 1:   1100        -8311.070             0.063            0.019
Chain 1:   1200        -8047.627             0.032            0.019
Chain 1:   1300        -7988.309             0.016            0.018
Chain 1:   1400        -8013.917             0.013            0.014
Chain 1:   1500        -8111.093             0.014            0.014
Chain 1:   1600        -8029.307             0.012            0.012
Chain 1:   1700        -7990.936             0.011            0.010
Chain 1:   1800        -7962.242             0.011            0.010
Chain 1:   1900        -7988.166             0.011            0.010
Chain 1:   2000        -7925.505             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00148 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58988.355             1.000            1.000
Chain 1:    200       -18021.911             1.637            2.273
Chain 1:    300        -9032.501             1.423            1.000
Chain 1:    400        -8330.239             1.088            1.000
Chain 1:    500        -8312.931             0.871            0.995
Chain 1:    600        -8371.615             0.727            0.995
Chain 1:    700        -7760.772             0.634            0.084
Chain 1:    800        -8384.635             0.564            0.084
Chain 1:    900        -8087.422             0.506            0.079
Chain 1:   1000        -7774.272             0.459            0.079
Chain 1:   1100        -7730.669             0.360            0.074
Chain 1:   1200        -7892.437             0.134            0.040
Chain 1:   1300        -7690.942             0.038            0.037
Chain 1:   1400        -7654.609             0.030            0.026
Chain 1:   1500        -7551.340             0.031            0.026
Chain 1:   1600        -7743.673             0.033            0.026
Chain 1:   1700        -7634.093             0.026            0.025
Chain 1:   1800        -7624.746             0.019            0.020
Chain 1:   1900        -7591.199             0.016            0.014
Chain 1:   2000        -7674.303             0.013            0.014
Chain 1:   2100        -7592.861             0.013            0.014
Chain 1:   2200        -7755.596             0.013            0.014
Chain 1:   2300        -7562.438             0.013            0.014
Chain 1:   2400        -7561.146             0.013            0.014
Chain 1:   2500        -7679.656             0.013            0.014
Chain 1:   2600        -7555.355             0.012            0.014
Chain 1:   2700        -7514.291             0.011            0.011
Chain 1:   2800        -7537.655             0.011            0.011
Chain 1:   2900        -7411.428             0.013            0.015
Chain 1:   3000        -7561.792             0.013            0.016
Chain 1:   3100        -7551.753             0.013            0.016
Chain 1:   3200        -7761.891             0.013            0.016
Chain 1:   3300        -7475.694             0.014            0.016
Chain 1:   3400        -7713.432             0.017            0.017
Chain 1:   3500        -7464.448             0.019            0.020
Chain 1:   3600        -7530.857             0.019            0.020
Chain 1:   3700        -7480.468             0.019            0.020
Chain 1:   3800        -7479.551             0.018            0.020
Chain 1:   3900        -7442.665             0.017            0.020
Chain 1:   4000        -7434.140             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003643 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86748.889             1.000            1.000
Chain 1:    200       -13872.611             3.127            5.253
Chain 1:    300       -10136.444             2.207            1.000
Chain 1:    400       -11725.291             1.689            1.000
Chain 1:    500        -8825.929             1.417            0.369
Chain 1:    600        -9640.017             1.195            0.369
Chain 1:    700        -8798.398             1.038            0.329
Chain 1:    800        -8817.402             0.909            0.329
Chain 1:    900        -8860.408             0.808            0.136
Chain 1:   1000        -9068.351             0.730            0.136
Chain 1:   1100        -8788.092             0.633            0.096   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8483.693             0.111            0.084
Chain 1:   1300        -8854.985             0.078            0.042
Chain 1:   1400        -8557.714             0.068            0.036
Chain 1:   1500        -8633.454             0.036            0.035
Chain 1:   1600        -8742.177             0.029            0.032
Chain 1:   1700        -8803.178             0.020            0.023
Chain 1:   1800        -8361.490             0.025            0.032
Chain 1:   1900        -8465.117             0.026            0.032
Chain 1:   2000        -8453.859             0.024            0.032
Chain 1:   2100        -8569.742             0.022            0.014
Chain 1:   2200        -8363.706             0.021            0.014
Chain 1:   2300        -8459.106             0.018            0.012
Chain 1:   2400        -8526.065             0.015            0.012
Chain 1:   2500        -8474.534             0.015            0.012
Chain 1:   2600        -8488.474             0.014            0.011
Chain 1:   2700        -8395.952             0.014            0.011
Chain 1:   2800        -8343.434             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00332 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422756.139             1.000            1.000
Chain 1:    200     -1587356.979             2.653            4.306
Chain 1:    300      -891399.521             2.029            1.000
Chain 1:    400      -457875.612             1.758            1.000
Chain 1:    500      -357769.466             1.463            0.947
Chain 1:    600      -232713.098             1.308            0.947
Chain 1:    700      -119258.839             1.257            0.947
Chain 1:    800       -86555.926             1.148            0.947
Chain 1:    900       -66974.495             1.052            0.781
Chain 1:   1000       -51839.727             0.976            0.781
Chain 1:   1100       -39374.035             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38564.887             0.480            0.378
Chain 1:   1300       -26568.129             0.447            0.378
Chain 1:   1400       -26295.399             0.353            0.317
Chain 1:   1500       -22893.981             0.340            0.317
Chain 1:   1600       -22115.074             0.290            0.292
Chain 1:   1700       -20993.605             0.200            0.292
Chain 1:   1800       -20939.568             0.162            0.149
Chain 1:   1900       -21266.189             0.135            0.053
Chain 1:   2000       -19779.088             0.113            0.053
Chain 1:   2100       -20017.398             0.083            0.035
Chain 1:   2200       -20243.742             0.082            0.035
Chain 1:   2300       -19860.958             0.038            0.019
Chain 1:   2400       -19632.914             0.038            0.019
Chain 1:   2500       -19434.708             0.025            0.015
Chain 1:   2600       -19064.495             0.023            0.015
Chain 1:   2700       -19021.484             0.018            0.012
Chain 1:   2800       -18737.923             0.019            0.015
Chain 1:   2900       -19019.403             0.019            0.015
Chain 1:   3000       -19005.632             0.012            0.012
Chain 1:   3100       -19090.635             0.011            0.012
Chain 1:   3200       -18781.018             0.011            0.015
Chain 1:   3300       -18986.044             0.011            0.012
Chain 1:   3400       -18460.273             0.012            0.015
Chain 1:   3500       -19073.017             0.014            0.015
Chain 1:   3600       -18378.635             0.016            0.015
Chain 1:   3700       -18766.113             0.018            0.016
Chain 1:   3800       -17724.050             0.023            0.021
Chain 1:   3900       -17720.131             0.021            0.021
Chain 1:   4000       -17837.483             0.022            0.021
Chain 1:   4100       -17751.052             0.022            0.021
Chain 1:   4200       -17567.008             0.021            0.021
Chain 1:   4300       -17705.644             0.021            0.021
Chain 1:   4400       -17662.140             0.018            0.010
Chain 1:   4500       -17564.615             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001561 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12343.614             1.000            1.000
Chain 1:    200        -9198.887             0.671            1.000
Chain 1:    300        -8054.114             0.495            0.342
Chain 1:    400        -8230.450             0.376            0.342
Chain 1:    500        -8091.483             0.305            0.142
Chain 1:    600        -8014.952             0.255            0.142
Chain 1:    700        -7928.839             0.220            0.021
Chain 1:    800        -7969.823             0.194            0.021
Chain 1:    900        -8090.010             0.174            0.017
Chain 1:   1000        -7984.715             0.158            0.017
Chain 1:   1100        -8183.007             0.060            0.017
Chain 1:   1200        -7946.357             0.029            0.017
Chain 1:   1300        -7901.787             0.015            0.015
Chain 1:   1400        -7918.052             0.013            0.013
Chain 1:   1500        -8016.346             0.013            0.012
Chain 1:   1600        -7963.622             0.012            0.012
Chain 1:   1700        -7898.032             0.012            0.012
Chain 1:   1800        -7885.793             0.012            0.012
Chain 1:   1900        -7864.910             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001615 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57156.656             1.000            1.000
Chain 1:    200       -17345.884             1.648            2.295
Chain 1:    300        -8695.089             1.430            1.000
Chain 1:    400        -8202.935             1.088            1.000
Chain 1:    500        -8645.563             0.880            0.995
Chain 1:    600        -8872.718             0.738            0.995
Chain 1:    700        -7912.482             0.650            0.121
Chain 1:    800        -8331.616             0.575            0.121
Chain 1:    900        -7915.995             0.517            0.060
Chain 1:   1000        -7778.659             0.467            0.060
Chain 1:   1100        -7693.102             0.368            0.053
Chain 1:   1200        -7596.826             0.140            0.051
Chain 1:   1300        -7753.183             0.042            0.050
Chain 1:   1400        -7826.133             0.037            0.026
Chain 1:   1500        -7611.644             0.035            0.026
Chain 1:   1600        -7646.568             0.033            0.020
Chain 1:   1700        -7536.154             0.022            0.018
Chain 1:   1800        -7575.280             0.018            0.015
Chain 1:   1900        -7588.378             0.013            0.013
Chain 1:   2000        -7656.289             0.012            0.011
Chain 1:   2100        -7616.161             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003343 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86105.252             1.000            1.000
Chain 1:    200       -13449.358             3.201            5.402
Chain 1:    300        -9864.786             2.255            1.000
Chain 1:    400       -10834.899             1.714            1.000
Chain 1:    500        -8791.832             1.417            0.363
Chain 1:    600        -8489.489             1.187            0.363
Chain 1:    700        -8465.388             1.018            0.232
Chain 1:    800        -8820.612             0.896            0.232
Chain 1:    900        -8722.214             0.797            0.090
Chain 1:   1000        -8402.776             0.722            0.090
Chain 1:   1100        -8718.442             0.625            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8436.459             0.088            0.038
Chain 1:   1300        -8579.539             0.054            0.036
Chain 1:   1400        -8573.247             0.045            0.036
Chain 1:   1500        -8449.785             0.023            0.033
Chain 1:   1600        -8559.561             0.021            0.017
Chain 1:   1700        -8645.023             0.021            0.017
Chain 1:   1800        -8244.872             0.022            0.017
Chain 1:   1900        -8344.160             0.022            0.017
Chain 1:   2000        -8315.414             0.019            0.015
Chain 1:   2100        -8435.299             0.017            0.014
Chain 1:   2200        -8226.248             0.016            0.014
Chain 1:   2300        -8375.982             0.016            0.014
Chain 1:   2400        -8256.719             0.017            0.014
Chain 1:   2500        -8319.707             0.017            0.014
Chain 1:   2600        -8341.283             0.016            0.014
Chain 1:   2700        -8260.267             0.016            0.014
Chain 1:   2800        -8234.143             0.011            0.012
Chain 1:   2900        -8289.515             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390951.634             1.000            1.000
Chain 1:    200     -1585071.603             2.647            4.294
Chain 1:    300      -890408.145             2.025            1.000
Chain 1:    400      -457316.341             1.755            1.000
Chain 1:    500      -357700.274             1.460            0.947
Chain 1:    600      -232774.227             1.306            0.947
Chain 1:    700      -119076.499             1.256            0.947
Chain 1:    800       -86322.952             1.146            0.947
Chain 1:    900       -66681.760             1.052            0.780
Chain 1:   1000       -51489.304             0.976            0.780
Chain 1:   1100       -38976.924             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38153.218             0.481            0.379
Chain 1:   1300       -26122.133             0.449            0.379
Chain 1:   1400       -25841.808             0.355            0.321
Chain 1:   1500       -22432.571             0.343            0.321
Chain 1:   1600       -21649.909             0.293            0.295
Chain 1:   1700       -20525.285             0.203            0.295
Chain 1:   1800       -20469.787             0.165            0.152
Chain 1:   1900       -20795.695             0.137            0.055
Chain 1:   2000       -19308.178             0.115            0.055
Chain 1:   2100       -19546.507             0.084            0.036
Chain 1:   2200       -19772.658             0.083            0.036
Chain 1:   2300       -19390.169             0.039            0.020
Chain 1:   2400       -19162.346             0.039            0.020
Chain 1:   2500       -18964.374             0.025            0.016
Chain 1:   2600       -18594.888             0.024            0.016
Chain 1:   2700       -18551.943             0.018            0.012
Chain 1:   2800       -18268.910             0.020            0.015
Chain 1:   2900       -18550.039             0.020            0.015
Chain 1:   3000       -18536.225             0.012            0.012
Chain 1:   3100       -18621.176             0.011            0.012
Chain 1:   3200       -18312.070             0.012            0.015
Chain 1:   3300       -18516.637             0.011            0.012
Chain 1:   3400       -17991.911             0.013            0.015
Chain 1:   3500       -18603.280             0.015            0.015
Chain 1:   3600       -17910.601             0.017            0.015
Chain 1:   3700       -18296.916             0.019            0.017
Chain 1:   3800       -17257.656             0.023            0.021
Chain 1:   3900       -17253.818             0.022            0.021
Chain 1:   4000       -17371.113             0.022            0.021
Chain 1:   4100       -17284.931             0.022            0.021
Chain 1:   4200       -17101.395             0.022            0.021
Chain 1:   4300       -17239.634             0.021            0.021
Chain 1:   4400       -17196.636             0.019            0.011
Chain 1:   4500       -17099.194             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001312 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12376.746             1.000            1.000
Chain 1:    200        -9200.085             0.673            1.000
Chain 1:    300        -7948.740             0.501            0.345
Chain 1:    400        -8127.814             0.381            0.345
Chain 1:    500        -8023.162             0.308            0.157
Chain 1:    600        -7935.770             0.258            0.157
Chain 1:    700        -7839.737             0.223            0.022
Chain 1:    800        -7884.962             0.196            0.022
Chain 1:    900        -8009.712             0.176            0.016
Chain 1:   1000        -7947.174             0.159            0.016
Chain 1:   1100        -7939.376             0.059            0.013
Chain 1:   1200        -7861.122             0.026            0.012
Chain 1:   1300        -7809.747             0.011            0.011
Chain 1:   1400        -7828.956             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46180.559             1.000            1.000
Chain 1:    200       -15454.876             1.494            1.988
Chain 1:    300        -8678.002             1.256            1.000
Chain 1:    400        -8536.579             0.946            1.000
Chain 1:    500        -8383.826             0.761            0.781
Chain 1:    600        -8644.416             0.639            0.781
Chain 1:    700        -7980.905             0.560            0.083
Chain 1:    800        -8256.427             0.494            0.083
Chain 1:    900        -7932.682             0.443            0.041
Chain 1:   1000        -7833.886             0.400            0.041
Chain 1:   1100        -7762.978             0.301            0.033
Chain 1:   1200        -7908.509             0.104            0.030
Chain 1:   1300        -7818.662             0.027            0.018
Chain 1:   1400        -7797.622             0.026            0.018
Chain 1:   1500        -7600.510             0.027            0.026
Chain 1:   1600        -7869.936             0.027            0.026
Chain 1:   1700        -7524.101             0.023            0.026
Chain 1:   1800        -7597.618             0.021            0.018
Chain 1:   1900        -7569.078             0.017            0.013
Chain 1:   2000        -7637.902             0.017            0.011
Chain 1:   2100        -7600.647             0.017            0.011
Chain 1:   2200        -7698.808             0.016            0.011
Chain 1:   2300        -7679.604             0.015            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003452 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86668.114             1.000            1.000
Chain 1:    200       -13449.795             3.222            5.444
Chain 1:    300        -9817.703             2.271            1.000
Chain 1:    400       -10733.023             1.725            1.000
Chain 1:    500        -8696.145             1.427            0.370
Chain 1:    600        -8272.500             1.197            0.370
Chain 1:    700        -8538.233             1.031            0.234
Chain 1:    800        -9185.837             0.911            0.234
Chain 1:    900        -8617.078             0.817            0.085
Chain 1:   1000        -8460.301             0.737            0.085
Chain 1:   1100        -8624.099             0.639            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8358.500             0.098            0.066
Chain 1:   1300        -8458.166             0.062            0.051
Chain 1:   1400        -8554.947             0.055            0.032
Chain 1:   1500        -8381.418             0.033            0.031
Chain 1:   1600        -8498.119             0.029            0.021
Chain 1:   1700        -8579.165             0.027            0.019
Chain 1:   1800        -8168.547             0.025            0.019
Chain 1:   1900        -8264.379             0.020            0.019
Chain 1:   2000        -8237.169             0.018            0.014
Chain 1:   2100        -8359.268             0.018            0.014
Chain 1:   2200        -8178.379             0.017            0.014
Chain 1:   2300        -8260.059             0.017            0.014
Chain 1:   2400        -8329.025             0.016            0.014
Chain 1:   2500        -8274.383             0.015            0.012
Chain 1:   2600        -8273.271             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004187 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409040.632             1.000            1.000
Chain 1:    200     -1586781.118             2.650            4.299
Chain 1:    300      -890579.008             2.027            1.000
Chain 1:    400      -457375.069             1.757            1.000
Chain 1:    500      -357469.643             1.462            0.947
Chain 1:    600      -232610.665             1.307            0.947
Chain 1:    700      -118967.844             1.257            0.947
Chain 1:    800       -86232.083             1.147            0.947
Chain 1:    900       -66610.711             1.053            0.782
Chain 1:   1000       -51437.206             0.977            0.782
Chain 1:   1100       -38944.514             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38124.756             0.481            0.380
Chain 1:   1300       -26111.128             0.449            0.380
Chain 1:   1400       -25834.055             0.355            0.321
Chain 1:   1500       -22428.574             0.343            0.321
Chain 1:   1600       -21647.178             0.293            0.295
Chain 1:   1700       -20524.263             0.202            0.295
Chain 1:   1800       -20469.262             0.165            0.152
Chain 1:   1900       -20795.329             0.137            0.055
Chain 1:   2000       -19308.233             0.115            0.055
Chain 1:   2100       -19546.733             0.084            0.036
Chain 1:   2200       -19772.756             0.083            0.036
Chain 1:   2300       -19390.286             0.039            0.020
Chain 1:   2400       -19162.361             0.039            0.020
Chain 1:   2500       -18964.274             0.025            0.016
Chain 1:   2600       -18594.742             0.024            0.016
Chain 1:   2700       -18551.755             0.018            0.012
Chain 1:   2800       -18268.579             0.020            0.016
Chain 1:   2900       -18549.729             0.020            0.015
Chain 1:   3000       -18536.038             0.012            0.012
Chain 1:   3100       -18620.999             0.011            0.012
Chain 1:   3200       -18311.771             0.012            0.015
Chain 1:   3300       -18516.401             0.011            0.012
Chain 1:   3400       -17991.461             0.013            0.015
Chain 1:   3500       -18603.119             0.015            0.016
Chain 1:   3600       -17909.998             0.017            0.016
Chain 1:   3700       -18296.607             0.019            0.017
Chain 1:   3800       -17256.673             0.023            0.021
Chain 1:   3900       -17252.761             0.022            0.021
Chain 1:   4000       -17370.108             0.022            0.021
Chain 1:   4100       -17283.874             0.022            0.021
Chain 1:   4200       -17100.170             0.022            0.021
Chain 1:   4300       -17238.558             0.021            0.021
Chain 1:   4400       -17195.431             0.019            0.011
Chain 1:   4500       -17097.915             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003459 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12226.233             1.000            1.000
Chain 1:    200        -9190.244             0.665            1.000
Chain 1:    300        -7877.649             0.499            0.330
Chain 1:    400        -8062.505             0.380            0.330
Chain 1:    500        -7918.649             0.308            0.167
Chain 1:    600        -7838.929             0.258            0.167
Chain 1:    700        -7747.748             0.223            0.023
Chain 1:    800        -7792.209             0.196            0.023
Chain 1:    900        -7915.425             0.176            0.018
Chain 1:   1000        -7824.045             0.159            0.018
Chain 1:   1100        -7767.479             0.060            0.016
Chain 1:   1200        -7781.031             0.027            0.012
Chain 1:   1300        -7712.786             0.011            0.012
Chain 1:   1400        -7732.592             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50642.285             1.000            1.000
Chain 1:    200       -16020.553             1.581            2.161
Chain 1:    300        -8622.720             1.340            1.000
Chain 1:    400        -8479.007             1.009            1.000
Chain 1:    500        -8013.811             0.819            0.858
Chain 1:    600        -8513.740             0.692            0.858
Chain 1:    700        -8080.100             0.601            0.059
Chain 1:    800        -8205.327             0.528            0.059
Chain 1:    900        -7663.767             0.477            0.059
Chain 1:   1000        -7716.286             0.430            0.059
Chain 1:   1100        -7693.045             0.330            0.058
Chain 1:   1200        -7654.698             0.115            0.054
Chain 1:   1300        -7658.931             0.029            0.017
Chain 1:   1400        -7845.763             0.030            0.024
Chain 1:   1500        -7537.875             0.028            0.024
Chain 1:   1600        -7580.669             0.023            0.015
Chain 1:   1700        -7470.660             0.019            0.015
Chain 1:   1800        -7532.251             0.018            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00353 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86379.824             1.000            1.000
Chain 1:    200       -13324.116             3.241            5.483
Chain 1:    300        -9695.986             2.286            1.000
Chain 1:    400       -10398.807             1.731            1.000
Chain 1:    500        -8637.627             1.426            0.374
Chain 1:    600        -8209.023             1.197            0.374
Chain 1:    700        -8525.639             1.031            0.204
Chain 1:    800        -9186.291             0.911            0.204
Chain 1:    900        -8499.533             0.819            0.081
Chain 1:   1000        -8248.704             0.740            0.081
Chain 1:   1100        -8565.315             0.644            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8065.479             0.102            0.068
Chain 1:   1300        -8403.461             0.068            0.062
Chain 1:   1400        -8406.449             0.062            0.052
Chain 1:   1500        -8281.126             0.043            0.040
Chain 1:   1600        -8387.847             0.039            0.037
Chain 1:   1700        -8472.860             0.036            0.037
Chain 1:   1800        -8064.839             0.034            0.037
Chain 1:   1900        -8161.361             0.027            0.030
Chain 1:   2000        -8133.581             0.024            0.015
Chain 1:   2100        -8254.554             0.022            0.015
Chain 1:   2200        -8082.616             0.018            0.015
Chain 1:   2300        -8198.805             0.015            0.014
Chain 1:   2400        -8210.327             0.016            0.014
Chain 1:   2500        -8171.940             0.014            0.013
Chain 1:   2600        -8171.803             0.013            0.012
Chain 1:   2700        -8086.965             0.013            0.012
Chain 1:   2800        -8051.651             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005071 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 50.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396723.407             1.000            1.000
Chain 1:    200     -1582476.164             2.653            4.306
Chain 1:    300      -891366.604             2.027            1.000
Chain 1:    400      -457853.272             1.757            1.000
Chain 1:    500      -358483.947             1.461            0.947
Chain 1:    600      -233246.257             1.307            0.947
Chain 1:    700      -119304.510             1.257            0.947
Chain 1:    800       -86434.728             1.147            0.947
Chain 1:    900       -66724.689             1.053            0.775
Chain 1:   1000       -51483.116             0.977            0.775
Chain 1:   1100       -38924.349             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38094.892             0.481            0.380
Chain 1:   1300       -26020.931             0.450            0.380
Chain 1:   1400       -25734.736             0.356            0.323
Chain 1:   1500       -22314.203             0.344            0.323
Chain 1:   1600       -21527.790             0.294            0.296
Chain 1:   1700       -20398.616             0.204            0.295
Chain 1:   1800       -20341.892             0.166            0.153
Chain 1:   1900       -20667.873             0.138            0.055
Chain 1:   2000       -19177.616             0.116            0.055
Chain 1:   2100       -19416.077             0.085            0.037
Chain 1:   2200       -19642.619             0.084            0.037
Chain 1:   2300       -19259.796             0.040            0.020
Chain 1:   2400       -19031.939             0.040            0.020
Chain 1:   2500       -18833.951             0.025            0.016
Chain 1:   2600       -18464.258             0.024            0.016
Chain 1:   2700       -18421.286             0.018            0.012
Chain 1:   2800       -18138.224             0.020            0.016
Chain 1:   2900       -18419.449             0.020            0.015
Chain 1:   3000       -18405.595             0.012            0.012
Chain 1:   3100       -18490.581             0.011            0.012
Chain 1:   3200       -18181.341             0.012            0.015
Chain 1:   3300       -18386.008             0.011            0.012
Chain 1:   3400       -17861.097             0.013            0.015
Chain 1:   3500       -18472.718             0.015            0.016
Chain 1:   3600       -17779.775             0.017            0.016
Chain 1:   3700       -18166.338             0.019            0.017
Chain 1:   3800       -17126.573             0.023            0.021
Chain 1:   3900       -17122.761             0.022            0.021
Chain 1:   4000       -17240.043             0.022            0.021
Chain 1:   4100       -17153.808             0.022            0.021
Chain 1:   4200       -16970.223             0.022            0.021
Chain 1:   4300       -17108.511             0.021            0.021
Chain 1:   4400       -17065.457             0.019            0.011
Chain 1:   4500       -16968.011             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004324 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12426.495             1.000            1.000
Chain 1:    200        -9273.436             0.670            1.000
Chain 1:    300        -8180.639             0.491            0.340
Chain 1:    400        -8293.671             0.372            0.340
Chain 1:    500        -8191.970             0.300            0.134
Chain 1:    600        -8107.363             0.252            0.134
Chain 1:    700        -8023.736             0.217            0.014
Chain 1:    800        -8056.819             0.191            0.014
Chain 1:    900        -8208.268             0.171            0.014
Chain 1:   1000        -8058.541             0.156            0.018
Chain 1:   1100        -8094.893             0.057            0.014
Chain 1:   1200        -8059.637             0.023            0.012
Chain 1:   1300        -7992.639             0.011            0.010
Chain 1:   1400        -8008.199             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001541 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56983.486             1.000            1.000
Chain 1:    200       -17433.478             1.634            2.269
Chain 1:    300        -8750.436             1.420            1.000
Chain 1:    400        -8222.079             1.081            1.000
Chain 1:    500        -8524.352             0.872            0.992
Chain 1:    600        -8379.677             0.730            0.992
Chain 1:    700        -8677.227             0.630            0.064
Chain 1:    800        -8150.956             0.560            0.065
Chain 1:    900        -7876.964             0.501            0.064
Chain 1:   1000        -8002.424             0.453            0.064
Chain 1:   1100        -8001.315             0.353            0.035
Chain 1:   1200        -7680.191             0.130            0.035
Chain 1:   1300        -7786.013             0.032            0.035
Chain 1:   1400        -7834.011             0.026            0.034
Chain 1:   1500        -7631.670             0.025            0.027
Chain 1:   1600        -7779.625             0.026            0.027
Chain 1:   1700        -7538.041             0.025            0.027
Chain 1:   1800        -7626.575             0.020            0.019
Chain 1:   1900        -7596.292             0.017            0.016
Chain 1:   2000        -7639.380             0.016            0.014
Chain 1:   2100        -7613.762             0.016            0.014
Chain 1:   2200        -7728.246             0.014            0.014
Chain 1:   2300        -7635.769             0.014            0.012
Chain 1:   2400        -7677.757             0.013            0.012
Chain 1:   2500        -7623.806             0.012            0.012
Chain 1:   2600        -7585.496             0.010            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003023 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85931.036             1.000            1.000
Chain 1:    200       -13519.142             3.178            5.356
Chain 1:    300        -9944.735             2.239            1.000
Chain 1:    400       -10931.294             1.701            1.000
Chain 1:    500        -8889.467             1.407            0.359
Chain 1:    600        -8838.040             1.174            0.359
Chain 1:    700        -8473.420             1.012            0.230
Chain 1:    800        -8917.178             0.892            0.230
Chain 1:    900        -8781.783             0.794            0.090
Chain 1:   1000        -8519.102             0.718            0.090
Chain 1:   1100        -8765.926             0.621            0.050   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8419.042             0.089            0.043
Chain 1:   1300        -8731.071             0.057            0.041
Chain 1:   1400        -8640.906             0.049            0.036
Chain 1:   1500        -8543.582             0.027            0.031
Chain 1:   1600        -8644.951             0.028            0.031
Chain 1:   1700        -8734.335             0.024            0.028
Chain 1:   1800        -8336.643             0.024            0.028
Chain 1:   1900        -8437.929             0.024            0.028
Chain 1:   2000        -8408.720             0.021            0.012
Chain 1:   2100        -8530.112             0.020            0.012
Chain 1:   2200        -8309.160             0.018            0.012
Chain 1:   2300        -8466.768             0.017            0.012
Chain 1:   2400        -8479.781             0.016            0.012
Chain 1:   2500        -8450.449             0.015            0.012
Chain 1:   2600        -8453.119             0.014            0.012
Chain 1:   2700        -8359.316             0.014            0.012
Chain 1:   2800        -8329.879             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002924 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390054.204             1.000            1.000
Chain 1:    200     -1585936.319             2.645            4.290
Chain 1:    300      -891911.923             2.023            1.000
Chain 1:    400      -458546.449             1.753            1.000
Chain 1:    500      -358836.432             1.458            0.945
Chain 1:    600      -233580.914             1.305            0.945
Chain 1:    700      -119475.790             1.255            0.945
Chain 1:    800       -86629.009             1.145            0.945
Chain 1:    900       -66912.421             1.051            0.778
Chain 1:   1000       -51668.735             0.975            0.778
Chain 1:   1100       -39113.299             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38280.214             0.480            0.379
Chain 1:   1300       -26210.466             0.449            0.379
Chain 1:   1400       -25925.211             0.355            0.321
Chain 1:   1500       -22506.436             0.343            0.321
Chain 1:   1600       -21720.445             0.293            0.295
Chain 1:   1700       -20591.610             0.203            0.295
Chain 1:   1800       -20534.860             0.165            0.152
Chain 1:   1900       -20860.606             0.137            0.055
Chain 1:   2000       -19371.016             0.115            0.055
Chain 1:   2100       -19609.409             0.084            0.036
Chain 1:   2200       -19835.905             0.083            0.036
Chain 1:   2300       -19453.136             0.039            0.020
Chain 1:   2400       -19225.306             0.039            0.020
Chain 1:   2500       -19027.500             0.025            0.016
Chain 1:   2600       -18657.964             0.024            0.016
Chain 1:   2700       -18614.924             0.018            0.012
Chain 1:   2800       -18332.050             0.020            0.015
Chain 1:   2900       -18613.127             0.020            0.015
Chain 1:   3000       -18599.329             0.012            0.012
Chain 1:   3100       -18684.303             0.011            0.012
Chain 1:   3200       -18375.175             0.012            0.015
Chain 1:   3300       -18579.701             0.011            0.012
Chain 1:   3400       -18055.085             0.013            0.015
Chain 1:   3500       -18666.370             0.015            0.015
Chain 1:   3600       -17973.769             0.017            0.015
Chain 1:   3700       -18360.073             0.019            0.017
Chain 1:   3800       -17321.005             0.023            0.021
Chain 1:   3900       -17317.183             0.021            0.021
Chain 1:   4000       -17434.461             0.022            0.021
Chain 1:   4100       -17348.336             0.022            0.021
Chain 1:   4200       -17164.795             0.021            0.021
Chain 1:   4300       -17303.015             0.021            0.021
Chain 1:   4400       -17260.041             0.019            0.011
Chain 1:   4500       -17162.630             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001324 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12977.634             1.000            1.000
Chain 1:    200        -9798.814             0.662            1.000
Chain 1:    300        -8344.694             0.500            0.324
Chain 1:    400        -8584.570             0.382            0.324
Chain 1:    500        -8515.955             0.307            0.174
Chain 1:    600        -8289.441             0.260            0.174
Chain 1:    700        -8154.526             0.226            0.028
Chain 1:    800        -8164.411             0.197            0.028
Chain 1:    900        -8168.257             0.176            0.027
Chain 1:   1000        -8248.089             0.159            0.027
Chain 1:   1100        -8262.317             0.059            0.017
Chain 1:   1200        -8197.960             0.028            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001492 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62357.862             1.000            1.000
Chain 1:    200       -18548.002             1.681            2.362
Chain 1:    300        -9233.529             1.457            1.009
Chain 1:    400        -8463.893             1.115            1.009
Chain 1:    500        -8561.501             0.895            1.000
Chain 1:    600        -9106.674             0.755            1.000
Chain 1:    700        -7670.691             0.674            0.187
Chain 1:    800        -8348.802             0.600            0.187
Chain 1:    900        -7521.737             0.546            0.110
Chain 1:   1000        -7765.489             0.494            0.110
Chain 1:   1100        -7864.054             0.396            0.091
Chain 1:   1200        -7898.305             0.160            0.081
Chain 1:   1300        -7877.065             0.059            0.060
Chain 1:   1400        -7824.997             0.051            0.031
Chain 1:   1500        -7471.723             0.054            0.047
Chain 1:   1600        -7713.135             0.051            0.031
Chain 1:   1700        -7507.226             0.035            0.031
Chain 1:   1800        -7608.634             0.029            0.027
Chain 1:   1900        -7494.617             0.019            0.015
Chain 1:   2000        -7498.401             0.016            0.013
Chain 1:   2100        -7481.589             0.015            0.013
Chain 1:   2200        -7716.522             0.018            0.015
Chain 1:   2300        -7598.449             0.019            0.016
Chain 1:   2400        -7539.307             0.019            0.016
Chain 1:   2500        -7550.452             0.015            0.015
Chain 1:   2600        -7493.607             0.012            0.013
Chain 1:   2700        -7487.625             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003051 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86428.371             1.000            1.000
Chain 1:    200       -14178.854             3.048            5.096
Chain 1:    300       -10375.107             2.154            1.000
Chain 1:    400       -12282.424             1.654            1.000
Chain 1:    500        -9070.649             1.394            0.367
Chain 1:    600        -9654.259             1.172            0.367
Chain 1:    700        -8872.878             1.017            0.354
Chain 1:    800        -9460.554             0.898            0.354
Chain 1:    900        -9065.881             0.803            0.155
Chain 1:   1000        -9328.016             0.725            0.155
Chain 1:   1100        -9085.514             0.628            0.088   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8645.532             0.124            0.062
Chain 1:   1300        -8964.422             0.090            0.060
Chain 1:   1400        -8828.145             0.076            0.051
Chain 1:   1500        -8872.903             0.042            0.044
Chain 1:   1600        -8919.788             0.036            0.036
Chain 1:   1700        -8971.944             0.028            0.028
Chain 1:   1800        -8527.828             0.027            0.028
Chain 1:   1900        -8627.652             0.024            0.027
Chain 1:   2000        -8648.101             0.021            0.015
Chain 1:   2100        -8734.108             0.019            0.012
Chain 1:   2200        -8512.608             0.017            0.012
Chain 1:   2300        -8730.716             0.016            0.012
Chain 1:   2400        -8525.354             0.017            0.012
Chain 1:   2500        -8599.061             0.017            0.012
Chain 1:   2600        -8509.097             0.018            0.012
Chain 1:   2700        -8542.866             0.017            0.012
Chain 1:   2800        -8493.569             0.013            0.011
Chain 1:   2900        -8608.734             0.013            0.011
Chain 1:   3000        -8518.862             0.014            0.011
Chain 1:   3100        -8485.500             0.013            0.011
Chain 1:   3200        -8456.710             0.011            0.011
Chain 1:   3300        -8719.093             0.011            0.011
Chain 1:   3400        -8764.385             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003263 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412342.699             1.000            1.000
Chain 1:    200     -1583822.868             2.656            4.311
Chain 1:    300      -891653.250             2.029            1.000
Chain 1:    400      -458820.341             1.758            1.000
Chain 1:    500      -359010.098             1.462            0.943
Chain 1:    600      -234046.471             1.307            0.943
Chain 1:    700      -120106.948             1.256            0.943
Chain 1:    800       -87282.931             1.146            0.943
Chain 1:    900       -67602.392             1.051            0.776
Chain 1:   1000       -52387.388             0.975            0.776
Chain 1:   1100       -39846.710             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39029.409             0.477            0.376
Chain 1:   1300       -26949.792             0.445            0.376
Chain 1:   1400       -26670.752             0.351            0.315
Chain 1:   1500       -23247.657             0.338            0.315
Chain 1:   1600       -22462.847             0.288            0.291
Chain 1:   1700       -21331.122             0.199            0.290
Chain 1:   1800       -21274.755             0.161            0.147
Chain 1:   1900       -21601.749             0.134            0.053
Chain 1:   2000       -20108.608             0.112            0.053
Chain 1:   2100       -20347.195             0.082            0.035
Chain 1:   2200       -20574.702             0.081            0.035
Chain 1:   2300       -20190.795             0.038            0.019
Chain 1:   2400       -19962.515             0.038            0.019
Chain 1:   2500       -19764.698             0.024            0.015
Chain 1:   2600       -19393.767             0.023            0.015
Chain 1:   2700       -19350.465             0.018            0.012
Chain 1:   2800       -19066.948             0.019            0.015
Chain 1:   2900       -19348.707             0.019            0.015
Chain 1:   3000       -19334.824             0.011            0.012
Chain 1:   3100       -19419.904             0.011            0.011
Chain 1:   3200       -19109.960             0.011            0.015
Chain 1:   3300       -19315.210             0.010            0.011
Chain 1:   3400       -18789.011             0.012            0.015
Chain 1:   3500       -19402.560             0.014            0.015
Chain 1:   3600       -18707.136             0.016            0.015
Chain 1:   3700       -19095.488             0.018            0.016
Chain 1:   3800       -18051.897             0.022            0.020
Chain 1:   3900       -18047.984             0.021            0.020
Chain 1:   4000       -18165.290             0.021            0.020
Chain 1:   4100       -18078.845             0.021            0.020
Chain 1:   4200       -17894.409             0.021            0.020
Chain 1:   4300       -18033.272             0.020            0.020
Chain 1:   4400       -17989.499             0.018            0.010
Chain 1:   4500       -17891.959             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48731.051             1.000            1.000
Chain 1:    200       -14867.582             1.639            2.278
Chain 1:    300       -13771.824             1.119            1.000
Chain 1:    400       -15019.064             0.860            1.000
Chain 1:    500       -18201.589             0.723            0.175
Chain 1:    600       -12527.214             0.678            0.453
Chain 1:    700       -11944.881             0.588            0.175
Chain 1:    800       -17501.487             0.554            0.317
Chain 1:    900       -19248.872             0.503            0.175
Chain 1:   1000       -10939.815             0.528            0.317
Chain 1:   1100       -10983.678             0.429            0.175
Chain 1:   1200       -13891.052             0.222            0.175
Chain 1:   1300       -11840.951             0.231            0.175
Chain 1:   1400       -11636.830             0.225            0.175
Chain 1:   1500       -12228.698             0.212            0.173
Chain 1:   1600       -10417.014             0.184            0.173
Chain 1:   1700       -21829.515             0.232            0.174
Chain 1:   1800       -12734.784             0.271            0.174
Chain 1:   1900       -12543.083             0.264            0.174
Chain 1:   2000       -10962.822             0.202            0.173
Chain 1:   2100       -11361.349             0.205            0.173
Chain 1:   2200        -9669.418             0.202            0.173
Chain 1:   2300       -11847.565             0.203            0.174
Chain 1:   2400       -10572.366             0.213            0.174
Chain 1:   2500        -9602.808             0.219            0.174
Chain 1:   2600       -10060.911             0.206            0.144
Chain 1:   2700       -10942.006             0.162            0.121
Chain 1:   2800       -10130.359             0.098            0.101
Chain 1:   2900        -9505.235             0.103            0.101
Chain 1:   3000       -14284.712             0.122            0.101
Chain 1:   3100        -9894.374             0.163            0.121
Chain 1:   3200       -12268.634             0.165            0.121
Chain 1:   3300       -17690.370             0.177            0.121
Chain 1:   3400       -10734.729             0.230            0.194
Chain 1:   3500        -8968.757             0.240            0.197
Chain 1:   3600       -10020.565             0.245            0.197
Chain 1:   3700        -9408.169             0.244            0.197
Chain 1:   3800        -9080.898             0.240            0.197
Chain 1:   3900        -9878.764             0.241            0.197
Chain 1:   4000        -9265.272             0.214            0.194
Chain 1:   4100        -8741.642             0.176            0.105
Chain 1:   4200        -9244.411             0.162            0.081
Chain 1:   4300        -8601.206             0.139            0.075
Chain 1:   4400       -10994.556             0.096            0.075
Chain 1:   4500        -9074.610             0.097            0.075
Chain 1:   4600       -12278.445             0.113            0.075
Chain 1:   4700       -11917.299             0.109            0.075
Chain 1:   4800        -8611.917             0.144            0.081
Chain 1:   4900       -11510.136             0.161            0.212
Chain 1:   5000       -16406.673             0.184            0.218
Chain 1:   5100       -10443.425             0.235            0.252
Chain 1:   5200        -8657.537             0.251            0.252
Chain 1:   5300       -11504.443             0.268            0.252
Chain 1:   5400        -8661.225             0.279            0.261
Chain 1:   5500       -11894.618             0.285            0.272
Chain 1:   5600       -12971.822             0.267            0.272
Chain 1:   5700        -8913.371             0.310            0.298
Chain 1:   5800        -9511.694             0.278            0.272
Chain 1:   5900        -9381.156             0.254            0.272
Chain 1:   6000        -8344.051             0.236            0.247
Chain 1:   6100        -8739.245             0.184            0.206
Chain 1:   6200        -8331.621             0.168            0.124
Chain 1:   6300        -9792.937             0.158            0.124
Chain 1:   6400       -13215.008             0.151            0.124
Chain 1:   6500        -8411.064             0.181            0.124
Chain 1:   6600        -8415.930             0.173            0.124
Chain 1:   6700        -8624.045             0.130            0.063
Chain 1:   6800        -8318.775             0.127            0.049
Chain 1:   6900       -10834.086             0.149            0.124
Chain 1:   7000        -9457.804             0.151            0.146
Chain 1:   7100       -10760.060             0.159            0.146
Chain 1:   7200       -11731.952             0.162            0.146
Chain 1:   7300       -10375.351             0.160            0.131
Chain 1:   7400        -8809.386             0.152            0.131
Chain 1:   7500       -10825.605             0.114            0.131
Chain 1:   7600        -8386.301             0.143            0.146
Chain 1:   7700        -8399.837             0.141            0.146
Chain 1:   7800       -12773.956             0.171            0.178
Chain 1:   7900       -10820.006             0.166            0.178
Chain 1:   8000        -8364.248             0.181            0.181
Chain 1:   8100       -11813.499             0.198            0.186
Chain 1:   8200       -11841.962             0.190            0.186
Chain 1:   8300        -8526.922             0.216            0.291
Chain 1:   8400        -9976.225             0.212            0.291
Chain 1:   8500        -8535.496             0.211            0.291
Chain 1:   8600        -9427.937             0.191            0.181
Chain 1:   8700       -10894.115             0.204            0.181
Chain 1:   8800        -8364.355             0.200            0.181
Chain 1:   8900        -9254.277             0.192            0.169
Chain 1:   9000        -8572.366             0.170            0.145
Chain 1:   9100        -8274.587             0.145            0.135
Chain 1:   9200       -11513.088             0.173            0.145
Chain 1:   9300        -8275.077             0.173            0.145
Chain 1:   9400        -8388.793             0.160            0.135
Chain 1:   9500        -9171.200             0.151            0.096
Chain 1:   9600        -9415.343             0.145            0.096
Chain 1:   9700        -8822.626             0.138            0.085
Chain 1:   9800       -11174.520             0.129            0.085
Chain 1:   9900       -11029.296             0.120            0.080
Chain 1:   10000        -8117.130             0.148            0.085
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001425 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57198.885             1.000            1.000
Chain 1:    200       -17516.900             1.633            2.265
Chain 1:    300        -8731.047             1.424            1.006
Chain 1:    400        -8325.134             1.080            1.006
Chain 1:    500        -8280.385             0.865            1.000
Chain 1:    600        -8609.434             0.727            1.000
Chain 1:    700        -7814.451             0.638            0.102
Chain 1:    800        -8113.021             0.563            0.102
Chain 1:    900        -7792.685             0.505            0.049
Chain 1:   1000        -7823.765             0.455            0.049
Chain 1:   1100        -7709.259             0.356            0.041
Chain 1:   1200        -7606.319             0.131            0.038
Chain 1:   1300        -8017.921             0.036            0.038
Chain 1:   1400        -7784.493             0.034            0.037
Chain 1:   1500        -7584.398             0.036            0.037
Chain 1:   1600        -7725.281             0.034            0.030
Chain 1:   1700        -7484.300             0.027            0.030
Chain 1:   1800        -7624.717             0.025            0.026
Chain 1:   1900        -7539.706             0.022            0.018
Chain 1:   2000        -7585.621             0.022            0.018
Chain 1:   2100        -7566.127             0.021            0.018
Chain 1:   2200        -7683.796             0.021            0.018
Chain 1:   2300        -7520.451             0.018            0.018
Chain 1:   2400        -7628.852             0.017            0.018
Chain 1:   2500        -7544.587             0.015            0.015
Chain 1:   2600        -7530.104             0.013            0.014
Chain 1:   2700        -7519.576             0.010            0.011
Chain 1:   2800        -7558.985             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002963 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85598.217             1.000            1.000
Chain 1:    200       -13515.732             3.167            5.333
Chain 1:    300        -9859.557             2.235            1.000
Chain 1:    400       -10877.565             1.699            1.000
Chain 1:    500        -8850.090             1.405            0.371
Chain 1:    600        -8317.183             1.182            0.371
Chain 1:    700        -8388.755             1.014            0.229
Chain 1:    800        -8755.976             0.893            0.229
Chain 1:    900        -8609.781             0.795            0.094
Chain 1:   1000        -8430.040             0.718            0.094
Chain 1:   1100        -8469.671             0.618            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8200.006             0.088            0.042
Chain 1:   1300        -8485.380             0.055            0.034
Chain 1:   1400        -8538.939             0.046            0.033
Chain 1:   1500        -8427.309             0.024            0.021
Chain 1:   1600        -8537.389             0.019            0.017
Chain 1:   1700        -8611.107             0.019            0.017
Chain 1:   1800        -8189.746             0.020            0.017
Chain 1:   1900        -8289.392             0.020            0.013
Chain 1:   2000        -8263.684             0.018            0.013
Chain 1:   2100        -8388.723             0.019            0.013
Chain 1:   2200        -8194.619             0.018            0.013
Chain 1:   2300        -8284.157             0.016            0.013
Chain 1:   2400        -8353.211             0.016            0.013
Chain 1:   2500        -8299.379             0.015            0.012
Chain 1:   2600        -8300.346             0.014            0.011
Chain 1:   2700        -8217.272             0.014            0.011
Chain 1:   2800        -8177.707             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003113 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378723.814             1.000            1.000
Chain 1:    200     -1579562.103             2.652            4.304
Chain 1:    300      -890945.904             2.026            1.000
Chain 1:    400      -458139.571             1.756            1.000
Chain 1:    500      -358836.062             1.460            0.945
Chain 1:    600      -233832.418             1.306            0.945
Chain 1:    700      -119675.445             1.255            0.945
Chain 1:    800       -86791.350             1.146            0.945
Chain 1:    900       -67058.417             1.051            0.773
Chain 1:   1000       -51792.269             0.976            0.773
Chain 1:   1100       -39207.781             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38376.864             0.479            0.379
Chain 1:   1300       -26268.002             0.448            0.379
Chain 1:   1400       -25981.295             0.355            0.321
Chain 1:   1500       -22551.637             0.342            0.321
Chain 1:   1600       -21763.234             0.292            0.295
Chain 1:   1700       -20629.007             0.203            0.294
Chain 1:   1800       -20571.340             0.165            0.152
Chain 1:   1900       -20897.558             0.137            0.055
Chain 1:   2000       -19404.323             0.115            0.055
Chain 1:   2100       -19642.917             0.084            0.036
Chain 1:   2200       -19870.193             0.083            0.036
Chain 1:   2300       -19486.655             0.039            0.020
Chain 1:   2400       -19258.636             0.039            0.020
Chain 1:   2500       -19060.968             0.025            0.016
Chain 1:   2600       -18690.807             0.024            0.016
Chain 1:   2700       -18647.602             0.018            0.012
Chain 1:   2800       -18364.592             0.020            0.015
Chain 1:   2900       -18645.939             0.020            0.015
Chain 1:   3000       -18632.066             0.012            0.012
Chain 1:   3100       -18717.112             0.011            0.012
Chain 1:   3200       -18407.653             0.012            0.015
Chain 1:   3300       -18612.453             0.011            0.012
Chain 1:   3400       -18087.289             0.013            0.015
Chain 1:   3500       -18699.449             0.015            0.015
Chain 1:   3600       -18005.763             0.017            0.015
Chain 1:   3700       -18392.919             0.018            0.017
Chain 1:   3800       -17352.155             0.023            0.021
Chain 1:   3900       -17348.330             0.021            0.021
Chain 1:   4000       -17465.580             0.022            0.021
Chain 1:   4100       -17379.381             0.022            0.021
Chain 1:   4200       -17195.480             0.021            0.021
Chain 1:   4300       -17333.941             0.021            0.021
Chain 1:   4400       -17290.682             0.019            0.011
Chain 1:   4500       -17193.229             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00164 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12397.993             1.000            1.000
Chain 1:    200        -9378.292             0.661            1.000
Chain 1:    300        -8070.103             0.495            0.322
Chain 1:    400        -8224.357             0.376            0.322
Chain 1:    500        -8085.356             0.304            0.162
Chain 1:    600        -8011.375             0.255            0.162
Chain 1:    700        -7865.627             0.221            0.019
Chain 1:    800        -7900.607             0.194            0.019
Chain 1:    900        -7936.090             0.173            0.019
Chain 1:   1000        -7905.694             0.156            0.019
Chain 1:   1100        -8002.684             0.057            0.017
Chain 1:   1200        -7907.802             0.026            0.012
Chain 1:   1300        -7850.499             0.011            0.012
Chain 1:   1400        -7873.156             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45519.113             1.000            1.000
Chain 1:    200       -15533.273             1.465            1.930
Chain 1:    300        -8705.205             1.238            1.000
Chain 1:    400        -8711.614             0.929            1.000
Chain 1:    500        -8021.248             0.760            0.784
Chain 1:    600        -8607.645             0.645            0.784
Chain 1:    700        -8272.933             0.559            0.086
Chain 1:    800        -8087.995             0.492            0.086
Chain 1:    900        -8080.301             0.437            0.068
Chain 1:   1000        -7847.908             0.396            0.068
Chain 1:   1100        -7808.532             0.297            0.040
Chain 1:   1200        -7582.783             0.107            0.030
Chain 1:   1300        -7790.200             0.031            0.030
Chain 1:   1400        -7913.450             0.033            0.030
Chain 1:   1500        -7623.291             0.028            0.030
Chain 1:   1600        -7781.494             0.023            0.027
Chain 1:   1700        -7536.126             0.022            0.027
Chain 1:   1800        -7614.084             0.021            0.027
Chain 1:   1900        -7727.971             0.022            0.027
Chain 1:   2000        -7584.953             0.021            0.020
Chain 1:   2100        -7622.138             0.021            0.020
Chain 1:   2200        -7723.080             0.019            0.019
Chain 1:   2300        -7615.423             0.018            0.016
Chain 1:   2400        -7663.014             0.017            0.015
Chain 1:   2500        -7585.608             0.015            0.014
Chain 1:   2600        -7546.743             0.013            0.013
Chain 1:   2700        -7549.313             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00357 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87100.641             1.000            1.000
Chain 1:    200       -13532.860             3.218            5.436
Chain 1:    300        -9873.614             2.269            1.000
Chain 1:    400       -10708.515             1.721            1.000
Chain 1:    500        -8849.371             1.419            0.371
Chain 1:    600        -8681.626             1.186            0.371
Chain 1:    700        -8722.190             1.017            0.210
Chain 1:    800        -9030.907             0.894            0.210
Chain 1:    900        -8716.432             0.799            0.078
Chain 1:   1000        -8630.858             0.720            0.078
Chain 1:   1100        -8607.924             0.620            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8438.456             0.079            0.034
Chain 1:   1300        -8532.946             0.043            0.020
Chain 1:   1400        -8541.239             0.035            0.019
Chain 1:   1500        -8418.669             0.015            0.015
Chain 1:   1600        -8536.314             0.015            0.014
Chain 1:   1700        -8619.876             0.015            0.014
Chain 1:   1800        -8201.755             0.017            0.014
Chain 1:   1900        -8299.151             0.015            0.012
Chain 1:   2000        -8273.234             0.014            0.012
Chain 1:   2100        -8397.078             0.015            0.014
Chain 1:   2200        -8211.664             0.015            0.014
Chain 1:   2300        -8293.938             0.015            0.014
Chain 1:   2400        -8363.496             0.016            0.014
Chain 1:   2500        -8309.400             0.015            0.012
Chain 1:   2600        -8309.652             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003178 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399801.904             1.000            1.000
Chain 1:    200     -1583577.699             2.652            4.304
Chain 1:    300      -892156.435             2.026            1.000
Chain 1:    400      -458911.816             1.756            1.000
Chain 1:    500      -359387.819             1.460            0.944
Chain 1:    600      -233892.694             1.306            0.944
Chain 1:    700      -119677.504             1.256            0.944
Chain 1:    800       -86757.935             1.146            0.944
Chain 1:    900       -67014.960             1.052            0.775
Chain 1:   1000       -51746.904             0.976            0.775
Chain 1:   1100       -39168.500             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38335.699             0.480            0.379
Chain 1:   1300       -26244.302             0.448            0.379
Chain 1:   1400       -25957.541             0.355            0.321
Chain 1:   1500       -22532.759             0.343            0.321
Chain 1:   1600       -21745.198             0.293            0.295
Chain 1:   1700       -20613.914             0.203            0.295
Chain 1:   1800       -20556.662             0.165            0.152
Chain 1:   1900       -20882.778             0.137            0.055
Chain 1:   2000       -19391.097             0.115            0.055
Chain 1:   2100       -19629.680             0.084            0.036
Chain 1:   2200       -19856.532             0.083            0.036
Chain 1:   2300       -19473.347             0.039            0.020
Chain 1:   2400       -19245.401             0.039            0.020
Chain 1:   2500       -19047.488             0.025            0.016
Chain 1:   2600       -18677.598             0.024            0.016
Chain 1:   2700       -18634.480             0.018            0.012
Chain 1:   2800       -18351.412             0.020            0.015
Chain 1:   2900       -18632.716             0.020            0.015
Chain 1:   3000       -18618.825             0.012            0.012
Chain 1:   3100       -18703.863             0.011            0.012
Chain 1:   3200       -18394.494             0.012            0.015
Chain 1:   3300       -18599.221             0.011            0.012
Chain 1:   3400       -18074.137             0.013            0.015
Chain 1:   3500       -18686.086             0.015            0.015
Chain 1:   3600       -17992.653             0.017            0.015
Chain 1:   3700       -18379.631             0.019            0.017
Chain 1:   3800       -17339.150             0.023            0.021
Chain 1:   3900       -17335.290             0.021            0.021
Chain 1:   4000       -17452.577             0.022            0.021
Chain 1:   4100       -17366.376             0.022            0.021
Chain 1:   4200       -17182.521             0.022            0.021
Chain 1:   4300       -17320.963             0.021            0.021
Chain 1:   4400       -17277.789             0.019            0.011
Chain 1:   4500       -17180.293             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001285 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12100.285             1.000            1.000
Chain 1:    200        -8888.814             0.681            1.000
Chain 1:    300        -7875.031             0.497            0.361
Chain 1:    400        -8029.545             0.377            0.361
Chain 1:    500        -7923.225             0.305            0.129
Chain 1:    600        -7784.051             0.257            0.129
Chain 1:    700        -7715.358             0.221            0.019
Chain 1:    800        -7720.425             0.194            0.019
Chain 1:    900        -7700.205             0.173            0.018
Chain 1:   1000        -7775.621             0.156            0.018
Chain 1:   1100        -7831.696             0.057            0.013
Chain 1:   1200        -7728.695             0.022            0.013
Chain 1:   1300        -7729.288             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56695.550             1.000            1.000
Chain 1:    200       -17032.271             1.664            2.329
Chain 1:    300        -8535.891             1.441            1.000
Chain 1:    400        -7816.584             1.104            1.000
Chain 1:    500        -8230.505             0.893            0.995
Chain 1:    600        -8003.823             0.749            0.995
Chain 1:    700        -7798.651             0.646            0.092
Chain 1:    800        -7974.857             0.568            0.092
Chain 1:    900        -7845.257             0.507            0.050
Chain 1:   1000        -7765.707             0.457            0.050
Chain 1:   1100        -7621.240             0.359            0.028
Chain 1:   1200        -7594.005             0.126            0.026
Chain 1:   1300        -7739.401             0.029            0.022
Chain 1:   1400        -7802.583             0.020            0.019
Chain 1:   1500        -7510.309             0.019            0.019
Chain 1:   1600        -7685.752             0.019            0.019
Chain 1:   1700        -7444.568             0.019            0.019
Chain 1:   1800        -7526.880             0.018            0.019
Chain 1:   1900        -7487.129             0.017            0.019
Chain 1:   2000        -7516.764             0.016            0.019
Chain 1:   2100        -7535.326             0.015            0.011
Chain 1:   2200        -7613.592             0.015            0.011
Chain 1:   2300        -7527.381             0.015            0.011
Chain 1:   2400        -7563.792             0.014            0.011
Chain 1:   2500        -7403.416             0.013            0.011
Chain 1:   2600        -7462.072             0.011            0.010
Chain 1:   2700        -7501.794             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004453 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 44.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86390.328             1.000            1.000
Chain 1:    200       -13148.776             3.285            5.570
Chain 1:    300        -9597.679             2.313            1.000
Chain 1:    400       -10538.916             1.757            1.000
Chain 1:    500        -8503.395             1.454            0.370
Chain 1:    600        -8136.447             1.219            0.370
Chain 1:    700        -8358.435             1.049            0.239
Chain 1:    800        -8557.104             0.920            0.239
Chain 1:    900        -8430.130             0.820            0.089
Chain 1:   1000        -8186.196             0.741            0.089
Chain 1:   1100        -8471.254             0.644            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8110.461             0.092            0.044
Chain 1:   1300        -8329.652             0.057            0.034
Chain 1:   1400        -8317.598             0.049            0.030
Chain 1:   1500        -8223.771             0.026            0.027
Chain 1:   1600        -8320.581             0.022            0.026
Chain 1:   1700        -8409.055             0.021            0.023
Chain 1:   1800        -8020.551             0.023            0.026
Chain 1:   1900        -8123.016             0.023            0.026
Chain 1:   2000        -8092.978             0.020            0.013
Chain 1:   2100        -8224.495             0.019            0.013
Chain 1:   2200        -8010.470             0.017            0.013
Chain 1:   2300        -8152.322             0.016            0.013
Chain 1:   2400        -8164.753             0.016            0.013
Chain 1:   2500        -8132.784             0.015            0.013
Chain 1:   2600        -8132.634             0.014            0.013
Chain 1:   2700        -8040.812             0.014            0.013
Chain 1:   2800        -8016.732             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003076 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431057.830             1.000            1.000
Chain 1:    200     -1585790.998             2.658            4.317
Chain 1:    300      -889704.598             2.033            1.000
Chain 1:    400      -456830.848             1.762            1.000
Chain 1:    500      -356917.622             1.465            0.948
Chain 1:    600      -231969.646             1.311            0.948
Chain 1:    700      -118475.017             1.260            0.948
Chain 1:    800       -85793.410             1.151            0.948
Chain 1:    900       -66196.668             1.056            0.782
Chain 1:   1000       -51043.755             0.980            0.782
Chain 1:   1100       -38578.009             0.912            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37756.680             0.483            0.381
Chain 1:   1300       -25773.592             0.451            0.381
Chain 1:   1400       -25496.709             0.357            0.323
Chain 1:   1500       -22100.785             0.344            0.323
Chain 1:   1600       -21322.115             0.294            0.297
Chain 1:   1700       -20203.194             0.204            0.296
Chain 1:   1800       -20148.920             0.166            0.154
Chain 1:   1900       -20474.622             0.138            0.055
Chain 1:   2000       -18990.883             0.116            0.055
Chain 1:   2100       -19228.785             0.085            0.037
Chain 1:   2200       -19454.378             0.084            0.037
Chain 1:   2300       -19072.492             0.040            0.020
Chain 1:   2400       -18844.854             0.040            0.020
Chain 1:   2500       -18646.777             0.026            0.016
Chain 1:   2600       -18277.525             0.024            0.016
Chain 1:   2700       -18234.764             0.019            0.012
Chain 1:   2800       -17951.815             0.020            0.016
Chain 1:   2900       -18232.752             0.020            0.015
Chain 1:   3000       -18218.955             0.012            0.012
Chain 1:   3100       -18303.854             0.011            0.012
Chain 1:   3200       -17994.924             0.012            0.015
Chain 1:   3300       -18199.385             0.011            0.012
Chain 1:   3400       -17674.943             0.013            0.015
Chain 1:   3500       -18285.774             0.015            0.016
Chain 1:   3600       -17593.810             0.017            0.016
Chain 1:   3700       -17979.558             0.019            0.017
Chain 1:   3800       -16941.321             0.023            0.021
Chain 1:   3900       -16937.518             0.022            0.021
Chain 1:   4000       -17054.824             0.023            0.021
Chain 1:   4100       -16968.666             0.023            0.021
Chain 1:   4200       -16785.401             0.022            0.021
Chain 1:   4300       -16923.453             0.022            0.021
Chain 1:   4400       -16880.637             0.019            0.011
Chain 1:   4500       -16783.240             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001125 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49201.027             1.000            1.000
Chain 1:    200       -53813.066             0.543            1.000
Chain 1:    300       -21674.170             0.856            1.000
Chain 1:    400       -22026.059             0.646            1.000
Chain 1:    500       -23020.587             0.526            0.086
Chain 1:    600       -15249.809             0.523            0.510
Chain 1:    700       -16814.284             0.461            0.093
Chain 1:    800       -15426.778             0.415            0.093
Chain 1:    900       -14192.227             0.379            0.090
Chain 1:   1000       -12537.426             0.354            0.093
Chain 1:   1100       -20206.321             0.292            0.093
Chain 1:   1200       -11004.634             0.367            0.132
Chain 1:   1300       -10919.780             0.219            0.093
Chain 1:   1400       -14509.926             0.243            0.132
Chain 1:   1500       -11197.430             0.268            0.247
Chain 1:   1600        -9942.458             0.229            0.132
Chain 1:   1700       -12985.626             0.244            0.234
Chain 1:   1800       -10528.024             0.258            0.234
Chain 1:   1900       -16888.453             0.287            0.247
Chain 1:   2000       -14707.994             0.289            0.247
Chain 1:   2100        -9570.227             0.304            0.247
Chain 1:   2200       -13788.064             0.251            0.247
Chain 1:   2300        -9364.617             0.298            0.296
Chain 1:   2400        -9856.274             0.278            0.296
Chain 1:   2500       -11555.379             0.263            0.234
Chain 1:   2600       -11797.691             0.253            0.234
Chain 1:   2700        -9356.584             0.255            0.261
Chain 1:   2800       -10933.546             0.246            0.261
Chain 1:   2900        -9570.694             0.223            0.148
Chain 1:   3000        -8981.836             0.215            0.147
Chain 1:   3100        -8760.197             0.163            0.144
Chain 1:   3200       -10110.617             0.146            0.142
Chain 1:   3300       -10479.183             0.102            0.134
Chain 1:   3400        -9875.101             0.104            0.134
Chain 1:   3500        -9583.279             0.092            0.066
Chain 1:   3600       -11537.846             0.107            0.134
Chain 1:   3700       -12097.233             0.085            0.066
Chain 1:   3800        -9849.241             0.094            0.066
Chain 1:   3900        -9554.771             0.083            0.061
Chain 1:   4000        -9756.999             0.078            0.046
Chain 1:   4100        -8805.023             0.086            0.061
Chain 1:   4200        -8883.772             0.074            0.046
Chain 1:   4300        -9122.511             0.073            0.046
Chain 1:   4400       -12981.521             0.097            0.046
Chain 1:   4500        -9618.532             0.129            0.108
Chain 1:   4600        -9121.950             0.117            0.054
Chain 1:   4700        -8586.424             0.119            0.062
Chain 1:   4800        -8790.417             0.098            0.054
Chain 1:   4900       -12792.907             0.126            0.062
Chain 1:   5000       -14675.946             0.137            0.108
Chain 1:   5100        -8808.220             0.193            0.128
Chain 1:   5200        -8764.668             0.193            0.128
Chain 1:   5300        -9137.218             0.194            0.128
Chain 1:   5400        -8949.583             0.166            0.062
Chain 1:   5500       -13171.289             0.163            0.062
Chain 1:   5600       -15966.177             0.176            0.128
Chain 1:   5700       -12395.255             0.198            0.175
Chain 1:   5800        -8698.235             0.238            0.288
Chain 1:   5900       -15114.166             0.249            0.288
Chain 1:   6000        -8843.131             0.308            0.321
Chain 1:   6100        -9378.621             0.247            0.288
Chain 1:   6200        -8260.788             0.260            0.288
Chain 1:   6300        -8982.841             0.264            0.288
Chain 1:   6400        -9292.733             0.265            0.288
Chain 1:   6500        -8875.605             0.237            0.175
Chain 1:   6600       -11385.990             0.242            0.220
Chain 1:   6700        -8803.849             0.243            0.220
Chain 1:   6800       -12193.590             0.228            0.220
Chain 1:   6900       -12593.037             0.189            0.135
Chain 1:   7000       -12781.467             0.119            0.080
Chain 1:   7100       -13309.959             0.117            0.080
Chain 1:   7200       -10297.705             0.133            0.080
Chain 1:   7300       -10893.830             0.131            0.055
Chain 1:   7400       -11417.551             0.132            0.055
Chain 1:   7500        -8736.672             0.158            0.220
Chain 1:   7600        -8865.880             0.137            0.055
Chain 1:   7700        -8486.884             0.112            0.046
Chain 1:   7800       -13191.711             0.120            0.046
Chain 1:   7900        -8572.088             0.171            0.055
Chain 1:   8000        -8852.620             0.173            0.055
Chain 1:   8100        -9281.152             0.173            0.055
Chain 1:   8200        -9710.265             0.148            0.046
Chain 1:   8300        -9829.210             0.144            0.046
Chain 1:   8400       -11847.639             0.157            0.046
Chain 1:   8500        -8307.367             0.169            0.046
Chain 1:   8600        -9899.470             0.183            0.161
Chain 1:   8700        -8914.185             0.190            0.161
Chain 1:   8800        -8847.894             0.155            0.111
Chain 1:   8900        -9223.478             0.105            0.046
Chain 1:   9000        -9792.643             0.108            0.058
Chain 1:   9100        -9325.004             0.108            0.058
Chain 1:   9200       -11847.489             0.125            0.111
Chain 1:   9300        -8280.344             0.167            0.161
Chain 1:   9400        -8231.333             0.150            0.111
Chain 1:   9500        -8305.803             0.109            0.058
Chain 1:   9600        -9529.161             0.105            0.058
Chain 1:   9700        -8525.302             0.106            0.058
Chain 1:   9800        -8219.706             0.109            0.058
Chain 1:   9900        -9669.870             0.120            0.118
Chain 1:   10000        -8478.972             0.128            0.128
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001392 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61806.812             1.000            1.000
Chain 1:    200       -17969.050             1.720            2.440
Chain 1:    300        -8934.587             1.484            1.011
Chain 1:    400        -8921.501             1.113            1.011
Chain 1:    500        -8712.218             0.895            1.000
Chain 1:    600        -8974.331             0.751            1.000
Chain 1:    700        -7832.709             0.664            0.146
Chain 1:    800        -8322.844             0.589            0.146
Chain 1:    900        -8142.223             0.526            0.059
Chain 1:   1000        -7902.974             0.476            0.059
Chain 1:   1100        -7920.282             0.376            0.030
Chain 1:   1200        -7698.618             0.135            0.029
Chain 1:   1300        -7697.309             0.034            0.029
Chain 1:   1400        -8122.626             0.039            0.029
Chain 1:   1500        -7610.469             0.044            0.030
Chain 1:   1600        -7795.293             0.043            0.030
Chain 1:   1700        -7565.650             0.032            0.030
Chain 1:   1800        -7592.381             0.026            0.029
Chain 1:   1900        -7629.766             0.024            0.029
Chain 1:   2000        -7675.005             0.022            0.024
Chain 1:   2100        -7631.595             0.022            0.024
Chain 1:   2200        -7732.453             0.021            0.013
Chain 1:   2300        -7590.533             0.023            0.019
Chain 1:   2400        -7639.312             0.018            0.013
Chain 1:   2500        -7642.981             0.011            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0032 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85915.486             1.000            1.000
Chain 1:    200       -13650.497             3.147            5.294
Chain 1:    300       -10009.368             2.219            1.000
Chain 1:    400       -10951.667             1.686            1.000
Chain 1:    500        -8982.507             1.393            0.364
Chain 1:    600        -8408.704             1.172            0.364
Chain 1:    700        -8571.241             1.007            0.219
Chain 1:    800        -8937.776             0.886            0.219
Chain 1:    900        -8975.169             0.788            0.086
Chain 1:   1000        -8732.598             0.712            0.086
Chain 1:   1100        -8805.949             0.613            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8327.946             0.089            0.057
Chain 1:   1300        -8684.181             0.057            0.041
Chain 1:   1400        -8693.292             0.049            0.041
Chain 1:   1500        -8564.234             0.028            0.028
Chain 1:   1600        -8672.718             0.023            0.019
Chain 1:   1700        -8750.532             0.022            0.015
Chain 1:   1800        -8329.858             0.023            0.015
Chain 1:   1900        -8429.017             0.023            0.015
Chain 1:   2000        -8403.117             0.021            0.013
Chain 1:   2100        -8527.742             0.022            0.015
Chain 1:   2200        -8336.209             0.018            0.015
Chain 1:   2300        -8423.630             0.015            0.013
Chain 1:   2400        -8492.888             0.016            0.013
Chain 1:   2500        -8438.992             0.015            0.012
Chain 1:   2600        -8439.705             0.014            0.010
Chain 1:   2700        -8356.715             0.014            0.010
Chain 1:   2800        -8317.581             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8404188.139             1.000            1.000
Chain 1:    200     -1580562.464             2.659            4.317
Chain 1:    300      -889544.977             2.031            1.000
Chain 1:    400      -457190.310             1.760            1.000
Chain 1:    500      -357709.230             1.464            0.946
Chain 1:    600      -232916.469             1.309            0.946
Chain 1:    700      -119277.302             1.258            0.946
Chain 1:    800       -86544.443             1.148            0.946
Chain 1:    900       -66905.964             1.053            0.777
Chain 1:   1000       -51718.611             0.977            0.777
Chain 1:   1100       -39211.940             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38391.106             0.479            0.378
Chain 1:   1300       -26353.336             0.447            0.378
Chain 1:   1400       -26074.148             0.354            0.319
Chain 1:   1500       -22663.314             0.341            0.319
Chain 1:   1600       -21881.156             0.291            0.294
Chain 1:   1700       -20754.915             0.201            0.294
Chain 1:   1800       -20699.449             0.164            0.151
Chain 1:   1900       -21025.656             0.136            0.054
Chain 1:   2000       -19537.079             0.114            0.054
Chain 1:   2100       -19775.247             0.084            0.036
Chain 1:   2200       -20001.875             0.083            0.036
Chain 1:   2300       -19618.941             0.039            0.020
Chain 1:   2400       -19391.028             0.039            0.020
Chain 1:   2500       -19193.149             0.025            0.016
Chain 1:   2600       -18823.097             0.023            0.016
Chain 1:   2700       -18780.075             0.018            0.012
Chain 1:   2800       -18496.971             0.019            0.015
Chain 1:   2900       -18778.204             0.019            0.015
Chain 1:   3000       -18764.351             0.012            0.012
Chain 1:   3100       -18849.373             0.011            0.012
Chain 1:   3200       -18539.964             0.012            0.015
Chain 1:   3300       -18744.784             0.011            0.012
Chain 1:   3400       -18219.623             0.012            0.015
Chain 1:   3500       -18831.645             0.015            0.015
Chain 1:   3600       -18138.114             0.016            0.015
Chain 1:   3700       -18525.061             0.018            0.017
Chain 1:   3800       -17484.506             0.023            0.021
Chain 1:   3900       -17480.679             0.021            0.021
Chain 1:   4000       -17597.939             0.022            0.021
Chain 1:   4100       -17511.696             0.022            0.021
Chain 1:   4200       -17327.934             0.021            0.021
Chain 1:   4300       -17466.332             0.021            0.021
Chain 1:   4400       -17423.085             0.018            0.011
Chain 1:   4500       -17325.652             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12734.887             1.000            1.000
Chain 1:    200        -9492.107             0.671            1.000
Chain 1:    300        -8137.715             0.503            0.342
Chain 1:    400        -8254.192             0.381            0.342
Chain 1:    500        -8214.129             0.305            0.166
Chain 1:    600        -8087.003             0.257            0.166
Chain 1:    700        -7993.854             0.222            0.016
Chain 1:    800        -7996.913             0.194            0.016
Chain 1:    900        -7906.465             0.174            0.014
Chain 1:   1000        -8103.277             0.159            0.016
Chain 1:   1100        -8137.564             0.059            0.014
Chain 1:   1200        -8035.077             0.027            0.013
Chain 1:   1300        -7966.906             0.011            0.012
Chain 1:   1400        -7985.995             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001555 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47345.163             1.000            1.000
Chain 1:    200       -15874.353             1.491            1.982
Chain 1:    300        -8616.915             1.275            1.000
Chain 1:    400        -8729.219             0.959            1.000
Chain 1:    500        -8644.137             0.769            0.842
Chain 1:    600        -7815.598             0.659            0.842
Chain 1:    700        -7822.977             0.565            0.106
Chain 1:    800        -8294.785             0.501            0.106
Chain 1:    900        -7840.979             0.452            0.058
Chain 1:   1000        -7646.261             0.409            0.058
Chain 1:   1100        -7719.396             0.310            0.057
Chain 1:   1200        -7722.436             0.112            0.025
Chain 1:   1300        -7684.526             0.028            0.013
Chain 1:   1400        -7534.082             0.029            0.020
Chain 1:   1500        -7484.947             0.029            0.020
Chain 1:   1600        -7701.240             0.021            0.020
Chain 1:   1700        -7475.292             0.024            0.025
Chain 1:   1800        -7514.892             0.019            0.020
Chain 1:   1900        -7521.997             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005976 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 59.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85810.559             1.000            1.000
Chain 1:    200       -13659.071             3.141            5.282
Chain 1:    300       -10013.020             2.215            1.000
Chain 1:    400       -10814.519             1.680            1.000
Chain 1:    500        -8995.076             1.385            0.364
Chain 1:    600        -8660.116             1.160            0.364
Chain 1:    700        -8463.281             0.998            0.202
Chain 1:    800        -8929.026             0.880            0.202
Chain 1:    900        -8812.989             0.783            0.074
Chain 1:   1000        -8592.361             0.708            0.074
Chain 1:   1100        -8814.079             0.610            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8452.804             0.086            0.043
Chain 1:   1300        -8695.261             0.053            0.039
Chain 1:   1400        -8712.360             0.045            0.028
Chain 1:   1500        -8558.675             0.027            0.026
Chain 1:   1600        -8673.303             0.024            0.025
Chain 1:   1700        -8749.979             0.023            0.025
Chain 1:   1800        -8327.687             0.023            0.025
Chain 1:   1900        -8428.248             0.023            0.025
Chain 1:   2000        -8402.622             0.020            0.018
Chain 1:   2100        -8527.841             0.019            0.015
Chain 1:   2200        -8332.429             0.017            0.015
Chain 1:   2300        -8423.006             0.016            0.013
Chain 1:   2400        -8491.969             0.016            0.013
Chain 1:   2500        -8438.171             0.015            0.012
Chain 1:   2600        -8439.307             0.014            0.011
Chain 1:   2700        -8356.135             0.014            0.011
Chain 1:   2800        -8316.342             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00363 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8379501.601             1.000            1.000
Chain 1:    200     -1582188.639             2.648            4.296
Chain 1:    300      -891323.595             2.024            1.000
Chain 1:    400      -458092.054             1.754            1.000
Chain 1:    500      -358428.457             1.459            0.946
Chain 1:    600      -233469.106             1.305            0.946
Chain 1:    700      -119582.187             1.255            0.946
Chain 1:    800       -86719.362             1.145            0.946
Chain 1:    900       -67037.261             1.051            0.775
Chain 1:   1000       -51816.494             0.975            0.775
Chain 1:   1100       -39269.695             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38446.873             0.479            0.379
Chain 1:   1300       -26382.091             0.448            0.379
Chain 1:   1400       -26099.998             0.354            0.320
Chain 1:   1500       -22680.453             0.341            0.320
Chain 1:   1600       -21894.925             0.291            0.294
Chain 1:   1700       -20766.301             0.202            0.294
Chain 1:   1800       -20710.011             0.164            0.151
Chain 1:   1900       -21036.222             0.136            0.054
Chain 1:   2000       -19545.767             0.114            0.054
Chain 1:   2100       -19784.379             0.084            0.036
Chain 1:   2200       -20010.913             0.083            0.036
Chain 1:   2300       -19628.030             0.039            0.020
Chain 1:   2400       -19400.042             0.039            0.020
Chain 1:   2500       -19201.993             0.025            0.016
Chain 1:   2600       -18832.100             0.023            0.016
Chain 1:   2700       -18789.117             0.018            0.012
Chain 1:   2800       -18505.811             0.019            0.015
Chain 1:   2900       -18787.218             0.019            0.015
Chain 1:   3000       -18773.479             0.012            0.012
Chain 1:   3100       -18858.395             0.011            0.012
Chain 1:   3200       -18549.039             0.012            0.015
Chain 1:   3300       -18753.833             0.011            0.012
Chain 1:   3400       -18228.574             0.012            0.015
Chain 1:   3500       -18840.646             0.015            0.015
Chain 1:   3600       -18147.203             0.016            0.015
Chain 1:   3700       -18534.048             0.018            0.017
Chain 1:   3800       -17493.431             0.023            0.021
Chain 1:   3900       -17489.578             0.021            0.021
Chain 1:   4000       -17606.917             0.022            0.021
Chain 1:   4100       -17520.549             0.022            0.021
Chain 1:   4200       -17336.814             0.021            0.021
Chain 1:   4300       -17475.234             0.021            0.021
Chain 1:   4400       -17432.013             0.018            0.011
Chain 1:   4500       -17334.537             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001174 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12589.909             1.000            1.000
Chain 1:    200        -9613.304             0.655            1.000
Chain 1:    300        -8343.210             0.487            0.310
Chain 1:    400        -8498.848             0.370            0.310
Chain 1:    500        -8363.411             0.299            0.152
Chain 1:    600        -8229.700             0.252            0.152
Chain 1:    700        -8178.559             0.217            0.018
Chain 1:    800        -8130.492             0.191            0.018
Chain 1:    900        -8051.474             0.171            0.016
Chain 1:   1000        -8239.011             0.156            0.018
Chain 1:   1100        -8268.287             0.056            0.016
Chain 1:   1200        -8163.213             0.026            0.016
Chain 1:   1300        -8101.494             0.012            0.013
Chain 1:   1400        -8124.329             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62408.612             1.000            1.000
Chain 1:    200       -18180.159             1.716            2.433
Chain 1:    300        -9029.905             1.482            1.013
Chain 1:    400        -9712.045             1.129            1.013
Chain 1:    500        -7849.319             0.951            1.000
Chain 1:    600        -8740.057             0.809            1.000
Chain 1:    700        -7937.546             0.708            0.237
Chain 1:    800        -8324.863             0.625            0.237
Chain 1:    900        -7712.215             0.565            0.102
Chain 1:   1000        -7922.233             0.511            0.102
Chain 1:   1100        -7887.933             0.411            0.101
Chain 1:   1200        -7615.843             0.172            0.079
Chain 1:   1300        -7863.309             0.073            0.070
Chain 1:   1400        -7962.700             0.068            0.047
Chain 1:   1500        -7623.310             0.048            0.045
Chain 1:   1600        -7704.318             0.039            0.036
Chain 1:   1700        -7607.236             0.030            0.031
Chain 1:   1800        -7648.444             0.026            0.027
Chain 1:   1900        -7665.460             0.019            0.013
Chain 1:   2000        -7767.818             0.017            0.013
Chain 1:   2100        -7641.324             0.018            0.013
Chain 1:   2200        -7762.822             0.016            0.013
Chain 1:   2300        -7625.810             0.015            0.013
Chain 1:   2400        -7685.190             0.015            0.013
Chain 1:   2500        -7603.524             0.011            0.013
Chain 1:   2600        -7566.934             0.011            0.013
Chain 1:   2700        -7533.567             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86424.919             1.000            1.000
Chain 1:    200       -13774.153             3.137            5.274
Chain 1:    300       -10133.763             2.211            1.000
Chain 1:    400       -11041.188             1.679            1.000
Chain 1:    500        -9108.688             1.386            0.359
Chain 1:    600        -8797.299             1.161            0.359
Chain 1:    700        -8630.871             0.998            0.212
Chain 1:    800        -8938.617             0.877            0.212
Chain 1:    900        -8950.836             0.780            0.082
Chain 1:   1000        -8749.446             0.704            0.082
Chain 1:   1100        -8937.162             0.606            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8579.851             0.083            0.035
Chain 1:   1300        -8795.114             0.049            0.034
Chain 1:   1400        -8814.675             0.041            0.024
Chain 1:   1500        -8690.924             0.022            0.023
Chain 1:   1600        -8796.025             0.019            0.021
Chain 1:   1700        -8878.000             0.018            0.021
Chain 1:   1800        -8459.358             0.020            0.021
Chain 1:   1900        -8557.815             0.021            0.021
Chain 1:   2000        -8531.728             0.019            0.014
Chain 1:   2100        -8655.706             0.018            0.014
Chain 1:   2200        -8469.123             0.016            0.014
Chain 1:   2300        -8552.366             0.015            0.012
Chain 1:   2400        -8621.889             0.015            0.012
Chain 1:   2500        -8567.857             0.015            0.012
Chain 1:   2600        -8568.187             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003611 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406744.835             1.000            1.000
Chain 1:    200     -1583435.516             2.655            4.309
Chain 1:    300      -891651.722             2.028            1.000
Chain 1:    400      -458827.839             1.757            1.000
Chain 1:    500      -359393.436             1.461            0.943
Chain 1:    600      -234010.624             1.307            0.943
Chain 1:    700      -119867.392             1.256            0.943
Chain 1:    800       -87004.870             1.146            0.943
Chain 1:    900       -67262.168             1.052            0.776
Chain 1:   1000       -51999.116             0.976            0.776
Chain 1:   1100       -39428.078             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38595.928             0.479            0.378
Chain 1:   1300       -26504.861             0.447            0.378
Chain 1:   1400       -26218.133             0.354            0.319
Chain 1:   1500       -22794.750             0.341            0.319
Chain 1:   1600       -22008.251             0.291            0.294
Chain 1:   1700       -20876.464             0.201            0.294
Chain 1:   1800       -20819.334             0.164            0.150
Chain 1:   1900       -21145.493             0.136            0.054
Chain 1:   2000       -19653.982             0.114            0.054
Chain 1:   2100       -19892.248             0.083            0.036
Chain 1:   2200       -20119.380             0.082            0.036
Chain 1:   2300       -19736.032             0.039            0.019
Chain 1:   2400       -19508.093             0.039            0.019
Chain 1:   2500       -19310.399             0.025            0.015
Chain 1:   2600       -18940.209             0.023            0.015
Chain 1:   2700       -18897.035             0.018            0.012
Chain 1:   2800       -18614.057             0.019            0.015
Chain 1:   2900       -18895.404             0.019            0.015
Chain 1:   3000       -18881.370             0.012            0.012
Chain 1:   3100       -18966.458             0.011            0.012
Chain 1:   3200       -18657.001             0.012            0.015
Chain 1:   3300       -18861.839             0.011            0.012
Chain 1:   3400       -18336.686             0.012            0.015
Chain 1:   3500       -18948.736             0.015            0.015
Chain 1:   3600       -18255.186             0.016            0.015
Chain 1:   3700       -18642.271             0.018            0.017
Chain 1:   3800       -17601.654             0.023            0.021
Chain 1:   3900       -17597.854             0.021            0.021
Chain 1:   4000       -17715.096             0.022            0.021
Chain 1:   4100       -17628.893             0.022            0.021
Chain 1:   4200       -17445.047             0.021            0.021
Chain 1:   4300       -17583.452             0.021            0.021
Chain 1:   4400       -17540.227             0.018            0.011
Chain 1:   4500       -17442.794             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001244 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48684.339             1.000            1.000
Chain 1:    200       -22173.441             1.098            1.196
Chain 1:    300       -24549.293             0.764            1.000
Chain 1:    400       -12683.231             0.807            1.000
Chain 1:    500       -12485.653             0.649            0.936
Chain 1:    600       -16172.524             0.579            0.936
Chain 1:    700       -13136.920             0.529            0.231
Chain 1:    800       -10979.961             0.487            0.231
Chain 1:    900       -13684.067             0.455            0.228
Chain 1:   1000       -12254.408             0.421            0.228
Chain 1:   1100       -10485.101             0.338            0.198
Chain 1:   1200       -12959.007             0.238            0.196
Chain 1:   1300       -10671.323             0.250            0.198
Chain 1:   1400       -12022.507             0.167            0.196
Chain 1:   1500       -11214.729             0.173            0.196
Chain 1:   1600       -18556.374             0.190            0.196
Chain 1:   1700       -11396.343             0.229            0.196
Chain 1:   1800        -9700.948             0.227            0.191
Chain 1:   1900       -10376.773             0.214            0.175
Chain 1:   2000       -14728.554             0.232            0.191
Chain 1:   2100        -9353.640             0.272            0.214
Chain 1:   2200       -12586.630             0.279            0.257
Chain 1:   2300        -9251.702             0.294            0.295
Chain 1:   2400       -10012.946             0.290            0.295
Chain 1:   2500        -9167.959             0.292            0.295
Chain 1:   2600       -10196.684             0.262            0.257
Chain 1:   2700        -8790.881             0.216            0.175
Chain 1:   2800       -10221.102             0.212            0.160
Chain 1:   2900        -9803.685             0.210            0.160
Chain 1:   3000        -8989.856             0.189            0.140
Chain 1:   3100        -9318.316             0.135            0.101
Chain 1:   3200        -9414.342             0.111            0.092
Chain 1:   3300       -10093.184             0.081            0.091
Chain 1:   3400       -11639.567             0.087            0.092
Chain 1:   3500        -9142.320             0.105            0.101
Chain 1:   3600       -10637.567             0.109            0.133
Chain 1:   3700        -8702.365             0.115            0.133
Chain 1:   3800       -10206.242             0.116            0.133
Chain 1:   3900        -9000.106             0.125            0.134
Chain 1:   4000        -8974.046             0.117            0.134
Chain 1:   4100       -12885.069             0.143            0.141
Chain 1:   4200       -11885.133             0.151            0.141
Chain 1:   4300        -9247.923             0.173            0.147
Chain 1:   4400       -12178.949             0.183            0.222
Chain 1:   4500        -9115.203             0.190            0.222
Chain 1:   4600       -10582.964             0.189            0.222
Chain 1:   4700       -10426.760             0.169            0.147
Chain 1:   4800        -8581.275             0.176            0.215
Chain 1:   4900       -12850.181             0.195            0.241
Chain 1:   5000       -11495.589             0.207            0.241
Chain 1:   5100        -8287.223             0.215            0.241
Chain 1:   5200        -8502.563             0.209            0.241
Chain 1:   5300       -12845.441             0.215            0.241
Chain 1:   5400        -8382.658             0.244            0.332
Chain 1:   5500        -8286.086             0.211            0.215
Chain 1:   5600       -13196.769             0.235            0.332
Chain 1:   5700       -12305.002             0.240            0.332
Chain 1:   5800        -8484.140             0.264            0.338
Chain 1:   5900       -12502.195             0.263            0.338
Chain 1:   6000       -11655.632             0.258            0.338
Chain 1:   6100        -8567.572             0.256            0.338
Chain 1:   6200        -8684.289             0.254            0.338
Chain 1:   6300        -8350.608             0.225            0.321
Chain 1:   6400       -10863.580             0.195            0.231
Chain 1:   6500       -12868.743             0.209            0.231
Chain 1:   6600        -8407.227             0.225            0.231
Chain 1:   6700       -10125.516             0.235            0.231
Chain 1:   6800        -9604.975             0.195            0.170
Chain 1:   6900        -8434.551             0.177            0.156
Chain 1:   7000        -8345.663             0.170            0.156
Chain 1:   7100        -8679.388             0.138            0.139
Chain 1:   7200        -9033.523             0.141            0.139
Chain 1:   7300        -8798.384             0.140            0.139
Chain 1:   7400        -8183.821             0.124            0.075
Chain 1:   7500       -10860.108             0.133            0.075
Chain 1:   7600        -8317.052             0.110            0.075
Chain 1:   7700       -10546.462             0.115            0.075
Chain 1:   7800        -8739.819             0.130            0.139
Chain 1:   7900        -8037.774             0.125            0.087
Chain 1:   8000        -8747.519             0.132            0.087
Chain 1:   8100       -10429.105             0.144            0.161
Chain 1:   8200        -8877.232             0.158            0.175
Chain 1:   8300       -11658.811             0.179            0.207
Chain 1:   8400        -9319.119             0.196            0.211
Chain 1:   8500        -8227.023             0.185            0.207
Chain 1:   8600        -8127.490             0.156            0.175
Chain 1:   8700        -8221.243             0.136            0.161
Chain 1:   8800        -8384.241             0.117            0.133
Chain 1:   8900        -8680.312             0.112            0.133
Chain 1:   9000       -10019.316             0.117            0.134
Chain 1:   9100        -8211.622             0.123            0.134
Chain 1:   9200        -8338.076             0.107            0.133
Chain 1:   9300        -9150.471             0.092            0.089
Chain 1:   9400       -10842.970             0.082            0.089
Chain 1:   9500        -8813.161             0.092            0.089
Chain 1:   9600        -8111.446             0.100            0.089
Chain 1:   9700        -8430.287             0.102            0.089
Chain 1:   9800       -12629.852             0.134            0.134
Chain 1:   9900        -8495.688             0.179            0.156
Chain 1:   10000        -8464.776             0.166            0.156
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56886.240             1.000            1.000
Chain 1:    200       -17179.018             1.656            2.311
Chain 1:    300        -8605.958             1.436            1.000
Chain 1:    400        -7963.633             1.097            1.000
Chain 1:    500        -8599.968             0.892            0.996
Chain 1:    600        -7908.239             0.758            0.996
Chain 1:    700        -8276.031             0.656            0.087
Chain 1:    800        -7971.366             0.579            0.087
Chain 1:    900        -7860.974             0.516            0.081
Chain 1:   1000        -7696.710             0.467            0.081
Chain 1:   1100        -7626.973             0.368            0.074
Chain 1:   1200        -7642.257             0.137            0.044
Chain 1:   1300        -7649.239             0.037            0.038
Chain 1:   1400        -7842.733             0.032            0.025
Chain 1:   1500        -7600.511             0.027            0.025
Chain 1:   1600        -7569.776             0.019            0.021
Chain 1:   1700        -7503.443             0.016            0.014
Chain 1:   1800        -7545.971             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003608 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85812.484             1.000            1.000
Chain 1:    200       -13232.533             3.242            5.485
Chain 1:    300        -9680.527             2.284            1.000
Chain 1:    400       -10539.413             1.733            1.000
Chain 1:    500        -8615.652             1.431            0.367
Chain 1:    600        -8491.660             1.195            0.367
Chain 1:    700        -8553.276             1.025            0.223
Chain 1:    800        -8980.950             0.903            0.223
Chain 1:    900        -8556.282             0.808            0.081
Chain 1:   1000        -8260.121             0.731            0.081
Chain 1:   1100        -8574.843             0.635            0.050   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.058             0.092            0.050
Chain 1:   1300        -8423.506             0.059            0.048
Chain 1:   1400        -8408.313             0.051            0.037
Chain 1:   1500        -8288.924             0.030            0.037
Chain 1:   1600        -8392.069             0.030            0.037
Chain 1:   1700        -8479.206             0.030            0.037
Chain 1:   1800        -8087.698             0.030            0.037
Chain 1:   1900        -8189.917             0.027            0.036
Chain 1:   2000        -8160.092             0.023            0.014
Chain 1:   2100        -8287.735             0.021            0.014
Chain 1:   2200        -8074.067             0.018            0.014
Chain 1:   2300        -8218.753             0.016            0.014
Chain 1:   2400        -8233.730             0.016            0.014
Chain 1:   2500        -8200.336             0.015            0.012
Chain 1:   2600        -8201.912             0.014            0.012
Chain 1:   2700        -8109.050             0.014            0.012
Chain 1:   2800        -8082.801             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002915 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8384806.404             1.000            1.000
Chain 1:    200     -1582033.615             2.650            4.300
Chain 1:    300      -890203.341             2.026            1.000
Chain 1:    400      -457557.993             1.756            1.000
Chain 1:    500      -358214.290             1.460            0.946
Chain 1:    600      -233128.865             1.306            0.946
Chain 1:    700      -119122.037             1.256            0.946
Chain 1:    800       -86316.922             1.147            0.946
Chain 1:    900       -66609.779             1.052            0.777
Chain 1:   1000       -51369.426             0.977            0.777
Chain 1:   1100       -38821.508             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37988.450             0.481            0.380
Chain 1:   1300       -25921.745             0.450            0.380
Chain 1:   1400       -25636.414             0.357            0.323
Chain 1:   1500       -22218.872             0.344            0.323
Chain 1:   1600       -21433.519             0.294            0.297
Chain 1:   1700       -20304.613             0.204            0.296
Chain 1:   1800       -20247.905             0.166            0.154
Chain 1:   1900       -20573.638             0.138            0.056
Chain 1:   2000       -19084.423             0.116            0.056
Chain 1:   2100       -19322.646             0.085            0.037
Chain 1:   2200       -19549.199             0.084            0.037
Chain 1:   2300       -19166.406             0.040            0.020
Chain 1:   2400       -18938.615             0.040            0.020
Chain 1:   2500       -18740.935             0.026            0.016
Chain 1:   2600       -18371.358             0.024            0.016
Chain 1:   2700       -18328.332             0.019            0.012
Chain 1:   2800       -18045.580             0.020            0.016
Chain 1:   2900       -18326.599             0.020            0.015
Chain 1:   3000       -18312.746             0.012            0.012
Chain 1:   3100       -18397.722             0.011            0.012
Chain 1:   3200       -18088.649             0.012            0.015
Chain 1:   3300       -18293.157             0.011            0.012
Chain 1:   3400       -17768.676             0.013            0.015
Chain 1:   3500       -18379.790             0.015            0.016
Chain 1:   3600       -17687.400             0.017            0.016
Chain 1:   3700       -18073.564             0.019            0.017
Chain 1:   3800       -17034.865             0.023            0.021
Chain 1:   3900       -17031.080             0.022            0.021
Chain 1:   4000       -17148.332             0.022            0.021
Chain 1:   4100       -17062.246             0.022            0.021
Chain 1:   4200       -16878.789             0.022            0.021
Chain 1:   4300       -17016.936             0.022            0.021
Chain 1:   4400       -16974.027             0.019            0.011
Chain 1:   4500       -16876.640             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00134 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12486.686             1.000            1.000
Chain 1:    200        -9077.238             0.688            1.000
Chain 1:    300        -7968.520             0.505            0.376
Chain 1:    400        -8089.227             0.382            0.376
Chain 1:    500        -8046.938             0.307            0.139
Chain 1:    600        -7886.360             0.259            0.139
Chain 1:    700        -7827.222             0.223            0.020
Chain 1:    800        -7845.322             0.196            0.020
Chain 1:    900        -7780.133             0.175            0.015
Chain 1:   1000        -7895.746             0.159            0.015
Chain 1:   1100        -7975.229             0.060            0.015
Chain 1:   1200        -7845.655             0.024            0.015
Chain 1:   1300        -7870.018             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55435.307             1.000            1.000
Chain 1:    200       -17065.136             1.624            2.248
Chain 1:    300        -8614.142             1.410            1.000
Chain 1:    400        -8175.385             1.071            1.000
Chain 1:    500        -7867.433             0.864            0.981
Chain 1:    600        -8888.913             0.740            0.981
Chain 1:    700        -8086.341             0.648            0.115
Chain 1:    800        -7786.942             0.572            0.115
Chain 1:    900        -7879.785             0.510            0.099
Chain 1:   1000        -7674.468             0.461            0.099
Chain 1:   1100        -7619.164             0.362            0.054
Chain 1:   1200        -7500.993             0.139            0.039
Chain 1:   1300        -7571.641             0.042            0.038
Chain 1:   1400        -7886.452             0.040            0.038
Chain 1:   1500        -7549.039             0.041            0.038
Chain 1:   1600        -7707.031             0.031            0.027
Chain 1:   1700        -7440.642             0.025            0.027
Chain 1:   1800        -7518.053             0.022            0.020
Chain 1:   1900        -7520.511             0.021            0.020
Chain 1:   2000        -7547.716             0.019            0.016
Chain 1:   2100        -7537.452             0.018            0.016
Chain 1:   2200        -7629.924             0.018            0.012
Chain 1:   2300        -7512.384             0.018            0.016
Chain 1:   2400        -7578.352             0.015            0.012
Chain 1:   2500        -7514.276             0.012            0.010
Chain 1:   2600        -7479.079             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002903 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86304.057             1.000            1.000
Chain 1:    200       -13427.984             3.214            5.427
Chain 1:    300        -9803.965             2.266            1.000
Chain 1:    400       -10549.417             1.717            1.000
Chain 1:    500        -8780.411             1.414            0.370
Chain 1:    600        -8261.214             1.189            0.370
Chain 1:    700        -8661.915             1.025            0.201
Chain 1:    800        -9323.948             0.906            0.201
Chain 1:    900        -8633.700             0.814            0.080
Chain 1:   1000        -8372.068             0.736            0.080
Chain 1:   1100        -8640.064             0.639            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8262.436             0.101            0.071
Chain 1:   1300        -8438.410             0.066            0.063
Chain 1:   1400        -8489.864             0.060            0.046
Chain 1:   1500        -8367.411             0.041            0.046
Chain 1:   1600        -8475.431             0.036            0.031
Chain 1:   1700        -8568.422             0.032            0.031
Chain 1:   1800        -8154.798             0.030            0.031
Chain 1:   1900        -8250.869             0.024            0.021
Chain 1:   2000        -8224.143             0.021            0.015
Chain 1:   2100        -8346.489             0.019            0.015
Chain 1:   2200        -8166.282             0.017            0.015
Chain 1:   2300        -8246.046             0.016            0.013
Chain 1:   2400        -8315.652             0.016            0.013
Chain 1:   2500        -8260.958             0.015            0.012
Chain 1:   2600        -8260.254             0.014            0.011
Chain 1:   2700        -8177.572             0.014            0.010
Chain 1:   2800        -8141.477             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418111.810             1.000            1.000
Chain 1:    200     -1588390.192             2.650            4.300
Chain 1:    300      -890727.042             2.028            1.000
Chain 1:    400      -456968.850             1.758            1.000
Chain 1:    500      -356637.230             1.463            0.949
Chain 1:    600      -231743.811             1.309            0.949
Chain 1:    700      -118562.514             1.258            0.949
Chain 1:    800       -85897.131             1.148            0.949
Chain 1:    900       -66368.680             1.054            0.783
Chain 1:   1000       -51265.435             0.978            0.783
Chain 1:   1100       -38831.163             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38022.196             0.482            0.380
Chain 1:   1300       -26068.790             0.449            0.380
Chain 1:   1400       -25797.771             0.355            0.320
Chain 1:   1500       -22407.199             0.342            0.320
Chain 1:   1600       -21630.524             0.292            0.295
Chain 1:   1700       -20515.064             0.202            0.294
Chain 1:   1800       -20461.941             0.164            0.151
Chain 1:   1900       -20788.025             0.137            0.054
Chain 1:   2000       -19304.777             0.115            0.054
Chain 1:   2100       -19543.038             0.084            0.036
Chain 1:   2200       -19768.377             0.083            0.036
Chain 1:   2300       -19386.583             0.039            0.020
Chain 1:   2400       -19158.773             0.039            0.020
Chain 1:   2500       -18960.325             0.025            0.016
Chain 1:   2600       -18591.038             0.023            0.016
Chain 1:   2700       -18548.284             0.018            0.012
Chain 1:   2800       -18264.855             0.020            0.016
Chain 1:   2900       -18546.019             0.020            0.015
Chain 1:   3000       -18532.380             0.012            0.012
Chain 1:   3100       -18617.253             0.011            0.012
Chain 1:   3200       -18308.143             0.012            0.015
Chain 1:   3300       -18512.764             0.011            0.012
Chain 1:   3400       -17987.747             0.013            0.015
Chain 1:   3500       -18599.310             0.015            0.016
Chain 1:   3600       -17906.460             0.017            0.016
Chain 1:   3700       -18292.760             0.019            0.017
Chain 1:   3800       -17253.029             0.023            0.021
Chain 1:   3900       -17249.125             0.022            0.021
Chain 1:   4000       -17366.518             0.022            0.021
Chain 1:   4100       -17280.177             0.022            0.021
Chain 1:   4200       -17096.636             0.022            0.021
Chain 1:   4300       -17234.952             0.021            0.021
Chain 1:   4400       -17191.882             0.019            0.011
Chain 1:   4500       -17094.370             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001332 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48684.148             1.000            1.000
Chain 1:    200       -20411.180             1.193            1.385
Chain 1:    300       -13521.784             0.965            1.000
Chain 1:    400       -13442.167             0.725            1.000
Chain 1:    500       -14669.392             0.597            0.510
Chain 1:    600       -13566.836             0.511            0.510
Chain 1:    700       -14694.591             0.449            0.084
Chain 1:    800       -14034.120             0.399            0.084
Chain 1:    900       -11215.006             0.382            0.084
Chain 1:   1000       -11297.898             0.345            0.084
Chain 1:   1100       -16441.278             0.276            0.084
Chain 1:   1200       -12584.626             0.168            0.084
Chain 1:   1300       -11349.367             0.128            0.084
Chain 1:   1400       -18080.982             0.165            0.109
Chain 1:   1500       -10534.016             0.228            0.251
Chain 1:   1600       -10224.758             0.223            0.251
Chain 1:   1700       -19231.532             0.262            0.306
Chain 1:   1800       -18283.999             0.263            0.306
Chain 1:   1900        -9181.224             0.337            0.313
Chain 1:   2000       -16314.090             0.380            0.372
Chain 1:   2100        -9282.582             0.424            0.437
Chain 1:   2200       -10018.858             0.401            0.437
Chain 1:   2300        -9144.862             0.399            0.437
Chain 1:   2400        -8946.214             0.364            0.437
Chain 1:   2500       -14232.769             0.330            0.371
Chain 1:   2600        -9921.548             0.370            0.435
Chain 1:   2700        -8851.964             0.336            0.371
Chain 1:   2800        -9610.691             0.338            0.371
Chain 1:   2900        -9076.026             0.245            0.121
Chain 1:   3000        -8698.275             0.206            0.096
Chain 1:   3100       -11865.097             0.157            0.096
Chain 1:   3200       -14180.456             0.166            0.121
Chain 1:   3300       -14300.101             0.157            0.121
Chain 1:   3400       -14094.252             0.156            0.121
Chain 1:   3500        -9355.370             0.170            0.121
Chain 1:   3600        -9409.509             0.127            0.079
Chain 1:   3700       -11469.825             0.133            0.079
Chain 1:   3800       -13720.811             0.141            0.163
Chain 1:   3900        -9027.145             0.187            0.164
Chain 1:   4000        -8402.341             0.190            0.164
Chain 1:   4100        -9084.941             0.171            0.163
Chain 1:   4200       -10397.312             0.167            0.126
Chain 1:   4300        -9619.890             0.175            0.126
Chain 1:   4400        -8867.265             0.182            0.126
Chain 1:   4500        -8713.776             0.133            0.085
Chain 1:   4600       -10873.563             0.152            0.126
Chain 1:   4700       -13308.698             0.152            0.126
Chain 1:   4800        -8700.445             0.189            0.126
Chain 1:   4900       -14565.466             0.177            0.126
Chain 1:   5000       -14890.854             0.172            0.126
Chain 1:   5100        -8311.999             0.244            0.183
Chain 1:   5200        -9106.287             0.240            0.183
Chain 1:   5300        -8399.753             0.240            0.183
Chain 1:   5400       -13235.360             0.268            0.199
Chain 1:   5500        -8597.732             0.320            0.365
Chain 1:   5600        -8969.595             0.305            0.365
Chain 1:   5700       -12726.119             0.316            0.365
Chain 1:   5800        -9034.943             0.304            0.365
Chain 1:   5900       -11304.991             0.284            0.295
Chain 1:   6000        -9806.532             0.297            0.295
Chain 1:   6100        -8347.685             0.235            0.201
Chain 1:   6200       -12680.473             0.260            0.295
Chain 1:   6300        -9557.597             0.285            0.327
Chain 1:   6400       -13599.897             0.278            0.297
Chain 1:   6500        -8739.501             0.280            0.297
Chain 1:   6600        -8319.645             0.280            0.297
Chain 1:   6700        -8308.269             0.251            0.297
Chain 1:   6800        -8143.616             0.212            0.201
Chain 1:   6900        -8153.815             0.192            0.175
Chain 1:   7000        -8486.895             0.181            0.175
Chain 1:   7100        -9752.786             0.176            0.130
Chain 1:   7200       -10436.944             0.149            0.066
Chain 1:   7300        -8804.240             0.135            0.066
Chain 1:   7400       -13235.033             0.138            0.066
Chain 1:   7500        -8590.416             0.137            0.066
Chain 1:   7600        -8183.240             0.137            0.066
Chain 1:   7700        -8188.657             0.137            0.066
Chain 1:   7800        -8932.632             0.143            0.083
Chain 1:   7900        -8085.107             0.153            0.105
Chain 1:   8000        -9244.929             0.162            0.125
Chain 1:   8100        -7979.398             0.165            0.125
Chain 1:   8200       -10701.872             0.184            0.159
Chain 1:   8300        -8791.572             0.187            0.159
Chain 1:   8400        -8723.605             0.154            0.125
Chain 1:   8500        -8037.549             0.109            0.105
Chain 1:   8600        -8364.152             0.108            0.105
Chain 1:   8700        -8347.659             0.108            0.105
Chain 1:   8800       -10377.195             0.119            0.125
Chain 1:   8900       -11630.743             0.119            0.125
Chain 1:   9000       -12074.487             0.110            0.108
Chain 1:   9100       -12175.323             0.095            0.085
Chain 1:   9200        -8062.754             0.121            0.085
Chain 1:   9300        -8227.760             0.101            0.039
Chain 1:   9400        -8443.853             0.103            0.039
Chain 1:   9500       -10358.517             0.113            0.039
Chain 1:   9600        -9356.914             0.120            0.107
Chain 1:   9700        -8274.408             0.133            0.108
Chain 1:   9800        -8462.015             0.115            0.107
Chain 1:   9900        -9007.863             0.111            0.061
Chain 1:   10000        -9732.356             0.114            0.074
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61490.162             1.000            1.000
Chain 1:    200       -17536.764             1.753            2.506
Chain 1:    300        -8686.536             1.508            1.019
Chain 1:    400        -8952.428             1.139            1.019
Chain 1:    500        -7753.519             0.942            1.000
Chain 1:    600        -8386.067             0.797            1.000
Chain 1:    700        -8241.638             0.686            0.155
Chain 1:    800        -8081.641             0.603            0.155
Chain 1:    900        -7790.803             0.540            0.075
Chain 1:   1000        -7769.135             0.486            0.075
Chain 1:   1100        -7818.804             0.387            0.037
Chain 1:   1200        -7528.978             0.140            0.037
Chain 1:   1300        -7716.329             0.041            0.030
Chain 1:   1400        -7860.320             0.039            0.024
Chain 1:   1500        -7607.249             0.027            0.024
Chain 1:   1600        -7503.371             0.021            0.020
Chain 1:   1700        -7488.570             0.020            0.020
Chain 1:   1800        -7530.539             0.018            0.018
Chain 1:   1900        -7575.411             0.015            0.014
Chain 1:   2000        -7567.533             0.015            0.014
Chain 1:   2100        -7581.782             0.014            0.014
Chain 1:   2200        -7656.550             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002555 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85181.582             1.000            1.000
Chain 1:    200       -13128.831             3.244            5.488
Chain 1:    300        -9590.950             2.286            1.000
Chain 1:    400       -10465.806             1.735            1.000
Chain 1:    500        -8515.145             1.434            0.369
Chain 1:    600        -8152.149             1.202            0.369
Chain 1:    700        -8395.137             1.035            0.229
Chain 1:    800        -8794.598             0.911            0.229
Chain 1:    900        -8431.523             0.815            0.084
Chain 1:   1000        -8212.368             0.736            0.084
Chain 1:   1100        -8459.628             0.639            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8125.893             0.094            0.045
Chain 1:   1300        -8342.621             0.060            0.043
Chain 1:   1400        -8330.475             0.052            0.041
Chain 1:   1500        -8228.096             0.030            0.029
Chain 1:   1600        -8322.291             0.027            0.029
Chain 1:   1700        -8416.944             0.025            0.027
Chain 1:   1800        -8030.506             0.025            0.027
Chain 1:   1900        -8132.900             0.022            0.026
Chain 1:   2000        -8102.686             0.020            0.013
Chain 1:   2100        -8238.164             0.018            0.013
Chain 1:   2200        -8021.897             0.017            0.013
Chain 1:   2300        -8163.211             0.016            0.013
Chain 1:   2400        -8173.601             0.016            0.013
Chain 1:   2500        -8141.981             0.015            0.013
Chain 1:   2600        -8139.714             0.014            0.013
Chain 1:   2700        -8049.267             0.014            0.013
Chain 1:   2800        -8027.811             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003449 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402745.249             1.000            1.000
Chain 1:    200     -1584653.257             2.651            4.303
Chain 1:    300      -890796.376             2.027            1.000
Chain 1:    400      -457307.647             1.757            1.000
Chain 1:    500      -357522.332             1.462            0.948
Chain 1:    600      -232610.839             1.308            0.948
Chain 1:    700      -118835.764             1.258            0.948
Chain 1:    800       -86038.994             1.148            0.948
Chain 1:    900       -66384.951             1.053            0.779
Chain 1:   1000       -51177.360             0.978            0.779
Chain 1:   1100       -38653.045             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37824.367             0.482            0.381
Chain 1:   1300       -25789.002             0.451            0.381
Chain 1:   1400       -25506.340             0.357            0.324
Chain 1:   1500       -22096.169             0.345            0.324
Chain 1:   1600       -21312.423             0.295            0.297
Chain 1:   1700       -20187.948             0.204            0.296
Chain 1:   1800       -20132.190             0.167            0.154
Chain 1:   1900       -20457.716             0.139            0.056
Chain 1:   2000       -18970.876             0.117            0.056
Chain 1:   2100       -19209.042             0.086            0.037
Chain 1:   2200       -19434.976             0.085            0.037
Chain 1:   2300       -19052.844             0.040            0.020
Chain 1:   2400       -18825.186             0.040            0.020
Chain 1:   2500       -18627.168             0.026            0.016
Chain 1:   2600       -18257.969             0.024            0.016
Chain 1:   2700       -18215.152             0.019            0.012
Chain 1:   2800       -17932.238             0.020            0.016
Chain 1:   2900       -18213.213             0.020            0.015
Chain 1:   3000       -18199.503             0.012            0.012
Chain 1:   3100       -18284.361             0.011            0.012
Chain 1:   3200       -17975.439             0.012            0.015
Chain 1:   3300       -18179.859             0.011            0.012
Chain 1:   3400       -17655.461             0.013            0.015
Chain 1:   3500       -18266.259             0.015            0.016
Chain 1:   3600       -17574.412             0.017            0.016
Chain 1:   3700       -17960.091             0.019            0.017
Chain 1:   3800       -16922.016             0.023            0.021
Chain 1:   3900       -16918.232             0.022            0.021
Chain 1:   4000       -17035.538             0.023            0.021
Chain 1:   4100       -16949.372             0.023            0.021
Chain 1:   4200       -16766.152             0.022            0.021
Chain 1:   4300       -16904.176             0.022            0.021
Chain 1:   4400       -16861.389             0.019            0.011
Chain 1:   4500       -16764.027             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003446 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49363.255             1.000            1.000
Chain 1:    200       -16500.164             1.496            1.992
Chain 1:    300       -21316.688             1.073            1.000
Chain 1:    400       -29100.025             0.871            1.000
Chain 1:    500       -15283.485             0.878            0.904
Chain 1:    600       -15924.908             0.738            0.904
Chain 1:    700       -13350.631             0.660            0.267
Chain 1:    800       -15312.649             0.594            0.267
Chain 1:    900       -11040.510             0.571            0.267
Chain 1:   1000       -12940.639             0.528            0.267
Chain 1:   1100       -12088.933             0.435            0.226
Chain 1:   1200       -12251.162             0.238            0.193
Chain 1:   1300       -14817.827             0.232            0.173
Chain 1:   1400       -11024.605             0.240            0.173
Chain 1:   1500       -10401.370             0.156            0.147
Chain 1:   1600       -12463.644             0.168            0.165
Chain 1:   1700       -10358.346             0.169            0.165
Chain 1:   1800       -13021.455             0.177            0.173
Chain 1:   1900        -9864.957             0.170            0.173
Chain 1:   2000       -10181.602             0.159            0.173
Chain 1:   2100        -9555.925             0.158            0.173
Chain 1:   2200       -11353.877             0.173            0.173
Chain 1:   2300        -9507.413             0.175            0.194
Chain 1:   2400       -10579.781             0.150            0.165
Chain 1:   2500       -15282.426             0.175            0.194
Chain 1:   2600       -10394.880             0.206            0.203
Chain 1:   2700        -9939.616             0.190            0.194
Chain 1:   2800       -10837.574             0.178            0.158
Chain 1:   2900        -9691.799             0.158            0.118
Chain 1:   3000       -11797.372             0.172            0.158
Chain 1:   3100       -10135.094             0.182            0.164
Chain 1:   3200       -11216.835             0.176            0.164
Chain 1:   3300       -10643.862             0.162            0.118
Chain 1:   3400       -10401.833             0.154            0.118
Chain 1:   3500       -10607.871             0.125            0.096
Chain 1:   3600       -17667.987             0.118            0.096
Chain 1:   3700        -9836.241             0.193            0.118
Chain 1:   3800        -9228.366             0.192            0.118
Chain 1:   3900       -12403.980             0.205            0.164
Chain 1:   4000       -17053.725             0.215            0.164
Chain 1:   4100       -10374.428             0.263            0.256
Chain 1:   4200       -13458.209             0.276            0.256
Chain 1:   4300        -9904.930             0.306            0.273
Chain 1:   4400        -9251.915             0.311            0.273
Chain 1:   4500       -12480.861             0.335            0.273
Chain 1:   4600        -8953.397             0.335            0.273
Chain 1:   4700       -11792.995             0.279            0.259
Chain 1:   4800        -9279.685             0.300            0.271
Chain 1:   4900       -12038.179             0.297            0.271
Chain 1:   5000       -14870.066             0.289            0.259
Chain 1:   5100        -9308.403             0.284            0.259
Chain 1:   5200        -8942.618             0.265            0.259
Chain 1:   5300       -10587.995             0.245            0.241
Chain 1:   5400        -8732.847             0.259            0.241
Chain 1:   5500        -8713.097             0.233            0.229
Chain 1:   5600        -8669.364             0.194            0.212
Chain 1:   5700        -8975.746             0.174            0.190
Chain 1:   5800        -8920.643             0.147            0.155
Chain 1:   5900       -10046.775             0.136            0.112
Chain 1:   6000        -9278.091             0.125            0.083
Chain 1:   6100        -8684.743             0.072            0.068
Chain 1:   6200        -8803.501             0.069            0.068
Chain 1:   6300       -12181.931             0.081            0.068
Chain 1:   6400       -13386.881             0.069            0.068
Chain 1:   6500       -10107.893             0.101            0.083
Chain 1:   6600       -14498.569             0.131            0.090
Chain 1:   6700        -8863.891             0.191            0.112
Chain 1:   6800       -11581.326             0.214            0.235
Chain 1:   6900        -8609.814             0.237            0.277
Chain 1:   7000       -10730.147             0.249            0.277
Chain 1:   7100        -8442.722             0.269            0.277
Chain 1:   7200        -8529.405             0.269            0.277
Chain 1:   7300       -14248.533             0.281            0.303
Chain 1:   7400       -10570.459             0.307            0.324
Chain 1:   7500       -10248.986             0.278            0.303
Chain 1:   7600        -9055.272             0.261            0.271
Chain 1:   7700       -10871.688             0.214            0.235
Chain 1:   7800       -11180.034             0.193            0.198
Chain 1:   7900        -8475.380             0.191            0.198
Chain 1:   8000        -9617.513             0.183            0.167
Chain 1:   8100        -8459.365             0.169            0.137
Chain 1:   8200        -9350.897             0.178            0.137
Chain 1:   8300        -8486.507             0.148            0.132
Chain 1:   8400        -9125.862             0.120            0.119
Chain 1:   8500        -9901.815             0.125            0.119
Chain 1:   8600       -13442.457             0.138            0.119
Chain 1:   8700        -8648.283             0.177            0.119
Chain 1:   8800        -8625.281             0.174            0.119
Chain 1:   8900       -11597.627             0.168            0.119
Chain 1:   9000        -9314.073             0.180            0.137
Chain 1:   9100        -9939.825             0.173            0.102
Chain 1:   9200       -10218.002             0.166            0.102
Chain 1:   9300        -8601.470             0.175            0.188
Chain 1:   9400       -12605.583             0.200            0.245
Chain 1:   9500        -8440.953             0.241            0.256
Chain 1:   9600       -11020.456             0.238            0.245
Chain 1:   9700       -10957.523             0.183            0.234
Chain 1:   9800       -11236.520             0.186            0.234
Chain 1:   9900        -8483.212             0.192            0.234
Chain 1:   10000        -8808.237             0.172            0.188
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63544.957             1.000            1.000
Chain 1:    200       -18484.735             1.719            2.438
Chain 1:    300        -8930.797             1.502            1.070
Chain 1:    400        -9221.750             1.135            1.070
Chain 1:    500        -8190.050             0.933            1.000
Chain 1:    600        -8530.952             0.784            1.000
Chain 1:    700        -8172.546             0.678            0.126
Chain 1:    800        -8333.433             0.596            0.126
Chain 1:    900        -7776.382             0.538            0.072
Chain 1:   1000        -8131.792             0.488            0.072
Chain 1:   1100        -7729.481             0.394            0.052
Chain 1:   1200        -7618.139             0.151            0.044
Chain 1:   1300        -7822.322             0.047            0.044
Chain 1:   1400        -7957.126             0.045            0.044
Chain 1:   1500        -7642.329             0.037            0.041
Chain 1:   1600        -7717.423             0.034            0.041
Chain 1:   1700        -7570.704             0.031            0.026
Chain 1:   1800        -7709.595             0.031            0.026
Chain 1:   1900        -7595.053             0.026            0.019
Chain 1:   2000        -7671.106             0.022            0.018
Chain 1:   2100        -7619.811             0.018            0.017
Chain 1:   2200        -7738.983             0.018            0.017
Chain 1:   2300        -7603.227             0.017            0.017
Chain 1:   2400        -7665.781             0.016            0.015
Chain 1:   2500        -7662.326             0.012            0.015
Chain 1:   2600        -7562.350             0.012            0.015
Chain 1:   2700        -7567.531             0.011            0.013
Chain 1:   2800        -7595.459             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00262 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86465.242             1.000            1.000
Chain 1:    200       -13776.194             3.138            5.276
Chain 1:    300       -10081.489             2.214            1.000
Chain 1:    400       -11149.370             1.685            1.000
Chain 1:    500        -9064.764             1.394            0.366
Chain 1:    600        -8572.557             1.171            0.366
Chain 1:    700        -8717.243             1.006            0.230
Chain 1:    800        -9115.448             0.886            0.230
Chain 1:    900        -8953.815             0.789            0.096
Chain 1:   1000        -8998.695             0.711            0.096
Chain 1:   1100        -8717.097             0.614            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8472.584             0.089            0.044
Chain 1:   1300        -8754.296             0.056            0.032
Chain 1:   1400        -8715.867             0.047            0.032
Chain 1:   1500        -8608.280             0.025            0.029
Chain 1:   1600        -8714.905             0.021            0.018
Chain 1:   1700        -8787.835             0.020            0.018
Chain 1:   1800        -8355.335             0.021            0.018
Chain 1:   1900        -8459.535             0.020            0.012
Chain 1:   2000        -8434.919             0.020            0.012
Chain 1:   2100        -8413.828             0.017            0.012
Chain 1:   2200        -8378.177             0.014            0.012
Chain 1:   2300        -8506.407             0.013            0.012
Chain 1:   2400        -8362.083             0.014            0.012
Chain 1:   2500        -8430.597             0.013            0.012
Chain 1:   2600        -8350.176             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002517 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416667.328             1.000            1.000
Chain 1:    200     -1582445.503             2.659            4.319
Chain 1:    300      -889742.444             2.032            1.000
Chain 1:    400      -457578.155             1.760            1.000
Chain 1:    500      -357954.690             1.464            0.944
Chain 1:    600      -233093.747             1.309            0.944
Chain 1:    700      -119432.244             1.258            0.944
Chain 1:    800       -86691.680             1.148            0.944
Chain 1:    900       -67045.770             1.053            0.779
Chain 1:   1000       -51858.309             0.977            0.779
Chain 1:   1100       -39349.448             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38528.278             0.479            0.378
Chain 1:   1300       -26488.071             0.447            0.378
Chain 1:   1400       -26209.677             0.353            0.318
Chain 1:   1500       -22798.023             0.340            0.318
Chain 1:   1600       -22015.831             0.290            0.293
Chain 1:   1700       -20889.167             0.201            0.293
Chain 1:   1800       -20833.623             0.163            0.150
Chain 1:   1900       -21160.095             0.135            0.054
Chain 1:   2000       -19670.715             0.114            0.054
Chain 1:   2100       -19908.980             0.083            0.036
Chain 1:   2200       -20135.822             0.082            0.036
Chain 1:   2300       -19752.656             0.039            0.019
Chain 1:   2400       -19524.603             0.039            0.019
Chain 1:   2500       -19326.745             0.025            0.015
Chain 1:   2600       -18956.477             0.023            0.015
Chain 1:   2700       -18913.341             0.018            0.012
Chain 1:   2800       -18630.100             0.019            0.015
Chain 1:   2900       -18911.498             0.019            0.015
Chain 1:   3000       -18897.630             0.012            0.012
Chain 1:   3100       -18982.676             0.011            0.012
Chain 1:   3200       -18673.128             0.012            0.015
Chain 1:   3300       -18878.040             0.011            0.012
Chain 1:   3400       -18352.584             0.012            0.015
Chain 1:   3500       -18965.052             0.015            0.015
Chain 1:   3600       -18270.942             0.016            0.015
Chain 1:   3700       -18658.328             0.018            0.017
Chain 1:   3800       -17616.855             0.023            0.021
Chain 1:   3900       -17612.981             0.021            0.021
Chain 1:   4000       -17730.277             0.022            0.021
Chain 1:   4100       -17643.986             0.022            0.021
Chain 1:   4200       -17459.968             0.021            0.021
Chain 1:   4300       -17598.541             0.021            0.021
Chain 1:   4400       -17555.138             0.018            0.011
Chain 1:   4500       -17457.641             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49464.089             1.000            1.000
Chain 1:    200       -16505.990             1.498            1.997
Chain 1:    300       -25212.273             1.114            1.000
Chain 1:    400       -19062.743             0.916            1.000
Chain 1:    500       -15598.358             0.777            0.345
Chain 1:    600       -13302.465             0.677            0.345
Chain 1:    700       -15165.666             0.597            0.323
Chain 1:    800       -14104.268             0.532            0.323
Chain 1:    900       -12420.419             0.488            0.222
Chain 1:   1000       -10949.604             0.453            0.222
Chain 1:   1100       -22162.264             0.403            0.222
Chain 1:   1200       -11352.827             0.299            0.222
Chain 1:   1300       -13274.681             0.279            0.173
Chain 1:   1400       -16017.552             0.264            0.171
Chain 1:   1500       -11496.843             0.281            0.171
Chain 1:   1600       -13311.830             0.277            0.145
Chain 1:   1700       -10844.399             0.288            0.171
Chain 1:   1800       -12393.178             0.293            0.171
Chain 1:   1900       -10893.798             0.293            0.171
Chain 1:   2000       -19084.004             0.322            0.228
Chain 1:   2100       -12707.336             0.322            0.228
Chain 1:   2200       -10714.188             0.245            0.186
Chain 1:   2300       -10607.906             0.232            0.186
Chain 1:   2400       -11533.082             0.223            0.186
Chain 1:   2500       -10792.862             0.190            0.138
Chain 1:   2600       -10126.882             0.183            0.138
Chain 1:   2700        -9554.148             0.166            0.125
Chain 1:   2800       -10790.382             0.165            0.115
Chain 1:   2900       -11803.389             0.160            0.086
Chain 1:   3000       -15190.300             0.140            0.086
Chain 1:   3100       -10068.935             0.140            0.086
Chain 1:   3200       -14852.410             0.154            0.086
Chain 1:   3300       -10028.866             0.201            0.115
Chain 1:   3400       -13780.235             0.220            0.223
Chain 1:   3500        -9795.899             0.254            0.272
Chain 1:   3600        -9460.216             0.251            0.272
Chain 1:   3700       -10153.933             0.252            0.272
Chain 1:   3800       -10371.479             0.242            0.272
Chain 1:   3900       -12349.721             0.250            0.272
Chain 1:   4000       -13051.846             0.233            0.272
Chain 1:   4100        -9077.520             0.226            0.272
Chain 1:   4200        -9697.869             0.200            0.160
Chain 1:   4300       -16134.920             0.192            0.160
Chain 1:   4400        -9917.384             0.227            0.160
Chain 1:   4500       -12321.270             0.206            0.160
Chain 1:   4600        -9246.895             0.236            0.195
Chain 1:   4700       -13510.791             0.261            0.316
Chain 1:   4800        -9155.714             0.306            0.332
Chain 1:   4900        -9227.303             0.291            0.332
Chain 1:   5000       -15176.524             0.325            0.392
Chain 1:   5100        -8995.582             0.350            0.392
Chain 1:   5200        -9068.483             0.344            0.392
Chain 1:   5300        -9635.030             0.310            0.332
Chain 1:   5400        -8880.651             0.256            0.316
Chain 1:   5500        -9318.011             0.241            0.316
Chain 1:   5600        -9294.034             0.208            0.085
Chain 1:   5700        -9072.981             0.179            0.059
Chain 1:   5800        -9058.339             0.131            0.047
Chain 1:   5900        -9075.191             0.131            0.047
Chain 1:   6000       -11986.863             0.116            0.047
Chain 1:   6100        -9319.299             0.076            0.047
Chain 1:   6200       -16919.580             0.120            0.059
Chain 1:   6300       -10031.166             0.183            0.085
Chain 1:   6400       -10391.506             0.178            0.047
Chain 1:   6500       -15216.197             0.205            0.243
Chain 1:   6600        -9014.078             0.273            0.286
Chain 1:   6700        -8785.973             0.273            0.286
Chain 1:   6800        -9368.210             0.279            0.286
Chain 1:   6900       -11763.945             0.300            0.286
Chain 1:   7000       -10246.752             0.290            0.286
Chain 1:   7100        -8632.267             0.280            0.204
Chain 1:   7200        -8691.371             0.236            0.187
Chain 1:   7300       -11552.207             0.192            0.187
Chain 1:   7400       -12826.060             0.199            0.187
Chain 1:   7500        -9047.608             0.209            0.187
Chain 1:   7600        -9458.296             0.144            0.148
Chain 1:   7700        -9820.908             0.145            0.148
Chain 1:   7800       -11919.526             0.157            0.176
Chain 1:   7900        -9290.947             0.165            0.176
Chain 1:   8000        -9158.835             0.151            0.176
Chain 1:   8100        -8845.825             0.136            0.099
Chain 1:   8200       -11025.322             0.155            0.176
Chain 1:   8300        -8725.733             0.157            0.176
Chain 1:   8400       -14086.426             0.185            0.198
Chain 1:   8500        -9129.411             0.197            0.198
Chain 1:   8600       -12168.525             0.218            0.250
Chain 1:   8700        -8917.771             0.251            0.264
Chain 1:   8800        -8633.104             0.236            0.264
Chain 1:   8900        -9478.462             0.217            0.250
Chain 1:   9000        -9634.790             0.217            0.250
Chain 1:   9100        -9685.284             0.214            0.250
Chain 1:   9200        -9074.964             0.201            0.250
Chain 1:   9300       -12167.696             0.200            0.250
Chain 1:   9400       -10011.228             0.184            0.215
Chain 1:   9500        -8477.336             0.148            0.181
Chain 1:   9600        -9145.705             0.130            0.089
Chain 1:   9700        -9318.093             0.095            0.073
Chain 1:   9800        -9234.570             0.093            0.073
Chain 1:   9900       -10106.957             0.093            0.073
Chain 1:   10000       -12671.170             0.111            0.086
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001506 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58473.607             1.000            1.000
Chain 1:    200       -18226.131             1.604            2.208
Chain 1:    300        -8931.451             1.416            1.041
Chain 1:    400        -8150.662             1.086            1.041
Chain 1:    500        -9224.920             0.892            1.000
Chain 1:    600        -8819.011             0.751            1.000
Chain 1:    700        -8519.915             0.649            0.116
Chain 1:    800        -8482.489             0.568            0.116
Chain 1:    900        -7824.074             0.515            0.096
Chain 1:   1000        -8169.379             0.467            0.096
Chain 1:   1100        -7751.096             0.373            0.084
Chain 1:   1200        -7705.125             0.152            0.054
Chain 1:   1300        -7717.661             0.049            0.046
Chain 1:   1400        -7892.420             0.041            0.042
Chain 1:   1500        -7653.198             0.033            0.035
Chain 1:   1600        -7640.131             0.028            0.031
Chain 1:   1700        -7649.815             0.025            0.022
Chain 1:   1800        -7785.612             0.026            0.022
Chain 1:   1900        -7610.150             0.020            0.022
Chain 1:   2000        -7644.875             0.016            0.017
Chain 1:   2100        -7583.398             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00257 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86481.875             1.000            1.000
Chain 1:    200       -14015.385             3.085            5.170
Chain 1:    300       -10336.746             2.175            1.000
Chain 1:    400       -11177.721             1.650            1.000
Chain 1:    500        -9166.008             1.364            0.356
Chain 1:    600        -8743.465             1.145            0.356
Chain 1:    700        -9109.589             0.987            0.219
Chain 1:    800        -9297.325             0.866            0.219
Chain 1:    900        -9070.327             0.773            0.075
Chain 1:   1000        -9032.090             0.696            0.075
Chain 1:   1100        -8980.900             0.596            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8752.416             0.082            0.040
Chain 1:   1300        -9021.547             0.049            0.030
Chain 1:   1400        -8987.129             0.042            0.026
Chain 1:   1500        -8876.825             0.022            0.025
Chain 1:   1600        -8984.361             0.018            0.020
Chain 1:   1700        -9063.486             0.015            0.012
Chain 1:   1800        -8636.042             0.018            0.012
Chain 1:   1900        -8738.915             0.016            0.012
Chain 1:   2000        -8713.845             0.016            0.012
Chain 1:   2100        -8841.255             0.017            0.012
Chain 1:   2200        -8639.760             0.017            0.012
Chain 1:   2300        -8734.395             0.015            0.012
Chain 1:   2400        -8802.065             0.015            0.012
Chain 1:   2500        -8748.270             0.015            0.012
Chain 1:   2600        -8750.900             0.014            0.011
Chain 1:   2700        -8666.968             0.014            0.011
Chain 1:   2800        -8625.349             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002752 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386467.698             1.000            1.000
Chain 1:    200     -1583637.767             2.648            4.296
Chain 1:    300      -891581.464             2.024            1.000
Chain 1:    400      -458702.372             1.754            1.000
Chain 1:    500      -359205.716             1.459            0.944
Chain 1:    600      -234098.363             1.305            0.944
Chain 1:    700      -120064.752             1.254            0.944
Chain 1:    800       -87171.499             1.144            0.944
Chain 1:    900       -67463.590             1.050            0.776
Chain 1:   1000       -52218.187             0.974            0.776
Chain 1:   1100       -39652.656             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38826.485             0.478            0.377
Chain 1:   1300       -26741.141             0.446            0.377
Chain 1:   1400       -26456.973             0.352            0.317
Chain 1:   1500       -23032.517             0.340            0.317
Chain 1:   1600       -22245.532             0.290            0.292
Chain 1:   1700       -21114.306             0.200            0.292
Chain 1:   1800       -21057.368             0.163            0.149
Chain 1:   1900       -21383.723             0.135            0.054
Chain 1:   2000       -19891.689             0.113            0.054
Chain 1:   2100       -20130.376             0.083            0.035
Chain 1:   2200       -20357.292             0.082            0.035
Chain 1:   2300       -19974.004             0.038            0.019
Chain 1:   2400       -19745.962             0.038            0.019
Chain 1:   2500       -19548.068             0.025            0.015
Chain 1:   2600       -19178.042             0.023            0.015
Chain 1:   2700       -19134.915             0.018            0.012
Chain 1:   2800       -18851.699             0.019            0.015
Chain 1:   2900       -19133.123             0.019            0.015
Chain 1:   3000       -19119.296             0.012            0.012
Chain 1:   3100       -19204.304             0.011            0.012
Chain 1:   3200       -18894.882             0.011            0.015
Chain 1:   3300       -19099.675             0.011            0.012
Chain 1:   3400       -18574.390             0.012            0.015
Chain 1:   3500       -19186.649             0.014            0.015
Chain 1:   3600       -18492.870             0.016            0.015
Chain 1:   3700       -18880.046             0.018            0.016
Chain 1:   3800       -17839.043             0.022            0.021
Chain 1:   3900       -17835.175             0.021            0.021
Chain 1:   4000       -17952.477             0.021            0.021
Chain 1:   4100       -17866.200             0.022            0.021
Chain 1:   4200       -17682.273             0.021            0.021
Chain 1:   4300       -17820.788             0.021            0.021
Chain 1:   4400       -17777.509             0.018            0.010
Chain 1:   4500       -17680.011             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001353 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12933.746             1.000            1.000
Chain 1:    200        -9867.847             0.655            1.000
Chain 1:    300        -8538.027             0.489            0.311
Chain 1:    400        -8725.753             0.372            0.311
Chain 1:    500        -8725.639             0.298            0.156
Chain 1:    600        -8491.727             0.253            0.156
Chain 1:    700        -8397.208             0.218            0.028
Chain 1:    800        -8420.756             0.191            0.028
Chain 1:    900        -8529.210             0.171            0.022
Chain 1:   1000        -8436.935             0.155            0.022
Chain 1:   1100        -8470.340             0.056            0.013
Chain 1:   1200        -8435.559             0.025            0.011
Chain 1:   1300        -8353.272             0.010            0.011
Chain 1:   1400        -8377.163             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001692 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46933.900             1.000            1.000
Chain 1:    200       -16239.263             1.445            1.890
Chain 1:    300        -9019.353             1.230            1.000
Chain 1:    400        -8637.752             0.934            1.000
Chain 1:    500        -9203.629             0.759            0.800
Chain 1:    600        -8891.491             0.639            0.800
Chain 1:    700        -8430.913             0.555            0.061
Chain 1:    800        -8041.486             0.492            0.061
Chain 1:    900        -7724.678             0.442            0.055
Chain 1:   1000        -7905.180             0.400            0.055
Chain 1:   1100        -8116.387             0.302            0.048
Chain 1:   1200        -7927.067             0.116            0.044
Chain 1:   1300        -7902.219             0.036            0.041
Chain 1:   1400        -8003.198             0.033            0.035
Chain 1:   1500        -7565.542             0.033            0.035
Chain 1:   1600        -7769.015             0.032            0.026
Chain 1:   1700        -7519.589             0.030            0.026
Chain 1:   1800        -7764.944             0.028            0.026
Chain 1:   1900        -7658.071             0.025            0.026
Chain 1:   2000        -7730.200             0.024            0.026
Chain 1:   2100        -7669.471             0.022            0.024
Chain 1:   2200        -7806.765             0.021            0.018
Chain 1:   2300        -7610.561             0.024            0.026
Chain 1:   2400        -7689.018             0.023            0.026
Chain 1:   2500        -7474.567             0.020            0.026
Chain 1:   2600        -7592.343             0.019            0.018
Chain 1:   2700        -7496.800             0.017            0.016
Chain 1:   2800        -7705.729             0.017            0.016
Chain 1:   2900        -7426.860             0.019            0.018
Chain 1:   3000        -7590.749             0.020            0.022
Chain 1:   3100        -7584.631             0.020            0.022
Chain 1:   3200        -7798.089             0.021            0.026
Chain 1:   3300        -7497.531             0.022            0.027
Chain 1:   3400        -7758.709             0.025            0.027
Chain 1:   3500        -7494.497             0.025            0.027
Chain 1:   3600        -7551.756             0.024            0.027
Chain 1:   3700        -7509.544             0.024            0.027
Chain 1:   3800        -7508.834             0.021            0.027
Chain 1:   3900        -7462.295             0.018            0.022
Chain 1:   4000        -7453.324             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002609 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87239.991             1.000            1.000
Chain 1:    200       -14118.695             3.090            5.179
Chain 1:    300       -10439.158             2.177            1.000
Chain 1:    400       -11671.279             1.659            1.000
Chain 1:    500        -9380.680             1.376            0.352
Chain 1:    600        -9677.713             1.152            0.352
Chain 1:    700        -9093.363             0.997            0.244
Chain 1:    800        -9821.141             0.881            0.244
Chain 1:    900        -9296.507             0.790            0.106
Chain 1:   1000        -8836.587             0.716            0.106
Chain 1:   1100        -9201.343             0.620            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8743.345             0.107            0.064
Chain 1:   1300        -8992.592             0.075            0.056
Chain 1:   1400        -9131.711             0.066            0.052
Chain 1:   1500        -8962.551             0.043            0.052
Chain 1:   1600        -9084.905             0.041            0.052
Chain 1:   1700        -9155.400             0.036            0.040
Chain 1:   1800        -8725.940             0.033            0.040
Chain 1:   1900        -8829.603             0.029            0.028
Chain 1:   2000        -8804.739             0.024            0.019
Chain 1:   2100        -8935.335             0.021            0.015
Chain 1:   2200        -8732.004             0.018            0.015
Chain 1:   2300        -8827.163             0.017            0.015
Chain 1:   2400        -8892.888             0.016            0.013
Chain 1:   2500        -8838.444             0.015            0.012
Chain 1:   2600        -8842.138             0.013            0.011
Chain 1:   2700        -8757.633             0.014            0.011
Chain 1:   2800        -8714.974             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003173 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410707.556             1.000            1.000
Chain 1:    200     -1585191.620             2.653            4.306
Chain 1:    300      -892063.173             2.028            1.000
Chain 1:    400      -458932.076             1.757            1.000
Chain 1:    500      -359093.198             1.461            0.944
Chain 1:    600      -233885.622             1.307            0.944
Chain 1:    700      -119967.772             1.256            0.944
Chain 1:    800       -87154.043             1.146            0.944
Chain 1:    900       -67470.462             1.051            0.777
Chain 1:   1000       -52253.690             0.975            0.777
Chain 1:   1100       -39717.594             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38894.372             0.478            0.377
Chain 1:   1300       -26834.329             0.445            0.377
Chain 1:   1400       -26552.543             0.352            0.316
Chain 1:   1500       -23136.169             0.339            0.316
Chain 1:   1600       -22351.901             0.289            0.292
Chain 1:   1700       -21223.480             0.199            0.291
Chain 1:   1800       -21167.312             0.162            0.148
Chain 1:   1900       -21493.672             0.134            0.053
Chain 1:   2000       -20003.504             0.113            0.053
Chain 1:   2100       -20241.787             0.082            0.035
Chain 1:   2200       -20468.678             0.081            0.035
Chain 1:   2300       -20085.516             0.038            0.019
Chain 1:   2400       -19857.516             0.038            0.019
Chain 1:   2500       -19659.622             0.024            0.015
Chain 1:   2600       -19289.392             0.023            0.015
Chain 1:   2700       -19246.289             0.018            0.012
Chain 1:   2800       -18963.077             0.019            0.015
Chain 1:   2900       -19244.514             0.019            0.015
Chain 1:   3000       -19230.596             0.012            0.012
Chain 1:   3100       -19315.620             0.011            0.011
Chain 1:   3200       -19006.110             0.011            0.015
Chain 1:   3300       -19211.017             0.010            0.011
Chain 1:   3400       -18685.616             0.012            0.015
Chain 1:   3500       -19297.950             0.014            0.015
Chain 1:   3600       -18604.115             0.016            0.015
Chain 1:   3700       -18991.304             0.018            0.016
Chain 1:   3800       -17950.144             0.022            0.020
Chain 1:   3900       -17946.303             0.021            0.020
Chain 1:   4000       -18063.601             0.021            0.020
Chain 1:   4100       -17977.292             0.021            0.020
Chain 1:   4200       -17793.376             0.021            0.020
Chain 1:   4300       -17931.866             0.021            0.020
Chain 1:   4400       -17888.535             0.018            0.010
Chain 1:   4500       -17791.073             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12111.165             1.000            1.000
Chain 1:    200        -9084.910             0.667            1.000
Chain 1:    300        -7837.684             0.497            0.333
Chain 1:    400        -8055.804             0.380            0.333
Chain 1:    500        -7922.353             0.307            0.159
Chain 1:    600        -7783.880             0.259            0.159
Chain 1:    700        -7700.596             0.224            0.027
Chain 1:    800        -7711.186             0.196            0.027
Chain 1:    900        -7626.869             0.175            0.018
Chain 1:   1000        -7805.288             0.160            0.023
Chain 1:   1100        -7839.024             0.060            0.018
Chain 1:   1200        -7734.615             0.028            0.017
Chain 1:   1300        -7683.025             0.013            0.013
Chain 1:   1400        -7698.430             0.011            0.011
Chain 1:   1500        -7784.747             0.010            0.011
Chain 1:   1600        -7753.354             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61196.333             1.000            1.000
Chain 1:    200       -17586.842             1.740            2.480
Chain 1:    300        -8749.756             1.497            1.010
Chain 1:    400        -8216.328             1.139            1.010
Chain 1:    500        -8262.154             0.912            1.000
Chain 1:    600        -8886.937             0.772            1.000
Chain 1:    700        -8089.824             0.676            0.099
Chain 1:    800        -8084.680             0.591            0.099
Chain 1:    900        -7939.516             0.528            0.070
Chain 1:   1000        -7738.711             0.477            0.070
Chain 1:   1100        -7796.636             0.378            0.065
Chain 1:   1200        -7633.316             0.132            0.026
Chain 1:   1300        -7662.383             0.032            0.021
Chain 1:   1400        -7765.558             0.027            0.018
Chain 1:   1500        -7634.584             0.028            0.018
Chain 1:   1600        -7771.530             0.022            0.018
Chain 1:   1700        -7509.677             0.016            0.018
Chain 1:   1800        -7567.312             0.017            0.018
Chain 1:   1900        -7616.496             0.016            0.017
Chain 1:   2000        -7661.000             0.014            0.013
Chain 1:   2100        -7661.435             0.013            0.013
Chain 1:   2200        -7693.614             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003134 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86604.829             1.000            1.000
Chain 1:    200       -13248.298             3.269            5.537
Chain 1:    300        -9644.311             2.304            1.000
Chain 1:    400       -10403.519             1.746            1.000
Chain 1:    500        -8570.438             1.440            0.374
Chain 1:    600        -8511.884             1.201            0.374
Chain 1:    700        -8486.549             1.030            0.214
Chain 1:    800        -9095.022             0.909            0.214
Chain 1:    900        -8398.066             0.817            0.083
Chain 1:   1000        -8289.961             0.737            0.083
Chain 1:   1100        -8452.954             0.639            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8216.570             0.088            0.067
Chain 1:   1300        -8363.027             0.053            0.029
Chain 1:   1400        -8365.060             0.045            0.019
Chain 1:   1500        -8229.893             0.026            0.018
Chain 1:   1600        -8341.412             0.026            0.018
Chain 1:   1700        -8427.703             0.027            0.018
Chain 1:   1800        -8026.910             0.025            0.018
Chain 1:   1900        -8125.873             0.018            0.016
Chain 1:   2000        -8097.169             0.017            0.016
Chain 1:   2100        -8217.047             0.017            0.015
Chain 1:   2200        -8008.048             0.016            0.015
Chain 1:   2300        -8158.009             0.016            0.015
Chain 1:   2400        -8038.222             0.018            0.015
Chain 1:   2500        -8101.411             0.017            0.015
Chain 1:   2600        -8123.136             0.016            0.015
Chain 1:   2700        -8042.103             0.016            0.015
Chain 1:   2800        -8015.843             0.011            0.012
Chain 1:   2900        -8071.243             0.011            0.010
Chain 1:   3000        -7955.278             0.012            0.015
Chain 1:   3100        -8093.285             0.012            0.015
Chain 1:   3200        -7973.104             0.011            0.015
Chain 1:   3300        -7994.735             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8438590.231             1.000            1.000
Chain 1:    200     -1587845.111             2.657            4.314
Chain 1:    300      -891765.627             2.032            1.000
Chain 1:    400      -457744.289             1.761            1.000
Chain 1:    500      -357877.277             1.464            0.948
Chain 1:    600      -232619.987             1.310            0.948
Chain 1:    700      -118905.275             1.260            0.948
Chain 1:    800       -86127.136             1.150            0.948
Chain 1:    900       -66467.221             1.055            0.781
Chain 1:   1000       -51268.558             0.979            0.781
Chain 1:   1100       -38756.033             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37931.468             0.482            0.381
Chain 1:   1300       -25903.735             0.450            0.381
Chain 1:   1400       -25622.334             0.357            0.323
Chain 1:   1500       -22214.584             0.344            0.323
Chain 1:   1600       -21432.311             0.294            0.296
Chain 1:   1700       -20308.405             0.204            0.296
Chain 1:   1800       -20252.994             0.166            0.153
Chain 1:   1900       -20578.851             0.138            0.055
Chain 1:   2000       -19091.927             0.116            0.055
Chain 1:   2100       -19330.083             0.085            0.036
Chain 1:   2200       -19556.170             0.084            0.036
Chain 1:   2300       -19173.811             0.040            0.020
Chain 1:   2400       -18946.061             0.040            0.020
Chain 1:   2500       -18747.993             0.025            0.016
Chain 1:   2600       -18378.458             0.024            0.016
Chain 1:   2700       -18335.566             0.019            0.012
Chain 1:   2800       -18052.500             0.020            0.016
Chain 1:   2900       -18333.639             0.020            0.015
Chain 1:   3000       -18319.775             0.012            0.012
Chain 1:   3100       -18404.748             0.011            0.012
Chain 1:   3200       -18095.583             0.012            0.015
Chain 1:   3300       -18300.219             0.011            0.012
Chain 1:   3400       -17775.425             0.013            0.015
Chain 1:   3500       -18386.775             0.015            0.016
Chain 1:   3600       -17694.168             0.017            0.016
Chain 1:   3700       -18080.447             0.019            0.017
Chain 1:   3800       -17041.174             0.023            0.021
Chain 1:   3900       -17037.358             0.022            0.021
Chain 1:   4000       -17154.658             0.022            0.021
Chain 1:   4100       -17068.431             0.022            0.021
Chain 1:   4200       -16884.961             0.022            0.021
Chain 1:   4300       -17023.161             0.022            0.021
Chain 1:   4400       -16980.181             0.019            0.011
Chain 1:   4500       -16882.751             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00133 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48714.700             1.000            1.000
Chain 1:    200       -22810.345             1.068            1.136
Chain 1:    300       -19955.981             0.760            1.000
Chain 1:    400       -14708.066             0.659            1.000
Chain 1:    500       -16272.123             0.546            0.357
Chain 1:    600       -11457.356             0.525            0.420
Chain 1:    700       -15415.812             0.487            0.357
Chain 1:    800       -21441.138             0.461            0.357
Chain 1:    900       -15775.026             0.450            0.357
Chain 1:   1000       -12809.670             0.428            0.357
Chain 1:   1100       -14363.747             0.339            0.281
Chain 1:   1200       -10334.446             0.264            0.281
Chain 1:   1300       -13910.363             0.276            0.281
Chain 1:   1400       -10544.766             0.272            0.281
Chain 1:   1500       -11736.386             0.272            0.281
Chain 1:   1600       -10701.490             0.240            0.257
Chain 1:   1700       -11125.937             0.218            0.257
Chain 1:   1800       -12739.146             0.203            0.231
Chain 1:   1900        -9931.311             0.195            0.231
Chain 1:   2000       -18369.765             0.218            0.257
Chain 1:   2100        -9539.609             0.300            0.283
Chain 1:   2200       -10066.023             0.266            0.257
Chain 1:   2300       -11468.094             0.252            0.127
Chain 1:   2400       -10542.558             0.229            0.122
Chain 1:   2500       -13855.697             0.243            0.127
Chain 1:   2600       -10792.019             0.262            0.239
Chain 1:   2700       -10434.205             0.261            0.239
Chain 1:   2800        -9966.664             0.253            0.239
Chain 1:   2900       -13236.947             0.250            0.239
Chain 1:   3000        -9550.463             0.243            0.239
Chain 1:   3100        -8735.342             0.159            0.122
Chain 1:   3200        -9794.718             0.165            0.122
Chain 1:   3300       -15800.256             0.191            0.239
Chain 1:   3400       -11066.705             0.225            0.247
Chain 1:   3500        -9476.577             0.218            0.247
Chain 1:   3600       -10203.199             0.196            0.168
Chain 1:   3700        -9023.249             0.206            0.168
Chain 1:   3800        -8663.340             0.205            0.168
Chain 1:   3900        -8883.156             0.183            0.131
Chain 1:   4000       -10397.083             0.159            0.131
Chain 1:   4100        -9688.219             0.157            0.131
Chain 1:   4200       -11532.512             0.162            0.146
Chain 1:   4300       -10065.997             0.139            0.146
Chain 1:   4400       -10422.483             0.099            0.131
Chain 1:   4500        -8978.606             0.099            0.131
Chain 1:   4600       -10234.208             0.104            0.131
Chain 1:   4700        -8801.998             0.107            0.146
Chain 1:   4800        -8587.812             0.105            0.146
Chain 1:   4900       -13609.098             0.140            0.146
Chain 1:   5000        -9434.270             0.170            0.160
Chain 1:   5100        -8799.082             0.169            0.160
Chain 1:   5200       -10445.048             0.169            0.158
Chain 1:   5300        -8527.016             0.177            0.161
Chain 1:   5400        -9033.825             0.179            0.161
Chain 1:   5500        -8599.948             0.168            0.158
Chain 1:   5600       -12411.793             0.187            0.163
Chain 1:   5700       -12399.961             0.171            0.158
Chain 1:   5800        -8576.402             0.213            0.225
Chain 1:   5900        -8849.671             0.179            0.158
Chain 1:   6000       -13199.835             0.168            0.158
Chain 1:   6100        -8791.356             0.210            0.225
Chain 1:   6200        -9564.306             0.203            0.225
Chain 1:   6300        -9446.431             0.182            0.081
Chain 1:   6400        -9977.144             0.181            0.081
Chain 1:   6500        -8671.359             0.191            0.151
Chain 1:   6600        -8835.333             0.162            0.081
Chain 1:   6700       -12697.364             0.193            0.151
Chain 1:   6800       -11387.798             0.160            0.115
Chain 1:   6900        -9142.615             0.181            0.151
Chain 1:   7000        -9639.862             0.153            0.115
Chain 1:   7100        -8368.894             0.118            0.115
Chain 1:   7200       -11158.758             0.135            0.151
Chain 1:   7300        -8551.944             0.165            0.152
Chain 1:   7400        -9387.871             0.168            0.152
Chain 1:   7500        -8902.661             0.159            0.152
Chain 1:   7600        -8835.466             0.157            0.152
Chain 1:   7700        -9211.093             0.131            0.115
Chain 1:   7800       -13001.372             0.149            0.152
Chain 1:   7900        -9560.167             0.160            0.152
Chain 1:   8000       -10527.037             0.164            0.152
Chain 1:   8100        -8229.707             0.177            0.250
Chain 1:   8200       -10255.651             0.172            0.198
Chain 1:   8300       -10010.160             0.144            0.092
Chain 1:   8400        -8936.742             0.147            0.120
Chain 1:   8500       -12763.033             0.171            0.198
Chain 1:   8600       -10505.558             0.192            0.215
Chain 1:   8700        -8464.073             0.212            0.241
Chain 1:   8800        -9183.773             0.191            0.215
Chain 1:   8900       -10783.141             0.170            0.198
Chain 1:   9000        -8838.597             0.182            0.215
Chain 1:   9100        -8540.996             0.158            0.198
Chain 1:   9200       -12457.976             0.170            0.215
Chain 1:   9300       -10027.888             0.191            0.220
Chain 1:   9400        -8712.617             0.195            0.220
Chain 1:   9500        -8385.034             0.168            0.215
Chain 1:   9600        -8489.165             0.148            0.151
Chain 1:   9700        -8413.836             0.125            0.148
Chain 1:   9800        -9403.146             0.128            0.148
Chain 1:   9900       -10042.102             0.119            0.105
Chain 1:   10000        -8677.586             0.113            0.105
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61810.940             1.000            1.000
Chain 1:    200       -17839.070             1.732            2.465
Chain 1:    300        -8833.914             1.495            1.019
Chain 1:    400        -9371.501             1.135            1.019
Chain 1:    500        -8333.299             0.933            1.000
Chain 1:    600        -8677.151             0.784            1.000
Chain 1:    700        -7912.795             0.686            0.125
Chain 1:    800        -7821.824             0.602            0.125
Chain 1:    900        -7943.674             0.537            0.097
Chain 1:   1000        -7771.068             0.485            0.097
Chain 1:   1100        -7740.434             0.386            0.057
Chain 1:   1200        -7777.197             0.140            0.040
Chain 1:   1300        -7742.440             0.038            0.022
Chain 1:   1400        -7939.378             0.035            0.022
Chain 1:   1500        -7596.618             0.027            0.022
Chain 1:   1600        -7746.793             0.025            0.019
Chain 1:   1700        -7533.960             0.018            0.019
Chain 1:   1800        -7635.076             0.018            0.019
Chain 1:   1900        -7473.991             0.019            0.022
Chain 1:   2000        -7576.272             0.018            0.019
Chain 1:   2100        -7617.009             0.018            0.019
Chain 1:   2200        -7699.285             0.019            0.019
Chain 1:   2300        -7600.589             0.019            0.019
Chain 1:   2400        -7649.086             0.018            0.014
Chain 1:   2500        -7567.774             0.014            0.013
Chain 1:   2600        -7518.817             0.013            0.013
Chain 1:   2700        -7515.003             0.010            0.011
Chain 1:   2800        -7576.485             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002645 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85734.496             1.000            1.000
Chain 1:    200       -13508.040             3.173            5.347
Chain 1:    300        -9947.293             2.235            1.000
Chain 1:    400       -10737.770             1.695            1.000
Chain 1:    500        -8770.391             1.401            0.358
Chain 1:    600        -8537.897             1.172            0.358
Chain 1:    700        -8651.727             1.006            0.224
Chain 1:    800        -9012.489             0.885            0.224
Chain 1:    900        -8757.982             0.790            0.074
Chain 1:   1000        -8542.260             0.714            0.074
Chain 1:   1100        -8769.372             0.616            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8420.502             0.086            0.040
Chain 1:   1300        -8484.112             0.051            0.029
Chain 1:   1400        -8588.507             0.045            0.027
Chain 1:   1500        -8510.622             0.023            0.026
Chain 1:   1600        -8517.528             0.020            0.025
Chain 1:   1700        -8440.978             0.020            0.025
Chain 1:   1800        -8327.189             0.017            0.014
Chain 1:   1900        -8447.145             0.016            0.014
Chain 1:   2000        -8407.311             0.014            0.012
Chain 1:   2100        -8532.034             0.013            0.012
Chain 1:   2200        -8314.891             0.011            0.012
Chain 1:   2300        -8468.636             0.012            0.014
Chain 1:   2400        -8477.878             0.011            0.014
Chain 1:   2500        -8453.170             0.011            0.014
Chain 1:   2600        -8454.877             0.010            0.014
Chain 1:   2700        -8360.600             0.011            0.014
Chain 1:   2800        -8331.850             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002916 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8368925.283             1.000            1.000
Chain 1:    200     -1575186.275             2.656            4.313
Chain 1:    300      -888945.757             2.028            1.000
Chain 1:    400      -456969.090             1.758            1.000
Chain 1:    500      -358107.344             1.461            0.945
Chain 1:    600      -233195.080             1.307            0.945
Chain 1:    700      -119376.095             1.256            0.945
Chain 1:    800       -86575.118             1.147            0.945
Chain 1:    900       -66885.652             1.052            0.772
Chain 1:   1000       -51654.444             0.976            0.772
Chain 1:   1100       -39106.385             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38277.867             0.479            0.379
Chain 1:   1300       -26207.122             0.448            0.379
Chain 1:   1400       -25922.044             0.355            0.321
Chain 1:   1500       -22502.738             0.342            0.321
Chain 1:   1600       -21717.180             0.292            0.295
Chain 1:   1700       -20587.576             0.203            0.294
Chain 1:   1800       -20530.920             0.165            0.152
Chain 1:   1900       -20856.777             0.137            0.055
Chain 1:   2000       -19367.019             0.115            0.055
Chain 1:   2100       -19605.261             0.084            0.036
Chain 1:   2200       -19831.884             0.083            0.036
Chain 1:   2300       -19449.045             0.039            0.020
Chain 1:   2400       -19221.250             0.039            0.020
Chain 1:   2500       -19023.476             0.025            0.016
Chain 1:   2600       -18653.801             0.024            0.016
Chain 1:   2700       -18610.848             0.018            0.012
Chain 1:   2800       -18327.992             0.020            0.015
Chain 1:   2900       -18609.094             0.020            0.015
Chain 1:   3000       -18595.214             0.012            0.012
Chain 1:   3100       -18680.186             0.011            0.012
Chain 1:   3200       -18371.056             0.012            0.015
Chain 1:   3300       -18575.650             0.011            0.012
Chain 1:   3400       -18051.029             0.013            0.015
Chain 1:   3500       -18662.320             0.015            0.015
Chain 1:   3600       -17969.768             0.017            0.015
Chain 1:   3700       -18356.052             0.019            0.017
Chain 1:   3800       -17317.028             0.023            0.021
Chain 1:   3900       -17313.267             0.021            0.021
Chain 1:   4000       -17430.497             0.022            0.021
Chain 1:   4100       -17344.362             0.022            0.021
Chain 1:   4200       -17160.918             0.022            0.021
Chain 1:   4300       -17299.075             0.021            0.021
Chain 1:   4400       -17256.124             0.019            0.011
Chain 1:   4500       -17158.751             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12745.384             1.000            1.000
Chain 1:    200        -9610.338             0.663            1.000
Chain 1:    300        -8256.876             0.497            0.326
Chain 1:    400        -8444.929             0.378            0.326
Chain 1:    500        -8310.290             0.306            0.164
Chain 1:    600        -8163.584             0.258            0.164
Chain 1:    700        -8249.135             0.222            0.022
Chain 1:    800        -8104.855             0.197            0.022
Chain 1:    900        -8199.024             0.176            0.018
Chain 1:   1000        -8096.241             0.160            0.018
Chain 1:   1100        -8149.811             0.061            0.018
Chain 1:   1200        -8070.369             0.029            0.016
Chain 1:   1300        -8060.421             0.013            0.013
Chain 1:   1400        -8043.216             0.011            0.011
Chain 1:   1500        -8131.523             0.010            0.011
Chain 1:   1600        -8080.676             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57374.131             1.000            1.000
Chain 1:    200       -17813.108             1.610            2.221
Chain 1:    300        -8936.056             1.405            1.000
Chain 1:    400        -8204.771             1.076            1.000
Chain 1:    500        -8884.151             0.876            0.993
Chain 1:    600        -8755.105             0.732            0.993
Chain 1:    700        -8386.800             0.634            0.089
Chain 1:    800        -8138.766             0.559            0.089
Chain 1:    900        -7886.087             0.500            0.076
Chain 1:   1000        -8050.669             0.452            0.076
Chain 1:   1100        -7932.488             0.354            0.044
Chain 1:   1200        -7595.616             0.136            0.044
Chain 1:   1300        -7795.701             0.039            0.032
Chain 1:   1400        -8112.796             0.034            0.032
Chain 1:   1500        -7684.442             0.032            0.032
Chain 1:   1600        -7850.485             0.033            0.032
Chain 1:   1700        -7524.997             0.033            0.032
Chain 1:   1800        -7684.162             0.032            0.032
Chain 1:   1900        -7756.460             0.029            0.026
Chain 1:   2000        -7797.498             0.028            0.026
Chain 1:   2100        -7554.703             0.030            0.032
Chain 1:   2200        -8028.391             0.031            0.032
Chain 1:   2300        -7652.474             0.033            0.039
Chain 1:   2400        -7738.747             0.031            0.032
Chain 1:   2500        -7663.929             0.026            0.021
Chain 1:   2600        -7570.340             0.025            0.021
Chain 1:   2700        -7563.616             0.021            0.012
Chain 1:   2800        -7529.541             0.019            0.011
Chain 1:   2900        -7432.051             0.020            0.012
Chain 1:   3000        -7588.962             0.021            0.013
Chain 1:   3100        -7580.117             0.018            0.012
Chain 1:   3200        -7784.103             0.015            0.012
Chain 1:   3300        -7506.221             0.014            0.012
Chain 1:   3400        -7734.369             0.016            0.013
Chain 1:   3500        -7485.453             0.018            0.021
Chain 1:   3600        -7553.716             0.018            0.021
Chain 1:   3700        -7502.223             0.018            0.021
Chain 1:   3800        -7500.012             0.018            0.021
Chain 1:   3900        -7465.424             0.017            0.021
Chain 1:   4000        -7457.875             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002612 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86167.725             1.000            1.000
Chain 1:    200       -13871.895             3.106            5.212
Chain 1:    300       -10153.194             2.193            1.000
Chain 1:    400       -11467.565             1.673            1.000
Chain 1:    500        -9172.785             1.389            0.366
Chain 1:    600        -8795.748             1.164            0.366
Chain 1:    700        -9052.854             1.002            0.250
Chain 1:    800        -9468.806             0.882            0.250
Chain 1:    900        -8952.929             0.791            0.115
Chain 1:   1000        -9011.325             0.712            0.115
Chain 1:   1100        -8791.705             0.615            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8525.912             0.097            0.044
Chain 1:   1300        -8795.817             0.063            0.043
Chain 1:   1400        -8783.908             0.052            0.031
Chain 1:   1500        -8659.396             0.028            0.031
Chain 1:   1600        -8773.398             0.025            0.028
Chain 1:   1700        -8832.527             0.023            0.025
Chain 1:   1800        -8394.954             0.024            0.025
Chain 1:   1900        -8498.736             0.019            0.014
Chain 1:   2000        -8477.119             0.019            0.014
Chain 1:   2100        -8458.544             0.017            0.013
Chain 1:   2200        -8417.050             0.014            0.012
Chain 1:   2300        -8551.857             0.013            0.012
Chain 1:   2400        -8396.904             0.014            0.013
Chain 1:   2500        -8468.058             0.014            0.012
Chain 1:   2600        -8380.657             0.013            0.010
Chain 1:   2700        -8418.168             0.013            0.010
Chain 1:   2800        -8376.142             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00266 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372644.597             1.000            1.000
Chain 1:    200     -1582069.919             2.646            4.292
Chain 1:    300      -891219.528             2.022            1.000
Chain 1:    400      -458022.957             1.753            1.000
Chain 1:    500      -358468.170             1.458            0.946
Chain 1:    600      -233592.454             1.304            0.946
Chain 1:    700      -119756.999             1.254            0.946
Chain 1:    800       -86932.827             1.144            0.946
Chain 1:    900       -67268.871             1.050            0.775
Chain 1:   1000       -52056.832             0.974            0.775
Chain 1:   1100       -39512.240             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38694.966             0.478            0.378
Chain 1:   1300       -26618.284             0.446            0.378
Chain 1:   1400       -26337.517             0.353            0.317
Chain 1:   1500       -22914.629             0.340            0.317
Chain 1:   1600       -22129.101             0.290            0.292
Chain 1:   1700       -20998.181             0.200            0.292
Chain 1:   1800       -20941.747             0.163            0.149
Chain 1:   1900       -21268.364             0.135            0.054
Chain 1:   2000       -19776.154             0.114            0.054
Chain 1:   2100       -20014.864             0.083            0.035
Chain 1:   2200       -20241.906             0.082            0.035
Chain 1:   2300       -19858.461             0.039            0.019
Chain 1:   2400       -19630.341             0.039            0.019
Chain 1:   2500       -19432.440             0.025            0.015
Chain 1:   2600       -19062.054             0.023            0.015
Chain 1:   2700       -19018.918             0.018            0.012
Chain 1:   2800       -18735.520             0.019            0.015
Chain 1:   2900       -19017.087             0.019            0.015
Chain 1:   3000       -19003.292             0.012            0.012
Chain 1:   3100       -19088.302             0.011            0.012
Chain 1:   3200       -18778.653             0.011            0.015
Chain 1:   3300       -18983.689             0.011            0.012
Chain 1:   3400       -18457.995             0.012            0.015
Chain 1:   3500       -19070.773             0.014            0.015
Chain 1:   3600       -18376.387             0.016            0.015
Chain 1:   3700       -18763.947             0.018            0.016
Chain 1:   3800       -17721.950             0.023            0.021
Chain 1:   3900       -17718.086             0.021            0.021
Chain 1:   4000       -17835.385             0.022            0.021
Chain 1:   4100       -17748.979             0.022            0.021
Chain 1:   4200       -17564.950             0.021            0.021
Chain 1:   4300       -17703.561             0.021            0.021
Chain 1:   4400       -17660.085             0.018            0.010
Chain 1:   4500       -17562.586             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00122 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12328.037             1.000            1.000
Chain 1:    200        -9293.095             0.663            1.000
Chain 1:    300        -8165.281             0.488            0.327
Chain 1:    400        -8161.753             0.366            0.327
Chain 1:    500        -8053.324             0.296            0.138
Chain 1:    600        -7966.074             0.248            0.138
Chain 1:    700        -7869.665             0.215            0.013
Chain 1:    800        -7914.398             0.188            0.013
Chain 1:    900        -8039.114             0.169            0.013
Chain 1:   1000        -7968.687             0.153            0.013
Chain 1:   1100        -7968.620             0.053            0.012
Chain 1:   1200        -7890.818             0.022            0.011
Chain 1:   1300        -7842.246             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56903.337             1.000            1.000
Chain 1:    200       -17411.117             1.634            2.268
Chain 1:    300        -8682.600             1.425            1.005
Chain 1:    400        -8295.532             1.080            1.005
Chain 1:    500        -8338.189             0.865            1.000
Chain 1:    600        -8238.064             0.723            1.000
Chain 1:    700        -7896.944             0.626            0.047
Chain 1:    800        -8154.742             0.552            0.047
Chain 1:    900        -7833.396             0.495            0.043
Chain 1:   1000        -7637.172             0.448            0.043
Chain 1:   1100        -7666.683             0.348            0.041
Chain 1:   1200        -7578.262             0.123            0.032
Chain 1:   1300        -7742.022             0.024            0.026
Chain 1:   1400        -7965.650             0.022            0.026
Chain 1:   1500        -7548.153             0.027            0.028
Chain 1:   1600        -7708.382             0.028            0.028
Chain 1:   1700        -7450.246             0.027            0.028
Chain 1:   1800        -7556.337             0.026            0.026
Chain 1:   1900        -7520.676             0.022            0.021
Chain 1:   2000        -7558.988             0.020            0.021
Chain 1:   2100        -7525.701             0.020            0.021
Chain 1:   2200        -7647.316             0.020            0.021
Chain 1:   2300        -7544.005             0.020            0.016
Chain 1:   2400        -7583.450             0.017            0.014
Chain 1:   2500        -7503.146             0.013            0.014
Chain 1:   2600        -7480.846             0.011            0.011
Chain 1:   2700        -7505.059             0.008            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002468 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86704.523             1.000            1.000
Chain 1:    200       -13465.586             3.219            5.439
Chain 1:    300        -9850.545             2.269            1.000
Chain 1:    400       -10571.749             1.719            1.000
Chain 1:    500        -8814.131             1.415            0.367
Chain 1:    600        -8678.802             1.182            0.367
Chain 1:    700        -8579.546             1.014            0.199
Chain 1:    800        -9166.440             0.896            0.199
Chain 1:    900        -8644.797             0.803            0.068
Chain 1:   1000        -8465.465             0.725            0.068
Chain 1:   1100        -8593.199             0.626            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8189.643             0.087            0.060
Chain 1:   1300        -8535.994             0.055            0.049
Chain 1:   1400        -8541.053             0.048            0.041
Chain 1:   1500        -8413.315             0.029            0.021
Chain 1:   1600        -8521.311             0.029            0.021
Chain 1:   1700        -8605.857             0.029            0.021
Chain 1:   1800        -8195.752             0.027            0.021
Chain 1:   1900        -8291.714             0.023            0.015
Chain 1:   2000        -8264.378             0.021            0.015
Chain 1:   2100        -8386.068             0.021            0.015
Chain 1:   2200        -8226.241             0.018            0.015
Chain 1:   2300        -8289.006             0.014            0.013
Chain 1:   2400        -8356.265             0.015            0.013
Chain 1:   2500        -8302.065             0.014            0.012
Chain 1:   2600        -8300.406             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00366 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398415.930             1.000            1.000
Chain 1:    200     -1583483.207             2.652            4.304
Chain 1:    300      -890534.891             2.027            1.000
Chain 1:    400      -457191.009             1.757            1.000
Chain 1:    500      -357779.715             1.462            0.948
Chain 1:    600      -232831.173             1.307            0.948
Chain 1:    700      -119143.615             1.257            0.948
Chain 1:    800       -86364.580             1.147            0.948
Chain 1:    900       -66719.788             1.052            0.778
Chain 1:   1000       -51522.946             0.977            0.778
Chain 1:   1100       -39004.675             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38182.006             0.481            0.380
Chain 1:   1300       -26145.080             0.449            0.380
Chain 1:   1400       -25864.427             0.355            0.321
Chain 1:   1500       -22453.440             0.343            0.321
Chain 1:   1600       -21670.285             0.292            0.295
Chain 1:   1700       -20544.896             0.203            0.294
Chain 1:   1800       -20489.203             0.165            0.152
Chain 1:   1900       -20815.307             0.137            0.055
Chain 1:   2000       -19327.006             0.115            0.055
Chain 1:   2100       -19565.397             0.084            0.036
Chain 1:   2200       -19791.707             0.083            0.036
Chain 1:   2300       -19409.047             0.039            0.020
Chain 1:   2400       -19181.177             0.039            0.020
Chain 1:   2500       -18983.129             0.025            0.016
Chain 1:   2600       -18613.508             0.024            0.016
Chain 1:   2700       -18570.513             0.018            0.012
Chain 1:   2800       -18287.386             0.020            0.015
Chain 1:   2900       -18568.605             0.020            0.015
Chain 1:   3000       -18554.798             0.012            0.012
Chain 1:   3100       -18639.763             0.011            0.012
Chain 1:   3200       -18330.528             0.012            0.015
Chain 1:   3300       -18535.192             0.011            0.012
Chain 1:   3400       -18010.239             0.013            0.015
Chain 1:   3500       -18621.915             0.015            0.015
Chain 1:   3600       -17928.856             0.017            0.015
Chain 1:   3700       -18315.477             0.019            0.017
Chain 1:   3800       -17275.562             0.023            0.021
Chain 1:   3900       -17271.698             0.022            0.021
Chain 1:   4000       -17389.013             0.022            0.021
Chain 1:   4100       -17302.797             0.022            0.021
Chain 1:   4200       -17119.106             0.022            0.021
Chain 1:   4300       -17257.463             0.021            0.021
Chain 1:   4400       -17214.368             0.019            0.011
Chain 1:   4500       -17116.893             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001593 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49254.700             1.000            1.000
Chain 1:    200       -18304.646             1.345            1.691
Chain 1:    300       -14122.998             0.996            1.000
Chain 1:    400       -25122.250             0.856            1.000
Chain 1:    500       -24851.685             0.687            0.438
Chain 1:    600       -24704.540             0.574            0.438
Chain 1:    700       -11984.466             0.643            0.438
Chain 1:    800       -14398.945             0.584            0.438
Chain 1:    900       -11810.915             0.543            0.296
Chain 1:   1000       -10614.968             0.500            0.296
Chain 1:   1100       -12256.804             0.414            0.219
Chain 1:   1200       -17330.885             0.274            0.219
Chain 1:   1300       -11522.307             0.295            0.219
Chain 1:   1400       -11865.408             0.254            0.168
Chain 1:   1500       -21323.314             0.297            0.219
Chain 1:   1600       -12435.133             0.368            0.293
Chain 1:   1700       -17851.184             0.292            0.293
Chain 1:   1800       -14964.547             0.295            0.293
Chain 1:   1900       -11254.674             0.306            0.303
Chain 1:   2000       -10571.149             0.301            0.303
Chain 1:   2100       -11834.064             0.298            0.303
Chain 1:   2200       -10343.965             0.283            0.303
Chain 1:   2300       -14491.167             0.261            0.286
Chain 1:   2400       -12754.829             0.272            0.286
Chain 1:   2500        -9487.722             0.262            0.286
Chain 1:   2600       -10809.535             0.203            0.193
Chain 1:   2700       -10765.234             0.173            0.144
Chain 1:   2800       -14483.529             0.179            0.144
Chain 1:   2900       -15880.555             0.155            0.136
Chain 1:   3000       -16162.622             0.151            0.136
Chain 1:   3100        -9469.838             0.211            0.144
Chain 1:   3200       -15397.939             0.235            0.257
Chain 1:   3300        -9929.436             0.261            0.257
Chain 1:   3400       -10087.670             0.249            0.257
Chain 1:   3500       -10934.383             0.222            0.122
Chain 1:   3600        -9117.664             0.230            0.199
Chain 1:   3700        -9640.861             0.235            0.199
Chain 1:   3800       -10152.213             0.214            0.088
Chain 1:   3900        -8989.008             0.219            0.129
Chain 1:   4000        -8935.069             0.217            0.129
Chain 1:   4100        -9188.514             0.150            0.077
Chain 1:   4200       -14657.511             0.148            0.077
Chain 1:   4300       -15106.768             0.096            0.054
Chain 1:   4400       -10090.913             0.144            0.077
Chain 1:   4500       -10172.897             0.137            0.054
Chain 1:   4600       -12722.808             0.138            0.054
Chain 1:   4700       -14089.047             0.142            0.097
Chain 1:   4800        -9257.514             0.189            0.129
Chain 1:   4900        -9006.035             0.179            0.097
Chain 1:   5000       -18656.439             0.230            0.200
Chain 1:   5100       -10909.605             0.298            0.373
Chain 1:   5200        -9467.836             0.276            0.200
Chain 1:   5300       -16449.205             0.316            0.424
Chain 1:   5400        -8926.626             0.350            0.424
Chain 1:   5500        -9071.882             0.351            0.424
Chain 1:   5600        -9386.805             0.334            0.424
Chain 1:   5700       -10286.954             0.333            0.424
Chain 1:   5800       -10010.973             0.284            0.152
Chain 1:   5900        -8644.825             0.297            0.158
Chain 1:   6000        -8491.870             0.247            0.152
Chain 1:   6100        -9773.776             0.189            0.131
Chain 1:   6200        -9049.323             0.182            0.088
Chain 1:   6300        -9372.284             0.143            0.080
Chain 1:   6400       -12140.846             0.081            0.080
Chain 1:   6500        -9206.178             0.112            0.088
Chain 1:   6600       -15502.408             0.149            0.131
Chain 1:   6700       -10110.461             0.194            0.158
Chain 1:   6800       -10333.477             0.193            0.158
Chain 1:   6900       -11243.392             0.185            0.131
Chain 1:   7000       -12399.595             0.193            0.131
Chain 1:   7100        -9418.616             0.211            0.228
Chain 1:   7200        -8646.104             0.212            0.228
Chain 1:   7300       -10394.012             0.226            0.228
Chain 1:   7400       -12290.419             0.218            0.168
Chain 1:   7500        -8436.445             0.232            0.168
Chain 1:   7600        -9076.622             0.198            0.154
Chain 1:   7700        -9269.227             0.147            0.093
Chain 1:   7800       -12661.545             0.172            0.154
Chain 1:   7900        -9279.218             0.200            0.168
Chain 1:   8000        -9818.153             0.196            0.168
Chain 1:   8100        -9439.987             0.169            0.154
Chain 1:   8200        -9698.266             0.162            0.154
Chain 1:   8300        -8484.485             0.160            0.143
Chain 1:   8400       -12234.871             0.175            0.143
Chain 1:   8500        -8493.623             0.174            0.143
Chain 1:   8600       -10598.137             0.186            0.199
Chain 1:   8700        -8391.973             0.211            0.263
Chain 1:   8800        -8741.504             0.188            0.199
Chain 1:   8900       -13406.869             0.186            0.199
Chain 1:   9000       -10283.470             0.211            0.263
Chain 1:   9100        -8392.811             0.230            0.263
Chain 1:   9200        -9290.184             0.237            0.263
Chain 1:   9300        -8961.017             0.226            0.263
Chain 1:   9400        -8600.884             0.199            0.225
Chain 1:   9500        -8340.505             0.158            0.199
Chain 1:   9600        -9312.541             0.149            0.104
Chain 1:   9700        -8751.436             0.129            0.097
Chain 1:   9800       -12257.296             0.154            0.104
Chain 1:   9900        -9398.392             0.149            0.104
Chain 1:   10000        -8693.135             0.127            0.097
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58573.190             1.000            1.000
Chain 1:    200       -18106.485             1.617            2.235
Chain 1:    300        -8835.165             1.428            1.049
Chain 1:    400        -8094.572             1.094            1.049
Chain 1:    500        -8267.156             0.879            1.000
Chain 1:    600        -9081.307             0.748            1.000
Chain 1:    700        -8806.646             0.645            0.091
Chain 1:    800        -8052.625             0.576            0.094
Chain 1:    900        -7924.427             0.514            0.091
Chain 1:   1000        -7723.949             0.465            0.091
Chain 1:   1100        -7572.248             0.367            0.090
Chain 1:   1200        -7530.622             0.144            0.031
Chain 1:   1300        -7698.945             0.042            0.026
Chain 1:   1400        -7714.489             0.033            0.022
Chain 1:   1500        -7570.319             0.033            0.022
Chain 1:   1600        -7730.174             0.026            0.021
Chain 1:   1700        -7597.570             0.024            0.020
Chain 1:   1800        -7693.538             0.016            0.019
Chain 1:   1900        -7569.403             0.016            0.019
Chain 1:   2000        -7639.202             0.014            0.017
Chain 1:   2100        -7533.106             0.014            0.016
Chain 1:   2200        -7714.259             0.016            0.017
Chain 1:   2300        -7518.871             0.016            0.017
Chain 1:   2400        -7569.117             0.017            0.017
Chain 1:   2500        -7545.420             0.015            0.016
Chain 1:   2600        -7531.942             0.013            0.014
Chain 1:   2700        -7453.807             0.012            0.012
Chain 1:   2800        -7479.848             0.011            0.010
Chain 1:   2900        -7340.791             0.012            0.010
Chain 1:   3000        -7490.925             0.013            0.014
Chain 1:   3100        -7490.927             0.011            0.010
Chain 1:   3200        -7717.858             0.012            0.010
Chain 1:   3300        -7422.964             0.013            0.010
Chain 1:   3400        -7665.491             0.016            0.019
Chain 1:   3500        -7405.245             0.019            0.020
Chain 1:   3600        -7471.771             0.020            0.020
Chain 1:   3700        -7421.248             0.019            0.020
Chain 1:   3800        -7422.663             0.019            0.020
Chain 1:   3900        -7379.692             0.018            0.020
Chain 1:   4000        -7372.297             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003662 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86421.936             1.000            1.000
Chain 1:    200       -13894.755             3.110            5.220
Chain 1:    300       -10177.694             2.195            1.000
Chain 1:    400       -11314.719             1.671            1.000
Chain 1:    500        -9164.866             1.384            0.365
Chain 1:    600        -9554.415             1.160            0.365
Chain 1:    700        -8945.148             1.004            0.235
Chain 1:    800        -8484.538             0.885            0.235
Chain 1:    900        -8566.604             0.788            0.100
Chain 1:   1000        -8821.752             0.712            0.100
Chain 1:   1100        -8951.483             0.614            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8538.175             0.096            0.054
Chain 1:   1300        -8852.504             0.064            0.048
Chain 1:   1400        -8784.929             0.054            0.041
Chain 1:   1500        -8681.784             0.032            0.036
Chain 1:   1600        -8792.645             0.029            0.029
Chain 1:   1700        -8855.832             0.023            0.014
Chain 1:   1800        -8416.311             0.023            0.014
Chain 1:   1900        -8521.376             0.023            0.014
Chain 1:   2000        -8500.896             0.020            0.013
Chain 1:   2100        -8491.988             0.019            0.012
Chain 1:   2200        -8438.876             0.015            0.012
Chain 1:   2300        -8574.752             0.013            0.012
Chain 1:   2400        -8419.859             0.014            0.012
Chain 1:   2500        -8491.130             0.014            0.012
Chain 1:   2600        -8403.773             0.013            0.010
Chain 1:   2700        -8441.213             0.013            0.010
Chain 1:   2800        -8399.181             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400046.355             1.000            1.000
Chain 1:    200     -1583613.947             2.652            4.304
Chain 1:    300      -890443.582             2.028            1.000
Chain 1:    400      -458053.459             1.757            1.000
Chain 1:    500      -358564.274             1.461            0.944
Chain 1:    600      -233545.228             1.307            0.944
Chain 1:    700      -119727.859             1.256            0.944
Chain 1:    800       -86935.368             1.146            0.944
Chain 1:    900       -67263.297             1.051            0.778
Chain 1:   1000       -52051.755             0.975            0.778
Chain 1:   1100       -39518.990             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38697.149             0.479            0.377
Chain 1:   1300       -26633.884             0.446            0.377
Chain 1:   1400       -26352.839             0.353            0.317
Chain 1:   1500       -22935.304             0.340            0.317
Chain 1:   1600       -22151.319             0.290            0.292
Chain 1:   1700       -21022.087             0.200            0.292
Chain 1:   1800       -20965.921             0.163            0.149
Chain 1:   1900       -21292.557             0.135            0.054
Chain 1:   2000       -19801.450             0.113            0.054
Chain 1:   2100       -20039.855             0.083            0.035
Chain 1:   2200       -20267.000             0.082            0.035
Chain 1:   2300       -19883.477             0.038            0.019
Chain 1:   2400       -19655.380             0.039            0.019
Chain 1:   2500       -19457.541             0.025            0.015
Chain 1:   2600       -19087.109             0.023            0.015
Chain 1:   2700       -19043.886             0.018            0.012
Chain 1:   2800       -18760.615             0.019            0.015
Chain 1:   2900       -19042.115             0.019            0.015
Chain 1:   3000       -19028.178             0.012            0.012
Chain 1:   3100       -19113.274             0.011            0.012
Chain 1:   3200       -18803.623             0.011            0.015
Chain 1:   3300       -19008.617             0.011            0.012
Chain 1:   3400       -18482.983             0.012            0.015
Chain 1:   3500       -19095.760             0.014            0.015
Chain 1:   3600       -18401.262             0.016            0.015
Chain 1:   3700       -18788.969             0.018            0.016
Chain 1:   3800       -17746.891             0.022            0.021
Chain 1:   3900       -17743.019             0.021            0.021
Chain 1:   4000       -17860.298             0.022            0.021
Chain 1:   4100       -17773.996             0.022            0.021
Chain 1:   4200       -17589.836             0.021            0.021
Chain 1:   4300       -17728.498             0.021            0.021
Chain 1:   4400       -17685.002             0.018            0.010
Chain 1:   4500       -17587.495             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001599 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48934.270             1.000            1.000
Chain 1:    200       -18291.271             1.338            1.675
Chain 1:    300       -19281.089             0.909            1.000
Chain 1:    400       -24572.222             0.735            1.000
Chain 1:    500       -12777.974             0.773            0.923
Chain 1:    600       -17840.029             0.691            0.923
Chain 1:    700       -15249.780             0.617            0.284
Chain 1:    800       -13586.099             0.555            0.284
Chain 1:    900       -14897.411             0.503            0.215
Chain 1:   1000       -12817.055             0.469            0.215
Chain 1:   1100       -19251.038             0.403            0.215
Chain 1:   1200       -11199.697             0.307            0.215
Chain 1:   1300       -14146.817             0.323            0.215
Chain 1:   1400       -11982.185             0.319            0.208
Chain 1:   1500       -11468.221             0.231            0.181
Chain 1:   1600       -12342.965             0.210            0.170
Chain 1:   1700       -10572.404             0.210            0.167
Chain 1:   1800       -10426.671             0.199            0.167
Chain 1:   1900       -22596.073             0.244            0.181
Chain 1:   2000       -11127.964             0.331            0.208
Chain 1:   2100        -9550.162             0.314            0.181
Chain 1:   2200       -11213.023             0.257            0.167
Chain 1:   2300        -9640.507             0.252            0.165
Chain 1:   2400       -11271.738             0.249            0.163
Chain 1:   2500       -10421.317             0.252            0.163
Chain 1:   2600       -12408.337             0.261            0.163
Chain 1:   2700        -9741.147             0.272            0.163
Chain 1:   2800        -9491.822             0.273            0.163
Chain 1:   2900       -13536.339             0.249            0.163
Chain 1:   3000       -12239.146             0.157            0.160
Chain 1:   3100        -9102.272             0.175            0.160
Chain 1:   3200       -10143.674             0.170            0.160
Chain 1:   3300        -9507.089             0.161            0.145
Chain 1:   3400       -15663.744             0.185            0.160
Chain 1:   3500       -13626.027             0.192            0.160
Chain 1:   3600       -11019.071             0.200            0.237
Chain 1:   3700        -9200.180             0.192            0.198
Chain 1:   3800       -16610.089             0.234            0.237
Chain 1:   3900        -9295.208             0.283            0.237
Chain 1:   4000        -8853.526             0.277            0.237
Chain 1:   4100        -8861.328             0.243            0.198
Chain 1:   4200        -9169.179             0.236            0.198
Chain 1:   4300       -17723.117             0.278            0.237
Chain 1:   4400        -9805.389             0.319            0.237
Chain 1:   4500        -9064.487             0.312            0.237
Chain 1:   4600        -9675.052             0.295            0.198
Chain 1:   4700        -8589.093             0.288            0.126
Chain 1:   4800        -9940.655             0.257            0.126
Chain 1:   4900        -9563.051             0.182            0.082
Chain 1:   5000        -8704.128             0.187            0.099
Chain 1:   5100        -8613.299             0.188            0.099
Chain 1:   5200       -10468.696             0.202            0.126
Chain 1:   5300       -10217.708             0.157            0.099
Chain 1:   5400        -9063.094             0.089            0.099
Chain 1:   5500        -8813.533             0.083            0.099
Chain 1:   5600        -8646.062             0.079            0.099
Chain 1:   5700       -12859.175             0.099            0.099
Chain 1:   5800        -9062.926             0.127            0.099
Chain 1:   5900       -13309.729             0.155            0.127
Chain 1:   6000       -10321.863             0.174            0.177
Chain 1:   6100        -9291.502             0.184            0.177
Chain 1:   6200       -10267.297             0.176            0.127
Chain 1:   6300        -8513.227             0.194            0.206
Chain 1:   6400       -11579.730             0.208            0.265
Chain 1:   6500        -9634.279             0.225            0.265
Chain 1:   6600        -8835.253             0.232            0.265
Chain 1:   6700        -9896.763             0.210            0.206
Chain 1:   6800        -9693.978             0.171            0.202
Chain 1:   6900       -11015.394             0.151            0.120
Chain 1:   7000       -10091.259             0.131            0.111
Chain 1:   7100        -8557.727             0.138            0.120
Chain 1:   7200       -10710.471             0.148            0.179
Chain 1:   7300        -8544.105             0.153            0.179
Chain 1:   7400        -8972.496             0.131            0.120
Chain 1:   7500        -8555.710             0.116            0.107
Chain 1:   7600        -8695.283             0.109            0.107
Chain 1:   7700        -8452.719             0.101            0.092
Chain 1:   7800       -10316.500             0.117            0.120
Chain 1:   7900        -8386.894             0.128            0.179
Chain 1:   8000       -12023.227             0.149            0.181
Chain 1:   8100        -8416.910             0.174            0.201
Chain 1:   8200        -8238.847             0.156            0.181
Chain 1:   8300       -11579.603             0.159            0.181
Chain 1:   8400        -8599.068             0.189            0.230
Chain 1:   8500        -8194.971             0.189            0.230
Chain 1:   8600        -8297.388             0.189            0.230
Chain 1:   8700        -9736.454             0.201            0.230
Chain 1:   8800        -8271.090             0.200            0.230
Chain 1:   8900       -13343.935             0.215            0.289
Chain 1:   9000        -8235.792             0.247            0.289
Chain 1:   9100        -8280.810             0.205            0.177
Chain 1:   9200        -9039.777             0.211            0.177
Chain 1:   9300        -9190.145             0.184            0.148
Chain 1:   9400        -9656.672             0.154            0.084
Chain 1:   9500       -12239.669             0.170            0.148
Chain 1:   9600        -9686.166             0.195            0.177
Chain 1:   9700        -8334.197             0.197            0.177
Chain 1:   9800       -11178.870             0.205            0.211
Chain 1:   9900       -10616.512             0.172            0.162
Chain 1:   10000       -10657.461             0.110            0.084
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57149.516             1.000            1.000
Chain 1:    200       -17640.071             1.620            2.240
Chain 1:    300        -8847.601             1.411            1.000
Chain 1:    400        -8310.726             1.075            1.000
Chain 1:    500        -8618.855             0.867            0.994
Chain 1:    600        -8709.247             0.724            0.994
Chain 1:    700        -8016.038             0.633            0.086
Chain 1:    800        -8278.087             0.558            0.086
Chain 1:    900        -8014.030             0.499            0.065
Chain 1:   1000        -7910.543             0.451            0.065
Chain 1:   1100        -7781.795             0.352            0.036
Chain 1:   1200        -7703.786             0.130            0.033
Chain 1:   1300        -7925.142             0.033            0.032
Chain 1:   1400        -7871.883             0.027            0.028
Chain 1:   1500        -7648.338             0.027            0.028
Chain 1:   1600        -7865.857             0.028            0.028
Chain 1:   1700        -7589.591             0.023            0.028
Chain 1:   1800        -7647.891             0.021            0.028
Chain 1:   1900        -7654.212             0.018            0.017
Chain 1:   2000        -7618.918             0.017            0.017
Chain 1:   2100        -7670.553             0.016            0.010
Chain 1:   2200        -7778.022             0.016            0.014
Chain 1:   2300        -7607.917             0.016            0.014
Chain 1:   2400        -7697.532             0.016            0.014
Chain 1:   2500        -7684.738             0.013            0.012
Chain 1:   2600        -7582.443             0.012            0.012
Chain 1:   2700        -7625.174             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003486 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86165.000             1.000            1.000
Chain 1:    200       -13683.160             3.149            5.297
Chain 1:    300        -9996.451             2.222            1.000
Chain 1:    400       -11020.307             1.690            1.000
Chain 1:    500        -8990.624             1.397            0.369
Chain 1:    600        -8726.094             1.169            0.369
Chain 1:    700        -8363.735             1.008            0.226
Chain 1:    800        -8707.303             0.887            0.226
Chain 1:    900        -8713.634             0.789            0.093
Chain 1:   1000        -8763.144             0.710            0.093
Chain 1:   1100        -8694.800             0.611            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8418.854             0.085            0.039
Chain 1:   1300        -8667.979             0.051            0.033
Chain 1:   1400        -8684.980             0.042            0.030
Chain 1:   1500        -8532.973             0.021            0.029
Chain 1:   1600        -8647.137             0.019            0.018
Chain 1:   1700        -8718.186             0.016            0.013
Chain 1:   1800        -8288.986             0.017            0.013
Chain 1:   1900        -8392.671             0.018            0.013
Chain 1:   2000        -8367.766             0.018            0.013
Chain 1:   2100        -8498.916             0.019            0.015
Chain 1:   2200        -8295.327             0.018            0.015
Chain 1:   2300        -8390.420             0.016            0.013
Chain 1:   2400        -8455.980             0.017            0.013
Chain 1:   2500        -8401.393             0.015            0.012
Chain 1:   2600        -8405.191             0.014            0.011
Chain 1:   2700        -8320.690             0.014            0.011
Chain 1:   2800        -8277.885             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004226 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 42.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388378.433             1.000            1.000
Chain 1:    200     -1580353.255             2.654            4.308
Chain 1:    300      -891557.341             2.027            1.000
Chain 1:    400      -458846.188             1.756            1.000
Chain 1:    500      -359543.580             1.460            0.943
Chain 1:    600      -234219.028             1.306            0.943
Chain 1:    700      -119939.170             1.255            0.943
Chain 1:    800       -87031.399             1.146            0.943
Chain 1:    900       -67263.590             1.051            0.773
Chain 1:   1000       -51980.347             0.975            0.773
Chain 1:   1100       -39387.856             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38553.115             0.479            0.378
Chain 1:   1300       -26436.843             0.447            0.378
Chain 1:   1400       -26148.728             0.354            0.320
Chain 1:   1500       -22717.682             0.342            0.320
Chain 1:   1600       -21929.019             0.292            0.294
Chain 1:   1700       -20794.062             0.202            0.294
Chain 1:   1800       -20736.120             0.164            0.151
Chain 1:   1900       -21062.469             0.136            0.055
Chain 1:   2000       -19568.579             0.115            0.055
Chain 1:   2100       -19807.216             0.084            0.036
Chain 1:   2200       -20034.665             0.083            0.036
Chain 1:   2300       -19650.898             0.039            0.020
Chain 1:   2400       -19422.816             0.039            0.020
Chain 1:   2500       -19225.176             0.025            0.015
Chain 1:   2600       -18854.888             0.023            0.015
Chain 1:   2700       -18811.575             0.018            0.012
Chain 1:   2800       -18528.595             0.019            0.015
Chain 1:   2900       -18809.954             0.019            0.015
Chain 1:   3000       -18796.010             0.012            0.012
Chain 1:   3100       -18881.138             0.011            0.012
Chain 1:   3200       -18571.565             0.012            0.015
Chain 1:   3300       -18776.423             0.011            0.012
Chain 1:   3400       -18251.127             0.012            0.015
Chain 1:   3500       -18863.513             0.015            0.015
Chain 1:   3600       -18169.430             0.016            0.015
Chain 1:   3700       -18556.948             0.018            0.017
Chain 1:   3800       -17515.623             0.023            0.021
Chain 1:   3900       -17511.767             0.021            0.021
Chain 1:   4000       -17629.016             0.022            0.021
Chain 1:   4100       -17542.838             0.022            0.021
Chain 1:   4200       -17358.759             0.021            0.021
Chain 1:   4300       -17497.338             0.021            0.021
Chain 1:   4400       -17453.990             0.018            0.011
Chain 1:   4500       -17356.486             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12733.313             1.000            1.000
Chain 1:    200        -9689.158             0.657            1.000
Chain 1:    300        -8400.411             0.489            0.314
Chain 1:    400        -8578.024             0.372            0.314
Chain 1:    500        -8559.261             0.298            0.153
Chain 1:    600        -8341.935             0.253            0.153
Chain 1:    700        -8253.809             0.218            0.026
Chain 1:    800        -8275.455             0.191            0.026
Chain 1:    900        -8394.629             0.172            0.021
Chain 1:   1000        -8288.485             0.156            0.021
Chain 1:   1100        -8330.028             0.056            0.014
Chain 1:   1200        -8288.231             0.025            0.013
Chain 1:   1300        -8211.520             0.011            0.011
Chain 1:   1400        -8243.818             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62381.886             1.000            1.000
Chain 1:    200       -18343.654             1.700            2.401
Chain 1:    300        -9085.266             1.473            1.019
Chain 1:    400        -9400.062             1.113            1.019
Chain 1:    500        -8506.980             0.912            1.000
Chain 1:    600        -9097.691             0.771            1.000
Chain 1:    700        -8615.616             0.668            0.105
Chain 1:    800        -8686.505             0.586            0.105
Chain 1:    900        -8101.087             0.529            0.072
Chain 1:   1000        -7798.652             0.480            0.072
Chain 1:   1100        -7798.494             0.380            0.065
Chain 1:   1200        -7725.497             0.141            0.056
Chain 1:   1300        -7623.957             0.040            0.039
Chain 1:   1400        -7906.029             0.040            0.039
Chain 1:   1500        -7629.075             0.033            0.036
Chain 1:   1600        -7867.372             0.030            0.036
Chain 1:   1700        -7563.989             0.028            0.036
Chain 1:   1800        -7650.481             0.029            0.036
Chain 1:   1900        -7624.057             0.022            0.030
Chain 1:   2000        -7692.789             0.019            0.013
Chain 1:   2100        -7530.443             0.021            0.022
Chain 1:   2200        -7936.106             0.025            0.030
Chain 1:   2300        -7546.713             0.029            0.036
Chain 1:   2400        -7730.852             0.028            0.030
Chain 1:   2500        -7639.450             0.025            0.024
Chain 1:   2600        -7561.147             0.023            0.022
Chain 1:   2700        -7482.326             0.020            0.012
Chain 1:   2800        -7647.715             0.021            0.022
Chain 1:   2900        -7412.453             0.024            0.022
Chain 1:   3000        -7567.823             0.025            0.022
Chain 1:   3100        -7554.221             0.024            0.022
Chain 1:   3200        -7768.387             0.021            0.022
Chain 1:   3300        -7479.687             0.020            0.022
Chain 1:   3400        -7721.715             0.021            0.022
Chain 1:   3500        -7468.316             0.023            0.028
Chain 1:   3600        -7533.759             0.023            0.028
Chain 1:   3700        -7484.642             0.022            0.028
Chain 1:   3800        -7483.869             0.020            0.028
Chain 1:   3900        -7444.808             0.017            0.021
Chain 1:   4000        -7436.453             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003239 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86282.003             1.000            1.000
Chain 1:    200       -13930.844             3.097            5.194
Chain 1:    300       -10277.566             2.183            1.000
Chain 1:    400       -11422.471             1.662            1.000
Chain 1:    500        -9230.935             1.377            0.355
Chain 1:    600        -8769.009             1.157            0.355
Chain 1:    700        -8837.029             0.992            0.237
Chain 1:    800        -9044.414             0.871            0.237
Chain 1:    900        -9077.322             0.775            0.100
Chain 1:   1000        -8859.135             0.700            0.100
Chain 1:   1100        -9070.560             0.602            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8685.574             0.087            0.044
Chain 1:   1300        -8929.917             0.054            0.027
Chain 1:   1400        -8964.798             0.045            0.025
Chain 1:   1500        -8811.198             0.023            0.023
Chain 1:   1600        -8922.536             0.019            0.023
Chain 1:   1700        -9001.035             0.019            0.023
Chain 1:   1800        -8575.525             0.022            0.023
Chain 1:   1900        -8677.309             0.022            0.023
Chain 1:   2000        -8652.199             0.020            0.017
Chain 1:   2100        -8778.341             0.019            0.014
Chain 1:   2200        -8578.811             0.017            0.014
Chain 1:   2300        -8672.412             0.016            0.012
Chain 1:   2400        -8740.785             0.016            0.012
Chain 1:   2500        -8687.075             0.015            0.012
Chain 1:   2600        -8689.043             0.014            0.011
Chain 1:   2700        -8605.449             0.014            0.011
Chain 1:   2800        -8564.551             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003501 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418844.202             1.000            1.000
Chain 1:    200     -1582815.434             2.659            4.319
Chain 1:    300      -891212.491             2.032            1.000
Chain 1:    400      -458311.149             1.760            1.000
Chain 1:    500      -358868.786             1.463            0.945
Chain 1:    600      -233836.426             1.309            0.945
Chain 1:    700      -119888.446             1.257            0.945
Chain 1:    800       -87063.817             1.147            0.945
Chain 1:    900       -67357.653             1.052            0.776
Chain 1:   1000       -52121.077             0.976            0.776
Chain 1:   1100       -39569.107             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38739.972             0.478            0.377
Chain 1:   1300       -26661.179             0.446            0.377
Chain 1:   1400       -26376.834             0.353            0.317
Chain 1:   1500       -22955.968             0.340            0.317
Chain 1:   1600       -22170.602             0.290            0.293
Chain 1:   1700       -21040.013             0.200            0.292
Chain 1:   1800       -20983.265             0.163            0.149
Chain 1:   1900       -21309.534             0.135            0.054
Chain 1:   2000       -19818.507             0.113            0.054
Chain 1:   2100       -20056.723             0.083            0.035
Chain 1:   2200       -20283.786             0.082            0.035
Chain 1:   2300       -19900.482             0.038            0.019
Chain 1:   2400       -19672.538             0.039            0.019
Chain 1:   2500       -19474.776             0.025            0.015
Chain 1:   2600       -19104.517             0.023            0.015
Chain 1:   2700       -19061.388             0.018            0.012
Chain 1:   2800       -18778.322             0.019            0.015
Chain 1:   2900       -19059.667             0.019            0.015
Chain 1:   3000       -19045.733             0.012            0.012
Chain 1:   3100       -19130.782             0.011            0.012
Chain 1:   3200       -18821.273             0.011            0.015
Chain 1:   3300       -19026.162             0.011            0.012
Chain 1:   3400       -18500.872             0.012            0.015
Chain 1:   3500       -19113.087             0.014            0.015
Chain 1:   3600       -18419.351             0.016            0.015
Chain 1:   3700       -18806.521             0.018            0.016
Chain 1:   3800       -17765.593             0.022            0.021
Chain 1:   3900       -17761.781             0.021            0.021
Chain 1:   4000       -17879.029             0.022            0.021
Chain 1:   4100       -17792.781             0.022            0.021
Chain 1:   4200       -17608.915             0.021            0.021
Chain 1:   4300       -17747.358             0.021            0.021
Chain 1:   4400       -17704.070             0.018            0.010
Chain 1:   4500       -17606.635             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12728.572             1.000            1.000
Chain 1:    200        -9525.019             0.668            1.000
Chain 1:    300        -8077.499             0.505            0.336
Chain 1:    400        -8313.237             0.386            0.336
Chain 1:    500        -8172.686             0.312            0.179
Chain 1:    600        -8245.811             0.262            0.179
Chain 1:    700        -7910.779             0.230            0.042
Chain 1:    800        -7996.441             0.203            0.042
Chain 1:    900        -7839.194             0.183            0.028
Chain 1:   1000        -8066.633             0.167            0.028
Chain 1:   1100        -7950.829             0.069            0.028
Chain 1:   1200        -7932.255             0.035            0.020
Chain 1:   1300        -7882.176             0.018            0.017
Chain 1:   1400        -7903.883             0.015            0.015
Chain 1:   1500        -7996.414             0.015            0.012
Chain 1:   1600        -7914.943             0.015            0.012
Chain 1:   1700        -7882.395             0.011            0.011
Chain 1:   1800        -7855.252             0.010            0.010
Chain 1:   1900        -7882.396             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61978.673             1.000            1.000
Chain 1:    200       -18209.155             1.702            2.404
Chain 1:    300        -9024.271             1.474            1.018
Chain 1:    400        -8472.404             1.122            1.018
Chain 1:    500        -8647.845             0.901            1.000
Chain 1:    600        -8195.521             0.760            1.000
Chain 1:    700        -8465.488             0.656            0.065
Chain 1:    800        -8303.441             0.577            0.065
Chain 1:    900        -7970.299             0.517            0.055
Chain 1:   1000        -7705.704             0.469            0.055
Chain 1:   1100        -7834.143             0.371            0.042
Chain 1:   1200        -7695.887             0.132            0.034
Chain 1:   1300        -7573.887             0.032            0.032
Chain 1:   1400        -7780.316             0.028            0.027
Chain 1:   1500        -7530.390             0.029            0.032
Chain 1:   1600        -7808.208             0.027            0.032
Chain 1:   1700        -7425.870             0.029            0.033
Chain 1:   1800        -7577.488             0.029            0.033
Chain 1:   1900        -7559.485             0.025            0.027
Chain 1:   2000        -7636.179             0.023            0.020
Chain 1:   2100        -7547.440             0.023            0.020
Chain 1:   2200        -7709.819             0.023            0.021
Chain 1:   2300        -7559.155             0.023            0.021
Chain 1:   2400        -7560.796             0.021            0.020
Chain 1:   2500        -7554.094             0.017            0.020
Chain 1:   2600        -7485.472             0.015            0.012
Chain 1:   2700        -7476.747             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00252 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86915.837             1.000            1.000
Chain 1:    200       -13855.536             3.137            5.273
Chain 1:    300       -10096.711             2.215            1.000
Chain 1:    400       -11691.669             1.695            1.000
Chain 1:    500        -8722.035             1.424            0.372
Chain 1:    600        -9005.781             1.192            0.372
Chain 1:    700        -8532.483             1.030            0.340
Chain 1:    800        -9296.308             0.911            0.340
Chain 1:    900        -8986.340             0.814            0.136
Chain 1:   1000        -8413.457             0.739            0.136
Chain 1:   1100        -8885.283             0.645            0.082   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8376.924             0.123            0.068
Chain 1:   1300        -8710.308             0.090            0.061
Chain 1:   1400        -8700.011             0.077            0.055
Chain 1:   1500        -8550.401             0.044            0.053
Chain 1:   1600        -8664.599             0.042            0.053
Chain 1:   1700        -8712.258             0.037            0.038
Chain 1:   1800        -8257.525             0.035            0.038
Chain 1:   1900        -8369.333             0.033            0.038
Chain 1:   2000        -8374.008             0.026            0.017
Chain 1:   2100        -8484.361             0.022            0.013
Chain 1:   2200        -8263.380             0.018            0.013
Chain 1:   2300        -8404.623             0.016            0.013
Chain 1:   2400        -8270.644             0.018            0.016
Chain 1:   2500        -8342.814             0.017            0.013
Chain 1:   2600        -8253.090             0.017            0.013
Chain 1:   2700        -8286.004             0.017            0.013
Chain 1:   2800        -8237.395             0.012            0.013
Chain 1:   2900        -8349.989             0.012            0.013
Chain 1:   3000        -8278.242             0.012            0.013
Chain 1:   3100        -8229.484             0.012            0.011
Chain 1:   3200        -8202.317             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422611.169             1.000            1.000
Chain 1:    200     -1585286.027             2.656            4.313
Chain 1:    300      -889888.457             2.031            1.000
Chain 1:    400      -456933.081             1.760            1.000
Chain 1:    500      -357122.374             1.464            0.948
Chain 1:    600      -232257.265             1.310            0.948
Chain 1:    700      -119074.899             1.259            0.948
Chain 1:    800       -86436.970             1.148            0.948
Chain 1:    900       -66895.070             1.053            0.781
Chain 1:   1000       -51786.379             0.977            0.781
Chain 1:   1100       -39342.672             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38538.519             0.480            0.378
Chain 1:   1300       -26555.036             0.447            0.378
Chain 1:   1400       -26284.637             0.353            0.316
Chain 1:   1500       -22886.872             0.340            0.316
Chain 1:   1600       -22109.555             0.289            0.292
Chain 1:   1700       -20989.224             0.200            0.292
Chain 1:   1800       -20935.650             0.162            0.148
Chain 1:   1900       -21262.564             0.135            0.053
Chain 1:   2000       -19775.698             0.113            0.053
Chain 1:   2100       -20013.970             0.082            0.035
Chain 1:   2200       -20240.408             0.081            0.035
Chain 1:   2300       -19857.479             0.038            0.019
Chain 1:   2400       -19629.376             0.038            0.019
Chain 1:   2500       -19431.146             0.025            0.015
Chain 1:   2600       -19060.750             0.023            0.015
Chain 1:   2700       -19017.678             0.018            0.012
Chain 1:   2800       -18734.051             0.019            0.015
Chain 1:   2900       -19015.591             0.019            0.015
Chain 1:   3000       -19001.797             0.012            0.012
Chain 1:   3100       -19086.841             0.011            0.012
Chain 1:   3200       -18777.088             0.011            0.015
Chain 1:   3300       -18982.224             0.011            0.012
Chain 1:   3400       -18456.218             0.012            0.015
Chain 1:   3500       -19069.307             0.014            0.015
Chain 1:   3600       -18374.447             0.016            0.015
Chain 1:   3700       -18762.300             0.018            0.016
Chain 1:   3800       -17719.489             0.023            0.021
Chain 1:   3900       -17715.550             0.021            0.021
Chain 1:   4000       -17832.910             0.022            0.021
Chain 1:   4100       -17746.455             0.022            0.021
Chain 1:   4200       -17562.244             0.021            0.021
Chain 1:   4300       -17701.004             0.021            0.021
Chain 1:   4400       -17657.364             0.018            0.010
Chain 1:   4500       -17559.814             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001673 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12382.537             1.000            1.000
Chain 1:    200        -9285.391             0.667            1.000
Chain 1:    300        -8050.047             0.496            0.334
Chain 1:    400        -8221.065             0.377            0.334
Chain 1:    500        -8167.828             0.303            0.153
Chain 1:    600        -7995.388             0.256            0.153
Chain 1:    700        -7907.836             0.221            0.022
Chain 1:    800        -7915.826             0.193            0.022
Chain 1:    900        -7830.396             0.173            0.021
Chain 1:   1000        -8016.859             0.158            0.022
Chain 1:   1100        -8050.774             0.059            0.021
Chain 1:   1200        -7939.765             0.027            0.014
Chain 1:   1300        -7885.101             0.012            0.011
Chain 1:   1400        -7904.557             0.010            0.011
Chain 1:   1500        -7991.492             0.011            0.011
Chain 1:   1600        -7955.913             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001806 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61528.184             1.000            1.000
Chain 1:    200       -17893.656             1.719            2.439
Chain 1:    300        -8841.550             1.487            1.024
Chain 1:    400        -9416.211             1.131            1.024
Chain 1:    500        -8563.708             0.925            1.000
Chain 1:    600        -8655.318             0.772            1.000
Chain 1:    700        -7772.309             0.678            0.114
Chain 1:    800        -8865.258             0.609            0.123
Chain 1:    900        -7938.205             0.554            0.117
Chain 1:   1000        -7694.677             0.502            0.117
Chain 1:   1100        -7792.031             0.403            0.114
Chain 1:   1200        -7786.123             0.159            0.100
Chain 1:   1300        -7531.814             0.060            0.061
Chain 1:   1400        -7797.247             0.058            0.034
Chain 1:   1500        -7578.369             0.051            0.034
Chain 1:   1600        -7743.644             0.052            0.034
Chain 1:   1700        -7484.682             0.044            0.034
Chain 1:   1800        -7565.946             0.033            0.032
Chain 1:   1900        -7565.759             0.021            0.029
Chain 1:   2000        -7596.027             0.018            0.021
Chain 1:   2100        -7568.935             0.017            0.021
Chain 1:   2200        -7682.973             0.019            0.021
Chain 1:   2300        -7573.276             0.017            0.015
Chain 1:   2400        -7626.211             0.014            0.014
Chain 1:   2500        -7542.374             0.012            0.011
Chain 1:   2600        -7490.003             0.011            0.011
Chain 1:   2700        -7487.102             0.007            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003651 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85550.852             1.000            1.000
Chain 1:    200       -13511.171             3.166            5.332
Chain 1:    300        -9890.249             2.233            1.000
Chain 1:    400       -10637.004             1.692            1.000
Chain 1:    500        -8863.242             1.394            0.366
Chain 1:    600        -8359.410             1.171            0.366
Chain 1:    700        -8386.872             1.005            0.200
Chain 1:    800        -8847.744             0.885            0.200
Chain 1:    900        -8630.065             0.790            0.070
Chain 1:   1000        -8392.517             0.714            0.070
Chain 1:   1100        -8760.372             0.618            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8344.238             0.090            0.052
Chain 1:   1300        -8475.252             0.055            0.050
Chain 1:   1400        -8562.581             0.049            0.042
Chain 1:   1500        -8454.519             0.030            0.028
Chain 1:   1600        -8559.980             0.025            0.025
Chain 1:   1700        -8649.001             0.026            0.025
Chain 1:   1800        -8237.752             0.026            0.025
Chain 1:   1900        -8333.835             0.024            0.015
Chain 1:   2000        -8306.584             0.022            0.013
Chain 1:   2100        -8428.581             0.019            0.013
Chain 1:   2200        -8248.605             0.016            0.013
Chain 1:   2300        -8329.669             0.016            0.012
Chain 1:   2400        -8398.444             0.015            0.012
Chain 1:   2500        -8343.867             0.015            0.012
Chain 1:   2600        -8342.672             0.014            0.010
Chain 1:   2700        -8259.958             0.014            0.010
Chain 1:   2800        -8224.602             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372237.706             1.000            1.000
Chain 1:    200     -1578553.894             2.652            4.304
Chain 1:    300      -890764.230             2.025            1.000
Chain 1:    400      -457926.652             1.755            1.000
Chain 1:    500      -358801.920             1.459            0.945
Chain 1:    600      -233745.434             1.305            0.945
Chain 1:    700      -119633.431             1.255            0.945
Chain 1:    800       -86737.934             1.146            0.945
Chain 1:    900       -67005.359             1.051            0.772
Chain 1:   1000       -51745.773             0.975            0.772
Chain 1:   1100       -39165.047             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38335.874             0.479            0.379
Chain 1:   1300       -26235.504             0.448            0.379
Chain 1:   1400       -25949.004             0.355            0.321
Chain 1:   1500       -22521.316             0.342            0.321
Chain 1:   1600       -21733.127             0.293            0.295
Chain 1:   1700       -20600.190             0.203            0.294
Chain 1:   1800       -20542.932             0.165            0.152
Chain 1:   1900       -20868.890             0.137            0.055
Chain 1:   2000       -19376.774             0.115            0.055
Chain 1:   2100       -19615.296             0.084            0.036
Chain 1:   2200       -19842.216             0.083            0.036
Chain 1:   2300       -19459.067             0.039            0.020
Chain 1:   2400       -19231.139             0.039            0.020
Chain 1:   2500       -19033.349             0.025            0.016
Chain 1:   2600       -18663.398             0.024            0.016
Chain 1:   2700       -18620.363             0.018            0.012
Chain 1:   2800       -18337.301             0.020            0.015
Chain 1:   2900       -18618.620             0.020            0.015
Chain 1:   3000       -18604.782             0.012            0.012
Chain 1:   3100       -18689.748             0.011            0.012
Chain 1:   3200       -18380.426             0.012            0.015
Chain 1:   3300       -18585.161             0.011            0.012
Chain 1:   3400       -18060.167             0.013            0.015
Chain 1:   3500       -18671.991             0.015            0.015
Chain 1:   3600       -17978.821             0.017            0.015
Chain 1:   3700       -18365.530             0.019            0.017
Chain 1:   3800       -17325.479             0.023            0.021
Chain 1:   3900       -17321.682             0.021            0.021
Chain 1:   4000       -17438.941             0.022            0.021
Chain 1:   4100       -17352.696             0.022            0.021
Chain 1:   4200       -17169.050             0.022            0.021
Chain 1:   4300       -17307.354             0.021            0.021
Chain 1:   4400       -17264.216             0.019            0.011
Chain 1:   4500       -17166.818             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00149 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49572.401             1.000            1.000
Chain 1:    200       -17598.358             1.408            1.817
Chain 1:    300       -14294.679             1.016            1.000
Chain 1:    400       -12561.523             0.796            1.000
Chain 1:    500       -13881.521             0.656            0.231
Chain 1:    600       -12087.608             0.572            0.231
Chain 1:    700       -11531.190             0.497            0.148
Chain 1:    800       -12445.358             0.444            0.148
Chain 1:    900       -14324.624             0.409            0.138
Chain 1:   1000       -13277.937             0.376            0.138
Chain 1:   1100       -15171.201             0.289            0.131
Chain 1:   1200       -12291.937             0.130            0.131
Chain 1:   1300       -11037.864             0.119            0.125
Chain 1:   1400       -17442.338             0.142            0.125
Chain 1:   1500       -10591.038             0.197            0.131
Chain 1:   1600       -11137.086             0.187            0.125
Chain 1:   1700       -11188.343             0.182            0.125
Chain 1:   1800       -11836.674             0.181            0.125
Chain 1:   1900       -24198.236             0.218            0.125
Chain 1:   2000       -11807.210             0.316            0.234
Chain 1:   2100       -11111.108             0.309            0.234
Chain 1:   2200       -10798.723             0.289            0.114
Chain 1:   2300       -17945.254             0.317            0.367
Chain 1:   2400       -10267.689             0.355            0.398
Chain 1:   2500       -16798.871             0.330            0.389
Chain 1:   2600       -11061.748             0.376            0.398
Chain 1:   2700       -12045.329             0.384            0.398
Chain 1:   2800       -11808.331             0.381            0.398
Chain 1:   2900        -9685.075             0.352            0.389
Chain 1:   3000        -9614.292             0.247            0.219
Chain 1:   3100       -16035.248             0.281            0.389
Chain 1:   3200       -11490.948             0.318            0.395
Chain 1:   3300       -10662.288             0.286            0.389
Chain 1:   3400       -14912.541             0.239            0.285
Chain 1:   3500       -15437.436             0.204            0.219
Chain 1:   3600        -9780.878             0.210            0.219
Chain 1:   3700        -9569.301             0.204            0.219
Chain 1:   3800        -9564.399             0.202            0.219
Chain 1:   3900       -13845.116             0.211            0.285
Chain 1:   4000        -9501.504             0.256            0.309
Chain 1:   4100        -9325.193             0.218            0.285
Chain 1:   4200       -11495.997             0.197            0.189
Chain 1:   4300       -10063.683             0.204            0.189
Chain 1:   4400        -9115.847             0.186            0.142
Chain 1:   4500        -9166.025             0.183            0.142
Chain 1:   4600       -13201.281             0.155            0.142
Chain 1:   4700       -10425.384             0.180            0.189
Chain 1:   4800        -9214.674             0.193            0.189
Chain 1:   4900        -9280.779             0.163            0.142
Chain 1:   5000       -17221.477             0.163            0.142
Chain 1:   5100       -12952.320             0.194            0.189
Chain 1:   5200        -9268.661             0.215            0.266
Chain 1:   5300       -10257.427             0.210            0.266
Chain 1:   5400       -11364.707             0.210            0.266
Chain 1:   5500       -12973.910             0.222            0.266
Chain 1:   5600       -16474.122             0.212            0.212
Chain 1:   5700        -9835.900             0.253            0.212
Chain 1:   5800       -18814.122             0.288            0.330
Chain 1:   5900       -14211.715             0.319            0.330
Chain 1:   6000       -11743.086             0.294            0.324
Chain 1:   6100       -15010.605             0.283            0.218
Chain 1:   6200        -9186.710             0.307            0.218
Chain 1:   6300       -15650.840             0.338            0.324
Chain 1:   6400        -9495.021             0.394            0.413
Chain 1:   6500        -9076.379             0.386            0.413
Chain 1:   6600        -9007.379             0.365            0.413
Chain 1:   6700       -12805.236             0.327            0.324
Chain 1:   6800        -8942.518             0.323            0.324
Chain 1:   6900       -13276.032             0.323            0.326
Chain 1:   7000        -9610.328             0.340            0.381
Chain 1:   7100        -9574.445             0.319            0.381
Chain 1:   7200       -12334.507             0.278            0.326
Chain 1:   7300       -12311.373             0.237            0.297
Chain 1:   7400        -8666.078             0.214            0.297
Chain 1:   7500        -9470.182             0.218            0.297
Chain 1:   7600        -9045.240             0.222            0.297
Chain 1:   7700        -9139.362             0.193            0.224
Chain 1:   7800       -12232.190             0.175            0.224
Chain 1:   7900        -8698.317             0.183            0.224
Chain 1:   8000        -9679.981             0.155            0.101
Chain 1:   8100        -9378.247             0.158            0.101
Chain 1:   8200       -13047.922             0.164            0.101
Chain 1:   8300        -8786.595             0.212            0.253
Chain 1:   8400        -9057.914             0.173            0.101
Chain 1:   8500        -9319.408             0.167            0.101
Chain 1:   8600        -9287.445             0.163            0.101
Chain 1:   8700        -9049.243             0.165            0.101
Chain 1:   8800        -9271.419             0.142            0.032
Chain 1:   8900        -9874.521             0.107            0.032
Chain 1:   9000        -8576.148             0.112            0.032
Chain 1:   9100       -14169.868             0.149            0.061
Chain 1:   9200        -8794.866             0.182            0.061
Chain 1:   9300        -8879.276             0.134            0.030
Chain 1:   9400        -9529.307             0.138            0.061
Chain 1:   9500        -8689.593             0.145            0.068
Chain 1:   9600       -11616.124             0.169            0.097
Chain 1:   9700        -8859.623             0.198            0.151
Chain 1:   9800        -9667.799             0.204            0.151
Chain 1:   9900       -11571.933             0.214            0.165
Chain 1:   10000        -8589.986             0.234            0.252
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001851 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59023.376             1.000            1.000
Chain 1:    200       -18372.161             1.606            2.213
Chain 1:    300        -8959.756             1.421            1.051
Chain 1:    400        -8119.710             1.092            1.051
Chain 1:    500        -8176.313             0.875            1.000
Chain 1:    600        -8270.803             0.731            1.000
Chain 1:    700        -7768.138             0.636            0.103
Chain 1:    800        -8315.760             0.564            0.103
Chain 1:    900        -7848.737             0.508            0.066
Chain 1:   1000        -7831.688             0.458            0.066
Chain 1:   1100        -7696.978             0.359            0.065
Chain 1:   1200        -7626.022             0.139            0.060
Chain 1:   1300        -7544.025             0.035            0.018
Chain 1:   1400        -7577.948             0.025            0.011
Chain 1:   1500        -7480.344             0.026            0.013
Chain 1:   1600        -7662.615             0.027            0.018
Chain 1:   1700        -7592.538             0.022            0.013
Chain 1:   1800        -7540.547             0.016            0.011
Chain 1:   1900        -7488.503             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003825 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86174.310             1.000            1.000
Chain 1:    200       -14180.535             3.038            5.077
Chain 1:    300       -10420.224             2.146            1.000
Chain 1:    400       -12049.341             1.643            1.000
Chain 1:    500        -9301.482             1.374            0.361
Chain 1:    600        -8863.620             1.153            0.361
Chain 1:    700        -9202.603             0.994            0.295
Chain 1:    800       -10021.514             0.880            0.295
Chain 1:    900        -9270.892             0.791            0.135
Chain 1:   1000        -8782.099             0.717            0.135
Chain 1:   1100        -9255.914             0.622            0.082   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8719.046             0.121            0.081
Chain 1:   1300        -9040.700             0.088            0.062
Chain 1:   1400        -8906.594             0.076            0.056
Chain 1:   1500        -8900.781             0.047            0.051
Chain 1:   1600        -8995.442             0.043            0.051
Chain 1:   1700        -9048.528             0.040            0.051
Chain 1:   1800        -8594.551             0.037            0.051
Chain 1:   1900        -8705.322             0.030            0.036
Chain 1:   2000        -8709.865             0.025            0.015
Chain 1:   2100        -8655.246             0.020            0.013
Chain 1:   2200        -8627.662             0.014            0.011
Chain 1:   2300        -8808.674             0.013            0.011
Chain 1:   2400        -8601.336             0.014            0.011
Chain 1:   2500        -8673.783             0.014            0.011
Chain 1:   2600        -8585.345             0.014            0.010
Chain 1:   2700        -8622.539             0.014            0.010
Chain 1:   2800        -8573.847             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003136 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8366860.229             1.000            1.000
Chain 1:    200     -1583276.693             2.642            4.285
Chain 1:    300      -892033.721             2.020            1.000
Chain 1:    400      -458712.523             1.751            1.000
Chain 1:    500      -359289.732             1.456            0.945
Chain 1:    600      -234149.050             1.303            0.945
Chain 1:    700      -120185.920             1.252            0.945
Chain 1:    800       -87339.812             1.142            0.945
Chain 1:    900       -67648.394             1.048            0.775
Chain 1:   1000       -52427.529             0.972            0.775
Chain 1:   1100       -39871.353             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39053.095             0.477            0.376
Chain 1:   1300       -26960.844             0.445            0.376
Chain 1:   1400       -26679.576             0.351            0.315
Chain 1:   1500       -23252.837             0.338            0.315
Chain 1:   1600       -22466.488             0.288            0.291
Chain 1:   1700       -21333.542             0.199            0.290
Chain 1:   1800       -21276.806             0.161            0.147
Chain 1:   1900       -21603.673             0.134            0.053
Chain 1:   2000       -20109.833             0.112            0.053
Chain 1:   2100       -20348.640             0.082            0.035
Chain 1:   2200       -20576.091             0.081            0.035
Chain 1:   2300       -20192.208             0.038            0.019
Chain 1:   2400       -19963.942             0.038            0.019
Chain 1:   2500       -19766.137             0.024            0.015
Chain 1:   2600       -19395.446             0.023            0.015
Chain 1:   2700       -19352.169             0.018            0.012
Chain 1:   2800       -19068.695             0.019            0.015
Chain 1:   2900       -19350.407             0.019            0.015
Chain 1:   3000       -19336.541             0.011            0.012
Chain 1:   3100       -19421.626             0.011            0.011
Chain 1:   3200       -19111.804             0.011            0.015
Chain 1:   3300       -19316.947             0.010            0.011
Chain 1:   3400       -18790.958             0.012            0.015
Chain 1:   3500       -19404.267             0.014            0.015
Chain 1:   3600       -18709.153             0.016            0.015
Chain 1:   3700       -19097.276             0.018            0.016
Chain 1:   3800       -18054.232             0.022            0.020
Chain 1:   3900       -18050.337             0.021            0.020
Chain 1:   4000       -18167.628             0.021            0.020
Chain 1:   4100       -18081.216             0.021            0.020
Chain 1:   4200       -17896.898             0.021            0.020
Chain 1:   4300       -18035.690             0.020            0.020
Chain 1:   4400       -17992.018             0.018            0.010
Chain 1:   4500       -17894.484             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001862 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48856.766             1.000            1.000
Chain 1:    200       -22633.214             1.079            1.159
Chain 1:    300       -16272.758             0.850            1.000
Chain 1:    400       -23327.939             0.713            1.000
Chain 1:    500       -12074.688             0.757            0.932
Chain 1:    600       -30662.019             0.732            0.932
Chain 1:    700       -13340.661             0.813            0.932
Chain 1:    800       -10701.548             0.742            0.932
Chain 1:    900       -12830.452             0.678            0.606
Chain 1:   1000       -18546.235             0.641            0.606
Chain 1:   1100       -14370.894             0.570            0.391   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12864.001             0.466            0.308
Chain 1:   1300       -11310.570             0.440            0.302
Chain 1:   1400       -12070.319             0.417            0.291
Chain 1:   1500       -10320.729             0.340            0.247
Chain 1:   1600        -9897.401             0.284            0.170
Chain 1:   1700       -11481.793             0.168            0.166
Chain 1:   1800       -12487.177             0.151            0.138
Chain 1:   1900       -10310.256             0.156            0.138
Chain 1:   2000       -10316.663             0.125            0.137
Chain 1:   2100        -9490.606             0.105            0.117
Chain 1:   2200       -10549.406             0.103            0.100
Chain 1:   2300        -9508.348             0.100            0.100
Chain 1:   2400       -11534.454             0.112            0.109
Chain 1:   2500        -9610.781             0.115            0.109
Chain 1:   2600        -9935.494             0.114            0.109
Chain 1:   2700        -9261.934             0.107            0.100
Chain 1:   2800       -14335.944             0.134            0.109
Chain 1:   2900        -9201.114             0.169            0.109
Chain 1:   3000       -17662.339             0.217            0.176
Chain 1:   3100        -8629.262             0.313            0.200
Chain 1:   3200        -9074.226             0.308            0.200
Chain 1:   3300       -13359.563             0.329            0.321
Chain 1:   3400       -17651.044             0.336            0.321
Chain 1:   3500        -9069.741             0.410            0.354
Chain 1:   3600        -9789.113             0.414            0.354
Chain 1:   3700       -13219.155             0.433            0.354
Chain 1:   3800        -9190.568             0.441            0.438
Chain 1:   3900       -10560.637             0.399            0.321
Chain 1:   4000        -9600.107             0.361            0.259
Chain 1:   4100        -9620.070             0.256            0.243
Chain 1:   4200       -15303.625             0.288            0.259
Chain 1:   4300       -10004.516             0.309            0.259
Chain 1:   4400       -13090.809             0.309            0.259
Chain 1:   4500        -9326.885             0.254            0.259
Chain 1:   4600       -10195.152             0.256            0.259
Chain 1:   4700        -9517.941             0.237            0.236
Chain 1:   4800        -8676.834             0.203            0.130
Chain 1:   4900       -13604.093             0.226            0.236
Chain 1:   5000        -9758.993             0.255            0.362
Chain 1:   5100        -8823.967             0.266            0.362
Chain 1:   5200       -11167.216             0.249            0.236
Chain 1:   5300       -12835.605             0.209            0.210
Chain 1:   5400       -10385.059             0.209            0.210
Chain 1:   5500        -8520.300             0.191            0.210
Chain 1:   5600       -13347.501             0.219            0.219
Chain 1:   5700       -14720.934             0.221            0.219
Chain 1:   5800        -8760.244             0.279            0.236
Chain 1:   5900       -10458.576             0.259            0.219
Chain 1:   6000       -10217.493             0.222            0.210
Chain 1:   6100        -9213.124             0.223            0.210
Chain 1:   6200        -8664.776             0.208            0.162
Chain 1:   6300       -10336.109             0.211            0.162
Chain 1:   6400       -12026.594             0.201            0.162
Chain 1:   6500        -9783.014             0.203            0.162
Chain 1:   6600        -9301.737             0.172            0.141
Chain 1:   6700        -9550.804             0.165            0.141
Chain 1:   6800       -10604.348             0.107            0.109
Chain 1:   6900       -11483.043             0.098            0.099
Chain 1:   7000       -12219.650             0.102            0.099
Chain 1:   7100        -8900.328             0.128            0.099
Chain 1:   7200        -8516.454             0.126            0.099
Chain 1:   7300        -9327.485             0.119            0.087
Chain 1:   7400        -8523.503             0.114            0.087
Chain 1:   7500       -10473.191             0.110            0.087
Chain 1:   7600        -8487.770             0.128            0.094
Chain 1:   7700        -8592.674             0.127            0.094
Chain 1:   7800        -9364.094             0.125            0.087
Chain 1:   7900       -11596.925             0.137            0.094
Chain 1:   8000        -9893.592             0.148            0.172
Chain 1:   8100        -8647.764             0.125            0.144
Chain 1:   8200        -8939.720             0.124            0.144
Chain 1:   8300       -11732.616             0.139            0.172
Chain 1:   8400        -8478.419             0.168            0.186
Chain 1:   8500        -9468.273             0.160            0.172
Chain 1:   8600        -8679.913             0.145            0.144
Chain 1:   8700        -8562.717             0.145            0.144
Chain 1:   8800        -8401.689             0.139            0.144
Chain 1:   8900        -8663.556             0.123            0.105
Chain 1:   9000       -10267.817             0.121            0.105
Chain 1:   9100        -8102.169             0.134            0.105
Chain 1:   9200       -11417.267             0.159            0.156
Chain 1:   9300       -10291.236             0.147            0.109
Chain 1:   9400        -8276.954             0.133            0.109
Chain 1:   9500       -10525.494             0.143            0.156
Chain 1:   9600        -8572.674             0.157            0.214
Chain 1:   9700        -9653.511             0.167            0.214
Chain 1:   9800       -11961.690             0.184            0.214
Chain 1:   9900        -9540.008             0.207            0.228
Chain 1:   10000       -11160.758             0.206            0.228
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001811 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58116.557             1.000            1.000
Chain 1:    200       -17638.084             1.647            2.295
Chain 1:    300        -8656.613             1.444            1.038
Chain 1:    400        -8133.751             1.099            1.038
Chain 1:    500        -8469.759             0.887            1.000
Chain 1:    600        -8653.647             0.743            1.000
Chain 1:    700        -7771.562             0.653            0.114
Chain 1:    800        -8120.220             0.577            0.114
Chain 1:    900        -7905.905             0.516            0.064
Chain 1:   1000        -8031.576             0.466            0.064
Chain 1:   1100        -7633.655             0.371            0.052
Chain 1:   1200        -7547.003             0.143            0.043
Chain 1:   1300        -7620.798             0.040            0.040
Chain 1:   1400        -7616.349             0.033            0.027
Chain 1:   1500        -7579.923             0.030            0.021
Chain 1:   1600        -7633.265             0.028            0.016
Chain 1:   1700        -7524.302             0.019            0.014
Chain 1:   1800        -7586.258             0.015            0.011
Chain 1:   1900        -7588.762             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86230.003             1.000            1.000
Chain 1:    200       -13484.881             3.197            5.395
Chain 1:    300        -9911.775             2.252            1.000
Chain 1:    400       -10880.786             1.711            1.000
Chain 1:    500        -8863.651             1.414            0.360
Chain 1:    600        -8732.023             1.181            0.360
Chain 1:    700        -8402.673             1.018            0.228
Chain 1:    800        -8796.872             0.896            0.228
Chain 1:    900        -8793.519             0.797            0.089
Chain 1:   1000        -8473.032             0.721            0.089
Chain 1:   1100        -8776.992             0.624            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8428.442             0.089            0.041
Chain 1:   1300        -8621.439             0.055            0.039
Chain 1:   1400        -8620.547             0.046            0.038
Chain 1:   1500        -8519.299             0.025            0.035
Chain 1:   1600        -8620.163             0.024            0.035
Chain 1:   1700        -8708.538             0.022            0.022
Chain 1:   1800        -8310.531             0.022            0.022
Chain 1:   1900        -8411.698             0.023            0.022
Chain 1:   2000        -8382.459             0.020            0.012
Chain 1:   2100        -8503.812             0.018            0.012
Chain 1:   2200        -8282.641             0.016            0.012
Chain 1:   2300        -8440.513             0.016            0.012
Chain 1:   2400        -8453.330             0.016            0.012
Chain 1:   2500        -8424.234             0.015            0.012
Chain 1:   2600        -8426.895             0.014            0.012
Chain 1:   2700        -8333.082             0.014            0.012
Chain 1:   2800        -8303.592             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003413 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399760.364             1.000            1.000
Chain 1:    200     -1584384.950             2.651            4.302
Chain 1:    300      -889912.558             2.027            1.000
Chain 1:    400      -457089.280             1.757            1.000
Chain 1:    500      -357651.662             1.461            0.947
Chain 1:    600      -232552.925             1.307            0.947
Chain 1:    700      -119019.005             1.257            0.947
Chain 1:    800       -86278.325             1.147            0.947
Chain 1:    900       -66656.723             1.053            0.780
Chain 1:   1000       -51480.962             0.977            0.780
Chain 1:   1100       -38983.884             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38160.902             0.481            0.379
Chain 1:   1300       -26148.446             0.449            0.379
Chain 1:   1400       -25868.117             0.355            0.321
Chain 1:   1500       -22463.867             0.342            0.321
Chain 1:   1600       -21682.488             0.292            0.295
Chain 1:   1700       -20560.381             0.202            0.294
Chain 1:   1800       -20505.346             0.165            0.152
Chain 1:   1900       -20831.206             0.137            0.055
Chain 1:   2000       -19345.218             0.115            0.055
Chain 1:   2100       -19583.347             0.084            0.036
Chain 1:   2200       -19809.255             0.083            0.036
Chain 1:   2300       -19427.033             0.039            0.020
Chain 1:   2400       -19199.304             0.039            0.020
Chain 1:   2500       -19001.191             0.025            0.016
Chain 1:   2600       -18631.844             0.024            0.016
Chain 1:   2700       -18589.000             0.018            0.012
Chain 1:   2800       -18305.947             0.020            0.015
Chain 1:   2900       -18587.008             0.020            0.015
Chain 1:   3000       -18573.217             0.012            0.012
Chain 1:   3100       -18658.151             0.011            0.012
Chain 1:   3200       -18349.116             0.012            0.015
Chain 1:   3300       -18553.641             0.011            0.012
Chain 1:   3400       -18028.989             0.013            0.015
Chain 1:   3500       -18640.184             0.015            0.015
Chain 1:   3600       -17947.775             0.017            0.015
Chain 1:   3700       -18333.866             0.019            0.017
Chain 1:   3800       -17294.949             0.023            0.021
Chain 1:   3900       -17291.140             0.021            0.021
Chain 1:   4000       -17408.440             0.022            0.021
Chain 1:   4100       -17322.248             0.022            0.021
Chain 1:   4200       -17138.840             0.022            0.021
Chain 1:   4300       -17277.016             0.021            0.021
Chain 1:   4400       -17234.089             0.019            0.011
Chain 1:   4500       -17136.665             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001522 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48874.189             1.000            1.000
Chain 1:    200       -16707.660             1.463            1.925
Chain 1:    300       -14851.874             1.017            1.000
Chain 1:    400       -12543.229             0.809            1.000
Chain 1:    500       -12886.247             0.652            0.184
Chain 1:    600       -27486.298             0.632            0.531
Chain 1:    700       -15356.628             0.655            0.531
Chain 1:    800       -13496.002             0.590            0.531
Chain 1:    900       -16185.630             0.543            0.184
Chain 1:   1000       -30596.060             0.536            0.471
Chain 1:   1100       -10839.512             0.618            0.471   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -21733.962             0.476            0.471
Chain 1:   1300       -10190.235             0.576            0.501   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -10639.596             0.562            0.501   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -19826.548             0.606            0.501   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1600       -12645.869             0.610            0.501   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700       -11297.409             0.542            0.471   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1800       -10034.995             0.541            0.471   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -11045.928             0.534            0.471   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -14620.986             0.511            0.463   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100       -10165.132             0.373            0.438
Chain 1:   2200        -9935.348             0.325            0.245
Chain 1:   2300       -12241.492             0.230            0.188
Chain 1:   2400        -9310.038             0.258            0.245
Chain 1:   2500        -9888.079             0.217            0.188
Chain 1:   2600       -10814.418             0.169            0.126
Chain 1:   2700        -9102.174             0.176            0.188
Chain 1:   2800        -9676.828             0.169            0.188
Chain 1:   2900        -9288.538             0.164            0.188
Chain 1:   3000        -8986.650             0.143            0.086
Chain 1:   3100        -8623.512             0.104            0.059
Chain 1:   3200        -9219.861             0.108            0.065
Chain 1:   3300       -17333.252             0.136            0.065
Chain 1:   3400       -13628.309             0.131            0.065
Chain 1:   3500        -9970.551             0.162            0.086
Chain 1:   3600       -16007.034             0.191            0.188
Chain 1:   3700        -9392.998             0.243            0.272
Chain 1:   3800       -15479.997             0.276            0.367
Chain 1:   3900        -8807.787             0.348            0.377
Chain 1:   4000       -11004.937             0.365            0.377
Chain 1:   4100        -9014.261             0.382            0.377
Chain 1:   4200       -13419.274             0.409            0.377
Chain 1:   4300       -10188.326             0.394            0.367
Chain 1:   4400        -9570.917             0.373            0.367
Chain 1:   4500       -11891.534             0.356            0.328
Chain 1:   4600        -8701.826             0.355            0.328
Chain 1:   4700        -8652.186             0.285            0.317
Chain 1:   4800        -8777.328             0.247            0.221
Chain 1:   4900        -8752.155             0.171            0.200
Chain 1:   5000        -9897.300             0.163            0.195
Chain 1:   5100       -16162.285             0.180            0.195
Chain 1:   5200        -8807.001             0.230            0.195
Chain 1:   5300        -9622.440             0.207            0.116
Chain 1:   5400        -8935.598             0.208            0.116
Chain 1:   5500        -9729.848             0.197            0.085
Chain 1:   5600        -8819.677             0.171            0.085
Chain 1:   5700       -10816.038             0.189            0.103
Chain 1:   5800       -10762.471             0.188            0.103
Chain 1:   5900       -10727.105             0.188            0.103
Chain 1:   6000        -9399.483             0.190            0.103
Chain 1:   6100        -9209.921             0.154            0.085
Chain 1:   6200        -8696.582             0.076            0.082
Chain 1:   6300        -8383.130             0.071            0.077
Chain 1:   6400       -13298.031             0.101            0.082
Chain 1:   6500        -8595.410             0.147            0.103
Chain 1:   6600        -8847.123             0.140            0.059
Chain 1:   6700       -12488.087             0.150            0.059
Chain 1:   6800       -11982.953             0.154            0.059
Chain 1:   6900        -8856.868             0.189            0.141
Chain 1:   7000        -8827.598             0.175            0.059
Chain 1:   7100        -8481.086             0.177            0.059
Chain 1:   7200        -8562.938             0.172            0.042
Chain 1:   7300        -9372.423             0.177            0.086
Chain 1:   7400        -8356.810             0.152            0.086
Chain 1:   7500        -8881.467             0.104            0.059
Chain 1:   7600        -8562.951             0.104            0.059
Chain 1:   7700        -8179.678             0.080            0.047
Chain 1:   7800       -11292.374             0.103            0.059
Chain 1:   7900       -10674.077             0.074            0.058
Chain 1:   8000        -8357.171             0.101            0.059
Chain 1:   8100        -8703.971             0.101            0.059
Chain 1:   8200       -11028.243             0.121            0.086
Chain 1:   8300        -9915.327             0.124            0.112
Chain 1:   8400        -8576.638             0.127            0.112
Chain 1:   8500        -9096.638             0.127            0.112
Chain 1:   8600        -9613.614             0.129            0.112
Chain 1:   8700        -8142.967             0.142            0.156
Chain 1:   8800        -8344.039             0.117            0.112
Chain 1:   8900        -8384.955             0.112            0.112
Chain 1:   9000        -9908.223             0.099            0.112
Chain 1:   9100        -8246.042             0.115            0.154
Chain 1:   9200        -8740.369             0.100            0.112
Chain 1:   9300        -9728.562             0.099            0.102
Chain 1:   9400       -11721.214             0.100            0.102
Chain 1:   9500       -11634.915             0.095            0.102
Chain 1:   9600        -9015.665             0.119            0.154
Chain 1:   9700        -9820.009             0.109            0.102
Chain 1:   9800        -8959.123             0.116            0.102
Chain 1:   9900        -8462.511             0.122            0.102
Chain 1:   10000        -8319.579             0.108            0.096
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001488 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46392.601             1.000            1.000
Chain 1:    200       -15616.097             1.485            1.971
Chain 1:    300        -8748.716             1.252            1.000
Chain 1:    400        -8671.491             0.941            1.000
Chain 1:    500        -8287.772             0.762            0.785
Chain 1:    600        -8536.023             0.640            0.785
Chain 1:    700        -8253.909             0.553            0.046
Chain 1:    800        -8117.112             0.486            0.046
Chain 1:    900        -8093.401             0.433            0.034
Chain 1:   1000        -7844.855             0.393            0.034
Chain 1:   1100        -7822.876             0.293            0.032
Chain 1:   1200        -7940.928             0.097            0.029
Chain 1:   1300        -7790.794             0.021            0.019
Chain 1:   1400        -7726.734             0.021            0.019
Chain 1:   1500        -7689.008             0.016            0.017
Chain 1:   1600        -7762.169             0.015            0.015
Chain 1:   1700        -7616.655             0.013            0.015
Chain 1:   1800        -7682.349             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002613 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85970.734             1.000            1.000
Chain 1:    200       -13501.836             3.184            5.367
Chain 1:    300        -9902.794             2.244            1.000
Chain 1:    400       -10627.056             1.700            1.000
Chain 1:    500        -8856.946             1.400            0.363
Chain 1:    600        -8388.378             1.176            0.363
Chain 1:    700        -8420.708             1.008            0.200
Chain 1:    800        -9072.812             0.891            0.200
Chain 1:    900        -8680.554             0.797            0.072
Chain 1:   1000        -8530.490             0.719            0.072
Chain 1:   1100        -8772.428             0.622            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8308.349             0.091            0.056
Chain 1:   1300        -8607.102             0.058            0.056
Chain 1:   1400        -8612.788             0.051            0.045
Chain 1:   1500        -8492.616             0.033            0.035
Chain 1:   1600        -8598.916             0.028            0.028
Chain 1:   1700        -8685.145             0.029            0.028
Chain 1:   1800        -8282.543             0.027            0.028
Chain 1:   1900        -8381.019             0.023            0.018
Chain 1:   2000        -8352.624             0.022            0.014
Chain 1:   2100        -8472.462             0.021            0.014
Chain 1:   2200        -8278.916             0.017            0.014
Chain 1:   2300        -8416.173             0.015            0.014
Chain 1:   2400        -8291.382             0.017            0.014
Chain 1:   2500        -8356.278             0.016            0.014
Chain 1:   2600        -8379.700             0.015            0.014
Chain 1:   2700        -8298.147             0.015            0.014
Chain 1:   2800        -8270.792             0.011            0.012
Chain 1:   2900        -8326.209             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002731 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390450.948             1.000            1.000
Chain 1:    200     -1582627.386             2.651            4.302
Chain 1:    300      -891551.068             2.026            1.000
Chain 1:    400      -458040.114             1.756            1.000
Chain 1:    500      -358418.478             1.460            0.946
Chain 1:    600      -233499.353             1.306            0.946
Chain 1:    700      -119509.380             1.256            0.946
Chain 1:    800       -86612.977             1.146            0.946
Chain 1:    900       -66918.873             1.052            0.775
Chain 1:   1000       -51670.328             0.976            0.775
Chain 1:   1100       -39105.716             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38277.345             0.480            0.380
Chain 1:   1300       -26203.061             0.449            0.380
Chain 1:   1400       -25918.304             0.355            0.321
Chain 1:   1500       -22496.458             0.342            0.321
Chain 1:   1600       -21709.457             0.293            0.295
Chain 1:   1700       -20580.251             0.203            0.294
Chain 1:   1800       -20523.413             0.165            0.152
Chain 1:   1900       -20849.290             0.137            0.055
Chain 1:   2000       -19359.008             0.115            0.055
Chain 1:   2100       -19597.681             0.084            0.036
Chain 1:   2200       -19824.026             0.083            0.036
Chain 1:   2300       -19441.370             0.039            0.020
Chain 1:   2400       -19213.513             0.039            0.020
Chain 1:   2500       -19015.476             0.025            0.016
Chain 1:   2600       -18646.030             0.024            0.016
Chain 1:   2700       -18603.066             0.018            0.012
Chain 1:   2800       -18319.984             0.020            0.015
Chain 1:   2900       -18601.207             0.020            0.015
Chain 1:   3000       -18587.474             0.012            0.012
Chain 1:   3100       -18672.369             0.011            0.012
Chain 1:   3200       -18363.241             0.012            0.015
Chain 1:   3300       -18567.813             0.011            0.012
Chain 1:   3400       -18043.025             0.013            0.015
Chain 1:   3500       -18654.438             0.015            0.015
Chain 1:   3600       -17961.800             0.017            0.015
Chain 1:   3700       -18348.114             0.019            0.017
Chain 1:   3800       -17308.782             0.023            0.021
Chain 1:   3900       -17304.935             0.021            0.021
Chain 1:   4000       -17422.263             0.022            0.021
Chain 1:   4100       -17336.025             0.022            0.021
Chain 1:   4200       -17152.490             0.022            0.021
Chain 1:   4300       -17290.750             0.021            0.021
Chain 1:   4400       -17247.781             0.019            0.011
Chain 1:   4500       -17150.320             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001282 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13458.418             1.000            1.000
Chain 1:    200       -10110.648             0.666            1.000
Chain 1:    300        -8680.909             0.499            0.331
Chain 1:    400        -8282.380             0.386            0.331
Chain 1:    500        -8174.234             0.311            0.165
Chain 1:    600        -8128.836             0.260            0.165
Chain 1:    700        -8010.319             0.225            0.048
Chain 1:    800        -8053.876             0.198            0.048
Chain 1:    900        -8157.239             0.177            0.015
Chain 1:   1000        -8061.342             0.161            0.015
Chain 1:   1100        -8021.633             0.061            0.013
Chain 1:   1200        -8018.179             0.028            0.013
Chain 1:   1300        -8118.433             0.013            0.012
Chain 1:   1400        -7997.468             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003195 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47487.294             1.000            1.000
Chain 1:    200       -16224.010             1.463            1.927
Chain 1:    300        -8776.437             1.259            1.000
Chain 1:    400        -8227.694             0.961            1.000
Chain 1:    500        -8877.496             0.783            0.849
Chain 1:    600        -8282.938             0.665            0.849
Chain 1:    700        -8304.237             0.570            0.073
Chain 1:    800        -8361.719             0.500            0.073
Chain 1:    900        -8204.688             0.446            0.072
Chain 1:   1000        -7922.722             0.405            0.072
Chain 1:   1100        -7745.875             0.307            0.067
Chain 1:   1200        -7768.378             0.115            0.036
Chain 1:   1300        -7848.537             0.031            0.023
Chain 1:   1400        -7702.093             0.026            0.019
Chain 1:   1500        -7588.086             0.021            0.019
Chain 1:   1600        -7782.511             0.016            0.019
Chain 1:   1700        -7563.061             0.019            0.019
Chain 1:   1800        -7725.918             0.020            0.021
Chain 1:   1900        -7641.085             0.019            0.021
Chain 1:   2000        -7664.515             0.016            0.019
Chain 1:   2100        -7667.667             0.014            0.015
Chain 1:   2200        -7777.103             0.015            0.015
Chain 1:   2300        -7576.716             0.016            0.019
Chain 1:   2400        -7601.122             0.015            0.015
Chain 1:   2500        -7739.676             0.015            0.018
Chain 1:   2600        -7569.065             0.015            0.018
Chain 1:   2700        -7601.112             0.012            0.014
Chain 1:   2800        -7588.157             0.010            0.011
Chain 1:   2900        -7469.214             0.011            0.014
Chain 1:   3000        -7604.501             0.012            0.016
Chain 1:   3100        -7576.095             0.013            0.016
Chain 1:   3200        -7778.212             0.014            0.018
Chain 1:   3300        -7495.684             0.015            0.018
Chain 1:   3400        -7729.078             0.018            0.018
Chain 1:   3500        -7481.298             0.019            0.023
Chain 1:   3600        -7547.475             0.018            0.018
Chain 1:   3700        -7497.329             0.018            0.018
Chain 1:   3800        -7496.274             0.018            0.018
Chain 1:   3900        -7459.129             0.017            0.018
Chain 1:   4000        -7450.811             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003039 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87788.719             1.000            1.000
Chain 1:    200       -13893.251             3.159            5.319
Chain 1:    300       -10125.078             2.230            1.000
Chain 1:    400       -11744.284             1.707            1.000
Chain 1:    500        -8786.595             1.433            0.372
Chain 1:    600        -8552.572             1.199            0.372
Chain 1:    700        -8601.159             1.028            0.337
Chain 1:    800        -9648.220             0.913            0.337
Chain 1:    900        -8898.445             0.821            0.138
Chain 1:   1000        -9046.763             0.741            0.138
Chain 1:   1100        -8825.596             0.643            0.109   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8477.252             0.115            0.084
Chain 1:   1300        -8772.299             0.082            0.041
Chain 1:   1400        -8679.556             0.069            0.034
Chain 1:   1500        -8625.407             0.036            0.027
Chain 1:   1600        -8736.700             0.034            0.025
Chain 1:   1700        -8790.622             0.034            0.025
Chain 1:   1800        -8345.829             0.029            0.025
Chain 1:   1900        -8451.069             0.022            0.016
Chain 1:   2000        -8432.944             0.020            0.013
Chain 1:   2100        -8570.687             0.019            0.013
Chain 1:   2200        -8345.453             0.018            0.013
Chain 1:   2300        -8443.060             0.016            0.012
Chain 1:   2400        -8517.932             0.016            0.012
Chain 1:   2500        -8457.698             0.016            0.012
Chain 1:   2600        -8474.580             0.015            0.012
Chain 1:   2700        -8380.757             0.015            0.012
Chain 1:   2800        -8326.291             0.010            0.011
Chain 1:   2900        -8431.797             0.010            0.011
Chain 1:   3000        -8270.637             0.012            0.012
Chain 1:   3100        -8410.822             0.012            0.012
Chain 1:   3200        -8279.991             0.011            0.012
Chain 1:   3300        -8509.687             0.013            0.013
Chain 1:   3400        -8517.048             0.012            0.013
Chain 1:   3500        -8385.087             0.013            0.016
Chain 1:   3600        -8237.319             0.014            0.016
Chain 1:   3700        -8384.272             0.015            0.017
Chain 1:   3800        -8239.993             0.016            0.018
Chain 1:   3900        -8171.796             0.016            0.018
Chain 1:   4000        -8282.014             0.015            0.017
Chain 1:   4100        -8247.004             0.014            0.016
Chain 1:   4200        -8232.843             0.012            0.016
Chain 1:   4300        -8266.286             0.010            0.013
Chain 1:   4400        -8223.186             0.011            0.013
Chain 1:   4500        -8321.189             0.010            0.012
Chain 1:   4600        -8212.803             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0031 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8444436.133             1.000            1.000
Chain 1:    200     -1591610.979             2.653            4.306
Chain 1:    300      -891713.133             2.030            1.000
Chain 1:    400      -457696.601             1.760            1.000
Chain 1:    500      -357582.675             1.464            0.948
Chain 1:    600      -232447.523             1.310            0.948
Chain 1:    700      -119168.397             1.258            0.948
Chain 1:    800       -86463.325             1.148            0.948
Chain 1:    900       -66915.394             1.053            0.785
Chain 1:   1000       -51801.885             0.977            0.785
Chain 1:   1100       -39352.874             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38544.802             0.480            0.378
Chain 1:   1300       -26570.522             0.447            0.378
Chain 1:   1400       -26298.147             0.353            0.316
Chain 1:   1500       -22902.431             0.340            0.316
Chain 1:   1600       -22124.419             0.289            0.292
Chain 1:   1700       -21006.297             0.200            0.292
Chain 1:   1800       -20952.593             0.162            0.148
Chain 1:   1900       -21279.405             0.134            0.053
Chain 1:   2000       -19793.329             0.113            0.053
Chain 1:   2100       -20031.748             0.082            0.035
Chain 1:   2200       -20257.843             0.081            0.035
Chain 1:   2300       -19875.180             0.038            0.019
Chain 1:   2400       -19647.151             0.038            0.019
Chain 1:   2500       -19448.643             0.025            0.015
Chain 1:   2600       -19078.751             0.023            0.015
Chain 1:   2700       -19035.699             0.018            0.012
Chain 1:   2800       -18752.069             0.019            0.015
Chain 1:   2900       -19033.493             0.019            0.015
Chain 1:   3000       -19019.760             0.012            0.012
Chain 1:   3100       -19104.805             0.011            0.012
Chain 1:   3200       -18795.225             0.011            0.015
Chain 1:   3300       -19000.142             0.011            0.012
Chain 1:   3400       -18474.366             0.012            0.015
Chain 1:   3500       -19087.153             0.014            0.015
Chain 1:   3600       -18392.629             0.016            0.015
Chain 1:   3700       -18780.267             0.018            0.016
Chain 1:   3800       -17737.957             0.022            0.021
Chain 1:   3900       -17733.965             0.021            0.021
Chain 1:   4000       -17851.358             0.022            0.021
Chain 1:   4100       -17764.996             0.022            0.021
Chain 1:   4200       -17580.783             0.021            0.021
Chain 1:   4300       -17719.563             0.021            0.021
Chain 1:   4400       -17676.052             0.018            0.010
Chain 1:   4500       -17578.434             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001433 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12749.500             1.000            1.000
Chain 1:    200        -9700.973             0.657            1.000
Chain 1:    300        -8504.946             0.485            0.314
Chain 1:    400        -8698.310             0.369            0.314
Chain 1:    500        -8505.163             0.300            0.141
Chain 1:    600        -8428.156             0.251            0.141
Chain 1:    700        -8338.150             0.217            0.023
Chain 1:    800        -8344.428             0.190            0.023
Chain 1:    900        -8261.105             0.170            0.022
Chain 1:   1000        -8447.422             0.155            0.022
Chain 1:   1100        -8479.206             0.056            0.022
Chain 1:   1200        -8351.353             0.026            0.015
Chain 1:   1300        -8327.109             0.012            0.011
Chain 1:   1400        -8333.167             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62206.801             1.000            1.000
Chain 1:    200       -18368.221             1.693            2.387
Chain 1:    300        -9095.950             1.469            1.019
Chain 1:    400        -8728.526             1.112            1.019
Chain 1:    500        -8773.059             0.891            1.000
Chain 1:    600        -9387.979             0.753            1.000
Chain 1:    700        -8269.620             0.665            0.135
Chain 1:    800        -8625.963             0.587            0.135
Chain 1:    900        -7675.773             0.535            0.124
Chain 1:   1000        -8343.000             0.490            0.124
Chain 1:   1100        -7943.251             0.395            0.080
Chain 1:   1200        -7790.870             0.158            0.066
Chain 1:   1300        -7779.396             0.056            0.050
Chain 1:   1400        -7668.471             0.054            0.050
Chain 1:   1500        -7579.129             0.054            0.050
Chain 1:   1600        -7879.052             0.052            0.041
Chain 1:   1700        -7649.172             0.041            0.038
Chain 1:   1800        -7694.189             0.038            0.030
Chain 1:   1900        -7603.184             0.026            0.020
Chain 1:   2000        -7597.942             0.018            0.014
Chain 1:   2100        -7552.211             0.014            0.012
Chain 1:   2200        -7833.719             0.016            0.012
Chain 1:   2300        -7636.253             0.018            0.014
Chain 1:   2400        -7657.303             0.017            0.012
Chain 1:   2500        -7644.137             0.016            0.012
Chain 1:   2600        -7564.386             0.013            0.011
Chain 1:   2700        -7560.850             0.010            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002567 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86137.793             1.000            1.000
Chain 1:    200       -13952.708             3.087            5.174
Chain 1:    300       -10326.696             2.175            1.000
Chain 1:    400       -11175.920             1.650            1.000
Chain 1:    500        -9300.273             1.360            0.351
Chain 1:    600        -8925.560             1.141            0.351
Chain 1:    700        -8883.074             0.978            0.202
Chain 1:    800        -9252.219             0.861            0.202
Chain 1:    900        -9119.797             0.767            0.076
Chain 1:   1000        -9038.704             0.691            0.076
Chain 1:   1100        -9192.810             0.593            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8809.543             0.080            0.042
Chain 1:   1300        -9019.806             0.047            0.040
Chain 1:   1400        -9033.178             0.040            0.023
Chain 1:   1500        -8885.762             0.021            0.017
Chain 1:   1600        -8998.769             0.018            0.017
Chain 1:   1700        -9082.317             0.019            0.017
Chain 1:   1800        -8670.012             0.019            0.017
Chain 1:   1900        -8765.947             0.019            0.017
Chain 1:   2000        -8739.289             0.018            0.017
Chain 1:   2100        -8861.666             0.018            0.014
Chain 1:   2200        -8681.768             0.016            0.014
Chain 1:   2300        -8761.014             0.014            0.013
Chain 1:   2400        -8830.715             0.015            0.013
Chain 1:   2500        -8776.107             0.014            0.011
Chain 1:   2600        -8775.448             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003133 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8382400.872             1.000            1.000
Chain 1:    200     -1581644.141             2.650            4.300
Chain 1:    300      -892097.513             2.024            1.000
Chain 1:    400      -458689.292             1.754            1.000
Chain 1:    500      -359313.658             1.459            0.945
Chain 1:    600      -234209.998             1.305            0.945
Chain 1:    700      -120080.089             1.254            0.945
Chain 1:    800       -87173.267             1.145            0.945
Chain 1:    900       -67446.113             1.050            0.773
Chain 1:   1000       -52186.209             0.974            0.773
Chain 1:   1100       -39606.334             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38777.571             0.478            0.377
Chain 1:   1300       -26679.751             0.446            0.377
Chain 1:   1400       -26393.370             0.353            0.318
Chain 1:   1500       -22966.256             0.340            0.318
Chain 1:   1600       -22178.255             0.290            0.292
Chain 1:   1700       -21045.753             0.200            0.292
Chain 1:   1800       -20988.463             0.163            0.149
Chain 1:   1900       -21314.527             0.135            0.054
Chain 1:   2000       -19822.565             0.114            0.054
Chain 1:   2100       -20061.043             0.083            0.036
Chain 1:   2200       -20287.948             0.082            0.036
Chain 1:   2300       -19904.826             0.039            0.019
Chain 1:   2400       -19676.902             0.039            0.019
Chain 1:   2500       -19479.073             0.025            0.015
Chain 1:   2600       -19109.083             0.023            0.015
Chain 1:   2700       -19066.064             0.018            0.012
Chain 1:   2800       -18782.943             0.019            0.015
Chain 1:   2900       -19064.337             0.019            0.015
Chain 1:   3000       -19050.472             0.012            0.012
Chain 1:   3100       -19135.406             0.011            0.012
Chain 1:   3200       -18826.089             0.011            0.015
Chain 1:   3300       -19030.858             0.011            0.012
Chain 1:   3400       -18505.768             0.012            0.015
Chain 1:   3500       -19117.673             0.014            0.015
Chain 1:   3600       -18424.478             0.016            0.015
Chain 1:   3700       -18811.185             0.018            0.016
Chain 1:   3800       -17771.009             0.022            0.021
Chain 1:   3900       -17767.217             0.021            0.021
Chain 1:   4000       -17884.488             0.022            0.021
Chain 1:   4100       -17798.201             0.022            0.021
Chain 1:   4200       -17614.541             0.021            0.021
Chain 1:   4300       -17752.851             0.021            0.021
Chain 1:   4400       -17709.706             0.018            0.010
Chain 1:   4500       -17612.303             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49089.025             1.000            1.000
Chain 1:    200       -22030.971             1.114            1.228
Chain 1:    300       -16803.435             0.846            1.000
Chain 1:    400       -33087.165             0.758            1.000
Chain 1:    500       -12141.309             0.951            1.000
Chain 1:    600       -24548.379             0.877            1.000
Chain 1:    700       -10675.861             0.937            1.000
Chain 1:    800       -10787.779             0.821            1.000
Chain 1:    900       -18989.411             0.778            0.505
Chain 1:   1000       -12358.298             0.754            0.537
Chain 1:   1100       -12320.198             0.654            0.505   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -14660.610             0.547            0.492   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -13012.815             0.529            0.492   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -12363.751             0.485            0.432
Chain 1:   1500       -11521.344             0.320            0.160
Chain 1:   1600       -10413.095             0.280            0.127
Chain 1:   1700       -14267.611             0.177            0.127
Chain 1:   1800       -10758.342             0.209            0.160
Chain 1:   1900        -9828.177             0.175            0.127
Chain 1:   2000       -10997.483             0.132            0.106
Chain 1:   2100        -9384.103             0.149            0.127
Chain 1:   2200       -11148.389             0.149            0.127
Chain 1:   2300       -14953.454             0.161            0.158
Chain 1:   2400        -9333.482             0.216            0.172
Chain 1:   2500        -9496.065             0.211            0.172
Chain 1:   2600       -10157.472             0.207            0.172
Chain 1:   2700       -12330.895             0.197            0.172
Chain 1:   2800       -14986.165             0.182            0.172
Chain 1:   2900        -9380.613             0.233            0.176
Chain 1:   3000        -9100.403             0.225            0.176
Chain 1:   3100        -9787.254             0.215            0.176
Chain 1:   3200       -10780.301             0.208            0.176
Chain 1:   3300        -9521.222             0.196            0.132
Chain 1:   3400       -10421.531             0.144            0.092
Chain 1:   3500        -9409.069             0.154            0.108
Chain 1:   3600        -9757.643             0.151            0.108
Chain 1:   3700        -9905.617             0.134            0.092
Chain 1:   3800       -16387.966             0.156            0.092
Chain 1:   3900       -11076.595             0.145            0.092
Chain 1:   4000       -10490.498             0.147            0.092
Chain 1:   4100        -8867.637             0.158            0.108
Chain 1:   4200        -9477.773             0.156            0.108
Chain 1:   4300        -9915.927             0.147            0.086
Chain 1:   4400        -9043.225             0.148            0.097
Chain 1:   4500        -9225.611             0.139            0.064
Chain 1:   4600       -11637.014             0.156            0.097
Chain 1:   4700       -10836.919             0.162            0.097
Chain 1:   4800        -8677.140             0.147            0.097
Chain 1:   4900        -8850.024             0.101            0.074
Chain 1:   5000       -10980.125             0.115            0.097
Chain 1:   5100       -17335.311             0.133            0.097
Chain 1:   5200        -9099.714             0.218            0.194
Chain 1:   5300       -12599.725             0.241            0.207
Chain 1:   5400        -8822.409             0.274            0.249
Chain 1:   5500       -13306.294             0.306            0.278
Chain 1:   5600       -10254.777             0.315            0.298
Chain 1:   5700        -9746.305             0.313            0.298
Chain 1:   5800       -16326.108             0.328            0.337
Chain 1:   5900       -11914.944             0.363            0.367
Chain 1:   6000        -9237.662             0.373            0.367
Chain 1:   6100        -9883.087             0.343            0.337
Chain 1:   6200        -8259.868             0.272            0.298
Chain 1:   6300        -9241.484             0.255            0.298
Chain 1:   6400        -8650.383             0.219            0.290
Chain 1:   6500        -9328.324             0.192            0.197
Chain 1:   6600        -8883.570             0.167            0.106
Chain 1:   6700        -8525.037             0.166            0.106
Chain 1:   6800        -9055.173             0.132            0.073
Chain 1:   6900       -12676.299             0.124            0.073
Chain 1:   7000        -9746.644             0.125            0.073
Chain 1:   7100        -8339.752             0.135            0.106
Chain 1:   7200        -9878.353             0.131            0.106
Chain 1:   7300        -8329.396             0.139            0.156
Chain 1:   7400        -9794.019             0.147            0.156
Chain 1:   7500       -11522.021             0.155            0.156
Chain 1:   7600       -11174.655             0.153            0.156
Chain 1:   7700        -9112.491             0.171            0.169
Chain 1:   7800       -13011.615             0.195            0.186
Chain 1:   7900        -8418.399             0.221            0.186
Chain 1:   8000       -12827.924             0.226            0.186
Chain 1:   8100        -8364.195             0.262            0.226
Chain 1:   8200        -9092.241             0.255            0.226
Chain 1:   8300        -9346.624             0.239            0.226
Chain 1:   8400       -10912.508             0.238            0.226
Chain 1:   8500        -8439.341             0.252            0.293
Chain 1:   8600       -11106.119             0.273            0.293
Chain 1:   8700        -8941.267             0.275            0.293
Chain 1:   8800       -10980.155             0.263            0.242
Chain 1:   8900       -11330.979             0.212            0.240
Chain 1:   9000        -9198.422             0.201            0.232
Chain 1:   9100       -10353.836             0.159            0.186
Chain 1:   9200       -11955.717             0.164            0.186
Chain 1:   9300       -10133.617             0.179            0.186
Chain 1:   9400        -8901.304             0.179            0.186
Chain 1:   9500        -8337.536             0.156            0.180
Chain 1:   9600       -10041.176             0.149            0.170
Chain 1:   9700        -8330.827             0.145            0.170
Chain 1:   9800        -8470.613             0.129            0.138
Chain 1:   9900       -10343.636             0.144            0.170
Chain 1:   10000       -10631.802             0.123            0.138
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001531 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57149.348             1.000            1.000
Chain 1:    200       -17597.793             1.624            2.248
Chain 1:    300        -8810.933             1.415            1.000
Chain 1:    400        -8349.598             1.075            1.000
Chain 1:    500        -8272.844             0.862            0.997
Chain 1:    600        -9152.744             0.734            0.997
Chain 1:    700        -7860.637             0.653            0.164
Chain 1:    800        -8272.924             0.577            0.164
Chain 1:    900        -7973.086             0.517            0.096
Chain 1:   1000        -7836.550             0.467            0.096
Chain 1:   1100        -7686.619             0.369            0.055
Chain 1:   1200        -7803.197             0.146            0.050
Chain 1:   1300        -7772.160             0.047            0.038
Chain 1:   1400        -7873.093             0.043            0.020
Chain 1:   1500        -7600.726             0.045            0.036
Chain 1:   1600        -7572.091             0.036            0.020
Chain 1:   1700        -7531.656             0.020            0.017
Chain 1:   1800        -7607.446             0.016            0.015
Chain 1:   1900        -7609.182             0.012            0.013
Chain 1:   2000        -7660.868             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86907.751             1.000            1.000
Chain 1:    200       -13618.242             3.191            5.382
Chain 1:    300        -9970.624             2.249            1.000
Chain 1:    400       -10908.511             1.708            1.000
Chain 1:    500        -8948.683             1.411            0.366
Chain 1:    600        -8425.944             1.186            0.366
Chain 1:    700        -8475.848             1.017            0.219
Chain 1:    800        -8688.615             0.893            0.219
Chain 1:    900        -8804.200             0.795            0.086
Chain 1:   1000        -8568.928             0.719            0.086
Chain 1:   1100        -8827.720             0.621            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8409.265             0.088            0.050
Chain 1:   1300        -8682.096             0.055            0.031
Chain 1:   1400        -8658.749             0.047            0.029
Chain 1:   1500        -8542.159             0.026            0.027
Chain 1:   1600        -8650.735             0.021            0.024
Chain 1:   1700        -8737.063             0.021            0.024
Chain 1:   1800        -8322.751             0.024            0.027
Chain 1:   1900        -8419.355             0.024            0.027
Chain 1:   2000        -8392.706             0.021            0.014
Chain 1:   2100        -8515.511             0.020            0.014
Chain 1:   2200        -8335.484             0.017            0.014
Chain 1:   2300        -8414.145             0.015            0.013
Chain 1:   2400        -8483.884             0.015            0.013
Chain 1:   2500        -8429.394             0.015            0.011
Chain 1:   2600        -8428.949             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002996 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417033.184             1.000            1.000
Chain 1:    200     -1586061.682             2.653            4.307
Chain 1:    300      -891762.960             2.028            1.000
Chain 1:    400      -458032.852             1.758            1.000
Chain 1:    500      -358201.437             1.462            0.947
Chain 1:    600      -233086.689             1.308            0.947
Chain 1:    700      -119330.896             1.257            0.947
Chain 1:    800       -86514.851             1.148            0.947
Chain 1:    900       -66870.215             1.053            0.779
Chain 1:   1000       -51673.415             0.977            0.779
Chain 1:   1100       -39155.366             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38333.164             0.480            0.379
Chain 1:   1300       -26299.953             0.448            0.379
Chain 1:   1400       -26018.933             0.355            0.320
Chain 1:   1500       -22608.590             0.342            0.320
Chain 1:   1600       -21825.371             0.292            0.294
Chain 1:   1700       -20700.926             0.202            0.294
Chain 1:   1800       -20645.359             0.164            0.151
Chain 1:   1900       -20971.496             0.136            0.054
Chain 1:   2000       -19483.475             0.115            0.054
Chain 1:   2100       -19721.815             0.084            0.036
Chain 1:   2200       -19948.026             0.083            0.036
Chain 1:   2300       -19565.464             0.039            0.020
Chain 1:   2400       -19337.637             0.039            0.020
Chain 1:   2500       -19139.425             0.025            0.016
Chain 1:   2600       -18769.791             0.023            0.016
Chain 1:   2700       -18726.841             0.018            0.012
Chain 1:   2800       -18443.626             0.019            0.015
Chain 1:   2900       -18724.867             0.019            0.015
Chain 1:   3000       -18711.111             0.012            0.012
Chain 1:   3100       -18796.055             0.011            0.012
Chain 1:   3200       -18486.785             0.012            0.015
Chain 1:   3300       -18691.481             0.011            0.012
Chain 1:   3400       -18166.393             0.012            0.015
Chain 1:   3500       -18778.182             0.015            0.015
Chain 1:   3600       -18085.053             0.017            0.015
Chain 1:   3700       -18471.695             0.018            0.017
Chain 1:   3800       -17431.551             0.023            0.021
Chain 1:   3900       -17427.695             0.021            0.021
Chain 1:   4000       -17545.028             0.022            0.021
Chain 1:   4100       -17458.755             0.022            0.021
Chain 1:   4200       -17275.076             0.021            0.021
Chain 1:   4300       -17413.448             0.021            0.021
Chain 1:   4400       -17370.323             0.018            0.011
Chain 1:   4500       -17272.856             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00117 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13129.335             1.000            1.000
Chain 1:    200       -10017.203             0.655            1.000
Chain 1:    300        -8651.114             0.490            0.311
Chain 1:    400        -8852.466             0.373            0.311
Chain 1:    500        -8793.160             0.300            0.158
Chain 1:    600        -8568.188             0.254            0.158
Chain 1:    700        -8536.748             0.218            0.026
Chain 1:    800        -8511.863             0.191            0.026
Chain 1:    900        -8587.588             0.171            0.023
Chain 1:   1000        -8498.291             0.155            0.023
Chain 1:   1100        -8554.522             0.056            0.011
Chain 1:   1200        -8479.337             0.026            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001404 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -64293.135             1.000            1.000
Chain 1:    200       -19000.493             1.692            2.384
Chain 1:    300        -9210.493             1.482            1.063
Chain 1:    400        -9156.770             1.113            1.063
Chain 1:    500        -8640.939             0.902            1.000
Chain 1:    600        -8995.707             0.759            1.000
Chain 1:    700        -9228.824             0.654            0.060
Chain 1:    800        -8522.747             0.582            0.083
Chain 1:    900        -8481.626             0.518            0.060
Chain 1:   1000        -7644.430             0.477            0.083
Chain 1:   1100        -7816.081             0.380            0.060
Chain 1:   1200        -7616.061             0.144            0.039
Chain 1:   1300        -8026.922             0.043            0.039
Chain 1:   1400        -8149.521             0.044            0.039
Chain 1:   1500        -7598.217             0.045            0.039
Chain 1:   1600        -7703.484             0.042            0.026
Chain 1:   1700        -7688.339             0.040            0.026
Chain 1:   1800        -7618.944             0.033            0.022
Chain 1:   1900        -7608.995             0.032            0.022
Chain 1:   2000        -7716.480             0.023            0.015
Chain 1:   2100        -7538.799             0.023            0.015
Chain 1:   2200        -7809.070             0.024            0.015
Chain 1:   2300        -7693.663             0.020            0.015
Chain 1:   2400        -7673.465             0.019            0.014
Chain 1:   2500        -7630.978             0.012            0.014
Chain 1:   2600        -7594.365             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003187 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87262.199             1.000            1.000
Chain 1:    200       -14326.553             3.045            5.091
Chain 1:    300       -10596.386             2.148            1.000
Chain 1:    400       -12095.634             1.642            1.000
Chain 1:    500        -9459.848             1.369            0.352
Chain 1:    600        -9865.645             1.148            0.352
Chain 1:    700        -9180.474             0.994            0.279
Chain 1:    800        -9301.086             0.872            0.279
Chain 1:    900        -9701.353             0.780            0.124
Chain 1:   1000        -9183.752             0.707            0.124
Chain 1:   1100        -9245.204             0.608            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8911.433             0.103            0.056
Chain 1:   1300        -9229.021             0.071            0.041
Chain 1:   1400        -9011.087             0.061            0.041
Chain 1:   1500        -9082.614             0.034            0.037
Chain 1:   1600        -9187.504             0.031            0.034
Chain 1:   1700        -9244.981             0.024            0.024
Chain 1:   1800        -8797.741             0.028            0.034
Chain 1:   1900        -8906.113             0.025            0.024
Chain 1:   2000        -8888.080             0.019            0.012
Chain 1:   2100        -9019.081             0.020            0.015
Chain 1:   2200        -8802.351             0.019            0.015
Chain 1:   2300        -8905.908             0.017            0.012
Chain 1:   2400        -8966.656             0.015            0.012
Chain 1:   2500        -8914.732             0.015            0.012
Chain 1:   2600        -8929.415             0.014            0.012
Chain 1:   2700        -8836.301             0.014            0.012
Chain 1:   2800        -8782.174             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003208 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8420015.330             1.000            1.000
Chain 1:    200     -1582169.273             2.661            4.322
Chain 1:    300      -889835.694             2.033            1.000
Chain 1:    400      -458305.199             1.760            1.000
Chain 1:    500      -358765.392             1.464            0.942
Chain 1:    600      -233690.229             1.309            0.942
Chain 1:    700      -119957.080             1.257            0.942
Chain 1:    800       -87256.222             1.147            0.942
Chain 1:    900       -67605.660             1.052            0.778
Chain 1:   1000       -52422.080             0.976            0.778
Chain 1:   1100       -39920.538             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39100.904             0.477            0.375
Chain 1:   1300       -27058.526             0.444            0.375
Chain 1:   1400       -26780.683             0.351            0.313
Chain 1:   1500       -23369.862             0.337            0.313
Chain 1:   1600       -22588.604             0.287            0.291
Chain 1:   1700       -21461.119             0.198            0.290
Chain 1:   1800       -21405.660             0.161            0.146
Chain 1:   1900       -21732.423             0.133            0.053
Chain 1:   2000       -20242.471             0.111            0.053
Chain 1:   2100       -20480.542             0.081            0.035
Chain 1:   2200       -20707.820             0.080            0.035
Chain 1:   2300       -20324.193             0.038            0.019
Chain 1:   2400       -20096.089             0.038            0.019
Chain 1:   2500       -19898.369             0.024            0.015
Chain 1:   2600       -19527.624             0.023            0.015
Chain 1:   2700       -19484.337             0.018            0.012
Chain 1:   2800       -19201.120             0.019            0.015
Chain 1:   2900       -19482.600             0.019            0.014
Chain 1:   3000       -19468.595             0.011            0.012
Chain 1:   3100       -19553.759             0.011            0.011
Chain 1:   3200       -19243.953             0.011            0.014
Chain 1:   3300       -19449.067             0.010            0.011
Chain 1:   3400       -18923.287             0.012            0.014
Chain 1:   3500       -19536.280             0.014            0.015
Chain 1:   3600       -18841.416             0.016            0.015
Chain 1:   3700       -19229.413             0.018            0.016
Chain 1:   3800       -18186.841             0.022            0.020
Chain 1:   3900       -18182.967             0.021            0.020
Chain 1:   4000       -18300.220             0.021            0.020
Chain 1:   4100       -18213.955             0.021            0.020
Chain 1:   4200       -18029.659             0.021            0.020
Chain 1:   4300       -18168.386             0.020            0.020
Chain 1:   4400       -18124.775             0.018            0.010
Chain 1:   4500       -18027.276             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12280.188             1.000            1.000
Chain 1:    200        -9164.613             0.670            1.000
Chain 1:    300        -7893.223             0.500            0.340
Chain 1:    400        -8052.684             0.380            0.340
Chain 1:    500        -7953.304             0.307            0.161
Chain 1:    600        -7872.660             0.257            0.161
Chain 1:    700        -7780.399             0.222            0.020
Chain 1:    800        -7815.950             0.195            0.020
Chain 1:    900        -7943.895             0.175            0.016
Chain 1:   1000        -7841.416             0.159            0.016
Chain 1:   1100        -7920.127             0.060            0.013
Chain 1:   1200        -7787.853             0.028            0.013
Chain 1:   1300        -7795.161             0.012            0.012
Chain 1:   1400        -7773.444             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57012.902             1.000            1.000
Chain 1:    200       -17368.123             1.641            2.283
Chain 1:    300        -8675.193             1.428            1.002
Chain 1:    400        -8355.469             1.081            1.002
Chain 1:    500        -8131.780             0.870            1.000
Chain 1:    600        -8844.561             0.739            1.000
Chain 1:    700        -8000.982             0.648            0.105
Chain 1:    800        -8112.886             0.569            0.105
Chain 1:    900        -7819.893             0.510            0.081
Chain 1:   1000        -7922.475             0.460            0.081
Chain 1:   1100        -7751.487             0.362            0.038
Chain 1:   1200        -7783.430             0.134            0.037
Chain 1:   1300        -7684.545             0.036            0.028
Chain 1:   1400        -7622.128             0.032            0.022
Chain 1:   1500        -7578.226             0.030            0.014
Chain 1:   1600        -7741.700             0.024            0.014
Chain 1:   1700        -7500.317             0.017            0.014
Chain 1:   1800        -7554.134             0.016            0.013
Chain 1:   1900        -7571.247             0.013            0.013
Chain 1:   2000        -7593.278             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86345.877             1.000            1.000
Chain 1:    200       -13387.782             3.225            5.450
Chain 1:    300        -9756.477             2.274            1.000
Chain 1:    400       -10785.724             1.729            1.000
Chain 1:    500        -8699.604             1.431            0.372
Chain 1:    600        -8248.612             1.202            0.372
Chain 1:    700        -8437.236             1.033            0.240
Chain 1:    800        -8851.778             0.910            0.240
Chain 1:    900        -8589.459             0.812            0.095
Chain 1:   1000        -8432.296             0.733            0.095
Chain 1:   1100        -8608.130             0.635            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8242.598             0.095            0.047
Chain 1:   1300        -8455.110             0.060            0.044
Chain 1:   1400        -8467.865             0.050            0.031
Chain 1:   1500        -8322.936             0.028            0.025
Chain 1:   1600        -8435.857             0.024            0.022
Chain 1:   1700        -8519.224             0.023            0.020
Chain 1:   1800        -8107.366             0.023            0.020
Chain 1:   1900        -8203.356             0.021            0.019
Chain 1:   2000        -8176.457             0.020            0.017
Chain 1:   2100        -8298.829             0.019            0.015
Chain 1:   2200        -8118.524             0.017            0.015
Chain 1:   2300        -8198.492             0.015            0.013
Chain 1:   2400        -8268.049             0.016            0.013
Chain 1:   2500        -8213.339             0.015            0.012
Chain 1:   2600        -8212.598             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419906.276             1.000            1.000
Chain 1:    200     -1591744.096             2.645            4.290
Chain 1:    300      -892302.154             2.025            1.000
Chain 1:    400      -457781.840             1.756            1.000
Chain 1:    500      -357527.330             1.461            0.949
Chain 1:    600      -232359.429             1.307            0.949
Chain 1:    700      -118799.745             1.257            0.949
Chain 1:    800       -86057.874             1.147            0.949
Chain 1:    900       -66465.153             1.053            0.784
Chain 1:   1000       -51324.032             0.977            0.784
Chain 1:   1100       -38850.612             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38035.709             0.482            0.380
Chain 1:   1300       -26044.847             0.450            0.380
Chain 1:   1400       -25769.458             0.356            0.321
Chain 1:   1500       -22369.713             0.343            0.321
Chain 1:   1600       -21590.002             0.293            0.295
Chain 1:   1700       -20470.187             0.203            0.295
Chain 1:   1800       -20415.868             0.165            0.152
Chain 1:   1900       -20741.968             0.137            0.055
Chain 1:   2000       -19256.307             0.115            0.055
Chain 1:   2100       -19494.699             0.084            0.036
Chain 1:   2200       -19720.484             0.083            0.036
Chain 1:   2300       -19338.262             0.039            0.020
Chain 1:   2400       -19110.425             0.039            0.020
Chain 1:   2500       -18912.142             0.025            0.016
Chain 1:   2600       -18542.752             0.024            0.016
Chain 1:   2700       -18499.810             0.018            0.012
Chain 1:   2800       -18216.523             0.020            0.016
Chain 1:   2900       -18497.709             0.020            0.015
Chain 1:   3000       -18484.008             0.012            0.012
Chain 1:   3100       -18568.968             0.011            0.012
Chain 1:   3200       -18259.753             0.012            0.015
Chain 1:   3300       -18464.397             0.011            0.012
Chain 1:   3400       -17939.380             0.013            0.015
Chain 1:   3500       -18551.051             0.015            0.016
Chain 1:   3600       -17857.997             0.017            0.016
Chain 1:   3700       -18244.545             0.019            0.017
Chain 1:   3800       -17204.599             0.023            0.021
Chain 1:   3900       -17200.692             0.022            0.021
Chain 1:   4000       -17318.060             0.022            0.021
Chain 1:   4100       -17231.795             0.022            0.021
Chain 1:   4200       -17048.121             0.022            0.021
Chain 1:   4300       -17186.501             0.021            0.021
Chain 1:   4400       -17143.398             0.019            0.011
Chain 1:   4500       -17045.892             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001506 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49093.771             1.000            1.000
Chain 1:    200       -15426.644             1.591            2.182
Chain 1:    300       -19548.322             1.131            1.000
Chain 1:    400       -21851.895             0.875            1.000
Chain 1:    500       -12367.900             0.853            0.767
Chain 1:    600       -22091.037             0.784            0.767
Chain 1:    700       -12213.852             0.788            0.767
Chain 1:    800       -18963.845             0.734            0.767
Chain 1:    900       -13886.872             0.693            0.440
Chain 1:   1000       -12189.603             0.638            0.440
Chain 1:   1100       -10528.651             0.553            0.366   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10486.590             0.335            0.356
Chain 1:   1300       -13015.828             0.334            0.356
Chain 1:   1400       -10178.021             0.351            0.356
Chain 1:   1500       -10145.673             0.275            0.279
Chain 1:   1600       -11531.894             0.243            0.194
Chain 1:   1700       -10299.961             0.174            0.158
Chain 1:   1800       -18329.879             0.182            0.158
Chain 1:   1900        -9800.141             0.233            0.158
Chain 1:   2000       -11667.272             0.235            0.160
Chain 1:   2100        -9801.524             0.238            0.190
Chain 1:   2200       -10577.780             0.245            0.190
Chain 1:   2300       -15946.903             0.259            0.190
Chain 1:   2400        -9727.901             0.295            0.190
Chain 1:   2500       -10950.350             0.306            0.190
Chain 1:   2600        -9174.033             0.313            0.194
Chain 1:   2700       -10461.081             0.314            0.194
Chain 1:   2800       -14832.057             0.299            0.194
Chain 1:   2900       -10074.078             0.260            0.194
Chain 1:   3000        -9614.821             0.248            0.194
Chain 1:   3100        -8831.194             0.238            0.194
Chain 1:   3200        -9639.944             0.239            0.194
Chain 1:   3300        -9249.174             0.210            0.123
Chain 1:   3400        -9473.362             0.148            0.112
Chain 1:   3500        -9205.161             0.140            0.089
Chain 1:   3600        -9529.909             0.124            0.084
Chain 1:   3700       -10072.536             0.117            0.054
Chain 1:   3800        -8573.199             0.105            0.054
Chain 1:   3900       -12893.483             0.091            0.054
Chain 1:   4000        -9068.236             0.129            0.084
Chain 1:   4100        -9283.965             0.122            0.054
Chain 1:   4200        -9389.999             0.115            0.042
Chain 1:   4300       -10858.118             0.124            0.054
Chain 1:   4400       -15076.377             0.150            0.135
Chain 1:   4500       -12347.815             0.169            0.175
Chain 1:   4600        -9572.913             0.195            0.221
Chain 1:   4700       -10045.713             0.194            0.221
Chain 1:   4800        -8596.282             0.193            0.221
Chain 1:   4900        -8866.984             0.163            0.169
Chain 1:   5000       -13363.079             0.154            0.169
Chain 1:   5100        -8888.594             0.202            0.221
Chain 1:   5200        -8792.466             0.202            0.221
Chain 1:   5300       -13268.264             0.222            0.280
Chain 1:   5400       -11871.521             0.206            0.221
Chain 1:   5500       -12933.863             0.192            0.169
Chain 1:   5600        -8824.220             0.210            0.169
Chain 1:   5700       -11449.653             0.228            0.229
Chain 1:   5800        -8814.239             0.241            0.299
Chain 1:   5900        -8595.645             0.241            0.299
Chain 1:   6000        -8787.926             0.209            0.229
Chain 1:   6100       -10469.865             0.175            0.161
Chain 1:   6200        -9165.917             0.188            0.161
Chain 1:   6300        -8543.744             0.162            0.142
Chain 1:   6400       -11987.835             0.179            0.161
Chain 1:   6500        -8599.052             0.210            0.229
Chain 1:   6600        -8424.167             0.165            0.161
Chain 1:   6700        -9365.936             0.152            0.142
Chain 1:   6800       -10847.263             0.136            0.137
Chain 1:   6900        -9846.402             0.144            0.137
Chain 1:   7000        -8621.997             0.156            0.142
Chain 1:   7100       -12793.283             0.172            0.142
Chain 1:   7200        -9697.687             0.190            0.142
Chain 1:   7300       -11753.679             0.200            0.175
Chain 1:   7400       -11841.805             0.172            0.142
Chain 1:   7500       -10908.847             0.141            0.137
Chain 1:   7600        -9103.471             0.159            0.142
Chain 1:   7700        -8465.034             0.157            0.142
Chain 1:   7800        -9184.725             0.151            0.142
Chain 1:   7900        -8856.409             0.144            0.142
Chain 1:   8000        -9689.886             0.139            0.086
Chain 1:   8100        -9682.459             0.106            0.086
Chain 1:   8200       -11835.929             0.093            0.086
Chain 1:   8300        -8591.584             0.113            0.086
Chain 1:   8400       -10419.386             0.130            0.086
Chain 1:   8500        -8345.582             0.146            0.175
Chain 1:   8600        -9921.997             0.142            0.159
Chain 1:   8700        -8559.621             0.150            0.159
Chain 1:   8800        -8152.322             0.148            0.159
Chain 1:   8900        -9248.174             0.156            0.159
Chain 1:   9000       -10286.348             0.157            0.159
Chain 1:   9100        -8525.605             0.178            0.175
Chain 1:   9200        -9635.593             0.171            0.159
Chain 1:   9300        -8930.226             0.141            0.159
Chain 1:   9400        -8566.057             0.128            0.118
Chain 1:   9500        -8244.448             0.107            0.115
Chain 1:   9600        -9964.965             0.108            0.115
Chain 1:   9700        -8187.278             0.114            0.115
Chain 1:   9800       -12922.854             0.146            0.118
Chain 1:   9900        -8596.693             0.184            0.173
Chain 1:   10000        -8262.355             0.178            0.173
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001561 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61696.888             1.000            1.000
Chain 1:    200       -17908.151             1.723            2.445
Chain 1:    300        -8868.577             1.488            1.019
Chain 1:    400        -8328.848             1.132            1.019
Chain 1:    500        -8830.786             0.917            1.000
Chain 1:    600        -8826.879             0.764            1.000
Chain 1:    700        -8448.686             0.662            0.065
Chain 1:    800        -8113.182             0.584            0.065
Chain 1:    900        -7976.503             0.521            0.057
Chain 1:   1000        -7772.398             0.472            0.057
Chain 1:   1100        -7665.957             0.373            0.045
Chain 1:   1200        -7660.289             0.129            0.041
Chain 1:   1300        -7492.708             0.029            0.026
Chain 1:   1400        -7808.527             0.026            0.026
Chain 1:   1500        -7517.049             0.025            0.026
Chain 1:   1600        -7689.230             0.027            0.026
Chain 1:   1700        -7478.417             0.025            0.026
Chain 1:   1800        -7518.638             0.022            0.022
Chain 1:   1900        -7528.682             0.020            0.022
Chain 1:   2000        -7585.519             0.018            0.022
Chain 1:   2100        -7459.170             0.018            0.022
Chain 1:   2200        -7684.892             0.021            0.022
Chain 1:   2300        -7552.635             0.021            0.022
Chain 1:   2400        -7605.416             0.017            0.018
Chain 1:   2500        -7697.676             0.015            0.017
Chain 1:   2600        -7472.241             0.016            0.017
Chain 1:   2700        -7484.150             0.013            0.012
Chain 1:   2800        -7529.289             0.013            0.012
Chain 1:   2900        -7355.481             0.015            0.017
Chain 1:   3000        -7489.372             0.016            0.018
Chain 1:   3100        -7481.414             0.015            0.018
Chain 1:   3200        -7667.891             0.014            0.018
Chain 1:   3300        -7418.192             0.016            0.018
Chain 1:   3400        -7616.101             0.018            0.024
Chain 1:   3500        -7394.719             0.019            0.024
Chain 1:   3600        -7456.571             0.017            0.024
Chain 1:   3700        -7407.280             0.018            0.024
Chain 1:   3800        -7414.871             0.017            0.024
Chain 1:   3900        -7385.876             0.015            0.018
Chain 1:   4000        -7373.825             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003172 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86677.564             1.000            1.000
Chain 1:    200       -13542.069             3.200            5.401
Chain 1:    300        -9906.510             2.256            1.000
Chain 1:    400       -10878.204             1.714            1.000
Chain 1:    500        -8875.621             1.417            0.367
Chain 1:    600        -8611.401             1.186            0.367
Chain 1:    700        -8423.221             1.019            0.226
Chain 1:    800        -9245.791             0.903            0.226
Chain 1:    900        -8722.275             0.809            0.089
Chain 1:   1000        -8392.625             0.732            0.089
Chain 1:   1100        -8749.447             0.636            0.089   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8367.465             0.101            0.060
Chain 1:   1300        -8496.237             0.066            0.046
Chain 1:   1400        -8573.689             0.058            0.041
Chain 1:   1500        -8467.329             0.036            0.039
Chain 1:   1600        -8571.707             0.035            0.039
Chain 1:   1700        -8661.130             0.033            0.039
Chain 1:   1800        -8245.906             0.030            0.039
Chain 1:   1900        -8343.017             0.025            0.015
Chain 1:   2000        -8316.416             0.021            0.013
Chain 1:   2100        -8439.430             0.018            0.013
Chain 1:   2200        -8258.823             0.016            0.013
Chain 1:   2300        -8337.634             0.016            0.012
Chain 1:   2400        -8407.362             0.015            0.012
Chain 1:   2500        -8352.974             0.015            0.012
Chain 1:   2600        -8352.681             0.014            0.010
Chain 1:   2700        -8269.981             0.014            0.010
Chain 1:   2800        -8232.929             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8435311.541             1.000            1.000
Chain 1:    200     -1586493.565             2.658            4.317
Chain 1:    300      -890658.250             2.033            1.000
Chain 1:    400      -457669.436             1.761            1.000
Chain 1:    500      -357632.424             1.465            0.946
Chain 1:    600      -232652.546             1.310            0.946
Chain 1:    700      -119056.903             1.259            0.946
Chain 1:    800       -86333.460             1.149            0.946
Chain 1:    900       -66710.628             1.054            0.781
Chain 1:   1000       -51538.166             0.978            0.781
Chain 1:   1100       -39049.381             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38228.172             0.481            0.379
Chain 1:   1300       -26215.404             0.448            0.379
Chain 1:   1400       -25938.010             0.355            0.320
Chain 1:   1500       -22533.615             0.342            0.320
Chain 1:   1600       -21752.895             0.292            0.294
Chain 1:   1700       -20630.110             0.202            0.294
Chain 1:   1800       -20575.103             0.164            0.151
Chain 1:   1900       -20901.279             0.136            0.054
Chain 1:   2000       -19414.393             0.115            0.054
Chain 1:   2100       -19652.581             0.084            0.036
Chain 1:   2200       -19878.818             0.083            0.036
Chain 1:   2300       -19496.236             0.039            0.020
Chain 1:   2400       -19268.341             0.039            0.020
Chain 1:   2500       -19070.313             0.025            0.016
Chain 1:   2600       -18700.534             0.023            0.016
Chain 1:   2700       -18657.552             0.018            0.012
Chain 1:   2800       -18374.396             0.020            0.015
Chain 1:   2900       -18655.593             0.019            0.015
Chain 1:   3000       -18641.810             0.012            0.012
Chain 1:   3100       -18726.782             0.011            0.012
Chain 1:   3200       -18417.484             0.012            0.015
Chain 1:   3300       -18622.202             0.011            0.012
Chain 1:   3400       -18097.130             0.013            0.015
Chain 1:   3500       -18708.939             0.015            0.015
Chain 1:   3600       -18015.693             0.017            0.015
Chain 1:   3700       -18402.407             0.018            0.017
Chain 1:   3800       -17362.207             0.023            0.021
Chain 1:   3900       -17358.335             0.021            0.021
Chain 1:   4000       -17475.662             0.022            0.021
Chain 1:   4100       -17389.412             0.022            0.021
Chain 1:   4200       -17205.678             0.021            0.021
Chain 1:   4300       -17344.066             0.021            0.021
Chain 1:   4400       -17300.899             0.019            0.011
Chain 1:   4500       -17203.416             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001297 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13155.472             1.000            1.000
Chain 1:    200        -9624.350             0.683            1.000
Chain 1:    300        -8592.051             0.496            0.367
Chain 1:    400        -8406.154             0.377            0.367
Chain 1:    500        -8078.055             0.310            0.120
Chain 1:    600        -7897.887             0.262            0.120
Chain 1:    700        -7835.671             0.226            0.041
Chain 1:    800        -7850.524             0.198            0.041
Chain 1:    900        -7810.348             0.176            0.023
Chain 1:   1000        -7887.970             0.160            0.023
Chain 1:   1100        -7933.960             0.060            0.022
Chain 1:   1200        -7848.619             0.025            0.011
Chain 1:   1300        -7784.630             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001519 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63150.859             1.000            1.000
Chain 1:    200       -18259.529             1.729            2.459
Chain 1:    300        -8780.718             1.513            1.080
Chain 1:    400        -8667.150             1.138            1.080
Chain 1:    500        -8599.725             0.912            1.000
Chain 1:    600        -8845.357             0.764            1.000
Chain 1:    700        -7694.528             0.677            0.150
Chain 1:    800        -8069.828             0.598            0.150
Chain 1:    900        -8027.659             0.532            0.047
Chain 1:   1000        -7958.473             0.480            0.047
Chain 1:   1100        -7715.447             0.383            0.031
Chain 1:   1200        -7559.635             0.139            0.028
Chain 1:   1300        -7678.485             0.033            0.021
Chain 1:   1400        -7889.794             0.034            0.027
Chain 1:   1500        -7534.222             0.038            0.028
Chain 1:   1600        -7621.879             0.036            0.027
Chain 1:   1700        -7496.589             0.023            0.021
Chain 1:   1800        -7506.594             0.019            0.017
Chain 1:   1900        -7532.183             0.018            0.017
Chain 1:   2000        -7562.707             0.018            0.017
Chain 1:   2100        -7515.945             0.015            0.015
Chain 1:   2200        -7645.722             0.015            0.015
Chain 1:   2300        -7498.986             0.015            0.017
Chain 1:   2400        -7533.074             0.013            0.012
Chain 1:   2500        -7524.377             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003023 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86933.238             1.000            1.000
Chain 1:    200       -13568.404             3.204            5.407
Chain 1:    300        -9878.573             2.260            1.000
Chain 1:    400       -10890.300             1.718            1.000
Chain 1:    500        -8869.998             1.420            0.374
Chain 1:    600        -8316.157             1.195            0.374
Chain 1:    700        -8313.820             1.024            0.228
Chain 1:    800        -8506.181             0.899            0.228
Chain 1:    900        -8682.330             0.801            0.093
Chain 1:   1000        -8463.776             0.724            0.093
Chain 1:   1100        -8627.525             0.626            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8286.835             0.089            0.041
Chain 1:   1300        -8543.010             0.055            0.030
Chain 1:   1400        -8552.499             0.045            0.026
Chain 1:   1500        -8401.920             0.024            0.023
Chain 1:   1600        -8517.381             0.019            0.020
Chain 1:   1700        -8585.776             0.020            0.020
Chain 1:   1800        -8154.101             0.023            0.020
Chain 1:   1900        -8258.139             0.022            0.019
Chain 1:   2000        -8233.486             0.020            0.018
Chain 1:   2100        -8369.027             0.020            0.016
Chain 1:   2200        -8163.424             0.018            0.016
Chain 1:   2300        -8304.192             0.017            0.016
Chain 1:   2400        -8160.823             0.018            0.017
Chain 1:   2500        -8232.272             0.017            0.016
Chain 1:   2600        -8146.105             0.017            0.016
Chain 1:   2700        -8177.989             0.017            0.016
Chain 1:   2800        -8140.996             0.012            0.013
Chain 1:   2900        -8231.266             0.012            0.011
Chain 1:   3000        -8056.156             0.014            0.016
Chain 1:   3100        -8222.249             0.014            0.017
Chain 1:   3200        -8095.553             0.013            0.016
Chain 1:   3300        -8114.568             0.012            0.011
Chain 1:   3400        -8242.827             0.011            0.011
Chain 1:   3500        -8238.280             0.011            0.011
Chain 1:   3600        -8053.874             0.012            0.016
Chain 1:   3700        -8194.167             0.013            0.016
Chain 1:   3800        -8060.910             0.014            0.017
Chain 1:   3900        -7996.502             0.014            0.017
Chain 1:   4000        -8071.591             0.013            0.016
Chain 1:   4100        -8061.025             0.011            0.016
Chain 1:   4200        -8049.515             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002928 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8443592.390             1.000            1.000
Chain 1:    200     -1589019.810             2.657            4.314
Chain 1:    300      -890752.170             2.033            1.000
Chain 1:    400      -457507.091             1.761            1.000
Chain 1:    500      -357172.443             1.465            0.947
Chain 1:    600      -232238.285             1.311            0.947
Chain 1:    700      -118845.337             1.260            0.947
Chain 1:    800       -86170.445             1.150            0.947
Chain 1:    900       -66599.906             1.055            0.784
Chain 1:   1000       -51473.774             0.978            0.784
Chain 1:   1100       -39021.806             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38210.033             0.481            0.379
Chain 1:   1300       -26232.054             0.448            0.379
Chain 1:   1400       -25959.539             0.355            0.319
Chain 1:   1500       -22563.666             0.342            0.319
Chain 1:   1600       -21786.234             0.291            0.294
Chain 1:   1700       -20667.320             0.201            0.294
Chain 1:   1800       -20613.550             0.164            0.151
Chain 1:   1900       -20939.962             0.136            0.054
Chain 1:   2000       -19454.729             0.114            0.054
Chain 1:   2100       -19692.888             0.084            0.036
Chain 1:   2200       -19918.864             0.083            0.036
Chain 1:   2300       -19536.447             0.039            0.020
Chain 1:   2400       -19308.512             0.039            0.020
Chain 1:   2500       -19110.275             0.025            0.016
Chain 1:   2600       -18740.369             0.023            0.016
Chain 1:   2700       -18697.422             0.018            0.012
Chain 1:   2800       -18414.002             0.019            0.015
Chain 1:   2900       -18695.331             0.019            0.015
Chain 1:   3000       -18681.538             0.012            0.012
Chain 1:   3100       -18766.525             0.011            0.012
Chain 1:   3200       -18457.078             0.012            0.015
Chain 1:   3300       -18661.956             0.011            0.012
Chain 1:   3400       -18136.526             0.012            0.015
Chain 1:   3500       -18748.762             0.015            0.015
Chain 1:   3600       -18054.978             0.017            0.015
Chain 1:   3700       -18442.030             0.018            0.017
Chain 1:   3800       -17400.914             0.023            0.021
Chain 1:   3900       -17397.003             0.021            0.021
Chain 1:   4000       -17514.358             0.022            0.021
Chain 1:   4100       -17428.007             0.022            0.021
Chain 1:   4200       -17244.129             0.021            0.021
Chain 1:   4300       -17382.645             0.021            0.021
Chain 1:   4400       -17339.315             0.018            0.011
Chain 1:   4500       -17241.797             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001482 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12151.191             1.000            1.000
Chain 1:    200        -9081.091             0.669            1.000
Chain 1:    300        -7939.485             0.494            0.338
Chain 1:    400        -8148.686             0.377            0.338
Chain 1:    500        -8001.779             0.305            0.144
Chain 1:    600        -7871.086             0.257            0.144
Chain 1:    700        -7805.103             0.222            0.026
Chain 1:    800        -7813.124             0.194            0.026
Chain 1:    900        -7815.050             0.172            0.018
Chain 1:   1000        -7866.569             0.156            0.018
Chain 1:   1100        -7929.437             0.057            0.017
Chain 1:   1200        -7816.795             0.024            0.014
Chain 1:   1300        -7817.816             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61807.664             1.000            1.000
Chain 1:    200       -17521.654             1.764            2.528
Chain 1:    300        -8654.920             1.517            1.024
Chain 1:    400        -8087.121             1.156            1.024
Chain 1:    500        -8112.105             0.925            1.000
Chain 1:    600        -8751.580             0.783            1.000
Chain 1:    700        -7725.563             0.690            0.133
Chain 1:    800        -7927.012             0.607            0.133
Chain 1:    900        -7778.586             0.542            0.073
Chain 1:   1000        -7830.301             0.488            0.073
Chain 1:   1100        -7515.955             0.392            0.070
Chain 1:   1200        -7500.487             0.140            0.042
Chain 1:   1300        -7569.162             0.038            0.025
Chain 1:   1400        -7794.587             0.034            0.025
Chain 1:   1500        -7487.930             0.038            0.029
Chain 1:   1600        -7412.352             0.032            0.025
Chain 1:   1700        -7408.449             0.018            0.019
Chain 1:   1800        -7472.969             0.017            0.010
Chain 1:   1900        -7464.961             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002945 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85711.177             1.000            1.000
Chain 1:    200       -13230.405             3.239            5.478
Chain 1:    300        -9687.941             2.281            1.000
Chain 1:    400       -10550.102             1.731            1.000
Chain 1:    500        -8595.491             1.431            0.366
Chain 1:    600        -8225.241             1.200            0.366
Chain 1:    700        -8329.549             1.030            0.227
Chain 1:    800        -8686.849             0.906            0.227
Chain 1:    900        -8504.467             0.808            0.082
Chain 1:   1000        -8267.092             0.730            0.082
Chain 1:   1100        -8544.248             0.633            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8190.244             0.090            0.043
Chain 1:   1300        -8280.565             0.054            0.041
Chain 1:   1400        -8343.248             0.047            0.032
Chain 1:   1500        -8284.943             0.025            0.029
Chain 1:   1600        -8284.449             0.020            0.021
Chain 1:   1700        -8214.369             0.020            0.021
Chain 1:   1800        -8099.403             0.017            0.014
Chain 1:   1900        -8217.041             0.017            0.014
Chain 1:   2000        -8177.153             0.014            0.011
Chain 1:   2100        -8307.766             0.013            0.011
Chain 1:   2200        -8098.416             0.011            0.011
Chain 1:   2300        -8239.737             0.012            0.014
Chain 1:   2400        -8253.609             0.011            0.014
Chain 1:   2500        -8221.155             0.011            0.014
Chain 1:   2600        -8219.343             0.011            0.014
Chain 1:   2700        -8127.962             0.011            0.014
Chain 1:   2800        -8104.112             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8368602.781             1.000            1.000
Chain 1:    200     -1578867.352             2.650            4.300
Chain 1:    300      -889583.674             2.025            1.000
Chain 1:    400      -457106.656             1.755            1.000
Chain 1:    500      -358114.298             1.460            0.946
Chain 1:    600      -233099.121             1.306            0.946
Chain 1:    700      -119142.188             1.256            0.946
Chain 1:    800       -86334.503             1.146            0.946
Chain 1:    900       -66626.029             1.052            0.775
Chain 1:   1000       -51384.017             0.976            0.775
Chain 1:   1100       -38826.874             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37992.477             0.481            0.380
Chain 1:   1300       -25917.920             0.450            0.380
Chain 1:   1400       -25631.001             0.356            0.323
Chain 1:   1500       -22211.849             0.344            0.323
Chain 1:   1600       -21425.474             0.294            0.297
Chain 1:   1700       -20296.013             0.204            0.296
Chain 1:   1800       -20239.080             0.166            0.154
Chain 1:   1900       -20564.727             0.138            0.056
Chain 1:   2000       -19075.252             0.117            0.056
Chain 1:   2100       -19313.448             0.085            0.037
Chain 1:   2200       -19540.036             0.084            0.037
Chain 1:   2300       -19157.262             0.040            0.020
Chain 1:   2400       -18929.525             0.040            0.020
Chain 1:   2500       -18731.797             0.026            0.016
Chain 1:   2600       -18362.325             0.024            0.016
Chain 1:   2700       -18319.317             0.019            0.012
Chain 1:   2800       -18036.621             0.020            0.016
Chain 1:   2900       -18317.597             0.020            0.015
Chain 1:   3000       -18303.733             0.012            0.012
Chain 1:   3100       -18388.712             0.011            0.012
Chain 1:   3200       -18079.667             0.012            0.015
Chain 1:   3300       -18284.130             0.011            0.012
Chain 1:   3400       -17759.743             0.013            0.015
Chain 1:   3500       -18370.728             0.015            0.016
Chain 1:   3600       -17678.512             0.017            0.016
Chain 1:   3700       -18064.591             0.019            0.017
Chain 1:   3800       -17026.125             0.023            0.021
Chain 1:   3900       -17022.355             0.022            0.021
Chain 1:   4000       -17139.594             0.022            0.021
Chain 1:   4100       -17053.547             0.022            0.021
Chain 1:   4200       -16870.139             0.022            0.021
Chain 1:   4300       -17008.251             0.022            0.021
Chain 1:   4400       -16965.384             0.019            0.011
Chain 1:   4500       -16868.025             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001244 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50066.971             1.000            1.000
Chain 1:    200       -17434.888             1.436            1.872
Chain 1:    300       -20693.425             1.010            1.000
Chain 1:    400       -17265.278             0.807            1.000
Chain 1:    500       -16386.866             0.656            0.199
Chain 1:    600       -12217.948             0.604            0.341
Chain 1:    700       -15413.132             0.547            0.207
Chain 1:    800       -27471.724             0.534            0.341
Chain 1:    900       -11903.809             0.620            0.341
Chain 1:   1000       -25204.929             0.610            0.439
Chain 1:   1100       -13521.148             0.597            0.439   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11910.639             0.423            0.341
Chain 1:   1300       -14028.948             0.423            0.341
Chain 1:   1400       -13428.131             0.407            0.341
Chain 1:   1500       -15736.279             0.416            0.341
Chain 1:   1600       -13104.283             0.402            0.207
Chain 1:   1700       -10855.176             0.402            0.207
Chain 1:   1800       -10905.395             0.359            0.201
Chain 1:   1900       -12397.982             0.240            0.151
Chain 1:   2000       -10686.059             0.203            0.151
Chain 1:   2100       -11397.728             0.123            0.147
Chain 1:   2200       -10391.599             0.119            0.147
Chain 1:   2300       -10359.753             0.105            0.120
Chain 1:   2400       -10338.202             0.100            0.120
Chain 1:   2500       -12108.057             0.100            0.120
Chain 1:   2600       -10792.282             0.092            0.120
Chain 1:   2700       -10155.370             0.078            0.097
Chain 1:   2800       -10902.429             0.084            0.097
Chain 1:   2900       -10497.415             0.076            0.069
Chain 1:   3000        -9841.679             0.067            0.067
Chain 1:   3100       -10452.883             0.066            0.067
Chain 1:   3200        -9867.554             0.063            0.063
Chain 1:   3300       -12939.575             0.086            0.067
Chain 1:   3400        -9902.351             0.117            0.069
Chain 1:   3500       -10630.262             0.109            0.068
Chain 1:   3600       -11449.734             0.104            0.068
Chain 1:   3700        -9594.157             0.117            0.069
Chain 1:   3800       -16732.135             0.153            0.072
Chain 1:   3900       -12128.329             0.187            0.193
Chain 1:   4000        -9956.821             0.202            0.218
Chain 1:   4100       -11936.920             0.213            0.218
Chain 1:   4200       -16417.485             0.234            0.237
Chain 1:   4300       -11898.728             0.248            0.273
Chain 1:   4400       -10351.985             0.233            0.218
Chain 1:   4500       -12109.698             0.240            0.218
Chain 1:   4600        -9689.403             0.258            0.250
Chain 1:   4700       -11768.645             0.256            0.250
Chain 1:   4800        -9670.022             0.235            0.218
Chain 1:   4900        -9317.539             0.201            0.217
Chain 1:   5000       -10276.078             0.189            0.177
Chain 1:   5100        -9794.024             0.177            0.177
Chain 1:   5200       -15094.473             0.185            0.177
Chain 1:   5300       -12864.007             0.164            0.173
Chain 1:   5400        -9296.639             0.188            0.177
Chain 1:   5500        -9300.082             0.173            0.177
Chain 1:   5600       -15098.195             0.187            0.177
Chain 1:   5700        -9551.038             0.227            0.217
Chain 1:   5800        -9727.104             0.207            0.173
Chain 1:   5900       -14047.081             0.234            0.308
Chain 1:   6000       -10270.531             0.262            0.351
Chain 1:   6100        -9255.434             0.268            0.351
Chain 1:   6200        -9101.571             0.234            0.308
Chain 1:   6300       -12016.330             0.241            0.308
Chain 1:   6400       -11514.960             0.207            0.243
Chain 1:   6500       -13506.279             0.222            0.243
Chain 1:   6600        -9241.283             0.230            0.243
Chain 1:   6700       -13422.246             0.203            0.243
Chain 1:   6800       -10701.205             0.226            0.254
Chain 1:   6900        -9355.638             0.210            0.243
Chain 1:   7000       -12316.752             0.197            0.240
Chain 1:   7100       -13243.076             0.193            0.240
Chain 1:   7200        -9321.031             0.234            0.243
Chain 1:   7300       -10767.353             0.223            0.240
Chain 1:   7400        -9027.178             0.238            0.240
Chain 1:   7500        -9332.221             0.226            0.240
Chain 1:   7600        -9427.737             0.181            0.193
Chain 1:   7700        -9395.302             0.150            0.144
Chain 1:   7800        -9070.540             0.128            0.134
Chain 1:   7900        -8961.541             0.115            0.070
Chain 1:   8000        -9363.486             0.095            0.043
Chain 1:   8100       -12774.149             0.115            0.043
Chain 1:   8200        -9303.668             0.110            0.043
Chain 1:   8300       -12016.184             0.120            0.043
Chain 1:   8400       -13368.698             0.110            0.043
Chain 1:   8500       -12058.587             0.118            0.101
Chain 1:   8600        -9419.616             0.145            0.109
Chain 1:   8700        -9574.107             0.146            0.109
Chain 1:   8800        -9107.176             0.148            0.109
Chain 1:   8900        -9603.409             0.152            0.109
Chain 1:   9000       -13236.167             0.175            0.226
Chain 1:   9100        -8900.089             0.197            0.226
Chain 1:   9200        -9196.120             0.163            0.109
Chain 1:   9300       -10231.187             0.150            0.101
Chain 1:   9400        -9276.204             0.151            0.103
Chain 1:   9500        -8920.072             0.144            0.101
Chain 1:   9600        -9105.075             0.118            0.052
Chain 1:   9700        -8983.113             0.117            0.052
Chain 1:   9800        -8784.912             0.115            0.052
Chain 1:   9900       -10634.641             0.127            0.101
Chain 1:   10000        -9671.492             0.109            0.100
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58160.794             1.000            1.000
Chain 1:    200       -18389.470             1.581            2.163
Chain 1:    300        -9224.216             1.385            1.000
Chain 1:    400        -8353.881             1.065            1.000
Chain 1:    500        -8803.367             0.862            0.994
Chain 1:    600        -8785.652             0.719            0.994
Chain 1:    700        -8020.374             0.630            0.104
Chain 1:    800        -7965.117             0.552            0.104
Chain 1:    900        -8057.402             0.492            0.095
Chain 1:   1000        -8333.700             0.446            0.095
Chain 1:   1100        -7790.575             0.353            0.070
Chain 1:   1200        -8180.011             0.142            0.051
Chain 1:   1300        -8258.862             0.043            0.048
Chain 1:   1400        -8023.021             0.036            0.033
Chain 1:   1500        -8284.392             0.034            0.032
Chain 1:   1600        -7942.706             0.038            0.033
Chain 1:   1700        -7856.526             0.029            0.032
Chain 1:   1800        -7712.278             0.031            0.032
Chain 1:   1900        -7708.729             0.029            0.032
Chain 1:   2000        -7811.854             0.027            0.029
Chain 1:   2100        -7563.515             0.024            0.029
Chain 1:   2200        -8138.909             0.026            0.029
Chain 1:   2300        -7726.619             0.030            0.032
Chain 1:   2400        -7858.362             0.029            0.032
Chain 1:   2500        -7654.008             0.029            0.027
Chain 1:   2600        -7623.272             0.025            0.019
Chain 1:   2700        -7508.502             0.025            0.019
Chain 1:   2800        -7728.410             0.026            0.027
Chain 1:   2900        -7455.538             0.030            0.028
Chain 1:   3000        -7611.748             0.031            0.028
Chain 1:   3100        -7600.712             0.027            0.027
Chain 1:   3200        -7877.234             0.024            0.027
Chain 1:   3300        -7541.573             0.023            0.027
Chain 1:   3400        -7800.385             0.025            0.028
Chain 1:   3500        -7531.605             0.025            0.033
Chain 1:   3600        -7593.216             0.026            0.033
Chain 1:   3700        -7532.001             0.025            0.033
Chain 1:   3800        -7507.150             0.023            0.033
Chain 1:   3900        -7492.167             0.019            0.021
Chain 1:   4000        -7470.567             0.017            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003019 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86185.211             1.000            1.000
Chain 1:    200       -14439.187             2.984            4.969
Chain 1:    300       -10705.586             2.106            1.000
Chain 1:    400       -12092.271             1.608            1.000
Chain 1:    500        -9482.504             1.341            0.349
Chain 1:    600        -9370.625             1.120            0.349
Chain 1:    700        -9064.578             0.965            0.275
Chain 1:    800        -9619.995             0.851            0.275
Chain 1:    900        -9352.379             0.760            0.115
Chain 1:   1000        -9450.543             0.685            0.115
Chain 1:   1100        -9455.162             0.585            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8971.519             0.094            0.054
Chain 1:   1300        -9324.436             0.062            0.038
Chain 1:   1400        -9079.492             0.054            0.034
Chain 1:   1500        -9159.968             0.027            0.029
Chain 1:   1600        -9265.506             0.027            0.029
Chain 1:   1700        -9323.377             0.024            0.027
Chain 1:   1800        -8877.378             0.023            0.027
Chain 1:   1900        -8979.981             0.022            0.011
Chain 1:   2000        -8981.671             0.021            0.011
Chain 1:   2100        -9083.415             0.022            0.011
Chain 1:   2200        -8874.292             0.019            0.011
Chain 1:   2300        -9058.814             0.017            0.011
Chain 1:   2400        -8878.670             0.016            0.011
Chain 1:   2500        -8953.786             0.016            0.011
Chain 1:   2600        -8864.936             0.016            0.011
Chain 1:   2700        -8897.477             0.016            0.011
Chain 1:   2800        -8849.339             0.011            0.011
Chain 1:   2900        -8960.982             0.012            0.011
Chain 1:   3000        -8896.926             0.012            0.011
Chain 1:   3100        -8841.327             0.012            0.010
Chain 1:   3200        -8814.541             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003942 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400261.072             1.000            1.000
Chain 1:    200     -1581031.233             2.657            4.313
Chain 1:    300      -890499.801             2.030            1.000
Chain 1:    400      -458609.468             1.758            1.000
Chain 1:    500      -359275.613             1.461            0.942
Chain 1:    600      -234304.738             1.307            0.942
Chain 1:    700      -120355.222             1.255            0.942
Chain 1:    800       -87608.145             1.145            0.942
Chain 1:    900       -67905.740             1.050            0.775
Chain 1:   1000       -52681.833             0.974            0.775
Chain 1:   1100       -40137.149             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39315.119             0.476            0.374
Chain 1:   1300       -27222.849             0.443            0.374
Chain 1:   1400       -26941.974             0.350            0.313
Chain 1:   1500       -23517.816             0.337            0.313
Chain 1:   1600       -22732.940             0.287            0.290
Chain 1:   1700       -21599.182             0.197            0.289
Chain 1:   1800       -21542.454             0.160            0.146
Chain 1:   1900       -21869.311             0.133            0.052
Chain 1:   2000       -20375.963             0.111            0.052
Chain 1:   2100       -20614.237             0.081            0.035
Chain 1:   2200       -20842.074             0.080            0.035
Chain 1:   2300       -20457.949             0.038            0.019
Chain 1:   2400       -20229.705             0.038            0.019
Chain 1:   2500       -20032.263             0.024            0.015
Chain 1:   2600       -19661.132             0.022            0.015
Chain 1:   2700       -19617.795             0.017            0.012
Chain 1:   2800       -19334.565             0.019            0.015
Chain 1:   2900       -19616.229             0.019            0.014
Chain 1:   3000       -19602.176             0.011            0.012
Chain 1:   3100       -19687.318             0.011            0.011
Chain 1:   3200       -19377.407             0.011            0.014
Chain 1:   3300       -19582.639             0.010            0.011
Chain 1:   3400       -19056.696             0.012            0.014
Chain 1:   3500       -19669.998             0.014            0.015
Chain 1:   3600       -18974.833             0.016            0.015
Chain 1:   3700       -19363.037             0.018            0.016
Chain 1:   3800       -18320.011             0.022            0.020
Chain 1:   3900       -18316.176             0.020            0.020
Chain 1:   4000       -18433.399             0.021            0.020
Chain 1:   4100       -18347.056             0.021            0.020
Chain 1:   4200       -18162.724             0.020            0.020
Chain 1:   4300       -18301.451             0.020            0.020
Chain 1:   4400       -18257.740             0.018            0.010
Chain 1:   4500       -18160.280             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001276 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48430.794             1.000            1.000
Chain 1:    200       -15902.125             1.523            2.046
Chain 1:    300       -13061.335             1.088            1.000
Chain 1:    400       -12147.078             0.835            1.000
Chain 1:    500       -11619.136             0.677            0.217
Chain 1:    600       -12767.367             0.579            0.217
Chain 1:    700       -14092.498             0.510            0.094
Chain 1:    800       -16244.465             0.463            0.132
Chain 1:    900       -10799.841             0.467            0.132
Chain 1:   1000       -12245.518             0.432            0.132
Chain 1:   1100       -12232.745             0.332            0.118
Chain 1:   1200       -10158.720             0.148            0.118
Chain 1:   1300       -15607.957             0.161            0.118
Chain 1:   1400       -19149.060             0.172            0.132
Chain 1:   1500       -11404.989             0.236            0.185
Chain 1:   1600       -10535.670             0.235            0.185
Chain 1:   1700       -22038.302             0.278            0.204
Chain 1:   1800        -9673.540             0.392            0.349
Chain 1:   1900       -10081.821             0.346            0.204
Chain 1:   2000       -20441.872             0.385            0.349
Chain 1:   2100       -19152.289             0.391            0.349
Chain 1:   2200       -10865.673             0.447            0.507   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300        -9167.750             0.431            0.507   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400       -10100.550             0.422            0.507   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500       -11102.892             0.363            0.185
Chain 1:   2600        -9133.367             0.376            0.216
Chain 1:   2700        -9001.259             0.325            0.185
Chain 1:   2800       -16287.325             0.242            0.185
Chain 1:   2900       -11719.511             0.277            0.216
Chain 1:   3000        -8897.733             0.258            0.216
Chain 1:   3100       -15367.812             0.294            0.317
Chain 1:   3200        -8981.762             0.288            0.317
Chain 1:   3300        -9161.588             0.272            0.317
Chain 1:   3400       -15464.784             0.303            0.390
Chain 1:   3500        -9198.984             0.362            0.408
Chain 1:   3600        -8887.116             0.344            0.408
Chain 1:   3700        -8567.681             0.347            0.408
Chain 1:   3800       -15364.056             0.346            0.408
Chain 1:   3900        -8700.620             0.384            0.421
Chain 1:   4000        -9731.757             0.363            0.421
Chain 1:   4100        -8861.110             0.330            0.408
Chain 1:   4200       -13732.466             0.295            0.355
Chain 1:   4300       -12408.752             0.303            0.355
Chain 1:   4400        -9596.987             0.292            0.293
Chain 1:   4500        -8825.811             0.233            0.107
Chain 1:   4600       -11929.644             0.255            0.260
Chain 1:   4700        -9039.179             0.283            0.293
Chain 1:   4800        -8300.909             0.248            0.260
Chain 1:   4900        -8536.858             0.174            0.107
Chain 1:   5000        -8417.149             0.165            0.107
Chain 1:   5100       -10768.483             0.177            0.218
Chain 1:   5200       -14537.286             0.168            0.218
Chain 1:   5300        -9141.074             0.216            0.259
Chain 1:   5400       -13019.835             0.216            0.259
Chain 1:   5500       -12076.276             0.215            0.259
Chain 1:   5600        -8407.223             0.233            0.259
Chain 1:   5700       -13992.146             0.241            0.259
Chain 1:   5800        -8410.320             0.299            0.298
Chain 1:   5900       -15267.186             0.341            0.399
Chain 1:   6000        -8339.226             0.422            0.436
Chain 1:   6100       -12973.362             0.436            0.436
Chain 1:   6200        -8620.072             0.461            0.449
Chain 1:   6300        -8278.444             0.406            0.436
Chain 1:   6400       -12313.593             0.409            0.436
Chain 1:   6500       -13037.754             0.407            0.436
Chain 1:   6600        -8154.718             0.423            0.449
Chain 1:   6700       -11414.055             0.411            0.449
Chain 1:   6800        -9110.669             0.370            0.357
Chain 1:   6900        -8607.846             0.331            0.328
Chain 1:   7000        -9767.858             0.260            0.286
Chain 1:   7100        -8223.975             0.243            0.253
Chain 1:   7200        -8290.187             0.193            0.188
Chain 1:   7300        -9781.123             0.205            0.188
Chain 1:   7400        -8064.595             0.193            0.188
Chain 1:   7500        -8253.425             0.190            0.188
Chain 1:   7600        -9166.283             0.140            0.152
Chain 1:   7700        -9733.541             0.117            0.119
Chain 1:   7800        -8169.221             0.111            0.119
Chain 1:   7900        -9561.166             0.120            0.146
Chain 1:   8000        -8024.351             0.127            0.152
Chain 1:   8100        -8286.726             0.111            0.146
Chain 1:   8200        -8171.035             0.112            0.146
Chain 1:   8300        -8058.871             0.098            0.100
Chain 1:   8400       -11977.535             0.110            0.100
Chain 1:   8500        -7946.598             0.158            0.146
Chain 1:   8600        -8162.293             0.151            0.146
Chain 1:   8700        -8591.619             0.150            0.146
Chain 1:   8800        -9045.598             0.136            0.050
Chain 1:   8900        -8999.373             0.122            0.050
Chain 1:   9000        -8581.272             0.107            0.049
Chain 1:   9100       -10451.709             0.122            0.050
Chain 1:   9200        -9410.435             0.132            0.050
Chain 1:   9300        -9760.338             0.134            0.050
Chain 1:   9400        -8174.778             0.121            0.050
Chain 1:   9500        -9627.307             0.085            0.050
Chain 1:   9600        -8182.481             0.100            0.111
Chain 1:   9700        -9454.492             0.109            0.135
Chain 1:   9800        -8214.634             0.119            0.151
Chain 1:   9900        -8260.682             0.119            0.151
Chain 1:   10000        -7957.663             0.118            0.151
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56946.045             1.000            1.000
Chain 1:    200       -17248.724             1.651            2.301
Chain 1:    300        -8538.071             1.441            1.020
Chain 1:    400        -8185.946             1.091            1.020
Chain 1:    500        -8443.033             0.879            1.000
Chain 1:    600        -8552.782             0.735            1.000
Chain 1:    700        -7614.063             0.647            0.123
Chain 1:    800        -7954.011             0.572            0.123
Chain 1:    900        -7764.544             0.511            0.043
Chain 1:   1000        -7425.992             0.464            0.046
Chain 1:   1100        -7536.971             0.366            0.043
Chain 1:   1200        -7469.600             0.137            0.043
Chain 1:   1300        -7425.274             0.035            0.030
Chain 1:   1400        -7488.977             0.032            0.024
Chain 1:   1500        -7435.748             0.029            0.015
Chain 1:   1600        -7469.619             0.029            0.015
Chain 1:   1700        -7398.658             0.017            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003447 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86219.630             1.000            1.000
Chain 1:    200       -13222.120             3.260            5.521
Chain 1:    300        -9614.760             2.299            1.000
Chain 1:    400       -10479.064             1.745            1.000
Chain 1:    500        -8556.545             1.441            0.375
Chain 1:    600        -8480.696             1.202            0.375
Chain 1:    700        -8357.693             1.032            0.225
Chain 1:    800        -8403.137             0.904            0.225
Chain 1:    900        -8424.816             0.804            0.082
Chain 1:   1000        -8287.952             0.725            0.082
Chain 1:   1100        -8494.892             0.628            0.024   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8124.893             0.080            0.024
Chain 1:   1300        -8323.368             0.045            0.024
Chain 1:   1400        -8329.922             0.037            0.017
Chain 1:   1500        -8188.244             0.016            0.017
Chain 1:   1600        -8300.913             0.016            0.017
Chain 1:   1700        -8386.542             0.016            0.017
Chain 1:   1800        -7978.654             0.021            0.017
Chain 1:   1900        -8074.611             0.022            0.017
Chain 1:   2000        -8047.022             0.020            0.017
Chain 1:   2100        -8168.178             0.019            0.015
Chain 1:   2200        -8281.838             0.016            0.014
Chain 1:   2300        -8072.084             0.016            0.014
Chain 1:   2400        -8139.932             0.017            0.014
Chain 1:   2500        -8086.511             0.016            0.014
Chain 1:   2600        -8083.561             0.015            0.012
Chain 1:   2700        -8001.100             0.015            0.012
Chain 1:   2800        -7965.689             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003133 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407030.676             1.000            1.000
Chain 1:    200     -1584223.814             2.653            4.307
Chain 1:    300      -890446.543             2.029            1.000
Chain 1:    400      -457302.705             1.758            1.000
Chain 1:    500      -357579.160             1.462            0.947
Chain 1:    600      -232548.699             1.308            0.947
Chain 1:    700      -118867.607             1.258            0.947
Chain 1:    800       -86098.685             1.148            0.947
Chain 1:    900       -66459.332             1.054            0.779
Chain 1:   1000       -51267.661             0.978            0.779
Chain 1:   1100       -38758.477             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37933.502             0.482            0.381
Chain 1:   1300       -25908.011             0.450            0.381
Chain 1:   1400       -25627.658             0.356            0.323
Chain 1:   1500       -22219.985             0.344            0.323
Chain 1:   1600       -21437.760             0.294            0.296
Chain 1:   1700       -20313.895             0.204            0.296
Chain 1:   1800       -20258.426             0.166            0.153
Chain 1:   1900       -20584.449             0.138            0.055
Chain 1:   2000       -19097.082             0.116            0.055
Chain 1:   2100       -19335.360             0.085            0.036
Chain 1:   2200       -19561.571             0.084            0.036
Chain 1:   2300       -19179.028             0.040            0.020
Chain 1:   2400       -18951.174             0.040            0.020
Chain 1:   2500       -18753.112             0.025            0.016
Chain 1:   2600       -18383.541             0.024            0.016
Chain 1:   2700       -18340.559             0.019            0.012
Chain 1:   2800       -18057.474             0.020            0.016
Chain 1:   2900       -18338.602             0.020            0.015
Chain 1:   3000       -18324.830             0.012            0.012
Chain 1:   3100       -18409.798             0.011            0.012
Chain 1:   3200       -18100.597             0.012            0.015
Chain 1:   3300       -18305.220             0.011            0.012
Chain 1:   3400       -17780.324             0.013            0.015
Chain 1:   3500       -18391.919             0.015            0.016
Chain 1:   3600       -17698.947             0.017            0.016
Chain 1:   3700       -18085.474             0.019            0.017
Chain 1:   3800       -17045.727             0.023            0.021
Chain 1:   3900       -17041.873             0.022            0.021
Chain 1:   4000       -17159.184             0.022            0.021
Chain 1:   4100       -17072.985             0.022            0.021
Chain 1:   4200       -16889.328             0.022            0.021
Chain 1:   4300       -17027.667             0.022            0.021
Chain 1:   4400       -16984.585             0.019            0.011
Chain 1:   4500       -16887.114             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12336.552             1.000            1.000
Chain 1:    200        -9322.528             0.662            1.000
Chain 1:    300        -8074.356             0.493            0.323
Chain 1:    400        -8235.431             0.374            0.323
Chain 1:    500        -8163.191             0.301            0.155
Chain 1:    600        -8018.646             0.254            0.155
Chain 1:    700        -7944.875             0.219            0.020
Chain 1:    800        -7954.315             0.192            0.020
Chain 1:    900        -7855.496             0.172            0.018
Chain 1:   1000        -8053.345             0.157            0.020
Chain 1:   1100        -7986.219             0.058            0.018
Chain 1:   1200        -7984.499             0.026            0.013
Chain 1:   1300        -7912.954             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63158.183             1.000            1.000
Chain 1:    200       -18107.197             1.744            2.488
Chain 1:    300        -8755.118             1.519            1.068
Chain 1:    400        -8455.281             1.148            1.068
Chain 1:    500        -8401.306             0.920            1.000
Chain 1:    600        -8038.876             0.774            1.000
Chain 1:    700        -7760.250             0.668            0.045
Chain 1:    800        -8171.105             0.591            0.050
Chain 1:    900        -7982.445             0.528            0.045
Chain 1:   1000        -7899.278             0.476            0.045
Chain 1:   1100        -7681.810             0.379            0.036
Chain 1:   1200        -7580.845             0.132            0.035
Chain 1:   1300        -7803.207             0.028            0.028
Chain 1:   1400        -7686.287             0.026            0.028
Chain 1:   1500        -7651.602             0.026            0.028
Chain 1:   1600        -7549.328             0.022            0.024
Chain 1:   1700        -7652.964             0.020            0.015
Chain 1:   1800        -7649.706             0.015            0.014
Chain 1:   1900        -7621.539             0.013            0.014
Chain 1:   2000        -7582.900             0.013            0.014
Chain 1:   2100        -7620.228             0.010            0.013
Chain 1:   2200        -7720.263             0.010            0.013
Chain 1:   2300        -7623.912             0.009            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003037 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86387.630             1.000            1.000
Chain 1:    200       -13427.260             3.217            5.434
Chain 1:    300        -9855.059             2.265            1.000
Chain 1:    400       -10565.837             1.716            1.000
Chain 1:    500        -8759.327             1.414            0.362
Chain 1:    600        -8435.428             1.185            0.362
Chain 1:    700        -8482.742             1.016            0.206
Chain 1:    800        -8952.363             0.896            0.206
Chain 1:    900        -8699.552             0.799            0.067
Chain 1:   1000        -8430.098             0.723            0.067
Chain 1:   1100        -8672.700             0.626            0.052   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8338.859             0.086            0.040
Chain 1:   1300        -8407.380             0.051            0.038
Chain 1:   1400        -8501.686             0.045            0.032
Chain 1:   1500        -8436.731             0.025            0.029
Chain 1:   1600        -8433.976             0.021            0.028
Chain 1:   1700        -8357.959             0.022            0.028
Chain 1:   1800        -8245.834             0.018            0.014
Chain 1:   1900        -8365.192             0.016            0.014
Chain 1:   2000        -8325.571             0.014            0.011
Chain 1:   2100        -8450.902             0.012            0.011
Chain 1:   2200        -8237.924             0.011            0.011
Chain 1:   2300        -8387.070             0.012            0.014
Chain 1:   2400        -8400.958             0.011            0.014
Chain 1:   2500        -8370.395             0.011            0.014
Chain 1:   2600        -8372.154             0.011            0.014
Chain 1:   2700        -8278.579             0.011            0.014
Chain 1:   2800        -8250.592             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408287.034             1.000            1.000
Chain 1:    200     -1582851.889             2.656            4.312
Chain 1:    300      -891185.897             2.029            1.000
Chain 1:    400      -458477.714             1.758            1.000
Chain 1:    500      -358960.544             1.462            0.944
Chain 1:    600      -233651.415             1.308            0.944
Chain 1:    700      -119483.899             1.257            0.944
Chain 1:    800       -86625.149             1.148            0.944
Chain 1:    900       -66881.918             1.053            0.776
Chain 1:   1000       -51611.909             0.977            0.776
Chain 1:   1100       -39042.372             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38204.154             0.480            0.379
Chain 1:   1300       -26121.144             0.449            0.379
Chain 1:   1400       -25833.540             0.356            0.322
Chain 1:   1500       -22412.415             0.343            0.322
Chain 1:   1600       -21625.964             0.293            0.296
Chain 1:   1700       -20495.535             0.203            0.295
Chain 1:   1800       -20438.351             0.166            0.153
Chain 1:   1900       -20764.061             0.138            0.055
Chain 1:   2000       -19273.969             0.116            0.055
Chain 1:   2100       -19512.225             0.085            0.036
Chain 1:   2200       -19738.944             0.084            0.036
Chain 1:   2300       -19356.004             0.039            0.020
Chain 1:   2400       -19128.199             0.040            0.020
Chain 1:   2500       -18930.525             0.025            0.016
Chain 1:   2600       -18560.874             0.024            0.016
Chain 1:   2700       -18517.764             0.018            0.012
Chain 1:   2800       -18235.042             0.020            0.016
Chain 1:   2900       -18516.092             0.020            0.015
Chain 1:   3000       -18502.158             0.012            0.012
Chain 1:   3100       -18587.202             0.011            0.012
Chain 1:   3200       -18278.037             0.012            0.015
Chain 1:   3300       -18482.582             0.011            0.012
Chain 1:   3400       -17958.025             0.013            0.015
Chain 1:   3500       -18569.247             0.015            0.016
Chain 1:   3600       -17876.659             0.017            0.016
Chain 1:   3700       -18263.052             0.019            0.017
Chain 1:   3800       -17224.035             0.023            0.021
Chain 1:   3900       -17220.230             0.022            0.021
Chain 1:   4000       -17337.482             0.022            0.021
Chain 1:   4100       -17251.418             0.022            0.021
Chain 1:   4200       -17067.843             0.022            0.021
Chain 1:   4300       -17206.059             0.021            0.021
Chain 1:   4400       -17163.113             0.019            0.011
Chain 1:   4500       -17065.693             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001456 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13173.946             1.000            1.000
Chain 1:    200        -9780.841             0.673            1.000
Chain 1:    300        -8594.919             0.495            0.347
Chain 1:    400        -8759.718             0.376            0.347
Chain 1:    500        -8697.094             0.302            0.138
Chain 1:    600        -8482.976             0.256            0.138
Chain 1:    700        -8400.915             0.221            0.025
Chain 1:    800        -8405.188             0.193            0.025
Chain 1:    900        -8539.634             0.174            0.019
Chain 1:   1000        -8456.510             0.157            0.019
Chain 1:   1100        -8476.348             0.057            0.016
Chain 1:   1200        -8403.094             0.024            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001392 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62649.995             1.000            1.000
Chain 1:    200       -18780.040             1.668            2.336
Chain 1:    300        -9344.845             1.449            1.010
Chain 1:    400       -10150.294             1.106            1.010
Chain 1:    500        -8211.500             0.932            1.000
Chain 1:    600        -8569.336             0.784            1.000
Chain 1:    700        -8499.949             0.673            0.236
Chain 1:    800        -8346.993             0.591            0.236
Chain 1:    900        -7770.840             0.534            0.079
Chain 1:   1000        -7667.514             0.482            0.079
Chain 1:   1100        -7688.610             0.382            0.074
Chain 1:   1200        -7787.207             0.150            0.042
Chain 1:   1300        -7620.733             0.051            0.022
Chain 1:   1400        -7692.064             0.044            0.018
Chain 1:   1500        -7629.462             0.021            0.013
Chain 1:   1600        -7881.342             0.020            0.013
Chain 1:   1700        -7728.806             0.021            0.018
Chain 1:   1800        -7565.170             0.022            0.020
Chain 1:   1900        -7658.460             0.015            0.013
Chain 1:   2000        -7698.836             0.015            0.013
Chain 1:   2100        -7581.755             0.016            0.015
Chain 1:   2200        -7832.147             0.018            0.020
Chain 1:   2300        -7694.978             0.017            0.018
Chain 1:   2400        -7603.429             0.018            0.018
Chain 1:   2500        -7471.248             0.019            0.018
Chain 1:   2600        -7672.840             0.018            0.018
Chain 1:   2700        -7671.913             0.016            0.018
Chain 1:   2800        -7555.160             0.015            0.015
Chain 1:   2900        -7433.392             0.016            0.016
Chain 1:   3000        -7605.463             0.018            0.018
Chain 1:   3100        -7569.564             0.017            0.018
Chain 1:   3200        -7830.892             0.017            0.018
Chain 1:   3300        -7519.258             0.019            0.018
Chain 1:   3400        -7753.624             0.021            0.023
Chain 1:   3500        -7472.935             0.023            0.026
Chain 1:   3600        -7536.915             0.021            0.023
Chain 1:   3700        -7518.012             0.021            0.023
Chain 1:   3800        -7442.676             0.021            0.023
Chain 1:   3900        -7458.740             0.019            0.023
Chain 1:   4000        -7448.188             0.017            0.010
Chain 1:   4100        -7457.772             0.017            0.010
Chain 1:   4200        -7548.582             0.015            0.010
Chain 1:   4300        -7437.643             0.012            0.010
Chain 1:   4400        -7484.846             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003085 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87288.337             1.000            1.000
Chain 1:    200       -14340.587             3.043            5.087
Chain 1:    300       -10568.912             2.148            1.000
Chain 1:    400       -12448.576             1.649            1.000
Chain 1:    500        -8983.001             1.396            0.386
Chain 1:    600        -8835.619             1.166            0.386
Chain 1:    700        -9270.522             1.006            0.357
Chain 1:    800        -9195.652             0.882            0.357
Chain 1:    900        -9206.977             0.784            0.151
Chain 1:   1000        -9021.382             0.707            0.151
Chain 1:   1100        -9392.534             0.611            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8840.476             0.109            0.047
Chain 1:   1300        -9174.995             0.077            0.040
Chain 1:   1400        -9043.803             0.063            0.036
Chain 1:   1500        -9039.291             0.025            0.021
Chain 1:   1600        -9130.388             0.024            0.021
Chain 1:   1700        -9182.759             0.020            0.015
Chain 1:   1800        -8727.328             0.024            0.021
Chain 1:   1900        -8837.944             0.025            0.021
Chain 1:   2000        -8847.637             0.023            0.015
Chain 1:   2100        -8770.809             0.020            0.013
Chain 1:   2200        -8760.386             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003067 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426588.485             1.000            1.000
Chain 1:    200     -1588474.557             2.652            4.305
Chain 1:    300      -891574.725             2.029            1.000
Chain 1:    400      -458636.658             1.758            1.000
Chain 1:    500      -358573.301             1.462            0.944
Chain 1:    600      -233632.773             1.307            0.944
Chain 1:    700      -119933.991             1.256            0.944
Chain 1:    800       -87214.013             1.146            0.944
Chain 1:    900       -67584.249             1.051            0.782
Chain 1:   1000       -52415.446             0.975            0.782
Chain 1:   1100       -39920.324             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39106.893             0.478            0.375
Chain 1:   1300       -27072.529             0.444            0.375
Chain 1:   1400       -26797.834             0.351            0.313
Chain 1:   1500       -23387.051             0.337            0.313
Chain 1:   1600       -22605.753             0.287            0.290
Chain 1:   1700       -21479.156             0.198            0.289
Chain 1:   1800       -21424.054             0.160            0.146
Chain 1:   1900       -21750.913             0.133            0.052
Chain 1:   2000       -20260.703             0.111            0.052
Chain 1:   2100       -20499.264             0.081            0.035
Chain 1:   2200       -20726.249             0.080            0.035
Chain 1:   2300       -20342.784             0.038            0.019
Chain 1:   2400       -20114.528             0.038            0.019
Chain 1:   2500       -19916.629             0.024            0.015
Chain 1:   2600       -19545.979             0.023            0.015
Chain 1:   2700       -19502.708             0.018            0.012
Chain 1:   2800       -19219.201             0.019            0.015
Chain 1:   2900       -19500.811             0.019            0.014
Chain 1:   3000       -19486.968             0.011            0.012
Chain 1:   3100       -19572.079             0.011            0.011
Chain 1:   3200       -19262.225             0.011            0.014
Chain 1:   3300       -19467.377             0.010            0.011
Chain 1:   3400       -18941.368             0.012            0.014
Chain 1:   3500       -19554.644             0.014            0.015
Chain 1:   3600       -18859.441             0.016            0.015
Chain 1:   3700       -19247.585             0.018            0.016
Chain 1:   3800       -18204.457             0.022            0.020
Chain 1:   3900       -18200.503             0.021            0.020
Chain 1:   4000       -18317.824             0.021            0.020
Chain 1:   4100       -18231.418             0.021            0.020
Chain 1:   4200       -18047.043             0.021            0.020
Chain 1:   4300       -18185.882             0.020            0.020
Chain 1:   4400       -18142.156             0.018            0.010
Chain 1:   4500       -18044.582             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001192 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12112.838             1.000            1.000
Chain 1:    200        -9070.306             0.668            1.000
Chain 1:    300        -7885.137             0.495            0.335
Chain 1:    400        -8099.480             0.378            0.335
Chain 1:    500        -7968.919             0.306            0.150
Chain 1:    600        -7830.216             0.258            0.150
Chain 1:    700        -7752.905             0.222            0.026
Chain 1:    800        -7762.991             0.195            0.026
Chain 1:    900        -7661.138             0.175            0.018
Chain 1:   1000        -7806.117             0.159            0.019
Chain 1:   1100        -7801.390             0.059            0.018
Chain 1:   1200        -7790.460             0.026            0.016
Chain 1:   1300        -7731.243             0.011            0.013
Chain 1:   1400        -7751.673             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001422 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61581.698             1.000            1.000
Chain 1:    200       -17573.128             1.752            2.504
Chain 1:    300        -8703.867             1.508            1.019
Chain 1:    400        -8977.978             1.138            1.019
Chain 1:    500        -7939.700             0.937            1.000
Chain 1:    600        -8898.533             0.799            1.000
Chain 1:    700        -7813.792             0.704            0.139
Chain 1:    800        -8698.708             0.629            0.139
Chain 1:    900        -7955.639             0.570            0.131
Chain 1:   1000        -7728.970             0.516            0.131
Chain 1:   1100        -7697.985             0.416            0.108
Chain 1:   1200        -7761.860             0.166            0.102
Chain 1:   1300        -7691.625             0.065            0.093
Chain 1:   1400        -7626.941             0.063            0.093
Chain 1:   1500        -7566.130             0.051            0.029
Chain 1:   1600        -7498.176             0.041            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003793 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85247.181             1.000            1.000
Chain 1:    200       -13233.543             3.221            5.442
Chain 1:    300        -9662.290             2.270            1.000
Chain 1:    400       -10542.125             1.724            1.000
Chain 1:    500        -8581.915             1.425            0.370
Chain 1:    600        -8365.854             1.192            0.370
Chain 1:    700        -8522.521             1.024            0.228
Chain 1:    800        -8704.468             0.899            0.228
Chain 1:    900        -8510.156             0.801            0.083
Chain 1:   1000        -8341.829             0.723            0.083
Chain 1:   1100        -8525.687             0.625            0.026   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8070.413             0.087            0.026
Chain 1:   1300        -8360.860             0.053            0.026
Chain 1:   1400        -8377.612             0.045            0.023
Chain 1:   1500        -8261.712             0.024            0.022
Chain 1:   1600        -8367.091             0.022            0.021
Chain 1:   1700        -8452.042             0.022            0.021
Chain 1:   1800        -8055.760             0.024            0.022
Chain 1:   1900        -8156.717             0.023            0.020
Chain 1:   2000        -8127.483             0.022            0.014
Chain 1:   2100        -8248.472             0.021            0.014
Chain 1:   2200        -8026.051             0.018            0.014
Chain 1:   2300        -8185.621             0.017            0.014
Chain 1:   2400        -8196.010             0.016            0.014
Chain 1:   2500        -8169.751             0.015            0.013
Chain 1:   2600        -8172.183             0.014            0.012
Chain 1:   2700        -8078.240             0.014            0.012
Chain 1:   2800        -8048.426             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8369181.869             1.000            1.000
Chain 1:    200     -1579470.292             2.649            4.299
Chain 1:    300      -889366.991             2.025            1.000
Chain 1:    400      -456593.893             1.756            1.000
Chain 1:    500      -357294.399             1.460            0.948
Chain 1:    600      -232661.176             1.306            0.948
Chain 1:    700      -118982.392             1.256            0.948
Chain 1:    800       -86196.149             1.146            0.948
Chain 1:    900       -66546.593             1.052            0.776
Chain 1:   1000       -51334.418             0.976            0.776
Chain 1:   1100       -38800.525             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37975.520             0.481            0.380
Chain 1:   1300       -25923.096             0.450            0.380
Chain 1:   1400       -25640.990             0.356            0.323
Chain 1:   1500       -22225.320             0.344            0.323
Chain 1:   1600       -21440.740             0.294            0.296
Chain 1:   1700       -20313.568             0.204            0.295
Chain 1:   1800       -20257.447             0.166            0.154
Chain 1:   1900       -20583.272             0.138            0.055
Chain 1:   2000       -19094.528             0.116            0.055
Chain 1:   2100       -19332.978             0.085            0.037
Chain 1:   2200       -19559.198             0.084            0.037
Chain 1:   2300       -19176.697             0.040            0.020
Chain 1:   2400       -18948.870             0.040            0.020
Chain 1:   2500       -18750.953             0.026            0.016
Chain 1:   2600       -18381.494             0.024            0.016
Chain 1:   2700       -18338.612             0.019            0.012
Chain 1:   2800       -18055.589             0.020            0.016
Chain 1:   2900       -18336.732             0.020            0.015
Chain 1:   3000       -18322.977             0.012            0.012
Chain 1:   3100       -18407.863             0.011            0.012
Chain 1:   3200       -18098.823             0.012            0.015
Chain 1:   3300       -18303.369             0.011            0.012
Chain 1:   3400       -17778.728             0.013            0.015
Chain 1:   3500       -18389.957             0.015            0.016
Chain 1:   3600       -17697.538             0.017            0.016
Chain 1:   3700       -18083.642             0.019            0.017
Chain 1:   3800       -17044.751             0.023            0.021
Chain 1:   3900       -17040.949             0.022            0.021
Chain 1:   4000       -17158.238             0.022            0.021
Chain 1:   4100       -17072.019             0.022            0.021
Chain 1:   4200       -16888.627             0.022            0.021
Chain 1:   4300       -17026.780             0.022            0.021
Chain 1:   4400       -16983.853             0.019            0.011
Chain 1:   4500       -16886.436             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001342 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48845.976             1.000            1.000
Chain 1:    200       -16173.786             1.510            2.020
Chain 1:    300       -18494.247             1.049            1.000
Chain 1:    400       -13878.222             0.870            1.000
Chain 1:    500       -20906.567             0.763            0.336
Chain 1:    600       -15987.898             0.687            0.336
Chain 1:    700       -11408.819             0.646            0.336
Chain 1:    800       -14632.709             0.593            0.336
Chain 1:    900       -19997.909             0.557            0.333
Chain 1:   1000       -10868.418             0.585            0.336
Chain 1:   1100       -22680.079             0.537            0.336   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10141.239             0.459            0.336
Chain 1:   1300       -13203.554             0.470            0.336
Chain 1:   1400       -18049.735             0.463            0.336
Chain 1:   1500       -11775.867             0.483            0.401
Chain 1:   1600       -10415.939             0.465            0.401
Chain 1:   1700        -9452.357             0.435            0.268
Chain 1:   1800       -15795.493             0.453            0.402
Chain 1:   1900       -10037.791             0.484            0.521   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000        -9386.964             0.407            0.402
Chain 1:   2100       -10050.631             0.361            0.268
Chain 1:   2200       -10688.437             0.244            0.232
Chain 1:   2300       -18032.659             0.261            0.268
Chain 1:   2400        -9518.003             0.324            0.402
Chain 1:   2500       -11586.558             0.288            0.179
Chain 1:   2600       -10543.935             0.285            0.179
Chain 1:   2700        -9435.286             0.287            0.179
Chain 1:   2800       -11516.184             0.265            0.179
Chain 1:   2900        -9696.873             0.226            0.179
Chain 1:   3000        -9896.411             0.221            0.179
Chain 1:   3100       -12032.093             0.232            0.179
Chain 1:   3200        -9598.363             0.252            0.181
Chain 1:   3300        -9397.165             0.213            0.179
Chain 1:   3400        -9015.009             0.128            0.177
Chain 1:   3500        -9368.299             0.114            0.118
Chain 1:   3600       -17045.954             0.149            0.177
Chain 1:   3700        -9256.404             0.221            0.181
Chain 1:   3800       -11002.443             0.219            0.177
Chain 1:   3900        -9803.577             0.213            0.159
Chain 1:   4000       -11317.424             0.224            0.159
Chain 1:   4100       -10562.377             0.213            0.134
Chain 1:   4200       -10438.461             0.189            0.122
Chain 1:   4300       -14916.009             0.217            0.134
Chain 1:   4400        -9135.972             0.276            0.159
Chain 1:   4500        -9627.066             0.277            0.159
Chain 1:   4600       -10300.202             0.239            0.134
Chain 1:   4700       -15323.256             0.188            0.134
Chain 1:   4800        -9668.636             0.230            0.134
Chain 1:   4900        -9132.783             0.224            0.134
Chain 1:   5000       -13684.408             0.244            0.300
Chain 1:   5100       -12244.403             0.248            0.300
Chain 1:   5200       -14643.752             0.263            0.300
Chain 1:   5300       -10860.110             0.268            0.328
Chain 1:   5400        -9093.793             0.224            0.194
Chain 1:   5500        -8419.314             0.227            0.194
Chain 1:   5600        -9163.754             0.229            0.194
Chain 1:   5700        -9083.504             0.197            0.164
Chain 1:   5800        -8488.242             0.146            0.118
Chain 1:   5900       -14059.097             0.179            0.164
Chain 1:   6000        -9055.751             0.201            0.164
Chain 1:   6100       -13865.923             0.224            0.194
Chain 1:   6200        -8579.717             0.269            0.347
Chain 1:   6300        -9365.569             0.243            0.194
Chain 1:   6400        -8222.751             0.237            0.139
Chain 1:   6500       -13995.506             0.271            0.347
Chain 1:   6600       -10901.321             0.291            0.347
Chain 1:   6700        -8874.158             0.313            0.347
Chain 1:   6800        -9059.010             0.308            0.347
Chain 1:   6900        -8469.645             0.275            0.284
Chain 1:   7000       -11642.704             0.247            0.273
Chain 1:   7100        -9122.688             0.240            0.273
Chain 1:   7200        -8260.211             0.189            0.228
Chain 1:   7300       -11837.040             0.211            0.273
Chain 1:   7400       -13422.582             0.209            0.273
Chain 1:   7500        -9516.765             0.209            0.273
Chain 1:   7600        -9691.968             0.182            0.228
Chain 1:   7700        -8375.215             0.175            0.157
Chain 1:   7800       -10590.599             0.194            0.209
Chain 1:   7900        -8245.019             0.215            0.273
Chain 1:   8000        -8118.282             0.190            0.209
Chain 1:   8100        -8859.334             0.170            0.157
Chain 1:   8200        -8166.457             0.168            0.157
Chain 1:   8300        -8257.907             0.139            0.118
Chain 1:   8400        -8229.710             0.128            0.085
Chain 1:   8500        -8081.389             0.089            0.084
Chain 1:   8600       -11054.446             0.114            0.085
Chain 1:   8700        -8745.386             0.124            0.085
Chain 1:   8800        -9085.935             0.107            0.084
Chain 1:   8900        -8722.589             0.083            0.042
Chain 1:   9000        -8518.055             0.084            0.042
Chain 1:   9100        -8200.521             0.079            0.039
Chain 1:   9200        -8412.162             0.073            0.037
Chain 1:   9300        -8145.057             0.075            0.037
Chain 1:   9400       -10439.689             0.097            0.039
Chain 1:   9500        -8337.024             0.120            0.042
Chain 1:   9600       -10121.594             0.111            0.042
Chain 1:   9700       -10253.329             0.086            0.039
Chain 1:   9800       -11119.878             0.090            0.042
Chain 1:   9900        -8649.018             0.115            0.078
Chain 1:   10000        -9862.015             0.124            0.123
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57326.863             1.000            1.000
Chain 1:    200       -17460.492             1.642            2.283
Chain 1:    300        -8694.259             1.431            1.008
Chain 1:    400        -8317.151             1.084            1.008
Chain 1:    500        -8153.684             0.871            1.000
Chain 1:    600        -8713.289             0.737            1.000
Chain 1:    700        -7767.520             0.649            0.122
Chain 1:    800        -8217.965             0.575            0.122
Chain 1:    900        -7919.353             0.515            0.064
Chain 1:   1000        -7677.851             0.467            0.064
Chain 1:   1100        -7724.173             0.367            0.055
Chain 1:   1200        -7610.294             0.140            0.045
Chain 1:   1300        -7661.077             0.040            0.038
Chain 1:   1400        -7761.660             0.037            0.031
Chain 1:   1500        -7565.355             0.038            0.031
Chain 1:   1600        -7719.824             0.033            0.026
Chain 1:   1700        -7472.581             0.024            0.026
Chain 1:   1800        -7550.191             0.020            0.020
Chain 1:   1900        -7499.946             0.017            0.015
Chain 1:   2000        -7518.351             0.014            0.013
Chain 1:   2100        -7473.998             0.014            0.013
Chain 1:   2200        -7687.136             0.015            0.013
Chain 1:   2300        -7545.491             0.016            0.019
Chain 1:   2400        -7596.506             0.016            0.019
Chain 1:   2500        -7546.931             0.014            0.010
Chain 1:   2600        -7499.452             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002742 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87030.570             1.000            1.000
Chain 1:    200       -13443.878             3.237            5.474
Chain 1:    300        -9822.875             2.281            1.000
Chain 1:    400       -10781.653             1.733            1.000
Chain 1:    500        -8769.064             1.432            0.369
Chain 1:    600        -8290.080             1.203            0.369
Chain 1:    700        -8662.955             1.037            0.230
Chain 1:    800        -9366.786             0.917            0.230
Chain 1:    900        -8681.738             0.824            0.089
Chain 1:   1000        -8406.793             0.745            0.089
Chain 1:   1100        -8620.554             0.647            0.079   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8199.053             0.105            0.075
Chain 1:   1300        -8465.344             0.071            0.058
Chain 1:   1400        -8502.180             0.063            0.051
Chain 1:   1500        -8416.282             0.041            0.043
Chain 1:   1600        -8516.459             0.036            0.033
Chain 1:   1700        -8595.179             0.033            0.031
Chain 1:   1800        -8189.673             0.030            0.031
Chain 1:   1900        -8285.892             0.024            0.025
Chain 1:   2000        -8258.369             0.021            0.012
Chain 1:   2100        -8379.361             0.020            0.012
Chain 1:   2200        -8276.542             0.016            0.012
Chain 1:   2300        -8326.048             0.013            0.012
Chain 1:   2400        -8210.492             0.014            0.012
Chain 1:   2500        -8262.224             0.014            0.012
Chain 1:   2600        -8290.387             0.013            0.012
Chain 1:   2700        -8206.442             0.013            0.012
Chain 1:   2800        -8174.783             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003792 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8438937.065             1.000            1.000
Chain 1:    200     -1590400.281             2.653            4.306
Chain 1:    300      -891782.537             2.030            1.000
Chain 1:    400      -457802.814             1.759            1.000
Chain 1:    500      -357688.018             1.463            0.948
Chain 1:    600      -232396.788             1.309            0.948
Chain 1:    700      -118851.129             1.259            0.948
Chain 1:    800       -86144.048             1.149            0.948
Chain 1:    900       -66541.581             1.054            0.783
Chain 1:   1000       -51386.147             0.978            0.783
Chain 1:   1100       -38910.616             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38089.834             0.482            0.380
Chain 1:   1300       -26095.702             0.449            0.380
Chain 1:   1400       -25818.435             0.356            0.321
Chain 1:   1500       -22419.626             0.343            0.321
Chain 1:   1600       -21640.048             0.292            0.295
Chain 1:   1700       -20519.821             0.202            0.295
Chain 1:   1800       -20465.267             0.165            0.152
Chain 1:   1900       -20791.324             0.137            0.055
Chain 1:   2000       -19305.791             0.115            0.055
Chain 1:   2100       -19543.935             0.084            0.036
Chain 1:   2200       -19769.979             0.083            0.036
Chain 1:   2300       -19387.570             0.039            0.020
Chain 1:   2400       -19159.760             0.039            0.020
Chain 1:   2500       -18961.625             0.025            0.016
Chain 1:   2600       -18592.165             0.024            0.016
Chain 1:   2700       -18549.124             0.018            0.012
Chain 1:   2800       -18266.061             0.020            0.015
Chain 1:   2900       -18547.135             0.020            0.015
Chain 1:   3000       -18533.313             0.012            0.012
Chain 1:   3100       -18618.356             0.011            0.012
Chain 1:   3200       -18309.133             0.012            0.015
Chain 1:   3300       -18513.733             0.011            0.012
Chain 1:   3400       -17988.854             0.013            0.015
Chain 1:   3500       -18600.428             0.015            0.015
Chain 1:   3600       -17907.389             0.017            0.015
Chain 1:   3700       -18294.012             0.019            0.017
Chain 1:   3800       -17254.204             0.023            0.021
Chain 1:   3900       -17250.312             0.022            0.021
Chain 1:   4000       -17367.641             0.022            0.021
Chain 1:   4100       -17281.507             0.022            0.021
Chain 1:   4200       -17097.758             0.022            0.021
Chain 1:   4300       -17236.146             0.021            0.021
Chain 1:   4400       -17193.057             0.019            0.011
Chain 1:   4500       -17095.563             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001175 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48875.117             1.000            1.000
Chain 1:    200       -23248.666             1.051            1.102
Chain 1:    300       -14792.095             0.891            1.000
Chain 1:    400       -13626.089             0.690            1.000
Chain 1:    500       -21617.253             0.626            0.572
Chain 1:    600       -15652.430             0.585            0.572
Chain 1:    700       -11888.416             0.547            0.381
Chain 1:    800       -13216.267             0.491            0.381
Chain 1:    900       -12067.931             0.447            0.370
Chain 1:   1000       -21921.918             0.447            0.381
Chain 1:   1100       -13249.667             0.413            0.381
Chain 1:   1200       -11048.924             0.322            0.370
Chain 1:   1300       -12053.964             0.274            0.317
Chain 1:   1400        -9768.073             0.288            0.317
Chain 1:   1500       -21388.743             0.306            0.317
Chain 1:   1600       -11238.014             0.358            0.317
Chain 1:   1700       -12909.798             0.339            0.234
Chain 1:   1800       -11693.192             0.340            0.234
Chain 1:   1900       -10062.014             0.346            0.234
Chain 1:   2000       -11086.204             0.311            0.199
Chain 1:   2100       -13856.330             0.265            0.199
Chain 1:   2200        -9760.885             0.287            0.200
Chain 1:   2300       -17438.470             0.323            0.234
Chain 1:   2400        -9271.861             0.388            0.420
Chain 1:   2500        -9570.034             0.336            0.200
Chain 1:   2600        -9521.368             0.246            0.162
Chain 1:   2700        -9162.166             0.237            0.162
Chain 1:   2800        -9021.696             0.229            0.162
Chain 1:   2900        -9299.360             0.215            0.092
Chain 1:   3000       -15337.956             0.246            0.200
Chain 1:   3100        -9796.118             0.282            0.394
Chain 1:   3200        -9854.901             0.241            0.039
Chain 1:   3300        -9200.518             0.204            0.039
Chain 1:   3400        -9499.302             0.119            0.031
Chain 1:   3500        -9313.111             0.118            0.031
Chain 1:   3600        -9886.279             0.123            0.039
Chain 1:   3700        -9714.540             0.121            0.031
Chain 1:   3800        -8733.874             0.131            0.058
Chain 1:   3900       -10513.099             0.145            0.071
Chain 1:   4000       -10456.460             0.106            0.058
Chain 1:   4100        -9017.435             0.065            0.058
Chain 1:   4200        -9521.186             0.070            0.058
Chain 1:   4300        -8975.073             0.069            0.058
Chain 1:   4400        -8571.936             0.070            0.058
Chain 1:   4500        -8913.695             0.072            0.058
Chain 1:   4600       -10542.841             0.082            0.061
Chain 1:   4700        -8452.767             0.105            0.112
Chain 1:   4800        -9005.596             0.100            0.061
Chain 1:   4900        -8737.529             0.086            0.061
Chain 1:   5000        -9134.425             0.090            0.061
Chain 1:   5100       -11362.868             0.093            0.061
Chain 1:   5200        -9334.693             0.110            0.061
Chain 1:   5300       -10035.829             0.111            0.070
Chain 1:   5400       -13931.332             0.134            0.155
Chain 1:   5500       -10297.578             0.165            0.196
Chain 1:   5600        -9703.658             0.156            0.196
Chain 1:   5700       -13568.692             0.160            0.196
Chain 1:   5800        -8501.635             0.213            0.217
Chain 1:   5900       -12411.912             0.242            0.280
Chain 1:   6000       -10330.430             0.257            0.280
Chain 1:   6100        -9338.356             0.248            0.280
Chain 1:   6200        -8518.516             0.236            0.280
Chain 1:   6300       -14032.227             0.269            0.285
Chain 1:   6400        -9028.080             0.296            0.315
Chain 1:   6500        -8457.874             0.268            0.285
Chain 1:   6600        -8468.841             0.262            0.285
Chain 1:   6700        -9482.978             0.244            0.201
Chain 1:   6800       -11899.852             0.204            0.201
Chain 1:   6900       -11401.873             0.177            0.107
Chain 1:   7000        -8492.038             0.191            0.107
Chain 1:   7100       -12975.172             0.215            0.203
Chain 1:   7200        -8364.137             0.261            0.343
Chain 1:   7300        -9463.024             0.233            0.203
Chain 1:   7400        -9091.954             0.182            0.116
Chain 1:   7500        -8956.430             0.177            0.116
Chain 1:   7600        -9833.547             0.185            0.116
Chain 1:   7700        -9626.853             0.177            0.116
Chain 1:   7800        -9110.550             0.162            0.089
Chain 1:   7900        -8544.704             0.165            0.089
Chain 1:   8000       -10620.376             0.150            0.089
Chain 1:   8100       -10528.084             0.116            0.066
Chain 1:   8200        -9672.087             0.070            0.066
Chain 1:   8300       -11560.248             0.075            0.066
Chain 1:   8400        -8299.041             0.110            0.089
Chain 1:   8500        -8331.806             0.109            0.089
Chain 1:   8600        -8733.125             0.104            0.066
Chain 1:   8700        -8264.169             0.108            0.066
Chain 1:   8800        -8532.738             0.105            0.066
Chain 1:   8900        -9142.159             0.105            0.067
Chain 1:   9000        -8377.455             0.095            0.067
Chain 1:   9100        -9405.273             0.105            0.089
Chain 1:   9200       -10649.307             0.108            0.091
Chain 1:   9300        -8401.099             0.118            0.091
Chain 1:   9400        -8308.410             0.080            0.067
Chain 1:   9500        -8273.675             0.080            0.067
Chain 1:   9600        -9023.412             0.084            0.083
Chain 1:   9700       -11060.829             0.097            0.091
Chain 1:   9800        -8380.174             0.125            0.109
Chain 1:   9900        -9689.079             0.132            0.117
Chain 1:   10000        -8653.805             0.135            0.120
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00141 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61837.571             1.000            1.000
Chain 1:    200       -17771.067             1.740            2.480
Chain 1:    300        -8825.665             1.498            1.014
Chain 1:    400        -9178.010             1.133            1.014
Chain 1:    500        -8014.384             0.935            1.000
Chain 1:    600        -8897.897             0.796            1.000
Chain 1:    700        -7714.240             0.704            0.153
Chain 1:    800        -8154.385             0.623            0.153
Chain 1:    900        -7838.485             0.558            0.145
Chain 1:   1000        -7759.512             0.503            0.145
Chain 1:   1100        -7791.106             0.404            0.099
Chain 1:   1200        -7812.260             0.156            0.054
Chain 1:   1300        -7703.664             0.056            0.040
Chain 1:   1400        -7670.637             0.053            0.040
Chain 1:   1500        -7612.099             0.039            0.014
Chain 1:   1600        -7860.969             0.032            0.014
Chain 1:   1700        -7511.363             0.022            0.014
Chain 1:   1800        -7621.222             0.018            0.014
Chain 1:   1900        -7558.658             0.014            0.010
Chain 1:   2000        -7593.060             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002604 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85982.936             1.000            1.000
Chain 1:    200       -13442.277             3.198            5.396
Chain 1:    300        -9871.148             2.253            1.000
Chain 1:    400       -10696.312             1.709            1.000
Chain 1:    500        -8813.831             1.410            0.362
Chain 1:    600        -8494.645             1.181            0.362
Chain 1:    700        -8412.144             1.014            0.214
Chain 1:    800        -9093.675             0.896            0.214
Chain 1:    900        -8672.515             0.802            0.077
Chain 1:   1000        -8433.759             0.725            0.077
Chain 1:   1100        -8590.153             0.627            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8363.517             0.090            0.049
Chain 1:   1300        -8567.727             0.056            0.038
Chain 1:   1400        -8564.477             0.048            0.028
Chain 1:   1500        -8461.834             0.028            0.027
Chain 1:   1600        -8564.421             0.026            0.024
Chain 1:   1700        -8652.059             0.026            0.024
Chain 1:   1800        -8252.113             0.023            0.024
Chain 1:   1900        -8352.612             0.019            0.018
Chain 1:   2000        -8323.558             0.017            0.012
Chain 1:   2100        -8443.906             0.016            0.012
Chain 1:   2200        -8220.744             0.016            0.012
Chain 1:   2300        -8381.970             0.016            0.012
Chain 1:   2400        -8263.972             0.017            0.014
Chain 1:   2500        -8327.784             0.017            0.014
Chain 1:   2600        -8348.967             0.016            0.014
Chain 1:   2700        -8268.416             0.016            0.014
Chain 1:   2800        -8243.076             0.011            0.012
Chain 1:   2900        -8297.826             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003807 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399556.120             1.000            1.000
Chain 1:    200     -1580699.512             2.657            4.314
Chain 1:    300      -889768.962             2.030            1.000
Chain 1:    400      -457572.114             1.759            1.000
Chain 1:    500      -358167.740             1.462            0.945
Chain 1:    600      -233164.161             1.308            0.945
Chain 1:    700      -119271.452             1.258            0.945
Chain 1:    800       -86489.338             1.148            0.945
Chain 1:    900       -66798.517             1.053            0.777
Chain 1:   1000       -51568.386             0.977            0.777
Chain 1:   1100       -39028.621             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38196.748             0.480            0.379
Chain 1:   1300       -26134.731             0.449            0.379
Chain 1:   1400       -25850.752             0.355            0.321
Chain 1:   1500       -22434.536             0.343            0.321
Chain 1:   1600       -21650.122             0.293            0.295
Chain 1:   1700       -20521.587             0.203            0.295
Chain 1:   1800       -20465.107             0.165            0.152
Chain 1:   1900       -20790.923             0.137            0.055
Chain 1:   2000       -19301.907             0.115            0.055
Chain 1:   2100       -19540.015             0.085            0.036
Chain 1:   2200       -19766.627             0.084            0.036
Chain 1:   2300       -19383.818             0.039            0.020
Chain 1:   2400       -19156.008             0.039            0.020
Chain 1:   2500       -18958.329             0.025            0.016
Chain 1:   2600       -18588.559             0.024            0.016
Chain 1:   2700       -18545.565             0.018            0.012
Chain 1:   2800       -18262.733             0.020            0.015
Chain 1:   2900       -18543.806             0.020            0.015
Chain 1:   3000       -18529.938             0.012            0.012
Chain 1:   3100       -18614.916             0.011            0.012
Chain 1:   3200       -18305.770             0.012            0.015
Chain 1:   3300       -18510.364             0.011            0.012
Chain 1:   3400       -17985.718             0.013            0.015
Chain 1:   3500       -18597.052             0.015            0.015
Chain 1:   3600       -17904.407             0.017            0.015
Chain 1:   3700       -18290.725             0.019            0.017
Chain 1:   3800       -17251.627             0.023            0.021
Chain 1:   3900       -17247.854             0.022            0.021
Chain 1:   4000       -17365.085             0.022            0.021
Chain 1:   4100       -17278.959             0.022            0.021
Chain 1:   4200       -17095.459             0.022            0.021
Chain 1:   4300       -17233.637             0.021            0.021
Chain 1:   4400       -17190.645             0.019            0.011
Chain 1:   4500       -17093.268             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001408 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48233.421             1.000            1.000
Chain 1:    200       -17885.800             1.348            1.697
Chain 1:    300       -13004.736             1.024            1.000
Chain 1:    400       -13507.058             0.777            1.000
Chain 1:    500       -13474.266             0.622            0.375
Chain 1:    600       -14876.244             0.534            0.375
Chain 1:    700       -12168.151             0.490            0.223
Chain 1:    800       -13300.175             0.439            0.223
Chain 1:    900       -10115.011             0.425            0.223
Chain 1:   1000       -25339.296             0.443            0.315
Chain 1:   1100       -10227.272             0.491            0.315
Chain 1:   1200       -10458.604             0.323            0.223
Chain 1:   1300       -11986.561             0.298            0.127
Chain 1:   1400        -9995.463             0.315            0.199
Chain 1:   1500       -25019.934             0.374            0.223
Chain 1:   1600       -10857.783             0.495            0.315
Chain 1:   1700       -11177.039             0.476            0.315
Chain 1:   1800        -9367.594             0.487            0.315
Chain 1:   1900       -10520.931             0.466            0.199
Chain 1:   2000       -17340.601             0.446            0.199
Chain 1:   2100       -11163.545             0.353            0.199
Chain 1:   2200       -12040.919             0.358            0.199
Chain 1:   2300       -11454.340             0.351            0.199
Chain 1:   2400        -9363.169             0.353            0.223
Chain 1:   2500        -9424.305             0.294            0.193
Chain 1:   2600        -8879.051             0.169            0.110
Chain 1:   2700        -9562.798             0.174            0.110
Chain 1:   2800       -10142.056             0.160            0.073
Chain 1:   2900       -14730.748             0.180            0.073
Chain 1:   3000        -9407.695             0.197            0.073
Chain 1:   3100       -14165.249             0.176            0.073
Chain 1:   3200       -14165.631             0.168            0.072
Chain 1:   3300       -12787.178             0.174            0.108
Chain 1:   3400        -9067.042             0.193            0.108
Chain 1:   3500        -9773.633             0.199            0.108
Chain 1:   3600        -9204.292             0.199            0.108
Chain 1:   3700        -9545.893             0.196            0.108
Chain 1:   3800       -14766.550             0.225            0.312
Chain 1:   3900        -8666.638             0.265            0.336
Chain 1:   4000       -10828.076             0.228            0.200
Chain 1:   4100        -8852.394             0.217            0.200
Chain 1:   4200       -10356.609             0.231            0.200
Chain 1:   4300        -9338.730             0.231            0.200
Chain 1:   4400       -10646.257             0.203            0.145
Chain 1:   4500        -9185.170             0.211            0.159
Chain 1:   4600        -8657.650             0.211            0.159
Chain 1:   4700        -8941.504             0.211            0.159
Chain 1:   4800        -8421.967             0.182            0.145
Chain 1:   4900        -8145.642             0.115            0.123
Chain 1:   5000        -8841.157             0.103            0.109
Chain 1:   5100        -8365.900             0.086            0.079
Chain 1:   5200       -14548.236             0.114            0.079
Chain 1:   5300       -10729.595             0.139            0.079
Chain 1:   5400       -13653.418             0.148            0.079
Chain 1:   5500        -8931.949             0.185            0.079
Chain 1:   5600        -8846.548             0.180            0.079
Chain 1:   5700        -8700.077             0.178            0.079
Chain 1:   5800        -8344.866             0.176            0.079
Chain 1:   5900       -11617.785             0.201            0.214
Chain 1:   6000        -8976.868             0.223            0.282
Chain 1:   6100       -11520.085             0.239            0.282
Chain 1:   6200       -11038.199             0.201            0.221
Chain 1:   6300        -9061.657             0.187            0.218
Chain 1:   6400        -8795.226             0.169            0.218
Chain 1:   6500        -9230.873             0.120            0.047
Chain 1:   6600        -8751.606             0.125            0.055
Chain 1:   6700        -8715.710             0.124            0.055
Chain 1:   6800        -8810.143             0.121            0.055
Chain 1:   6900       -11949.454             0.119            0.055
Chain 1:   7000        -8262.655             0.134            0.055
Chain 1:   7100        -8123.865             0.113            0.047
Chain 1:   7200       -10231.759             0.130            0.055
Chain 1:   7300        -8304.282             0.131            0.055
Chain 1:   7400        -8850.225             0.134            0.062
Chain 1:   7500        -8423.709             0.135            0.062
Chain 1:   7600        -8232.881             0.131            0.062
Chain 1:   7700        -8932.118             0.139            0.078
Chain 1:   7800        -8912.814             0.138            0.078
Chain 1:   7900        -8048.532             0.122            0.078
Chain 1:   8000       -11295.841             0.107            0.078
Chain 1:   8100        -7900.064             0.148            0.107
Chain 1:   8200        -9872.842             0.147            0.107
Chain 1:   8300        -9022.552             0.133            0.094
Chain 1:   8400        -8630.245             0.132            0.094
Chain 1:   8500       -10608.648             0.145            0.107
Chain 1:   8600        -7989.136             0.176            0.186
Chain 1:   8700        -8267.070             0.171            0.186
Chain 1:   8800        -9775.368             0.187            0.186
Chain 1:   8900        -9583.173             0.178            0.186
Chain 1:   9000       -10311.908             0.156            0.154
Chain 1:   9100        -8336.846             0.137            0.154
Chain 1:   9200        -8203.810             0.119            0.094
Chain 1:   9300        -7862.696             0.113            0.071
Chain 1:   9400        -8160.043             0.113            0.071
Chain 1:   9500       -10404.255             0.116            0.071
Chain 1:   9600        -8046.622             0.112            0.071
Chain 1:   9700        -8213.471             0.111            0.071
Chain 1:   9800        -8096.644             0.097            0.043
Chain 1:   9900        -9856.081             0.113            0.071
Chain 1:   10000        -7890.118             0.130            0.179
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001681 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61465.587             1.000            1.000
Chain 1:    200       -17302.500             1.776            2.552
Chain 1:    300        -8641.692             1.518            1.002
Chain 1:    400        -8159.612             1.153            1.002
Chain 1:    500        -8260.426             0.925            1.000
Chain 1:    600        -9025.719             0.785            1.000
Chain 1:    700        -7966.014             0.692            0.133
Chain 1:    800        -8046.197             0.607            0.133
Chain 1:    900        -7851.649             0.542            0.085
Chain 1:   1000        -7654.301             0.490            0.085
Chain 1:   1100        -7666.163             0.391            0.059
Chain 1:   1200        -7612.831             0.136            0.026
Chain 1:   1300        -7709.523             0.037            0.025
Chain 1:   1400        -7837.884             0.033            0.016
Chain 1:   1500        -7646.388             0.034            0.025
Chain 1:   1600        -7558.701             0.027            0.016
Chain 1:   1700        -7520.600             0.014            0.013
Chain 1:   1800        -7556.400             0.013            0.013
Chain 1:   1900        -7618.468             0.012            0.012
Chain 1:   2000        -7614.463             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002559 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85962.535             1.000            1.000
Chain 1:    200       -12961.434             3.316            5.632
Chain 1:    300        -9498.262             2.332            1.000
Chain 1:    400        -9919.317             1.760            1.000
Chain 1:    500        -8840.689             1.432            0.365
Chain 1:    600        -8184.882             1.207            0.365
Chain 1:    700        -8354.576             1.037            0.122
Chain 1:    800        -8527.652             0.910            0.122
Chain 1:    900        -8416.809             0.811            0.080
Chain 1:   1000        -8169.604             0.733            0.080
Chain 1:   1100        -8441.158             0.636            0.042   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8214.068             0.075            0.032
Chain 1:   1300        -8159.769             0.040            0.030
Chain 1:   1400        -8170.443             0.035            0.028
Chain 1:   1500        -8188.361             0.023            0.020
Chain 1:   1600        -8184.578             0.015            0.020
Chain 1:   1700        -8136.623             0.014            0.013
Chain 1:   1800        -8015.638             0.013            0.013
Chain 1:   1900        -8124.970             0.014            0.013
Chain 1:   2000        -8091.676             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003282 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415584.762             1.000            1.000
Chain 1:    200     -1587890.428             2.650            4.300
Chain 1:    300      -891108.556             2.027            1.000
Chain 1:    400      -457181.733             1.758            1.000
Chain 1:    500      -357195.217             1.462            0.949
Chain 1:    600      -232086.638             1.308            0.949
Chain 1:    700      -118447.731             1.258            0.949
Chain 1:    800       -85701.590             1.149            0.949
Chain 1:    900       -66074.072             1.054            0.782
Chain 1:   1000       -50886.052             0.979            0.782
Chain 1:   1100       -38391.282             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37560.527             0.483            0.382
Chain 1:   1300       -25565.314             0.452            0.382
Chain 1:   1400       -25283.860             0.358            0.325
Chain 1:   1500       -21884.740             0.346            0.325
Chain 1:   1600       -21103.768             0.296            0.298
Chain 1:   1700       -19984.383             0.205            0.297
Chain 1:   1800       -19929.479             0.167            0.155
Chain 1:   1900       -20254.498             0.139            0.056
Chain 1:   2000       -18771.291             0.117            0.056
Chain 1:   2100       -19009.328             0.086            0.037
Chain 1:   2200       -19234.497             0.085            0.037
Chain 1:   2300       -18853.118             0.040            0.020
Chain 1:   2400       -18625.631             0.040            0.020
Chain 1:   2500       -18427.542             0.026            0.016
Chain 1:   2600       -18059.057             0.024            0.016
Chain 1:   2700       -18016.368             0.019            0.013
Chain 1:   2800       -17733.669             0.020            0.016
Chain 1:   2900       -18014.335             0.020            0.016
Chain 1:   3000       -18000.627             0.012            0.013
Chain 1:   3100       -18085.461             0.011            0.012
Chain 1:   3200       -17776.933             0.012            0.016
Chain 1:   3300       -17981.027             0.011            0.012
Chain 1:   3400       -17457.325             0.013            0.016
Chain 1:   3500       -18067.117             0.015            0.016
Chain 1:   3600       -17376.458             0.017            0.016
Chain 1:   3700       -17761.244             0.019            0.017
Chain 1:   3800       -16725.142             0.024            0.022
Chain 1:   3900       -16721.362             0.022            0.022
Chain 1:   4000       -16838.670             0.023            0.022
Chain 1:   4100       -16752.644             0.023            0.022
Chain 1:   4200       -16569.779             0.022            0.022
Chain 1:   4300       -16707.560             0.022            0.022
Chain 1:   4400       -16665.120             0.019            0.011
Chain 1:   4500       -16567.758             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001275 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12205.630             1.000            1.000
Chain 1:    200        -9110.796             0.670            1.000
Chain 1:    300        -7798.285             0.503            0.340
Chain 1:    400        -7942.338             0.382            0.340
Chain 1:    500        -7932.246             0.305            0.168
Chain 1:    600        -7726.615             0.259            0.168
Chain 1:    700        -7663.712             0.223            0.027
Chain 1:    800        -7696.739             0.196            0.027
Chain 1:    900        -7863.770             0.176            0.021
Chain 1:   1000        -7699.175             0.161            0.021
Chain 1:   1100        -7697.023             0.061            0.021
Chain 1:   1200        -7679.434             0.027            0.018
Chain 1:   1300        -7648.022             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46784.756             1.000            1.000
Chain 1:    200       -15343.959             1.525            2.049
Chain 1:    300        -8373.220             1.294            1.000
Chain 1:    400        -8387.666             0.971            1.000
Chain 1:    500        -8308.412             0.779            0.833
Chain 1:    600        -8506.513             0.653            0.833
Chain 1:    700        -7814.697             0.572            0.089
Chain 1:    800        -7976.891             0.503            0.089
Chain 1:    900        -7825.029             0.449            0.023
Chain 1:   1000        -7810.714             0.405            0.023
Chain 1:   1100        -7599.331             0.307            0.023
Chain 1:   1200        -7599.914             0.103            0.020
Chain 1:   1300        -7633.759             0.020            0.019
Chain 1:   1400        -7790.781             0.022            0.020
Chain 1:   1500        -7580.458             0.023            0.020
Chain 1:   1600        -7470.318             0.023            0.020
Chain 1:   1700        -7450.908             0.014            0.019
Chain 1:   1800        -7504.673             0.013            0.015
Chain 1:   1900        -7531.021             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00325 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86058.931             1.000            1.000
Chain 1:    200       -13077.870             3.290            5.581
Chain 1:    300        -9527.304             2.318            1.000
Chain 1:    400       -10262.466             1.756            1.000
Chain 1:    500        -8461.121             1.448            0.373
Chain 1:    600        -8042.172             1.215            0.373
Chain 1:    700        -8494.466             1.049            0.213
Chain 1:    800        -8865.275             0.923            0.213
Chain 1:    900        -8346.615             0.827            0.072
Chain 1:   1000        -8125.152             0.747            0.072
Chain 1:   1100        -8288.186             0.649            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8079.110             0.094            0.053
Chain 1:   1300        -8285.767             0.059            0.052
Chain 1:   1400        -8277.212             0.052            0.042
Chain 1:   1500        -8174.718             0.032            0.027
Chain 1:   1600        -8266.081             0.028            0.026
Chain 1:   1700        -8356.572             0.024            0.025
Chain 1:   1800        -7972.221             0.024            0.025
Chain 1:   1900        -8074.472             0.019            0.020
Chain 1:   2000        -8044.134             0.017            0.013
Chain 1:   2100        -8178.955             0.017            0.013
Chain 1:   2200        -7963.191             0.017            0.013
Chain 1:   2300        -8104.458             0.016            0.013
Chain 1:   2400        -8115.240             0.016            0.013
Chain 1:   2500        -8083.581             0.015            0.013
Chain 1:   2600        -8081.543             0.014            0.013
Chain 1:   2700        -7990.897             0.014            0.013
Chain 1:   2800        -7969.486             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003214 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403462.749             1.000            1.000
Chain 1:    200     -1586009.491             2.649            4.298
Chain 1:    300      -890729.232             2.026            1.000
Chain 1:    400      -457222.654             1.757            1.000
Chain 1:    500      -357279.768             1.461            0.948
Chain 1:    600      -232283.324             1.308            0.948
Chain 1:    700      -118663.961             1.258            0.948
Chain 1:    800       -85859.427             1.148            0.948
Chain 1:    900       -66239.362             1.053            0.781
Chain 1:   1000       -51054.445             0.978            0.781
Chain 1:   1100       -38549.846             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37726.875             0.483            0.382
Chain 1:   1300       -25718.173             0.451            0.382
Chain 1:   1400       -25437.164             0.358            0.324
Chain 1:   1500       -22033.291             0.345            0.324
Chain 1:   1600       -21251.180             0.295            0.297
Chain 1:   1700       -20130.284             0.205            0.296
Chain 1:   1800       -20075.221             0.167            0.154
Chain 1:   1900       -20400.850             0.139            0.056
Chain 1:   2000       -18915.615             0.117            0.056
Chain 1:   2100       -19153.811             0.086            0.037
Chain 1:   2200       -19379.339             0.085            0.037
Chain 1:   2300       -18997.532             0.040            0.020
Chain 1:   2400       -18769.914             0.040            0.020
Chain 1:   2500       -18571.621             0.026            0.016
Chain 1:   2600       -18202.629             0.024            0.016
Chain 1:   2700       -18159.919             0.019            0.012
Chain 1:   2800       -17876.855             0.020            0.016
Chain 1:   2900       -18157.869             0.020            0.015
Chain 1:   3000       -18144.159             0.012            0.012
Chain 1:   3100       -18228.984             0.011            0.012
Chain 1:   3200       -17920.137             0.012            0.015
Chain 1:   3300       -18124.537             0.011            0.012
Chain 1:   3400       -17600.103             0.013            0.015
Chain 1:   3500       -18210.845             0.015            0.016
Chain 1:   3600       -17519.132             0.017            0.016
Chain 1:   3700       -17904.692             0.019            0.017
Chain 1:   3800       -16866.664             0.024            0.022
Chain 1:   3900       -16862.862             0.022            0.022
Chain 1:   4000       -16980.213             0.023            0.022
Chain 1:   4100       -16893.985             0.023            0.022
Chain 1:   4200       -16710.813             0.022            0.022
Chain 1:   4300       -16848.842             0.022            0.022
Chain 1:   4400       -16806.103             0.019            0.011
Chain 1:   4500       -16708.700             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001276 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49881.490             1.000            1.000
Chain 1:    200       -17156.358             1.454            1.907
Chain 1:    300       -20056.902             1.017            1.000
Chain 1:    400       -14619.894             0.856            1.000
Chain 1:    500       -12472.526             0.719            0.372
Chain 1:    600       -16064.521             0.637            0.372
Chain 1:    700       -14269.887             0.564            0.224
Chain 1:    800       -14334.657             0.494            0.224
Chain 1:    900       -12625.788             0.454            0.172
Chain 1:   1000       -13999.533             0.418            0.172
Chain 1:   1100       -12497.291             0.330            0.145
Chain 1:   1200       -12228.765             0.142            0.135
Chain 1:   1300       -13195.869             0.135            0.126
Chain 1:   1400       -13120.449             0.098            0.120
Chain 1:   1500       -11347.098             0.096            0.120
Chain 1:   1600       -12751.504             0.085            0.110
Chain 1:   1700       -13242.513             0.076            0.098
Chain 1:   1800       -16613.315             0.096            0.110
Chain 1:   1900       -11002.468             0.134            0.110
Chain 1:   2000       -11532.378             0.128            0.110
Chain 1:   2100       -13225.014             0.129            0.110
Chain 1:   2200        -9607.557             0.165            0.128
Chain 1:   2300       -12435.041             0.180            0.156
Chain 1:   2400       -10409.170             0.199            0.195
Chain 1:   2500        -9432.561             0.194            0.195
Chain 1:   2600        -9683.946             0.185            0.195
Chain 1:   2700        -9650.373             0.182            0.195
Chain 1:   2800       -10954.128             0.173            0.128
Chain 1:   2900        -9934.606             0.133            0.119
Chain 1:   3000       -11796.175             0.144            0.128
Chain 1:   3100        -8987.899             0.162            0.158
Chain 1:   3200       -15181.393             0.165            0.158
Chain 1:   3300       -11784.977             0.172            0.158
Chain 1:   3400       -11139.608             0.158            0.119
Chain 1:   3500       -11422.917             0.150            0.119
Chain 1:   3600        -9539.610             0.167            0.158
Chain 1:   3700        -8969.724             0.173            0.158
Chain 1:   3800        -8973.406             0.161            0.158
Chain 1:   3900        -9647.269             0.158            0.158
Chain 1:   4000       -10326.441             0.149            0.070
Chain 1:   4100       -14749.557             0.148            0.070
Chain 1:   4200       -12126.296             0.128            0.070
Chain 1:   4300       -10413.249             0.116            0.070
Chain 1:   4400        -9268.493             0.123            0.124
Chain 1:   4500        -9633.903             0.124            0.124
Chain 1:   4600        -9211.308             0.109            0.070
Chain 1:   4700       -11233.969             0.120            0.124
Chain 1:   4800       -14073.162             0.141            0.165
Chain 1:   4900        -9723.036             0.178            0.180
Chain 1:   5000       -14885.140             0.206            0.202
Chain 1:   5100        -9432.709             0.234            0.202
Chain 1:   5200        -9462.529             0.213            0.180
Chain 1:   5300       -13232.944             0.225            0.202
Chain 1:   5400        -8968.503             0.260            0.285
Chain 1:   5500        -9395.244             0.261            0.285
Chain 1:   5600       -11371.288             0.274            0.285
Chain 1:   5700        -9405.580             0.277            0.285
Chain 1:   5800       -16320.974             0.299            0.347
Chain 1:   5900        -9343.624             0.329            0.347
Chain 1:   6000       -10168.587             0.302            0.285
Chain 1:   6100       -16324.612             0.282            0.285
Chain 1:   6200       -10445.348             0.338            0.377
Chain 1:   6300       -14097.016             0.335            0.377
Chain 1:   6400       -17036.411             0.305            0.259
Chain 1:   6500        -9436.580             0.381            0.377
Chain 1:   6600       -10129.416             0.371            0.377
Chain 1:   6700        -8446.174             0.370            0.377
Chain 1:   6800        -9134.740             0.335            0.259
Chain 1:   6900        -8719.361             0.265            0.199
Chain 1:   7000       -12372.215             0.286            0.259
Chain 1:   7100       -10070.371             0.271            0.229
Chain 1:   7200        -8755.026             0.230            0.199
Chain 1:   7300        -8597.610             0.206            0.173
Chain 1:   7400        -9028.677             0.194            0.150
Chain 1:   7500       -11705.201             0.136            0.150
Chain 1:   7600        -8625.778             0.165            0.199
Chain 1:   7700        -9227.811             0.151            0.150
Chain 1:   7800        -9718.051             0.149            0.150
Chain 1:   7900        -9045.731             0.152            0.150
Chain 1:   8000        -8747.029             0.125            0.074
Chain 1:   8100        -8437.690             0.106            0.065
Chain 1:   8200       -12585.707             0.124            0.065
Chain 1:   8300       -10930.427             0.138            0.074
Chain 1:   8400        -9016.894             0.154            0.151
Chain 1:   8500        -9936.871             0.140            0.093
Chain 1:   8600        -8788.932             0.118            0.093
Chain 1:   8700        -9282.692             0.117            0.093
Chain 1:   8800        -8544.821             0.120            0.093
Chain 1:   8900        -9384.002             0.122            0.093
Chain 1:   9000        -8742.758             0.126            0.093
Chain 1:   9100        -8431.690             0.126            0.093
Chain 1:   9200        -8481.249             0.093            0.089
Chain 1:   9300        -9710.320             0.091            0.089
Chain 1:   9400       -12652.211             0.093            0.089
Chain 1:   9500        -8472.509             0.133            0.089
Chain 1:   9600        -8594.719             0.121            0.086
Chain 1:   9700        -9126.312             0.122            0.086
Chain 1:   9800        -9702.017             0.119            0.073
Chain 1:   9900       -10993.092             0.122            0.073
Chain 1:   10000        -8278.425             0.147            0.117
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004398 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58642.290             1.000            1.000
Chain 1:    200       -18310.562             1.601            2.203
Chain 1:    300        -8994.624             1.413            1.036
Chain 1:    400        -8137.651             1.086            1.036
Chain 1:    500        -8852.864             0.885            1.000
Chain 1:    600        -9462.537             0.748            1.000
Chain 1:    700        -8313.350             0.661            0.138
Chain 1:    800        -8220.322             0.580            0.138
Chain 1:    900        -8067.755             0.517            0.105
Chain 1:   1000        -8112.122             0.466            0.105
Chain 1:   1100        -7635.950             0.373            0.081
Chain 1:   1200        -7760.597             0.154            0.064
Chain 1:   1300        -7966.886             0.053            0.062
Chain 1:   1400        -7812.077             0.044            0.026
Chain 1:   1500        -7623.883             0.039            0.025
Chain 1:   1600        -7844.066             0.035            0.025
Chain 1:   1700        -7679.758             0.023            0.021
Chain 1:   1800        -7795.008             0.024            0.021
Chain 1:   1900        -7681.077             0.023            0.021
Chain 1:   2000        -7795.359             0.024            0.021
Chain 1:   2100        -7677.968             0.020            0.020
Chain 1:   2200        -7889.029             0.021            0.021
Chain 1:   2300        -7623.114             0.022            0.021
Chain 1:   2400        -7658.045             0.020            0.021
Chain 1:   2500        -7681.688             0.018            0.015
Chain 1:   2600        -7614.056             0.016            0.015
Chain 1:   2700        -7572.054             0.014            0.015
Chain 1:   2800        -7742.494             0.015            0.015
Chain 1:   2900        -7488.943             0.017            0.015
Chain 1:   3000        -7617.664             0.017            0.017
Chain 1:   3100        -7618.783             0.016            0.017
Chain 1:   3200        -7823.335             0.016            0.017
Chain 1:   3300        -7529.333             0.016            0.017
Chain 1:   3400        -7782.279             0.019            0.022
Chain 1:   3500        -7521.351             0.022            0.026
Chain 1:   3600        -7584.998             0.022            0.026
Chain 1:   3700        -7538.319             0.022            0.026
Chain 1:   3800        -7525.279             0.020            0.026
Chain 1:   3900        -7493.640             0.017            0.017
Chain 1:   4000        -7487.146             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003245 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87603.087             1.000            1.000
Chain 1:    200       -14031.047             3.122            5.244
Chain 1:    300       -10215.505             2.206            1.000
Chain 1:    400       -12084.050             1.693            1.000
Chain 1:    500        -8632.636             1.434            0.400
Chain 1:    600        -8564.582             1.197            0.400
Chain 1:    700        -8598.218             1.026            0.374
Chain 1:    800        -8852.834             0.902            0.374
Chain 1:    900        -8867.194             0.802            0.155
Chain 1:   1000        -9216.718             0.725            0.155
Chain 1:   1100        -8766.171             0.630            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8490.693             0.109            0.038
Chain 1:   1300        -8885.633             0.076            0.038
Chain 1:   1400        -8762.515             0.062            0.032
Chain 1:   1500        -8678.584             0.023            0.029
Chain 1:   1600        -8758.979             0.023            0.029
Chain 1:   1700        -8829.125             0.024            0.029
Chain 1:   1800        -8364.465             0.026            0.032
Chain 1:   1900        -8487.017             0.028            0.032
Chain 1:   2000        -8504.267             0.024            0.014
Chain 1:   2100        -8589.680             0.020            0.014
Chain 1:   2200        -8374.134             0.019            0.014
Chain 1:   2300        -8536.036             0.017            0.014
Chain 1:   2400        -8382.630             0.017            0.014
Chain 1:   2500        -8456.218             0.017            0.014
Chain 1:   2600        -8366.865             0.017            0.014
Chain 1:   2700        -8400.845             0.017            0.014
Chain 1:   2800        -8352.010             0.012            0.011
Chain 1:   2900        -8466.798             0.012            0.011
Chain 1:   3000        -8379.423             0.013            0.011
Chain 1:   3100        -8344.178             0.012            0.011
Chain 1:   3200        -8315.951             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002754 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8450569.201             1.000            1.000
Chain 1:    200     -1595225.985             2.649            4.297
Chain 1:    300      -892554.733             2.028            1.000
Chain 1:    400      -458245.997             1.758            1.000
Chain 1:    500      -357522.651             1.463            0.948
Chain 1:    600      -232433.661             1.309            0.948
Chain 1:    700      -119184.493             1.258            0.948
Chain 1:    800       -86509.463             1.148            0.948
Chain 1:    900       -66978.104             1.052            0.787
Chain 1:   1000       -51894.440             0.976            0.787
Chain 1:   1100       -39468.428             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38669.065             0.480            0.378
Chain 1:   1300       -26710.181             0.446            0.378
Chain 1:   1400       -26442.291             0.352            0.315
Chain 1:   1500       -23050.031             0.339            0.315
Chain 1:   1600       -22274.143             0.289            0.292
Chain 1:   1700       -21156.909             0.199            0.291
Chain 1:   1800       -21103.996             0.161            0.147
Chain 1:   1900       -21430.989             0.134            0.053
Chain 1:   2000       -19945.399             0.112            0.053
Chain 1:   2100       -20183.751             0.082            0.035
Chain 1:   2200       -20409.839             0.081            0.035
Chain 1:   2300       -20027.177             0.038            0.019
Chain 1:   2400       -19799.050             0.038            0.019
Chain 1:   2500       -19600.594             0.024            0.015
Chain 1:   2600       -19230.307             0.023            0.015
Chain 1:   2700       -19187.284             0.018            0.012
Chain 1:   2800       -18903.410             0.019            0.015
Chain 1:   2900       -19185.064             0.019            0.015
Chain 1:   3000       -19171.323             0.012            0.012
Chain 1:   3100       -19256.337             0.011            0.012
Chain 1:   3200       -18946.584             0.011            0.015
Chain 1:   3300       -19151.737             0.011            0.012
Chain 1:   3400       -18625.567             0.012            0.015
Chain 1:   3500       -19238.808             0.014            0.015
Chain 1:   3600       -18543.806             0.016            0.015
Chain 1:   3700       -18931.684             0.018            0.016
Chain 1:   3800       -17888.567             0.022            0.020
Chain 1:   3900       -17884.584             0.021            0.020
Chain 1:   4000       -18001.996             0.021            0.020
Chain 1:   4100       -17915.450             0.022            0.020
Chain 1:   4200       -17731.198             0.021            0.020
Chain 1:   4300       -17870.021             0.021            0.020
Chain 1:   4400       -17826.336             0.018            0.010
Chain 1:   4500       -17728.738             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49241.880             1.000            1.000
Chain 1:    200       -21065.458             1.169            1.338
Chain 1:    300       -20039.994             0.796            1.000
Chain 1:    400       -14574.916             0.691            1.000
Chain 1:    500       -12874.906             0.579            0.375
Chain 1:    600       -21677.101             0.550            0.406
Chain 1:    700       -12330.247             0.580            0.406
Chain 1:    800       -14825.030             0.529            0.406
Chain 1:    900       -14043.273             0.476            0.375
Chain 1:   1000       -19544.067             0.457            0.375
Chain 1:   1100       -14447.747             0.392            0.353
Chain 1:   1200       -14763.612             0.260            0.281
Chain 1:   1300       -11687.511             0.281            0.281
Chain 1:   1400       -10372.039             0.257            0.263
Chain 1:   1500       -11025.315             0.249            0.263
Chain 1:   1600        -9998.628             0.219            0.168
Chain 1:   1700        -9767.004             0.146            0.127
Chain 1:   1800       -10003.328             0.131            0.103
Chain 1:   1900       -13838.275             0.153            0.127
Chain 1:   2000       -18417.201             0.150            0.127
Chain 1:   2100        -9536.393             0.208            0.127
Chain 1:   2200       -14903.000             0.242            0.249
Chain 1:   2300       -14078.334             0.221            0.127
Chain 1:   2400        -9607.855             0.255            0.249
Chain 1:   2500        -9419.897             0.251            0.249
Chain 1:   2600       -11570.366             0.259            0.249
Chain 1:   2700        -9930.707             0.274            0.249
Chain 1:   2800       -11528.143             0.285            0.249
Chain 1:   2900        -9707.593             0.276            0.188
Chain 1:   3000        -9392.652             0.255            0.186
Chain 1:   3100       -10715.816             0.174            0.165
Chain 1:   3200       -12044.264             0.149            0.139
Chain 1:   3300       -16521.346             0.170            0.165
Chain 1:   3400        -9714.975             0.194            0.165
Chain 1:   3500        -9314.487             0.196            0.165
Chain 1:   3600       -10551.266             0.189            0.139
Chain 1:   3700        -8755.559             0.193            0.139
Chain 1:   3800        -8887.955             0.181            0.123
Chain 1:   3900       -10777.989             0.179            0.123
Chain 1:   4000       -10998.247             0.178            0.123
Chain 1:   4100        -8930.914             0.189            0.175
Chain 1:   4200       -12132.273             0.204            0.205
Chain 1:   4300        -8947.069             0.213            0.205
Chain 1:   4400        -9835.884             0.152            0.175
Chain 1:   4500        -8945.099             0.157            0.175
Chain 1:   4600       -10849.912             0.163            0.176
Chain 1:   4700        -8550.411             0.170            0.176
Chain 1:   4800        -8658.163             0.169            0.176
Chain 1:   4900        -8552.089             0.153            0.176
Chain 1:   5000       -17633.395             0.203            0.231
Chain 1:   5100        -9109.499             0.273            0.264
Chain 1:   5200        -9157.524             0.247            0.176
Chain 1:   5300       -10302.015             0.223            0.111
Chain 1:   5400        -8924.120             0.229            0.154
Chain 1:   5500        -9355.841             0.224            0.154
Chain 1:   5600        -9052.413             0.209            0.111
Chain 1:   5700        -9254.551             0.185            0.046
Chain 1:   5800        -9112.961             0.185            0.046
Chain 1:   5900       -14889.064             0.223            0.111
Chain 1:   6000       -10779.955             0.209            0.111
Chain 1:   6100       -10240.958             0.121            0.053
Chain 1:   6200        -9512.503             0.128            0.077
Chain 1:   6300       -13532.971             0.147            0.077
Chain 1:   6400       -13925.950             0.134            0.053
Chain 1:   6500       -10913.084             0.157            0.077
Chain 1:   6600        -9460.022             0.169            0.154
Chain 1:   6700        -9029.546             0.172            0.154
Chain 1:   6800       -10751.517             0.186            0.160
Chain 1:   6900       -11220.175             0.151            0.154
Chain 1:   7000        -8708.675             0.142            0.154
Chain 1:   7100        -8496.475             0.139            0.154
Chain 1:   7200        -8234.405             0.135            0.154
Chain 1:   7300       -11251.806             0.132            0.154
Chain 1:   7400        -8395.430             0.163            0.160
Chain 1:   7500       -11151.836             0.160            0.160
Chain 1:   7600       -10803.323             0.148            0.160
Chain 1:   7700        -8595.206             0.169            0.247
Chain 1:   7800        -8420.694             0.155            0.247
Chain 1:   7900       -10525.708             0.171            0.247
Chain 1:   8000        -8568.757             0.165            0.228
Chain 1:   8100       -10183.098             0.178            0.228
Chain 1:   8200       -12517.503             0.194            0.228
Chain 1:   8300        -8228.480             0.219            0.228
Chain 1:   8400        -8474.620             0.188            0.200
Chain 1:   8500        -8154.195             0.167            0.186
Chain 1:   8600        -8288.475             0.166            0.186
Chain 1:   8700        -9162.135             0.150            0.159
Chain 1:   8800        -8809.094             0.151            0.159
Chain 1:   8900        -9130.467             0.135            0.095
Chain 1:   9000       -10148.925             0.122            0.095
Chain 1:   9100        -8059.795             0.132            0.095
Chain 1:   9200       -11617.918             0.144            0.095
Chain 1:   9300        -9017.979             0.121            0.095
Chain 1:   9400       -11202.776             0.138            0.100
Chain 1:   9500        -8868.741             0.160            0.195
Chain 1:   9600        -8425.535             0.164            0.195
Chain 1:   9700        -8139.770             0.158            0.195
Chain 1:   9800        -9061.302             0.164            0.195
Chain 1:   9900        -8601.399             0.166            0.195
Chain 1:   10000        -9150.137             0.161            0.195
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56029.606             1.000            1.000
Chain 1:    200       -17425.812             1.608            2.215
Chain 1:    300        -8805.372             1.398            1.000
Chain 1:    400        -8619.625             1.054            1.000
Chain 1:    500        -8642.128             0.844            0.979
Chain 1:    600        -8758.825             0.705            0.979
Chain 1:    700        -8325.363             0.612            0.052
Chain 1:    800        -8352.210             0.536            0.052
Chain 1:    900        -7984.924             0.481            0.046
Chain 1:   1000        -8076.935             0.434            0.046
Chain 1:   1100        -7769.643             0.338            0.040
Chain 1:   1200        -7709.014             0.118            0.022
Chain 1:   1300        -7954.873             0.023            0.022
Chain 1:   1400        -7894.554             0.021            0.013
Chain 1:   1500        -7661.495             0.024            0.030
Chain 1:   1600        -7591.182             0.024            0.030
Chain 1:   1700        -7590.201             0.019            0.011
Chain 1:   1800        -7651.884             0.019            0.011
Chain 1:   1900        -7666.137             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002495 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85584.769             1.000            1.000
Chain 1:    200       -13701.113             3.123            5.247
Chain 1:    300       -10024.960             2.204            1.000
Chain 1:    400       -11108.685             1.678            1.000
Chain 1:    500        -8819.996             1.394            0.367
Chain 1:    600        -9148.852             1.168            0.367
Chain 1:    700        -8933.513             1.004            0.259
Chain 1:    800        -8718.681             0.882            0.259
Chain 1:    900        -8765.747             0.784            0.098
Chain 1:   1000        -8754.007             0.706            0.098
Chain 1:   1100        -8615.144             0.608            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8411.969             0.086            0.025
Chain 1:   1300        -8781.441             0.053            0.025
Chain 1:   1400        -8690.650             0.044            0.024
Chain 1:   1500        -8543.930             0.020            0.024
Chain 1:   1600        -8658.784             0.018            0.017
Chain 1:   1700        -8729.435             0.016            0.016
Chain 1:   1800        -8295.488             0.019            0.016
Chain 1:   1900        -8400.296             0.020            0.016
Chain 1:   2000        -8376.204             0.020            0.016
Chain 1:   2100        -8336.355             0.019            0.013
Chain 1:   2200        -8317.938             0.017            0.012
Chain 1:   2300        -8447.208             0.014            0.012
Chain 1:   2400        -8303.524             0.015            0.013
Chain 1:   2500        -8371.715             0.014            0.012
Chain 1:   2600        -8291.641             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003153 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372243.666             1.000            1.000
Chain 1:    200     -1578985.717             2.651            4.302
Chain 1:    300      -890794.107             2.025            1.000
Chain 1:    400      -458249.046             1.755            1.000
Chain 1:    500      -359258.222             1.459            0.944
Chain 1:    600      -233975.732             1.305            0.944
Chain 1:    700      -119842.285             1.255            0.944
Chain 1:    800       -86997.506             1.145            0.944
Chain 1:    900       -67251.780             1.050            0.773
Chain 1:   1000       -51991.850             0.975            0.773
Chain 1:   1100       -39413.921             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38584.434             0.479            0.378
Chain 1:   1300       -26472.640             0.447            0.378
Chain 1:   1400       -26186.414             0.354            0.319
Chain 1:   1500       -22756.777             0.341            0.319
Chain 1:   1600       -21969.136             0.291            0.294
Chain 1:   1700       -20833.954             0.201            0.294
Chain 1:   1800       -20776.429             0.164            0.151
Chain 1:   1900       -21102.885             0.136            0.054
Chain 1:   2000       -19609.191             0.114            0.054
Chain 1:   2100       -19847.588             0.084            0.036
Chain 1:   2200       -20075.182             0.083            0.036
Chain 1:   2300       -19691.334             0.039            0.019
Chain 1:   2400       -19463.235             0.039            0.019
Chain 1:   2500       -19265.706             0.025            0.015
Chain 1:   2600       -18895.094             0.023            0.015
Chain 1:   2700       -18851.860             0.018            0.012
Chain 1:   2800       -18568.781             0.019            0.015
Chain 1:   2900       -18850.265             0.019            0.015
Chain 1:   3000       -18836.279             0.012            0.012
Chain 1:   3100       -18921.361             0.011            0.012
Chain 1:   3200       -18611.736             0.012            0.015
Chain 1:   3300       -18816.726             0.011            0.012
Chain 1:   3400       -18291.262             0.012            0.015
Chain 1:   3500       -18903.884             0.015            0.015
Chain 1:   3600       -18209.628             0.016            0.015
Chain 1:   3700       -18597.162             0.018            0.017
Chain 1:   3800       -17555.562             0.023            0.021
Chain 1:   3900       -17551.766             0.021            0.021
Chain 1:   4000       -17668.976             0.022            0.021
Chain 1:   4100       -17582.718             0.022            0.021
Chain 1:   4200       -17398.693             0.021            0.021
Chain 1:   4300       -17537.223             0.021            0.021
Chain 1:   4400       -17493.790             0.018            0.011
Chain 1:   4500       -17396.367             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001293 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49198.130             1.000            1.000
Chain 1:    200       -16485.357             1.492            1.984
Chain 1:    300       -21905.293             1.077            1.000
Chain 1:    400       -26543.889             0.852            1.000
Chain 1:    500       -16709.516             0.799            0.589
Chain 1:    600       -13142.609             0.711            0.589
Chain 1:    700       -12709.684             0.614            0.271
Chain 1:    800       -14205.654             0.551            0.271
Chain 1:    900       -13960.766             0.491            0.247
Chain 1:   1000       -10995.836             0.469            0.270
Chain 1:   1100       -26971.738             0.429            0.270
Chain 1:   1200       -11768.602             0.359            0.270
Chain 1:   1300       -13448.336             0.347            0.270
Chain 1:   1400       -10915.639             0.353            0.270
Chain 1:   1500       -10205.694             0.301            0.232
Chain 1:   1600       -10656.800             0.278            0.125
Chain 1:   1700       -11241.837             0.280            0.125
Chain 1:   1800       -11339.281             0.270            0.125
Chain 1:   1900       -21629.847             0.316            0.232
Chain 1:   2000       -19525.641             0.300            0.125
Chain 1:   2100       -11290.158             0.313            0.125
Chain 1:   2200       -11598.673             0.187            0.108
Chain 1:   2300       -10103.904             0.189            0.108
Chain 1:   2400       -12246.312             0.183            0.108
Chain 1:   2500       -11218.958             0.186            0.108
Chain 1:   2600       -10631.241             0.187            0.108
Chain 1:   2700       -11816.020             0.192            0.108
Chain 1:   2800       -11127.042             0.197            0.108
Chain 1:   2900       -12372.315             0.160            0.101
Chain 1:   3000        -9345.106             0.181            0.101
Chain 1:   3100       -10680.271             0.121            0.101
Chain 1:   3200        -9667.231             0.129            0.105
Chain 1:   3300       -19674.933             0.165            0.105
Chain 1:   3400        -9276.715             0.259            0.105
Chain 1:   3500       -13348.929             0.281            0.125
Chain 1:   3600       -10254.597             0.305            0.302
Chain 1:   3700       -11077.827             0.303            0.302
Chain 1:   3800        -9129.427             0.318            0.302
Chain 1:   3900       -11826.214             0.331            0.302
Chain 1:   4000        -9787.783             0.319            0.228
Chain 1:   4100       -12484.974             0.328            0.228
Chain 1:   4200       -10806.467             0.333            0.228
Chain 1:   4300       -11659.397             0.290            0.216
Chain 1:   4400        -9874.349             0.196            0.213
Chain 1:   4500        -9269.327             0.172            0.208
Chain 1:   4600       -11607.790             0.162            0.201
Chain 1:   4700       -10815.062             0.162            0.201
Chain 1:   4800        -9284.268             0.157            0.181
Chain 1:   4900        -8958.178             0.137            0.165
Chain 1:   5000       -13119.682             0.148            0.165
Chain 1:   5100        -9008.961             0.172            0.165
Chain 1:   5200        -9125.508             0.158            0.165
Chain 1:   5300        -9445.448             0.154            0.165
Chain 1:   5400        -9857.231             0.140            0.073
Chain 1:   5500        -9715.836             0.135            0.073
Chain 1:   5600       -13434.583             0.143            0.073
Chain 1:   5700       -14052.673             0.140            0.044
Chain 1:   5800       -14211.197             0.124            0.042
Chain 1:   5900       -13921.003             0.123            0.042
Chain 1:   6000        -8790.095             0.150            0.042
Chain 1:   6100        -8779.452             0.104            0.034
Chain 1:   6200        -8606.369             0.105            0.034
Chain 1:   6300       -11175.971             0.124            0.042
Chain 1:   6400       -13677.910             0.139            0.044
Chain 1:   6500        -8803.909             0.192            0.183
Chain 1:   6600        -9076.182             0.168            0.044
Chain 1:   6700       -14144.162             0.199            0.183
Chain 1:   6800        -8933.359             0.256            0.230
Chain 1:   6900        -9482.363             0.260            0.230
Chain 1:   7000        -8897.582             0.208            0.183
Chain 1:   7100        -8663.120             0.211            0.183
Chain 1:   7200        -8878.668             0.211            0.183
Chain 1:   7300        -9709.258             0.197            0.086
Chain 1:   7400        -9045.677             0.186            0.073
Chain 1:   7500       -13380.812             0.163            0.073
Chain 1:   7600        -9110.874             0.207            0.086
Chain 1:   7700       -13691.961             0.204            0.086
Chain 1:   7800       -12648.818             0.154            0.082
Chain 1:   7900        -8807.685             0.192            0.086
Chain 1:   8000       -12506.691             0.215            0.296
Chain 1:   8100        -8784.538             0.255            0.324
Chain 1:   8200       -11836.819             0.278            0.324
Chain 1:   8300       -10346.774             0.284            0.324
Chain 1:   8400        -8460.834             0.299            0.324
Chain 1:   8500        -8484.852             0.267            0.296
Chain 1:   8600        -9936.618             0.235            0.258
Chain 1:   8700        -8752.372             0.215            0.223
Chain 1:   8800        -8717.264             0.207            0.223
Chain 1:   8900       -12276.620             0.192            0.223
Chain 1:   9000       -10719.584             0.177            0.146
Chain 1:   9100        -9237.597             0.151            0.146
Chain 1:   9200        -9779.735             0.131            0.145
Chain 1:   9300        -8384.125             0.133            0.146
Chain 1:   9400        -8841.029             0.116            0.145
Chain 1:   9500        -9333.450             0.121            0.145
Chain 1:   9600       -10659.502             0.119            0.135
Chain 1:   9700        -8635.311             0.128            0.145
Chain 1:   9800        -9087.805             0.133            0.145
Chain 1:   9900       -11060.374             0.122            0.145
Chain 1:   10000        -9090.237             0.129            0.160
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00155 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58569.181             1.000            1.000
Chain 1:    200       -18104.868             1.617            2.235
Chain 1:    300        -8869.817             1.425            1.041
Chain 1:    400        -8153.231             1.091            1.041
Chain 1:    500        -8247.585             0.875            1.000
Chain 1:    600        -9421.590             0.750            1.000
Chain 1:    700        -7853.514             0.671            0.200
Chain 1:    800        -8185.986             0.593            0.200
Chain 1:    900        -8012.524             0.529            0.125
Chain 1:   1000        -7920.790             0.477            0.125
Chain 1:   1100        -7615.663             0.381            0.088
Chain 1:   1200        -7728.307             0.159            0.041
Chain 1:   1300        -7700.591             0.056            0.040
Chain 1:   1400        -7699.417             0.047            0.022
Chain 1:   1500        -7566.742             0.047            0.022
Chain 1:   1600        -7773.202             0.038            0.022
Chain 1:   1700        -7592.702             0.020            0.022
Chain 1:   1800        -7713.780             0.018            0.018
Chain 1:   1900        -7593.471             0.017            0.016
Chain 1:   2000        -7668.712             0.017            0.016
Chain 1:   2100        -7585.025             0.014            0.016
Chain 1:   2200        -7752.437             0.015            0.016
Chain 1:   2300        -7575.328             0.017            0.018
Chain 1:   2400        -7699.973             0.018            0.018
Chain 1:   2500        -7637.247             0.017            0.016
Chain 1:   2600        -7540.685             0.016            0.016
Chain 1:   2700        -7522.537             0.014            0.016
Chain 1:   2800        -7517.376             0.012            0.013
Chain 1:   2900        -7395.076             0.012            0.013
Chain 1:   3000        -7541.794             0.013            0.016
Chain 1:   3100        -7543.235             0.012            0.016
Chain 1:   3200        -7759.411             0.013            0.016
Chain 1:   3300        -7482.391             0.014            0.016
Chain 1:   3400        -7710.218             0.015            0.017
Chain 1:   3500        -7453.140             0.018            0.019
Chain 1:   3600        -7521.563             0.018            0.019
Chain 1:   3700        -7470.332             0.018            0.019
Chain 1:   3800        -7469.986             0.018            0.019
Chain 1:   3900        -7431.843             0.017            0.019
Chain 1:   4000        -7423.513             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00288 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86555.217             1.000            1.000
Chain 1:    200       -13903.081             3.113            5.226
Chain 1:    300       -10218.633             2.195            1.000
Chain 1:    400       -11330.358             1.671            1.000
Chain 1:    500        -9214.191             1.383            0.361
Chain 1:    600        -8644.082             1.163            0.361
Chain 1:    700        -8704.430             0.998            0.230
Chain 1:    800        -9618.401             0.885            0.230
Chain 1:    900        -8991.593             0.795            0.098
Chain 1:   1000        -9014.384             0.715            0.098
Chain 1:   1100        -9009.660             0.615            0.095   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8622.238             0.097            0.070
Chain 1:   1300        -8893.042             0.064            0.066
Chain 1:   1400        -8905.745             0.055            0.045
Chain 1:   1500        -8749.749             0.034            0.030
Chain 1:   1600        -8864.635             0.028            0.018
Chain 1:   1700        -8934.633             0.028            0.018
Chain 1:   1800        -8504.108             0.024            0.018
Chain 1:   1900        -8607.924             0.018            0.013
Chain 1:   2000        -8583.220             0.018            0.013
Chain 1:   2100        -8718.946             0.020            0.016
Chain 1:   2200        -8512.617             0.018            0.016
Chain 1:   2300        -8608.933             0.016            0.013
Chain 1:   2400        -8672.248             0.016            0.013
Chain 1:   2500        -8616.473             0.015            0.012
Chain 1:   2600        -8620.652             0.014            0.011
Chain 1:   2700        -8535.930             0.014            0.011
Chain 1:   2800        -8492.589             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003363 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8369931.984             1.000            1.000
Chain 1:    200     -1577347.638             2.653            4.306
Chain 1:    300      -890720.808             2.026            1.000
Chain 1:    400      -458563.202             1.755            1.000
Chain 1:    500      -359628.556             1.459            0.942
Chain 1:    600      -234367.041             1.305            0.942
Chain 1:    700      -120144.755             1.254            0.942
Chain 1:    800       -87259.067             1.145            0.942
Chain 1:    900       -67499.154             1.050            0.771
Chain 1:   1000       -52218.012             0.974            0.771
Chain 1:   1100       -39625.075             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38792.610             0.478            0.377
Chain 1:   1300       -26667.959             0.446            0.377
Chain 1:   1400       -26380.435             0.353            0.318
Chain 1:   1500       -22947.023             0.340            0.318
Chain 1:   1600       -22158.210             0.290            0.293
Chain 1:   1700       -21021.520             0.201            0.293
Chain 1:   1800       -20963.454             0.163            0.150
Chain 1:   1900       -21289.917             0.135            0.054
Chain 1:   2000       -19795.273             0.114            0.054
Chain 1:   2100       -20033.834             0.083            0.036
Chain 1:   2200       -20261.530             0.082            0.036
Chain 1:   2300       -19877.528             0.039            0.019
Chain 1:   2400       -19649.407             0.039            0.019
Chain 1:   2500       -19451.913             0.025            0.015
Chain 1:   2600       -19081.407             0.023            0.015
Chain 1:   2700       -19038.104             0.018            0.012
Chain 1:   2800       -18755.118             0.019            0.015
Chain 1:   2900       -19036.551             0.019            0.015
Chain 1:   3000       -19022.559             0.012            0.012
Chain 1:   3100       -19107.678             0.011            0.012
Chain 1:   3200       -18798.082             0.011            0.015
Chain 1:   3300       -19002.997             0.011            0.012
Chain 1:   3400       -18477.652             0.012            0.015
Chain 1:   3500       -19090.190             0.014            0.015
Chain 1:   3600       -18395.952             0.016            0.015
Chain 1:   3700       -18783.549             0.018            0.016
Chain 1:   3800       -17742.049             0.022            0.021
Chain 1:   3900       -17738.223             0.021            0.021
Chain 1:   4000       -17855.429             0.022            0.021
Chain 1:   4100       -17769.255             0.022            0.021
Chain 1:   4200       -17585.151             0.021            0.021
Chain 1:   4300       -17723.729             0.021            0.021
Chain 1:   4400       -17680.342             0.018            0.010
Chain 1:   4500       -17582.869             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001619 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12275.802             1.000            1.000
Chain 1:    200        -9193.101             0.668            1.000
Chain 1:    300        -7859.795             0.502            0.335
Chain 1:    400        -8076.243             0.383            0.335
Chain 1:    500        -7808.243             0.313            0.170
Chain 1:    600        -7804.458             0.261            0.170
Chain 1:    700        -7745.513             0.225            0.034
Chain 1:    800        -7771.976             0.197            0.034
Chain 1:    900        -7760.387             0.175            0.027
Chain 1:   1000        -7805.581             0.158            0.027
Chain 1:   1100        -7842.567             0.059            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001766 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58081.995             1.000            1.000
Chain 1:    200       -17673.211             1.643            2.286
Chain 1:    300        -8627.945             1.445            1.048
Chain 1:    400        -8144.361             1.099            1.048
Chain 1:    500        -8346.703             0.884            1.000
Chain 1:    600        -8523.420             0.740            1.000
Chain 1:    700        -7926.039             0.645            0.075
Chain 1:    800        -8082.980             0.567            0.075
Chain 1:    900        -7866.443             0.507            0.059
Chain 1:   1000        -7797.738             0.457            0.059
Chain 1:   1100        -7667.728             0.359            0.028
Chain 1:   1200        -7665.117             0.130            0.024
Chain 1:   1300        -7606.719             0.026            0.021
Chain 1:   1400        -7843.209             0.023            0.021
Chain 1:   1500        -7559.790             0.024            0.021
Chain 1:   1600        -7615.803             0.023            0.019
Chain 1:   1700        -7478.632             0.017            0.018
Chain 1:   1800        -7530.394             0.016            0.017
Chain 1:   1900        -7559.947             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003876 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86045.228             1.000            1.000
Chain 1:    200       -13434.120             3.202            5.405
Chain 1:    300        -9767.964             2.260            1.000
Chain 1:    400       -10659.210             1.716            1.000
Chain 1:    500        -8752.711             1.416            0.375
Chain 1:    600        -8644.700             1.182            0.375
Chain 1:    700        -8241.211             1.020            0.218
Chain 1:    800        -8940.510             0.903            0.218
Chain 1:    900        -8594.121             0.807            0.084
Chain 1:   1000        -8345.462             0.729            0.084
Chain 1:   1100        -8547.575             0.632            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8079.007             0.097            0.058
Chain 1:   1300        -8397.767             0.063            0.049
Chain 1:   1400        -8449.026             0.055            0.040
Chain 1:   1500        -8313.037             0.035            0.038
Chain 1:   1600        -8420.597             0.035            0.038
Chain 1:   1700        -8486.526             0.031            0.030
Chain 1:   1800        -8061.995             0.029            0.030
Chain 1:   1900        -8163.992             0.026            0.024
Chain 1:   2000        -8138.773             0.023            0.016
Chain 1:   2100        -8265.973             0.022            0.015
Chain 1:   2200        -8065.038             0.019            0.015
Chain 1:   2300        -8159.405             0.016            0.013
Chain 1:   2400        -8227.328             0.017            0.013
Chain 1:   2500        -8173.506             0.016            0.012
Chain 1:   2600        -8175.894             0.014            0.012
Chain 1:   2700        -8092.161             0.015            0.012
Chain 1:   2800        -8050.735             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003664 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8366601.959             1.000            1.000
Chain 1:    200     -1577925.426             2.651            4.302
Chain 1:    300      -890647.129             2.025            1.000
Chain 1:    400      -458116.678             1.755            1.000
Chain 1:    500      -359149.061             1.459            0.944
Chain 1:    600      -234037.455             1.305            0.944
Chain 1:    700      -119747.564             1.255            0.944
Chain 1:    800       -86811.335             1.145            0.944
Chain 1:    900       -67043.320             1.051            0.772
Chain 1:   1000       -51755.594             0.975            0.772
Chain 1:   1100       -39150.754             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38319.069             0.479            0.379
Chain 1:   1300       -26189.942             0.449            0.379
Chain 1:   1400       -25901.679             0.355            0.322
Chain 1:   1500       -22466.293             0.343            0.322
Chain 1:   1600       -21676.392             0.293            0.295
Chain 1:   1700       -20539.578             0.203            0.295
Chain 1:   1800       -20481.466             0.166            0.153
Chain 1:   1900       -20807.781             0.138            0.055
Chain 1:   2000       -19313.123             0.116            0.055
Chain 1:   2100       -19551.812             0.085            0.036
Chain 1:   2200       -19779.245             0.084            0.036
Chain 1:   2300       -19395.537             0.040            0.020
Chain 1:   2400       -19167.455             0.040            0.020
Chain 1:   2500       -18969.840             0.025            0.016
Chain 1:   2600       -18599.487             0.024            0.016
Chain 1:   2700       -18556.306             0.018            0.012
Chain 1:   2800       -18273.212             0.020            0.015
Chain 1:   2900       -18554.716             0.020            0.015
Chain 1:   3000       -18540.770             0.012            0.012
Chain 1:   3100       -18625.800             0.011            0.012
Chain 1:   3200       -18316.311             0.012            0.015
Chain 1:   3300       -18521.186             0.011            0.012
Chain 1:   3400       -17995.895             0.013            0.015
Chain 1:   3500       -18608.250             0.015            0.015
Chain 1:   3600       -17914.379             0.017            0.015
Chain 1:   3700       -18301.646             0.019            0.017
Chain 1:   3800       -17260.580             0.023            0.021
Chain 1:   3900       -17256.765             0.022            0.021
Chain 1:   4000       -17374.005             0.022            0.021
Chain 1:   4100       -17287.740             0.022            0.021
Chain 1:   4200       -17103.820             0.022            0.021
Chain 1:   4300       -17242.294             0.021            0.021
Chain 1:   4400       -17198.990             0.019            0.011
Chain 1:   4500       -17101.547             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001403 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13208.714             1.000            1.000
Chain 1:    200        -9917.046             0.666            1.000
Chain 1:    300        -8509.730             0.499            0.332
Chain 1:    400        -8697.802             0.380            0.332
Chain 1:    500        -8593.406             0.306            0.165
Chain 1:    600        -8416.807             0.259            0.165
Chain 1:    700        -8550.776             0.224            0.022
Chain 1:    800        -8397.541             0.198            0.022
Chain 1:    900        -8410.460             0.176            0.021
Chain 1:   1000        -8404.684             0.159            0.021
Chain 1:   1100        -8416.527             0.059            0.018
Chain 1:   1200        -8340.173             0.027            0.016
Chain 1:   1300        -8282.847             0.011            0.012
Chain 1:   1400        -8303.085             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001393 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58433.234             1.000            1.000
Chain 1:    200       -18460.375             1.583            2.165
Chain 1:    300        -9278.430             1.385            1.000
Chain 1:    400        -8425.699             1.064            1.000
Chain 1:    500        -8845.264             0.861            0.990
Chain 1:    600        -8127.680             0.732            0.990
Chain 1:    700        -8456.562             0.633            0.101
Chain 1:    800        -8600.692             0.556            0.101
Chain 1:    900        -8013.312             0.502            0.088
Chain 1:   1000        -8081.884             0.453            0.088
Chain 1:   1100        -8222.980             0.355            0.073
Chain 1:   1200        -7937.241             0.142            0.047
Chain 1:   1300        -7753.392             0.045            0.039
Chain 1:   1400        -8106.047             0.039            0.039
Chain 1:   1500        -7646.723             0.041            0.039
Chain 1:   1600        -7920.034             0.035            0.036
Chain 1:   1700        -7800.694             0.033            0.035
Chain 1:   1800        -7798.433             0.031            0.035
Chain 1:   1900        -7750.979             0.025            0.024
Chain 1:   2000        -7810.470             0.024            0.024
Chain 1:   2100        -7677.937             0.024            0.024
Chain 1:   2200        -8106.167             0.026            0.024
Chain 1:   2300        -7782.120             0.028            0.035
Chain 1:   2400        -7832.979             0.024            0.017
Chain 1:   2500        -7707.559             0.020            0.016
Chain 1:   2600        -7652.018             0.017            0.015
Chain 1:   2700        -7586.886             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003106 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87888.912             1.000            1.000
Chain 1:    200       -14409.218             3.050            5.099
Chain 1:    300       -10574.938             2.154            1.000
Chain 1:    400       -12470.242             1.654            1.000
Chain 1:    500        -8953.960             1.401            0.393
Chain 1:    600        -8809.721             1.171            0.393
Chain 1:    700        -9392.085             1.012            0.363
Chain 1:    800        -9082.560             0.890            0.363
Chain 1:    900        -9296.306             0.794            0.152
Chain 1:   1000        -8797.614             0.720            0.152
Chain 1:   1100        -8979.563             0.622            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8765.436             0.114            0.057
Chain 1:   1300        -9025.121             0.081            0.034
Chain 1:   1400        -8971.808             0.066            0.029
Chain 1:   1500        -8995.519             0.027            0.024
Chain 1:   1600        -9081.744             0.027            0.024
Chain 1:   1700        -9139.930             0.021            0.023
Chain 1:   1800        -8672.240             0.023            0.023
Chain 1:   1900        -8786.781             0.022            0.020
Chain 1:   2000        -8805.737             0.017            0.013
Chain 1:   2100        -8895.360             0.016            0.010
Chain 1:   2200        -8665.059             0.016            0.010
Chain 1:   2300        -8835.148             0.015            0.010
Chain 1:   2400        -8696.184             0.016            0.013
Chain 1:   2500        -8756.098             0.016            0.013
Chain 1:   2600        -8662.218             0.017            0.013
Chain 1:   2700        -8697.731             0.016            0.013
Chain 1:   2800        -8658.551             0.011            0.011
Chain 1:   2900        -8765.101             0.011            0.011
Chain 1:   3000        -8672.922             0.012            0.011
Chain 1:   3100        -8639.795             0.011            0.011
Chain 1:   3200        -8608.469             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003312 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8428103.357             1.000            1.000
Chain 1:    200     -1586531.629             2.656            4.312
Chain 1:    300      -890781.004             2.031            1.000
Chain 1:    400      -458263.629             1.759            1.000
Chain 1:    500      -358404.564             1.463            0.944
Chain 1:    600      -233450.609             1.309            0.944
Chain 1:    700      -119885.171             1.257            0.944
Chain 1:    800       -87211.502             1.147            0.944
Chain 1:    900       -67599.029             1.051            0.781
Chain 1:   1000       -52450.618             0.975            0.781
Chain 1:   1100       -39973.636             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39163.807             0.477            0.375
Chain 1:   1300       -27143.769             0.443            0.375
Chain 1:   1400       -26870.453             0.350            0.312
Chain 1:   1500       -23464.084             0.337            0.312
Chain 1:   1600       -22684.763             0.287            0.290
Chain 1:   1700       -21559.476             0.197            0.289
Chain 1:   1800       -21504.874             0.160            0.145
Chain 1:   1900       -21832.009             0.132            0.052
Chain 1:   2000       -20342.221             0.111            0.052
Chain 1:   2100       -20580.646             0.081            0.034
Chain 1:   2200       -20807.776             0.080            0.034
Chain 1:   2300       -20424.128             0.037            0.019
Chain 1:   2400       -20195.826             0.038            0.019
Chain 1:   2500       -19997.879             0.024            0.015
Chain 1:   2600       -19626.956             0.022            0.015
Chain 1:   2700       -19583.622             0.017            0.012
Chain 1:   2800       -19300.019             0.019            0.015
Chain 1:   2900       -19581.733             0.019            0.014
Chain 1:   3000       -19567.772             0.011            0.012
Chain 1:   3100       -19652.965             0.011            0.011
Chain 1:   3200       -19342.924             0.011            0.014
Chain 1:   3300       -19548.239             0.010            0.011
Chain 1:   3400       -19021.925             0.012            0.014
Chain 1:   3500       -19635.652             0.014            0.015
Chain 1:   3600       -18939.831             0.016            0.015
Chain 1:   3700       -19328.449             0.018            0.016
Chain 1:   3800       -18284.367             0.022            0.020
Chain 1:   3900       -18280.392             0.020            0.020
Chain 1:   4000       -18397.707             0.021            0.020
Chain 1:   4100       -18311.287             0.021            0.020
Chain 1:   4200       -18126.679             0.020            0.020
Chain 1:   4300       -18265.670             0.020            0.020
Chain 1:   4400       -18221.787             0.018            0.010
Chain 1:   4500       -18124.177             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12768.857             1.000            1.000
Chain 1:    200        -9744.369             0.655            1.000
Chain 1:    300        -8458.286             0.487            0.310
Chain 1:    400        -8633.172             0.371            0.310
Chain 1:    500        -8599.876             0.297            0.152
Chain 1:    600        -8392.068             0.252            0.152
Chain 1:    700        -8311.465             0.217            0.025
Chain 1:    800        -8350.564             0.191            0.025
Chain 1:    900        -8379.765             0.170            0.020
Chain 1:   1000        -8331.040             0.154            0.020
Chain 1:   1100        -8467.449             0.055            0.016
Chain 1:   1200        -8317.884             0.026            0.016
Chain 1:   1300        -8264.745             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62166.967             1.000            1.000
Chain 1:    200       -18356.009             1.693            2.387
Chain 1:    300        -9095.299             1.468            1.018
Chain 1:    400        -9634.698             1.115            1.018
Chain 1:    500        -8893.965             0.909            1.000
Chain 1:    600        -8092.831             0.774            1.000
Chain 1:    700        -7788.841             0.669            0.099
Chain 1:    800        -8120.453             0.590            0.099
Chain 1:    900        -7861.624             0.528            0.083
Chain 1:   1000        -7823.687             0.476            0.083
Chain 1:   1100        -7771.348             0.377            0.056
Chain 1:   1200        -7621.919             0.140            0.041
Chain 1:   1300        -7724.776             0.040            0.039
Chain 1:   1400        -7780.841             0.035            0.033
Chain 1:   1500        -7584.735             0.029            0.026
Chain 1:   1600        -7746.305             0.021            0.021
Chain 1:   1700        -7776.351             0.018            0.020
Chain 1:   1800        -7663.556             0.015            0.015
Chain 1:   1900        -7579.493             0.013            0.013
Chain 1:   2000        -7642.960             0.013            0.013
Chain 1:   2100        -7443.799             0.015            0.015
Chain 1:   2200        -7785.981             0.018            0.015
Chain 1:   2300        -7531.417             0.020            0.021
Chain 1:   2400        -7677.379             0.021            0.021
Chain 1:   2500        -7481.343             0.021            0.021
Chain 1:   2600        -7511.413             0.019            0.019
Chain 1:   2700        -7517.050             0.019            0.019
Chain 1:   2800        -7475.912             0.018            0.019
Chain 1:   2900        -7357.279             0.018            0.019
Chain 1:   3000        -7513.895             0.020            0.021
Chain 1:   3100        -7504.159             0.017            0.019
Chain 1:   3200        -7722.148             0.016            0.019
Chain 1:   3300        -7446.955             0.016            0.019
Chain 1:   3400        -7681.967             0.017            0.021
Chain 1:   3500        -7416.404             0.018            0.021
Chain 1:   3600        -7482.809             0.018            0.021
Chain 1:   3700        -7433.998             0.019            0.021
Chain 1:   3800        -7434.886             0.019            0.021
Chain 1:   3900        -7390.531             0.018            0.021
Chain 1:   4000        -7383.668             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003031 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87180.204             1.000            1.000
Chain 1:    200       -13962.171             3.122            5.244
Chain 1:    300       -10300.266             2.200            1.000
Chain 1:    400       -11396.142             1.674            1.000
Chain 1:    500        -9280.532             1.385            0.356
Chain 1:    600        -9011.643             1.159            0.356
Chain 1:    700        -8695.426             0.999            0.228
Chain 1:    800        -9036.163             0.878            0.228
Chain 1:    900        -9125.203             0.782            0.096
Chain 1:   1000        -8886.991             0.706            0.096
Chain 1:   1100        -8992.296             0.608            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8746.567             0.086            0.036
Chain 1:   1300        -8975.577             0.053            0.030
Chain 1:   1400        -8987.726             0.044            0.028
Chain 1:   1500        -8856.400             0.022            0.027
Chain 1:   1600        -8964.370             0.020            0.026
Chain 1:   1700        -9048.998             0.018            0.015
Chain 1:   1800        -8627.587             0.019            0.015
Chain 1:   1900        -8727.570             0.019            0.015
Chain 1:   2000        -8701.768             0.017            0.012
Chain 1:   2100        -8826.436             0.017            0.014
Chain 1:   2200        -8634.084             0.016            0.014
Chain 1:   2300        -8722.164             0.015            0.012
Chain 1:   2400        -8791.407             0.015            0.012
Chain 1:   2500        -8737.560             0.015            0.011
Chain 1:   2600        -8738.348             0.013            0.010
Chain 1:   2700        -8655.294             0.013            0.010
Chain 1:   2800        -8616.069             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003121 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417680.231             1.000            1.000
Chain 1:    200     -1588092.859             2.650            4.300
Chain 1:    300      -893266.725             2.026            1.000
Chain 1:    400      -459097.666             1.756            1.000
Chain 1:    500      -359067.150             1.461            0.946
Chain 1:    600      -233822.306             1.306            0.946
Chain 1:    700      -119851.450             1.256            0.946
Chain 1:    800       -87015.971             1.146            0.946
Chain 1:    900       -67326.588             1.051            0.778
Chain 1:   1000       -52100.862             0.975            0.778
Chain 1:   1100       -39558.949             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38732.970             0.479            0.377
Chain 1:   1300       -26670.600             0.446            0.377
Chain 1:   1400       -26388.032             0.353            0.317
Chain 1:   1500       -22970.466             0.340            0.317
Chain 1:   1600       -22185.700             0.290            0.292
Chain 1:   1700       -21057.240             0.200            0.292
Chain 1:   1800       -21000.817             0.163            0.149
Chain 1:   1900       -21327.028             0.135            0.054
Chain 1:   2000       -19836.977             0.113            0.054
Chain 1:   2100       -20075.387             0.083            0.035
Chain 1:   2200       -20302.090             0.082            0.035
Chain 1:   2300       -19919.085             0.038            0.019
Chain 1:   2400       -19691.131             0.038            0.019
Chain 1:   2500       -19493.226             0.025            0.015
Chain 1:   2600       -19123.254             0.023            0.015
Chain 1:   2700       -19080.190             0.018            0.012
Chain 1:   2800       -18797.045             0.019            0.015
Chain 1:   2900       -19078.379             0.019            0.015
Chain 1:   3000       -19064.516             0.012            0.012
Chain 1:   3100       -19149.518             0.011            0.012
Chain 1:   3200       -18840.127             0.011            0.015
Chain 1:   3300       -19044.928             0.011            0.012
Chain 1:   3400       -18519.740             0.012            0.015
Chain 1:   3500       -19131.758             0.014            0.015
Chain 1:   3600       -18438.306             0.016            0.015
Chain 1:   3700       -18825.223             0.018            0.016
Chain 1:   3800       -17784.683             0.022            0.021
Chain 1:   3900       -17780.837             0.021            0.021
Chain 1:   4000       -17898.136             0.022            0.021
Chain 1:   4100       -17811.863             0.022            0.021
Chain 1:   4200       -17628.064             0.021            0.021
Chain 1:   4300       -17766.479             0.021            0.021
Chain 1:   4400       -17723.275             0.018            0.010
Chain 1:   4500       -17625.798             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0013 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12596.894             1.000            1.000
Chain 1:    200        -9339.588             0.674            1.000
Chain 1:    300        -8257.683             0.493            0.349
Chain 1:    400        -8348.925             0.373            0.349
Chain 1:    500        -8236.684             0.301            0.131
Chain 1:    600        -8177.913             0.252            0.131
Chain 1:    700        -8076.693             0.218            0.014
Chain 1:    800        -8082.355             0.191            0.014
Chain 1:    900        -8109.249             0.170            0.013
Chain 1:   1000        -8176.930             0.154            0.013
Chain 1:   1100        -8223.894             0.054            0.011
Chain 1:   1200        -8084.475             0.021            0.011
Chain 1:   1300        -8059.239             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61919.905             1.000            1.000
Chain 1:    200       -18058.204             1.714            2.429
Chain 1:    300        -8980.019             1.480            1.011
Chain 1:    400        -9601.762             1.126            1.011
Chain 1:    500        -8487.746             0.927            1.000
Chain 1:    600        -8928.618             0.781            1.000
Chain 1:    700        -7646.660             0.693            0.168
Chain 1:    800        -8192.168             0.615            0.168
Chain 1:    900        -7834.170             0.552            0.131
Chain 1:   1000        -7859.890             0.497            0.131
Chain 1:   1100        -7778.211             0.398            0.067
Chain 1:   1200        -7729.151             0.156            0.065
Chain 1:   1300        -7794.205             0.055            0.049
Chain 1:   1400        -7943.723             0.051            0.046
Chain 1:   1500        -7608.520             0.042            0.044
Chain 1:   1600        -7611.789             0.037            0.019
Chain 1:   1700        -7563.180             0.021            0.011
Chain 1:   1800        -7596.288             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004336 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86558.985             1.000            1.000
Chain 1:    200       -13757.788             3.146            5.292
Chain 1:    300       -10112.766             2.217            1.000
Chain 1:    400       -11076.411             1.685            1.000
Chain 1:    500        -9084.465             1.392            0.360
Chain 1:    600        -8536.744             1.170            0.360
Chain 1:    700        -8655.776             1.005            0.219
Chain 1:    800        -8895.516             0.883            0.219
Chain 1:    900        -8949.949             0.785            0.087
Chain 1:   1000        -8590.743             0.711            0.087
Chain 1:   1100        -8868.075             0.614            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8427.011             0.090            0.052
Chain 1:   1300        -8773.353             0.058            0.042
Chain 1:   1400        -8759.194             0.050            0.039
Chain 1:   1500        -8654.545             0.029            0.031
Chain 1:   1600        -8764.674             0.024            0.027
Chain 1:   1700        -8832.836             0.023            0.027
Chain 1:   1800        -8411.216             0.026            0.031
Chain 1:   1900        -8510.854             0.026            0.031
Chain 1:   2000        -8485.671             0.022            0.013
Chain 1:   2100        -8611.300             0.021            0.013
Chain 1:   2200        -8414.189             0.018            0.013
Chain 1:   2300        -8506.072             0.015            0.012
Chain 1:   2400        -8574.828             0.015            0.012
Chain 1:   2500        -8521.099             0.015            0.012
Chain 1:   2600        -8522.474             0.014            0.011
Chain 1:   2700        -8439.181             0.014            0.011
Chain 1:   2800        -8399.026             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003247 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407830.237             1.000            1.000
Chain 1:    200     -1584473.646             2.653            4.306
Chain 1:    300      -890457.027             2.029            1.000
Chain 1:    400      -457739.691             1.758            1.000
Chain 1:    500      -357966.376             1.462            0.945
Chain 1:    600      -233062.240             1.308            0.945
Chain 1:    700      -119370.007             1.257            0.945
Chain 1:    800       -86654.164             1.147            0.945
Chain 1:    900       -67014.418             1.052            0.779
Chain 1:   1000       -51826.222             0.976            0.779
Chain 1:   1100       -39317.847             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38497.789             0.479            0.378
Chain 1:   1300       -26455.631             0.447            0.378
Chain 1:   1400       -26177.471             0.354            0.318
Chain 1:   1500       -22765.805             0.341            0.318
Chain 1:   1600       -21983.674             0.291            0.293
Chain 1:   1700       -20856.602             0.201            0.293
Chain 1:   1800       -20800.983             0.163            0.150
Chain 1:   1900       -21127.312             0.136            0.054
Chain 1:   2000       -19638.195             0.114            0.054
Chain 1:   2100       -19876.459             0.083            0.036
Chain 1:   2200       -20103.218             0.082            0.036
Chain 1:   2300       -19720.132             0.039            0.019
Chain 1:   2400       -19492.137             0.039            0.019
Chain 1:   2500       -19294.410             0.025            0.015
Chain 1:   2600       -18924.230             0.023            0.015
Chain 1:   2700       -18881.129             0.018            0.012
Chain 1:   2800       -18598.007             0.019            0.015
Chain 1:   2900       -18879.344             0.019            0.015
Chain 1:   3000       -18865.457             0.012            0.012
Chain 1:   3100       -18950.476             0.011            0.012
Chain 1:   3200       -18641.025             0.012            0.015
Chain 1:   3300       -18845.892             0.011            0.012
Chain 1:   3400       -18320.650             0.012            0.015
Chain 1:   3500       -18932.793             0.015            0.015
Chain 1:   3600       -18239.118             0.016            0.015
Chain 1:   3700       -18626.179             0.018            0.017
Chain 1:   3800       -17585.398             0.023            0.021
Chain 1:   3900       -17581.552             0.021            0.021
Chain 1:   4000       -17698.831             0.022            0.021
Chain 1:   4100       -17612.569             0.022            0.021
Chain 1:   4200       -17428.714             0.021            0.021
Chain 1:   4300       -17567.144             0.021            0.021
Chain 1:   4400       -17523.863             0.018            0.011
Chain 1:   4500       -17426.404             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00127 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13053.191             1.000            1.000
Chain 1:    200       -10017.025             0.652            1.000
Chain 1:    300        -8528.246             0.493            0.303
Chain 1:    400        -8756.402             0.376            0.303
Chain 1:    500        -8600.091             0.304            0.175
Chain 1:    600        -8454.398             0.257            0.175
Chain 1:    700        -8576.759             0.222            0.026
Chain 1:    800        -8387.608             0.197            0.026
Chain 1:    900        -8460.923             0.176            0.023
Chain 1:   1000        -8389.530             0.159            0.023
Chain 1:   1100        -8434.361             0.060            0.018
Chain 1:   1200        -8361.997             0.030            0.017
Chain 1:   1300        -8296.295             0.014            0.014
Chain 1:   1400        -8328.739             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001459 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59110.469             1.000            1.000
Chain 1:    200       -18475.162             1.600            2.199
Chain 1:    300        -9299.129             1.395            1.000
Chain 1:    400        -8545.532             1.069            1.000
Chain 1:    500        -8337.394             0.860            0.987
Chain 1:    600        -9465.264             0.736            0.987
Chain 1:    700        -7932.106             0.659            0.193
Chain 1:    800        -8526.685             0.585            0.193
Chain 1:    900        -8390.231             0.522            0.119
Chain 1:   1000        -8183.943             0.472            0.119
Chain 1:   1100        -8058.403             0.374            0.088
Chain 1:   1200        -7993.372             0.155            0.070
Chain 1:   1300        -7938.699             0.057            0.025
Chain 1:   1400        -8050.705             0.049            0.025
Chain 1:   1500        -7662.513             0.052            0.025
Chain 1:   1600        -7765.505             0.041            0.016
Chain 1:   1700        -7772.887             0.022            0.016
Chain 1:   1800        -7690.332             0.016            0.014
Chain 1:   1900        -7743.302             0.015            0.013
Chain 1:   2000        -7814.037             0.014            0.011
Chain 1:   2100        -7707.981             0.013            0.011
Chain 1:   2200        -7951.466             0.016            0.013
Chain 1:   2300        -7738.280             0.018            0.014
Chain 1:   2400        -7789.138             0.017            0.013
Chain 1:   2500        -7664.932             0.014            0.013
Chain 1:   2600        -7680.780             0.012            0.011
Chain 1:   2700        -7653.059             0.013            0.011
Chain 1:   2800        -7785.657             0.013            0.014
Chain 1:   2900        -7521.819             0.016            0.016
Chain 1:   3000        -7680.725             0.017            0.017
Chain 1:   3100        -7676.814             0.016            0.017
Chain 1:   3200        -7868.749             0.015            0.017
Chain 1:   3300        -7583.388             0.016            0.017
Chain 1:   3400        -7802.728             0.019            0.021
Chain 1:   3500        -7583.401             0.020            0.024
Chain 1:   3600        -7645.594             0.020            0.024
Chain 1:   3700        -7602.742             0.021            0.024
Chain 1:   3800        -7573.322             0.019            0.024
Chain 1:   3900        -7545.815             0.016            0.021
Chain 1:   4000        -7541.698             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003542 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86910.449             1.000            1.000
Chain 1:    200       -14283.225             3.042            5.085
Chain 1:    300       -10503.521             2.148            1.000
Chain 1:    400       -12199.865             1.646            1.000
Chain 1:    500        -9282.615             1.380            0.360
Chain 1:    600        -9749.255             1.158            0.360
Chain 1:    700        -9143.858             1.002            0.314
Chain 1:    800        -8733.137             0.882            0.314
Chain 1:    900        -8835.519             0.786            0.139
Chain 1:   1000        -9601.817             0.715            0.139
Chain 1:   1100        -8995.579             0.622            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9411.384             0.118            0.067
Chain 1:   1300        -8790.078             0.089            0.067
Chain 1:   1400        -8976.636             0.077            0.066
Chain 1:   1500        -8931.625             0.046            0.048
Chain 1:   1600        -8889.472             0.042            0.047
Chain 1:   1700        -8765.581             0.037            0.044
Chain 1:   1800        -8819.440             0.032            0.021
Chain 1:   1900        -8758.393             0.032            0.021
Chain 1:   2000        -8768.678             0.024            0.014
Chain 1:   2100        -8828.958             0.018            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003374 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408977.789             1.000            1.000
Chain 1:    200     -1583301.238             2.656            4.311
Chain 1:    300      -893155.306             2.028            1.000
Chain 1:    400      -459953.808             1.756            1.000
Chain 1:    500      -360383.438             1.460            0.942
Chain 1:    600      -234952.732             1.306            0.942
Chain 1:    700      -120603.568             1.255            0.942
Chain 1:    800       -87673.038             1.145            0.942
Chain 1:    900       -67899.779             1.050            0.773
Chain 1:   1000       -52619.812             0.974            0.773
Chain 1:   1100       -40023.180             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39195.227             0.477            0.376
Chain 1:   1300       -27067.107             0.444            0.376
Chain 1:   1400       -26780.623             0.351            0.315
Chain 1:   1500       -23346.164             0.338            0.315
Chain 1:   1600       -22557.259             0.288            0.291
Chain 1:   1700       -21420.362             0.199            0.290
Chain 1:   1800       -21362.421             0.161            0.147
Chain 1:   1900       -21689.235             0.134            0.053
Chain 1:   2000       -20193.698             0.112            0.053
Chain 1:   2100       -20432.342             0.082            0.035
Chain 1:   2200       -20660.204             0.081            0.035
Chain 1:   2300       -20276.025             0.038            0.019
Chain 1:   2400       -20047.779             0.038            0.019
Chain 1:   2500       -19850.123             0.024            0.015
Chain 1:   2600       -19479.173             0.023            0.015
Chain 1:   2700       -19435.826             0.018            0.012
Chain 1:   2800       -19152.504             0.019            0.015
Chain 1:   2900       -19434.241             0.019            0.014
Chain 1:   3000       -19420.218             0.011            0.012
Chain 1:   3100       -19505.355             0.011            0.011
Chain 1:   3200       -19195.443             0.011            0.014
Chain 1:   3300       -19400.649             0.010            0.011
Chain 1:   3400       -18874.658             0.012            0.014
Chain 1:   3500       -19487.971             0.014            0.015
Chain 1:   3600       -18792.834             0.016            0.015
Chain 1:   3700       -19181.075             0.018            0.016
Chain 1:   3800       -18137.956             0.022            0.020
Chain 1:   3900       -18134.086             0.021            0.020
Chain 1:   4000       -18251.343             0.021            0.020
Chain 1:   4100       -18164.972             0.021            0.020
Chain 1:   4200       -17980.602             0.021            0.020
Chain 1:   4300       -18119.381             0.020            0.020
Chain 1:   4400       -18075.718             0.018            0.010
Chain 1:   4500       -17978.195             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0013 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48821.993             1.000            1.000
Chain 1:    200       -23440.720             1.041            1.083
Chain 1:    300       -13120.851             0.956            1.000
Chain 1:    400       -14995.613             0.749            1.000
Chain 1:    500       -17625.246             0.629            0.787
Chain 1:    600       -20380.262             0.546            0.787
Chain 1:    700       -13362.199             0.543            0.525
Chain 1:    800       -13172.062             0.477            0.525
Chain 1:    900       -16157.841             0.445            0.185
Chain 1:   1000       -31506.045             0.449            0.487
Chain 1:   1100       -17457.582             0.430            0.487
Chain 1:   1200       -20308.054             0.335            0.185
Chain 1:   1300       -11955.485             0.326            0.185
Chain 1:   1400       -10311.401             0.330            0.185
Chain 1:   1500       -10185.485             0.316            0.185
Chain 1:   1600       -34364.412             0.373            0.487
Chain 1:   1700       -13123.303             0.482            0.487
Chain 1:   1800        -9594.494             0.518            0.487   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1900       -17652.421             0.545            0.487   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -17794.578             0.497            0.456
Chain 1:   2100       -10198.484             0.491            0.456
Chain 1:   2200       -16193.419             0.514            0.456   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300        -9505.896             0.514            0.456   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400       -10365.110             0.507            0.456   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500       -10391.459             0.506            0.456   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600        -9818.570             0.441            0.370
Chain 1:   2700       -14812.284             0.313            0.368
Chain 1:   2800       -10218.533             0.321            0.370
Chain 1:   2900        -9688.900             0.281            0.337
Chain 1:   3000       -12621.106             0.304            0.337
Chain 1:   3100       -16352.096             0.252            0.232
Chain 1:   3200        -9336.417             0.290            0.232
Chain 1:   3300       -15550.016             0.260            0.232
Chain 1:   3400        -9858.529             0.309            0.337
Chain 1:   3500       -13236.615             0.334            0.337
Chain 1:   3600       -14719.873             0.339            0.337
Chain 1:   3700        -9901.834             0.354            0.400
Chain 1:   3800       -10266.612             0.312            0.255
Chain 1:   3900        -9552.557             0.314            0.255
Chain 1:   4000        -9270.262             0.294            0.255
Chain 1:   4100        -8929.058             0.275            0.255
Chain 1:   4200       -11880.910             0.225            0.248
Chain 1:   4300        -8917.111             0.218            0.248
Chain 1:   4400       -12009.255             0.186            0.248
Chain 1:   4500       -12487.340             0.164            0.101
Chain 1:   4600       -14072.683             0.165            0.113
Chain 1:   4700        -9238.573             0.169            0.113
Chain 1:   4800       -13429.328             0.197            0.248
Chain 1:   4900        -9472.351             0.231            0.257
Chain 1:   5000       -13560.757             0.258            0.301
Chain 1:   5100        -8522.805             0.313            0.312
Chain 1:   5200        -9384.644             0.298            0.312
Chain 1:   5300       -15197.431             0.303            0.312
Chain 1:   5400       -11279.885             0.312            0.347
Chain 1:   5500        -9250.258             0.330            0.347
Chain 1:   5600        -9282.439             0.319            0.347
Chain 1:   5700       -11531.677             0.286            0.312
Chain 1:   5800        -9803.689             0.273            0.301
Chain 1:   5900        -9050.362             0.239            0.219
Chain 1:   6000        -9067.112             0.209            0.195
Chain 1:   6100        -8709.004             0.154            0.176
Chain 1:   6200        -8486.354             0.148            0.176
Chain 1:   6300        -8832.087             0.113            0.083
Chain 1:   6400        -8805.880             0.079            0.041
Chain 1:   6500        -8984.008             0.059            0.039
Chain 1:   6600       -12551.542             0.087            0.041
Chain 1:   6700        -8330.534             0.118            0.041
Chain 1:   6800        -9151.132             0.109            0.041
Chain 1:   6900       -11274.117             0.120            0.041
Chain 1:   7000        -8882.638             0.147            0.090
Chain 1:   7100        -8597.717             0.146            0.090
Chain 1:   7200        -9413.253             0.152            0.090
Chain 1:   7300       -11213.249             0.164            0.161
Chain 1:   7400       -10982.396             0.166            0.161
Chain 1:   7500        -8662.254             0.191            0.188
Chain 1:   7600       -12164.197             0.191            0.188
Chain 1:   7700        -8273.345             0.187            0.188
Chain 1:   7800       -13257.981             0.216            0.268
Chain 1:   7900        -8742.043             0.249            0.269
Chain 1:   8000        -8395.984             0.226            0.268
Chain 1:   8100        -8370.556             0.223            0.268
Chain 1:   8200        -9854.803             0.229            0.268
Chain 1:   8300        -8217.329             0.233            0.268
Chain 1:   8400        -8605.640             0.236            0.268
Chain 1:   8500        -8643.252             0.209            0.199
Chain 1:   8600       -10462.704             0.198            0.174
Chain 1:   8700        -8708.511             0.171            0.174
Chain 1:   8800        -8392.194             0.137            0.151
Chain 1:   8900        -8472.858             0.087            0.045
Chain 1:   9000       -10756.896             0.104            0.151
Chain 1:   9100        -8640.003             0.128            0.174
Chain 1:   9200       -11418.725             0.137            0.199
Chain 1:   9300       -10465.311             0.126            0.174
Chain 1:   9400       -11447.593             0.130            0.174
Chain 1:   9500        -8907.696             0.159            0.201
Chain 1:   9600        -8395.609             0.147            0.201
Chain 1:   9700        -8551.744             0.129            0.091
Chain 1:   9800       -11802.214             0.153            0.212
Chain 1:   9900       -11247.257             0.157            0.212
Chain 1:   10000        -8315.783             0.171            0.243
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001425 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57165.938             1.000            1.000
Chain 1:    200       -17446.334             1.638            2.277
Chain 1:    300        -8724.925             1.425            1.000
Chain 1:    400        -8335.733             1.081            1.000
Chain 1:    500        -8428.198             0.867            1.000
Chain 1:    600        -8438.910             0.723            1.000
Chain 1:    700        -7767.524             0.632            0.086
Chain 1:    800        -8272.360             0.560            0.086
Chain 1:    900        -7968.862             0.502            0.061
Chain 1:   1000        -7767.639             0.455            0.061
Chain 1:   1100        -7754.136             0.355            0.047
Chain 1:   1200        -8028.330             0.131            0.038
Chain 1:   1300        -7610.681             0.036            0.038
Chain 1:   1400        -7855.341             0.035            0.034
Chain 1:   1500        -7621.492             0.037            0.034
Chain 1:   1600        -7669.766             0.037            0.034
Chain 1:   1700        -7545.752             0.030            0.031
Chain 1:   1800        -7621.381             0.025            0.031
Chain 1:   1900        -7598.319             0.021            0.026
Chain 1:   2000        -7618.784             0.019            0.016
Chain 1:   2100        -7602.512             0.019            0.016
Chain 1:   2200        -7718.429             0.017            0.015
Chain 1:   2300        -7614.685             0.013            0.014
Chain 1:   2400        -7620.882             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003099 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85944.488             1.000            1.000
Chain 1:    200       -13522.589             3.178            5.356
Chain 1:    300        -9920.537             2.240            1.000
Chain 1:    400       -10928.603             1.703            1.000
Chain 1:    500        -8810.317             1.410            0.363
Chain 1:    600        -8401.455             1.183            0.363
Chain 1:    700        -8508.642             1.016            0.240
Chain 1:    800        -8758.615             0.893            0.240
Chain 1:    900        -8690.725             0.794            0.092
Chain 1:   1000        -8613.012             0.716            0.092
Chain 1:   1100        -8760.485             0.617            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8306.447             0.087            0.049
Chain 1:   1300        -8620.668             0.055            0.036
Chain 1:   1400        -8621.267             0.046            0.029
Chain 1:   1500        -8495.272             0.023            0.017
Chain 1:   1600        -8602.322             0.019            0.015
Chain 1:   1700        -8688.540             0.019            0.015
Chain 1:   1800        -8281.245             0.021            0.015
Chain 1:   1900        -8377.733             0.021            0.015
Chain 1:   2000        -8349.969             0.021            0.015
Chain 1:   2100        -8470.774             0.021            0.014
Chain 1:   2200        -8290.390             0.017            0.014
Chain 1:   2300        -8416.973             0.015            0.014
Chain 1:   2400        -8427.551             0.015            0.014
Chain 1:   2500        -8389.592             0.014            0.012
Chain 1:   2600        -8388.434             0.013            0.012
Chain 1:   2700        -8303.278             0.013            0.012
Chain 1:   2800        -8268.149             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00344 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387627.417             1.000            1.000
Chain 1:    200     -1582486.425             2.650            4.300
Chain 1:    300      -890471.322             2.026            1.000
Chain 1:    400      -456986.478             1.756            1.000
Chain 1:    500      -357641.698             1.461            0.949
Chain 1:    600      -232756.103             1.307            0.949
Chain 1:    700      -119167.728             1.256            0.949
Chain 1:    800       -86404.865             1.147            0.949
Chain 1:    900       -66771.715             1.052            0.777
Chain 1:   1000       -51582.501             0.976            0.777
Chain 1:   1100       -39068.030             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38246.324             0.480            0.379
Chain 1:   1300       -26209.386             0.448            0.379
Chain 1:   1400       -25928.531             0.355            0.320
Chain 1:   1500       -22517.097             0.342            0.320
Chain 1:   1600       -21734.068             0.292            0.294
Chain 1:   1700       -20608.502             0.202            0.294
Chain 1:   1800       -20552.888             0.165            0.152
Chain 1:   1900       -20878.916             0.137            0.055
Chain 1:   2000       -19390.709             0.115            0.055
Chain 1:   2100       -19629.060             0.084            0.036
Chain 1:   2200       -19855.349             0.083            0.036
Chain 1:   2300       -19472.743             0.039            0.020
Chain 1:   2400       -19244.900             0.039            0.020
Chain 1:   2500       -19046.871             0.025            0.016
Chain 1:   2600       -18677.244             0.023            0.016
Chain 1:   2700       -18634.313             0.018            0.012
Chain 1:   2800       -18351.200             0.020            0.015
Chain 1:   2900       -18632.367             0.019            0.015
Chain 1:   3000       -18618.600             0.012            0.012
Chain 1:   3100       -18703.550             0.011            0.012
Chain 1:   3200       -18394.336             0.012            0.015
Chain 1:   3300       -18599.009             0.011            0.012
Chain 1:   3400       -18074.093             0.013            0.015
Chain 1:   3500       -18685.712             0.015            0.015
Chain 1:   3600       -17992.767             0.017            0.015
Chain 1:   3700       -18379.253             0.018            0.017
Chain 1:   3800       -17339.547             0.023            0.021
Chain 1:   3900       -17335.724             0.021            0.021
Chain 1:   4000       -17453.012             0.022            0.021
Chain 1:   4100       -17366.773             0.022            0.021
Chain 1:   4200       -17183.210             0.021            0.021
Chain 1:   4300       -17321.498             0.021            0.021
Chain 1:   4400       -17278.424             0.019            0.011
Chain 1:   4500       -17180.986             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001276 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12245.492             1.000            1.000
Chain 1:    200        -9155.835             0.669            1.000
Chain 1:    300        -8137.681             0.488            0.337
Chain 1:    400        -8149.436             0.366            0.337
Chain 1:    500        -8004.904             0.296            0.125
Chain 1:    600        -7933.150             0.249            0.125
Chain 1:    700        -7852.592             0.214            0.018
Chain 1:    800        -7883.833             0.188            0.018
Chain 1:    900        -8022.098             0.169            0.017
Chain 1:   1000        -7893.317             0.154            0.017
Chain 1:   1100        -7933.676             0.054            0.016
Chain 1:   1200        -7886.873             0.021            0.010
Chain 1:   1300        -7831.812             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56800.933             1.000            1.000
Chain 1:    200       -17271.657             1.644            2.289
Chain 1:    300        -8649.368             1.429            1.000
Chain 1:    400        -8246.123             1.084            1.000
Chain 1:    500        -8348.693             0.869            0.997
Chain 1:    600        -8601.820             0.729            0.997
Chain 1:    700        -8185.759             0.632            0.051
Chain 1:    800        -8082.998             0.555            0.051
Chain 1:    900        -7908.151             0.496            0.049
Chain 1:   1000        -7816.244             0.447            0.049
Chain 1:   1100        -7720.044             0.349            0.029
Chain 1:   1200        -7589.023             0.121            0.022
Chain 1:   1300        -7540.661             0.022            0.017
Chain 1:   1400        -7853.041             0.022            0.017
Chain 1:   1500        -7586.868             0.024            0.022
Chain 1:   1600        -7771.084             0.023            0.022
Chain 1:   1700        -7500.758             0.022            0.022
Chain 1:   1800        -7538.689             0.021            0.022
Chain 1:   1900        -7597.026             0.020            0.017
Chain 1:   2000        -7595.957             0.018            0.017
Chain 1:   2100        -7553.042             0.018            0.017
Chain 1:   2200        -7665.477             0.017            0.015
Chain 1:   2300        -7571.351             0.018            0.015
Chain 1:   2400        -7609.358             0.015            0.012
Chain 1:   2500        -7529.523             0.012            0.011
Chain 1:   2600        -7500.128             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002933 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86000.460             1.000            1.000
Chain 1:    200       -13322.469             3.228            5.455
Chain 1:    300        -9752.230             2.274            1.000
Chain 1:    400       -10507.221             1.723            1.000
Chain 1:    500        -8680.275             1.421            0.366
Chain 1:    600        -8304.878             1.191            0.366
Chain 1:    700        -8648.137             1.027            0.210
Chain 1:    800        -9138.563             0.905            0.210
Chain 1:    900        -8565.107             0.812            0.072
Chain 1:   1000        -8363.515             0.733            0.072
Chain 1:   1100        -8635.996             0.636            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8173.814             0.097            0.057
Chain 1:   1300        -8503.250             0.064            0.054
Chain 1:   1400        -8482.788             0.057            0.045
Chain 1:   1500        -8367.361             0.037            0.040
Chain 1:   1600        -8474.671             0.034            0.039
Chain 1:   1700        -8551.648             0.031            0.032
Chain 1:   1800        -8157.996             0.030            0.032
Chain 1:   1900        -8260.497             0.025            0.024
Chain 1:   2000        -8230.832             0.023            0.014
Chain 1:   2100        -8355.663             0.021            0.014
Chain 1:   2200        -8140.347             0.018            0.014
Chain 1:   2300        -8289.203             0.016            0.014
Chain 1:   2400        -8304.278             0.016            0.014
Chain 1:   2500        -8271.738             0.015            0.013
Chain 1:   2600        -8273.859             0.014            0.012
Chain 1:   2700        -8180.507             0.014            0.012
Chain 1:   2800        -8152.893             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003484 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415748.419             1.000            1.000
Chain 1:    200     -1589117.167             2.648            4.296
Chain 1:    300      -892269.924             2.026            1.000
Chain 1:    400      -457970.370             1.756            1.000
Chain 1:    500      -357777.430             1.461            0.948
Chain 1:    600      -232625.521             1.307            0.948
Chain 1:    700      -118924.264             1.257            0.948
Chain 1:    800       -86135.956             1.147            0.948
Chain 1:    900       -66501.520             1.053            0.781
Chain 1:   1000       -51315.137             0.977            0.781
Chain 1:   1100       -38810.480             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37985.828             0.482            0.381
Chain 1:   1300       -25973.280             0.450            0.381
Chain 1:   1400       -25692.894             0.356            0.322
Chain 1:   1500       -22288.279             0.344            0.322
Chain 1:   1600       -21506.354             0.293            0.296
Chain 1:   1700       -20384.673             0.203            0.295
Chain 1:   1800       -20329.560             0.166            0.153
Chain 1:   1900       -20655.214             0.138            0.055
Chain 1:   2000       -19169.483             0.116            0.055
Chain 1:   2100       -19407.714             0.085            0.036
Chain 1:   2200       -19633.436             0.084            0.036
Chain 1:   2300       -19251.415             0.039            0.020
Chain 1:   2400       -19023.713             0.040            0.020
Chain 1:   2500       -18825.511             0.025            0.016
Chain 1:   2600       -18456.341             0.024            0.016
Chain 1:   2700       -18413.509             0.018            0.012
Chain 1:   2800       -18130.441             0.020            0.016
Chain 1:   2900       -18411.468             0.020            0.015
Chain 1:   3000       -18397.773             0.012            0.012
Chain 1:   3100       -18482.654             0.011            0.012
Chain 1:   3200       -18173.657             0.012            0.015
Chain 1:   3300       -18378.140             0.011            0.012
Chain 1:   3400       -17853.543             0.013            0.015
Chain 1:   3500       -18464.573             0.015            0.016
Chain 1:   3600       -17772.406             0.017            0.016
Chain 1:   3700       -18158.295             0.019            0.017
Chain 1:   3800       -17119.685             0.023            0.021
Chain 1:   3900       -17115.854             0.022            0.021
Chain 1:   4000       -17233.197             0.022            0.021
Chain 1:   4100       -17146.980             0.022            0.021
Chain 1:   4200       -16963.638             0.022            0.021
Chain 1:   4300       -17101.780             0.021            0.021
Chain 1:   4400       -17058.906             0.019            0.011
Chain 1:   4500       -16961.479             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001348 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48380.090             1.000            1.000
Chain 1:    200       -18866.266             1.282            1.564
Chain 1:    300       -20867.626             0.887            1.000
Chain 1:    400       -17997.377             0.705            1.000
Chain 1:    500       -14775.454             0.608            0.218
Chain 1:    600       -25840.458             0.578            0.428
Chain 1:    700       -14196.322             0.612            0.428
Chain 1:    800       -14711.732             0.540            0.428
Chain 1:    900       -10667.220             0.522            0.379
Chain 1:   1000       -10890.797             0.472            0.379
Chain 1:   1100       -11085.541             0.374            0.218
Chain 1:   1200        -9677.287             0.232            0.159
Chain 1:   1300       -11417.255             0.238            0.159
Chain 1:   1400       -11458.946             0.222            0.152
Chain 1:   1500       -14683.493             0.222            0.152
Chain 1:   1600       -10779.217             0.216            0.152
Chain 1:   1700        -9995.454             0.141            0.146
Chain 1:   1800        -9071.361             0.148            0.146
Chain 1:   1900       -10456.999             0.123            0.133
Chain 1:   2000       -11871.565             0.133            0.133
Chain 1:   2100       -17282.294             0.163            0.146
Chain 1:   2200       -10237.551             0.217            0.152
Chain 1:   2300        -8956.205             0.216            0.143
Chain 1:   2400       -10407.325             0.230            0.143
Chain 1:   2500       -12431.474             0.224            0.143
Chain 1:   2600        -8953.444             0.227            0.143
Chain 1:   2700        -8657.203             0.222            0.143
Chain 1:   2800        -8907.118             0.215            0.143
Chain 1:   2900        -9196.116             0.205            0.143
Chain 1:   3000        -8733.210             0.198            0.143
Chain 1:   3100        -8487.915             0.170            0.139
Chain 1:   3200       -13768.957             0.139            0.139
Chain 1:   3300        -9063.953             0.177            0.139
Chain 1:   3400       -14566.093             0.201            0.163
Chain 1:   3500        -8896.908             0.248            0.378
Chain 1:   3600        -9358.405             0.214            0.053
Chain 1:   3700        -8851.359             0.217            0.057
Chain 1:   3800        -9819.053             0.224            0.099
Chain 1:   3900        -8915.070             0.231            0.101
Chain 1:   4000        -8852.836             0.226            0.101
Chain 1:   4100        -9371.352             0.229            0.101
Chain 1:   4200       -10652.883             0.202            0.101
Chain 1:   4300       -15564.172             0.182            0.101
Chain 1:   4400        -9553.982             0.207            0.101
Chain 1:   4500        -8675.338             0.154            0.101
Chain 1:   4600       -12780.717             0.181            0.101
Chain 1:   4700        -9894.783             0.204            0.120
Chain 1:   4800        -8598.283             0.209            0.151
Chain 1:   4900        -8386.717             0.202            0.151
Chain 1:   5000       -15876.582             0.248            0.292
Chain 1:   5100        -8970.977             0.320            0.316
Chain 1:   5200       -13339.893             0.340            0.321
Chain 1:   5300       -15208.110             0.321            0.321
Chain 1:   5400       -14577.746             0.263            0.292
Chain 1:   5500       -10769.887             0.288            0.321
Chain 1:   5600        -9772.402             0.266            0.292
Chain 1:   5700       -10907.752             0.247            0.151
Chain 1:   5800        -8664.592             0.258            0.259
Chain 1:   5900        -9059.303             0.260            0.259
Chain 1:   6000        -8240.848             0.222            0.123
Chain 1:   6100       -10909.653             0.170            0.123
Chain 1:   6200        -8421.246             0.167            0.123
Chain 1:   6300        -8190.805             0.157            0.104
Chain 1:   6400       -13049.361             0.190            0.245
Chain 1:   6500       -10554.600             0.178            0.236
Chain 1:   6600        -9950.988             0.174            0.236
Chain 1:   6700        -9229.914             0.172            0.236
Chain 1:   6800       -12618.571             0.173            0.236
Chain 1:   6900       -12469.218             0.170            0.236
Chain 1:   7000        -8410.167             0.208            0.245
Chain 1:   7100        -8164.502             0.186            0.236
Chain 1:   7200       -10112.910             0.176            0.193
Chain 1:   7300        -8169.621             0.197            0.236
Chain 1:   7400        -8477.475             0.164            0.193
Chain 1:   7500        -7945.457             0.147            0.078
Chain 1:   7600        -8372.126             0.146            0.078
Chain 1:   7700        -8126.213             0.141            0.067
Chain 1:   7800        -8464.559             0.118            0.051
Chain 1:   7900        -8157.234             0.121            0.051
Chain 1:   8000        -8117.164             0.073            0.040
Chain 1:   8100        -8255.475             0.071            0.040
Chain 1:   8200        -8292.513             0.053            0.038
Chain 1:   8300       -10549.031             0.050            0.038
Chain 1:   8400       -12643.516             0.063            0.040
Chain 1:   8500        -8221.954             0.110            0.040
Chain 1:   8600        -8308.890             0.106            0.038
Chain 1:   8700        -8475.609             0.105            0.038
Chain 1:   8800        -8372.809             0.102            0.020
Chain 1:   8900        -8853.822             0.104            0.020
Chain 1:   9000       -10553.704             0.120            0.054
Chain 1:   9100        -8270.952             0.146            0.161
Chain 1:   9200        -8154.285             0.147            0.161
Chain 1:   9300        -9078.105             0.135            0.102
Chain 1:   9400        -7909.427             0.134            0.102
Chain 1:   9500        -7938.881             0.080            0.054
Chain 1:   9600        -8067.552             0.081            0.054
Chain 1:   9700        -8999.226             0.089            0.102
Chain 1:   9800       -10563.091             0.103            0.104
Chain 1:   9900        -8472.830             0.122            0.148
Chain 1:   10000        -9572.858             0.117            0.115
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56588.056             1.000            1.000
Chain 1:    200       -17114.727             1.653            2.306
Chain 1:    300        -8575.108             1.434            1.000
Chain 1:    400        -8401.757             1.081            1.000
Chain 1:    500        -8043.745             0.873            0.996
Chain 1:    600        -8707.528             0.741            0.996
Chain 1:    700        -7791.201             0.652            0.118
Chain 1:    800        -8028.544             0.574            0.118
Chain 1:    900        -7857.596             0.513            0.076
Chain 1:   1000        -7844.980             0.461            0.076
Chain 1:   1100        -7753.948             0.363            0.045
Chain 1:   1200        -7534.547             0.135            0.030
Chain 1:   1300        -7760.791             0.038            0.029
Chain 1:   1400        -7924.435             0.038            0.029
Chain 1:   1500        -7559.864             0.039            0.029
Chain 1:   1600        -7500.895             0.032            0.029
Chain 1:   1700        -7495.967             0.020            0.022
Chain 1:   1800        -7523.780             0.017            0.021
Chain 1:   1900        -7569.520             0.016            0.012
Chain 1:   2000        -7560.878             0.016            0.012
Chain 1:   2100        -7568.351             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86739.561             1.000            1.000
Chain 1:    200       -13157.967             3.296            5.592
Chain 1:    300        -9599.477             2.321            1.000
Chain 1:    400       -10364.682             1.759            1.000
Chain 1:    500        -8517.007             1.451            0.371
Chain 1:    600        -8161.455             1.216            0.371
Chain 1:    700        -8481.465             1.048            0.217
Chain 1:    800        -8952.852             0.923            0.217
Chain 1:    900        -8393.202             0.828            0.074
Chain 1:   1000        -8299.576             0.747            0.074
Chain 1:   1100        -8517.187             0.649            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8255.839             0.093            0.053
Chain 1:   1300        -8305.401             0.057            0.044
Chain 1:   1400        -8374.539             0.050            0.038
Chain 1:   1500        -8229.803             0.030            0.032
Chain 1:   1600        -8334.477             0.027            0.026
Chain 1:   1700        -8417.715             0.024            0.018
Chain 1:   1800        -8032.304             0.024            0.018
Chain 1:   1900        -8134.616             0.018            0.013
Chain 1:   2000        -8104.205             0.018            0.013
Chain 1:   2100        -8237.356             0.017            0.013
Chain 1:   2200        -8022.671             0.016            0.013
Chain 1:   2300        -8163.994             0.017            0.016
Chain 1:   2400        -8175.689             0.017            0.016
Chain 1:   2500        -8143.896             0.015            0.013
Chain 1:   2600        -8142.713             0.014            0.013
Chain 1:   2700        -8051.501             0.014            0.013
Chain 1:   2800        -8028.523             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003084 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413972.497             1.000            1.000
Chain 1:    200     -1586505.006             2.652            4.303
Chain 1:    300      -891194.946             2.028            1.000
Chain 1:    400      -457571.699             1.758            1.000
Chain 1:    500      -357580.995             1.462            0.948
Chain 1:    600      -232433.954             1.308            0.948
Chain 1:    700      -118725.917             1.258            0.948
Chain 1:    800       -85953.667             1.149            0.948
Chain 1:    900       -66318.468             1.054            0.780
Chain 1:   1000       -51131.736             0.978            0.780
Chain 1:   1100       -38629.530             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37804.366             0.482            0.381
Chain 1:   1300       -25792.087             0.451            0.381
Chain 1:   1400       -25511.454             0.357            0.324
Chain 1:   1500       -22107.228             0.345            0.324
Chain 1:   1600       -21325.304             0.294            0.297
Chain 1:   1700       -20203.498             0.204            0.296
Chain 1:   1800       -20148.255             0.166            0.154
Chain 1:   1900       -20473.894             0.138            0.056
Chain 1:   2000       -18988.362             0.117            0.056
Chain 1:   2100       -19226.497             0.085            0.037
Chain 1:   2200       -19452.224             0.084            0.037
Chain 1:   2300       -19070.221             0.040            0.020
Chain 1:   2400       -18842.565             0.040            0.020
Chain 1:   2500       -18644.428             0.026            0.016
Chain 1:   2600       -18275.294             0.024            0.016
Chain 1:   2700       -18232.499             0.019            0.012
Chain 1:   2800       -17949.540             0.020            0.016
Chain 1:   2900       -18230.504             0.020            0.015
Chain 1:   3000       -18216.772             0.012            0.012
Chain 1:   3100       -18301.657             0.011            0.012
Chain 1:   3200       -17992.733             0.012            0.015
Chain 1:   3300       -18197.157             0.011            0.012
Chain 1:   3400       -17672.735             0.013            0.015
Chain 1:   3500       -18283.541             0.015            0.016
Chain 1:   3600       -17591.648             0.017            0.016
Chain 1:   3700       -17977.367             0.019            0.017
Chain 1:   3800       -16939.197             0.023            0.021
Chain 1:   3900       -16935.385             0.022            0.021
Chain 1:   4000       -17052.711             0.023            0.021
Chain 1:   4100       -16966.549             0.023            0.021
Chain 1:   4200       -16783.280             0.022            0.021
Chain 1:   4300       -16921.350             0.022            0.021
Chain 1:   4400       -16878.563             0.019            0.011
Chain 1:   4500       -16781.155             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001193 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49247.785             1.000            1.000
Chain 1:    200       -18320.194             1.344            1.688
Chain 1:    300       -19473.216             0.916            1.000
Chain 1:    400       -13822.933             0.789            1.000
Chain 1:    500       -20919.050             0.699            0.409
Chain 1:    600       -19815.682             0.592            0.409
Chain 1:    700       -16429.943             0.537            0.339
Chain 1:    800       -13574.248             0.496            0.339
Chain 1:    900       -15727.996             0.456            0.210
Chain 1:   1000       -12460.111             0.437            0.262
Chain 1:   1100       -16731.111             0.362            0.255
Chain 1:   1200       -19663.138             0.208            0.210
Chain 1:   1300       -11277.138             0.277            0.255
Chain 1:   1400       -12222.924             0.244            0.210
Chain 1:   1500       -10429.082             0.227            0.206
Chain 1:   1600       -10054.275             0.225            0.206
Chain 1:   1700        -9934.539             0.206            0.172
Chain 1:   1800       -16786.161             0.225            0.172
Chain 1:   1900       -11183.129             0.262            0.255
Chain 1:   2000        -9927.644             0.248            0.172
Chain 1:   2100       -16175.595             0.261            0.172
Chain 1:   2200       -22450.387             0.274            0.279
Chain 1:   2300       -11711.648             0.292            0.279
Chain 1:   2400       -10100.361             0.300            0.279
Chain 1:   2500       -10579.682             0.287            0.279
Chain 1:   2600       -13377.840             0.304            0.279
Chain 1:   2700        -9343.732             0.346            0.386
Chain 1:   2800       -10488.343             0.317            0.279
Chain 1:   2900       -10001.539             0.271            0.209
Chain 1:   3000        -9825.922             0.260            0.209
Chain 1:   3100        -9131.928             0.229            0.160
Chain 1:   3200       -11081.114             0.219            0.160
Chain 1:   3300        -9529.257             0.144            0.160
Chain 1:   3400        -9388.004             0.129            0.109
Chain 1:   3500        -9411.247             0.125            0.109
Chain 1:   3600        -9379.266             0.104            0.076
Chain 1:   3700       -13092.713             0.089            0.076
Chain 1:   3800       -16134.709             0.097            0.076
Chain 1:   3900        -9549.147             0.162            0.163
Chain 1:   4000        -9337.624             0.162            0.163
Chain 1:   4100        -9505.066             0.156            0.163
Chain 1:   4200       -14374.572             0.172            0.163
Chain 1:   4300        -9109.197             0.214            0.189
Chain 1:   4400        -9570.252             0.217            0.189
Chain 1:   4500       -15220.208             0.254            0.284
Chain 1:   4600       -10585.552             0.298            0.339
Chain 1:   4700       -10615.040             0.270            0.339
Chain 1:   4800        -8889.598             0.270            0.339
Chain 1:   4900        -9179.806             0.204            0.194
Chain 1:   5000       -16608.892             0.247            0.339
Chain 1:   5100        -9510.587             0.320            0.371
Chain 1:   5200        -9048.696             0.291            0.371
Chain 1:   5300       -15192.735             0.273            0.371
Chain 1:   5400       -15343.504             0.270            0.371
Chain 1:   5500       -11476.221             0.266            0.337
Chain 1:   5600       -13967.550             0.240            0.194
Chain 1:   5700       -15682.891             0.251            0.194
Chain 1:   5800        -9083.540             0.304            0.337
Chain 1:   5900       -13422.757             0.333            0.337
Chain 1:   6000        -8857.026             0.340            0.337
Chain 1:   6100        -8912.132             0.266            0.323
Chain 1:   6200       -10828.177             0.279            0.323
Chain 1:   6300       -15377.959             0.268            0.296
Chain 1:   6400       -12439.788             0.291            0.296
Chain 1:   6500       -12346.057             0.258            0.236
Chain 1:   6600       -10342.109             0.259            0.236
Chain 1:   6700        -9635.724             0.256            0.236
Chain 1:   6800        -9929.867             0.186            0.194
Chain 1:   6900        -9026.717             0.164            0.177
Chain 1:   7000        -9173.353             0.114            0.100
Chain 1:   7100        -8515.016             0.121            0.100
Chain 1:   7200       -10667.576             0.123            0.100
Chain 1:   7300       -11092.163             0.097            0.077
Chain 1:   7400        -9001.564             0.097            0.077
Chain 1:   7500        -8619.430             0.101            0.077
Chain 1:   7600        -9284.922             0.088            0.073
Chain 1:   7700        -9370.160             0.082            0.072
Chain 1:   7800        -9014.874             0.083            0.072
Chain 1:   7900       -12845.733             0.103            0.072
Chain 1:   8000       -10672.578             0.122            0.077
Chain 1:   8100        -9023.989             0.132            0.183
Chain 1:   8200        -9935.753             0.121            0.092
Chain 1:   8300        -8616.203             0.133            0.153
Chain 1:   8400        -9539.159             0.119            0.097
Chain 1:   8500        -8616.850             0.125            0.107
Chain 1:   8600       -10193.887             0.134            0.153
Chain 1:   8700        -9404.196             0.141            0.153
Chain 1:   8800       -10217.824             0.145            0.153
Chain 1:   8900        -9830.313             0.119            0.107
Chain 1:   9000        -8706.734             0.112            0.107
Chain 1:   9100       -10137.873             0.108            0.107
Chain 1:   9200        -8535.637             0.117            0.129
Chain 1:   9300        -8649.190             0.103            0.107
Chain 1:   9400        -8363.852             0.097            0.107
Chain 1:   9500        -8346.519             0.086            0.084
Chain 1:   9600       -11035.172             0.095            0.084
Chain 1:   9700        -8742.352             0.113            0.129
Chain 1:   9800       -11181.181             0.127            0.141
Chain 1:   9900       -10944.452             0.125            0.141
Chain 1:   10000        -9677.690             0.125            0.141
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46319.127             1.000            1.000
Chain 1:    200       -15844.540             1.462            1.923
Chain 1:    300        -8899.417             1.235            1.000
Chain 1:    400        -8258.660             0.945            1.000
Chain 1:    500        -9120.014             0.775            0.780
Chain 1:    600        -9372.689             0.650            0.780
Chain 1:    700        -8009.906             0.582            0.170
Chain 1:    800        -7810.234             0.512            0.170
Chain 1:    900        -8073.134             0.459            0.094
Chain 1:   1000        -7668.957             0.418            0.094
Chain 1:   1100        -7834.203             0.320            0.078
Chain 1:   1200        -7687.604             0.130            0.053
Chain 1:   1300        -7669.674             0.052            0.033
Chain 1:   1400        -7853.078             0.047            0.027
Chain 1:   1500        -7614.912             0.041            0.027
Chain 1:   1600        -7801.848             0.040            0.026
Chain 1:   1700        -7717.164             0.024            0.024
Chain 1:   1800        -7634.509             0.023            0.023
Chain 1:   1900        -7605.220             0.020            0.021
Chain 1:   2000        -7728.544             0.016            0.019
Chain 1:   2100        -7619.939             0.016            0.016
Chain 1:   2200        -7762.874             0.016            0.016
Chain 1:   2300        -7611.464             0.017            0.018
Chain 1:   2400        -7633.678             0.015            0.016
Chain 1:   2500        -7669.300             0.013            0.014
Chain 1:   2600        -7576.877             0.011            0.012
Chain 1:   2700        -7505.765             0.011            0.012
Chain 1:   2800        -7575.102             0.011            0.012
Chain 1:   2900        -7449.086             0.012            0.014
Chain 1:   3000        -7568.694             0.012            0.014
Chain 1:   3100        -7572.293             0.011            0.012
Chain 1:   3200        -7774.920             0.012            0.012
Chain 1:   3300        -7493.237             0.014            0.012
Chain 1:   3400        -7720.912             0.016            0.016
Chain 1:   3500        -7481.431             0.019            0.017
Chain 1:   3600        -7547.605             0.019            0.017
Chain 1:   3700        -7497.332             0.018            0.017
Chain 1:   3800        -7495.063             0.017            0.017
Chain 1:   3900        -7461.536             0.016            0.016
Chain 1:   4000        -7458.344             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003752 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86616.916             1.000            1.000
Chain 1:    200       -13874.321             3.121            5.243
Chain 1:    300       -10208.710             2.201            1.000
Chain 1:    400       -11060.203             1.670            1.000
Chain 1:    500        -9206.432             1.376            0.359
Chain 1:    600        -9094.892             1.149            0.359
Chain 1:    700        -8594.786             0.993            0.201
Chain 1:    800        -9439.180             0.880            0.201
Chain 1:    900        -8952.392             0.788            0.089
Chain 1:   1000        -8888.776             0.710            0.089
Chain 1:   1100        -8989.224             0.611            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8536.408             0.092            0.058
Chain 1:   1300        -8886.317             0.060            0.054
Chain 1:   1400        -8864.606             0.053            0.053
Chain 1:   1500        -8758.056             0.034            0.039
Chain 1:   1600        -8872.209             0.034            0.039
Chain 1:   1700        -8943.905             0.029            0.013
Chain 1:   1800        -8519.259             0.025            0.013
Chain 1:   1900        -8620.359             0.021            0.012
Chain 1:   2000        -8595.194             0.020            0.012
Chain 1:   2100        -8721.253             0.021            0.013
Chain 1:   2200        -8522.366             0.018            0.013
Chain 1:   2300        -8615.467             0.015            0.012
Chain 1:   2400        -8683.995             0.015            0.012
Chain 1:   2500        -8630.290             0.015            0.012
Chain 1:   2600        -8632.046             0.014            0.011
Chain 1:   2700        -8548.560             0.014            0.011
Chain 1:   2800        -8507.917             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002896 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8381316.692             1.000            1.000
Chain 1:    200     -1585219.654             2.644            4.287
Chain 1:    300      -891671.983             2.022            1.000
Chain 1:    400      -457737.366             1.753            1.000
Chain 1:    500      -357873.652             1.458            0.948
Chain 1:    600      -232982.520             1.305            0.948
Chain 1:    700      -119448.381             1.254            0.948
Chain 1:    800       -86662.075             1.145            0.948
Chain 1:    900       -67061.678             1.050            0.778
Chain 1:   1000       -51897.327             0.974            0.778
Chain 1:   1100       -39396.865             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38582.845             0.479            0.378
Chain 1:   1300       -26561.970             0.447            0.378
Chain 1:   1400       -26284.867             0.353            0.317
Chain 1:   1500       -22875.763             0.340            0.317
Chain 1:   1600       -22093.349             0.290            0.292
Chain 1:   1700       -20969.802             0.200            0.292
Chain 1:   1800       -20914.835             0.163            0.149
Chain 1:   1900       -21241.105             0.135            0.054
Chain 1:   2000       -19752.969             0.113            0.054
Chain 1:   2100       -19991.661             0.083            0.035
Chain 1:   2200       -20217.716             0.082            0.035
Chain 1:   2300       -19835.211             0.038            0.019
Chain 1:   2400       -19607.244             0.039            0.019
Chain 1:   2500       -19408.954             0.025            0.015
Chain 1:   2600       -19039.340             0.023            0.015
Chain 1:   2700       -18996.432             0.018            0.012
Chain 1:   2800       -18712.982             0.019            0.015
Chain 1:   2900       -18994.326             0.019            0.015
Chain 1:   3000       -18980.676             0.012            0.012
Chain 1:   3100       -19065.590             0.011            0.012
Chain 1:   3200       -18756.294             0.011            0.015
Chain 1:   3300       -18961.032             0.011            0.012
Chain 1:   3400       -18435.769             0.012            0.015
Chain 1:   3500       -19047.821             0.014            0.015
Chain 1:   3600       -18354.377             0.016            0.015
Chain 1:   3700       -18741.160             0.018            0.016
Chain 1:   3800       -17700.555             0.023            0.021
Chain 1:   3900       -17696.656             0.021            0.021
Chain 1:   4000       -17814.017             0.022            0.021
Chain 1:   4100       -17727.643             0.022            0.021
Chain 1:   4200       -17543.911             0.021            0.021
Chain 1:   4300       -17682.361             0.021            0.021
Chain 1:   4400       -17639.145             0.018            0.010
Chain 1:   4500       -17541.622             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12544.118             1.000            1.000
Chain 1:    200        -9342.862             0.671            1.000
Chain 1:    300        -8085.221             0.499            0.343
Chain 1:    400        -8278.497             0.380            0.343
Chain 1:    500        -8217.582             0.306            0.156
Chain 1:    600        -8004.040             0.259            0.156
Chain 1:    700        -7903.310             0.224            0.027
Chain 1:    800        -7933.370             0.197            0.027
Chain 1:    900        -8015.887             0.176            0.023
Chain 1:   1000        -7964.890             0.159            0.023
Chain 1:   1100        -7993.645             0.059            0.013
Chain 1:   1200        -7908.304             0.026            0.011
Chain 1:   1300        -7849.625             0.011            0.010
Chain 1:   1400        -7879.966             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001452 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62492.373             1.000            1.000
Chain 1:    200       -18168.499             1.720            2.440
Chain 1:    300        -9026.111             1.484            1.013
Chain 1:    400        -9656.640             1.129            1.013
Chain 1:    500        -8504.285             0.931            1.000
Chain 1:    600        -8494.751             0.776            1.000
Chain 1:    700        -8839.113             0.670            0.136
Chain 1:    800        -8077.621             0.598            0.136
Chain 1:    900        -8048.156             0.532            0.094
Chain 1:   1000        -7949.874             0.480            0.094
Chain 1:   1100        -7680.338             0.384            0.065
Chain 1:   1200        -7877.868             0.142            0.039
Chain 1:   1300        -7587.402             0.045            0.038
Chain 1:   1400        -7998.097             0.044            0.038
Chain 1:   1500        -7609.435             0.035            0.038
Chain 1:   1600        -7891.865             0.039            0.038
Chain 1:   1700        -7483.775             0.040            0.038
Chain 1:   1800        -7699.139             0.034            0.036
Chain 1:   1900        -7602.426             0.034            0.036
Chain 1:   2000        -7704.048             0.035            0.036
Chain 1:   2100        -7590.928             0.032            0.036
Chain 1:   2200        -7727.108             0.032            0.036
Chain 1:   2300        -7573.599             0.030            0.028
Chain 1:   2400        -7639.972             0.026            0.020
Chain 1:   2500        -7617.395             0.021            0.018
Chain 1:   2600        -7532.950             0.018            0.015
Chain 1:   2700        -7551.560             0.013            0.013
Chain 1:   2800        -7509.828             0.011            0.013
Chain 1:   2900        -7404.434             0.011            0.013
Chain 1:   3000        -7536.567             0.012            0.014
Chain 1:   3100        -7532.999             0.010            0.011
Chain 1:   3200        -7736.560             0.011            0.011
Chain 1:   3300        -7456.415             0.013            0.011
Chain 1:   3400        -7682.570             0.015            0.014
Chain 1:   3500        -7441.387             0.018            0.018
Chain 1:   3600        -7507.767             0.017            0.018
Chain 1:   3700        -7456.968             0.018            0.018
Chain 1:   3800        -7454.751             0.017            0.018
Chain 1:   3900        -7421.539             0.016            0.018
Chain 1:   4000        -7418.906             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86250.648             1.000            1.000
Chain 1:    200       -13777.434             3.130            5.260
Chain 1:    300       -10042.787             2.211            1.000
Chain 1:    400       -11385.429             1.688            1.000
Chain 1:    500        -8869.093             1.407            0.372
Chain 1:    600        -8390.695             1.182            0.372
Chain 1:    700        -8673.721             1.018            0.284
Chain 1:    800        -9520.824             0.902            0.284
Chain 1:    900        -8832.349             0.810            0.118
Chain 1:   1000        -8781.164             0.730            0.118
Chain 1:   1100        -8797.957             0.630            0.089   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8334.094             0.109            0.078
Chain 1:   1300        -8664.717             0.076            0.057
Chain 1:   1400        -8435.555             0.067            0.056
Chain 1:   1500        -8512.611             0.039            0.038
Chain 1:   1600        -8619.949             0.035            0.033
Chain 1:   1700        -8672.761             0.032            0.027
Chain 1:   1800        -8221.658             0.029            0.027
Chain 1:   1900        -8331.393             0.022            0.013
Chain 1:   2000        -8326.243             0.022            0.013
Chain 1:   2100        -8490.300             0.024            0.019
Chain 1:   2200        -8227.141             0.021            0.019
Chain 1:   2300        -8413.568             0.020            0.019
Chain 1:   2400        -8226.525             0.019            0.019
Chain 1:   2500        -8304.117             0.019            0.019
Chain 1:   2600        -8230.198             0.019            0.019
Chain 1:   2700        -8249.346             0.019            0.019
Chain 1:   2800        -8202.575             0.014            0.013
Chain 1:   2900        -8309.420             0.014            0.013
Chain 1:   3000        -8260.438             0.014            0.013
Chain 1:   3100        -8193.452             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003341 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386858.529             1.000            1.000
Chain 1:    200     -1582726.844             2.649            4.299
Chain 1:    300      -890643.890             2.025            1.000
Chain 1:    400      -457876.954             1.755            1.000
Chain 1:    500      -358491.199             1.460            0.945
Chain 1:    600      -233318.810             1.306            0.945
Chain 1:    700      -119540.826             1.255            0.945
Chain 1:    800       -86794.418             1.146            0.945
Chain 1:    900       -67129.821             1.051            0.777
Chain 1:   1000       -51938.048             0.975            0.777
Chain 1:   1100       -39415.418             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38598.618             0.479            0.377
Chain 1:   1300       -26534.602             0.447            0.377
Chain 1:   1400       -26255.441             0.353            0.318
Chain 1:   1500       -22837.814             0.340            0.318
Chain 1:   1600       -22054.666             0.290            0.293
Chain 1:   1700       -20924.527             0.201            0.292
Chain 1:   1800       -20868.560             0.163            0.150
Chain 1:   1900       -21195.417             0.135            0.054
Chain 1:   2000       -19703.777             0.114            0.054
Chain 1:   2100       -19942.084             0.083            0.036
Chain 1:   2200       -20169.504             0.082            0.036
Chain 1:   2300       -19785.759             0.039            0.019
Chain 1:   2400       -19557.577             0.039            0.019
Chain 1:   2500       -19359.874             0.025            0.015
Chain 1:   2600       -18989.060             0.023            0.015
Chain 1:   2700       -18945.815             0.018            0.012
Chain 1:   2800       -18662.451             0.019            0.015
Chain 1:   2900       -18944.105             0.019            0.015
Chain 1:   3000       -18930.091             0.012            0.012
Chain 1:   3100       -19015.217             0.011            0.012
Chain 1:   3200       -18705.405             0.011            0.015
Chain 1:   3300       -18910.585             0.011            0.012
Chain 1:   3400       -18384.678             0.012            0.015
Chain 1:   3500       -18997.848             0.015            0.015
Chain 1:   3600       -18302.893             0.016            0.015
Chain 1:   3700       -18690.907             0.018            0.017
Chain 1:   3800       -17648.141             0.023            0.021
Chain 1:   3900       -17644.293             0.021            0.021
Chain 1:   4000       -17761.543             0.022            0.021
Chain 1:   4100       -17675.186             0.022            0.021
Chain 1:   4200       -17490.934             0.021            0.021
Chain 1:   4300       -17629.641             0.021            0.021
Chain 1:   4400       -17586.002             0.018            0.011
Chain 1:   4500       -17488.521             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001318 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12294.043             1.000            1.000
Chain 1:    200        -9160.030             0.671            1.000
Chain 1:    300        -7938.667             0.499            0.342
Chain 1:    400        -8062.469             0.378            0.342
Chain 1:    500        -7930.529             0.306            0.154
Chain 1:    600        -7838.418             0.257            0.154
Chain 1:    700        -7744.099             0.222            0.017
Chain 1:    800        -7788.124             0.195            0.017
Chain 1:    900        -7913.620             0.175            0.016
Chain 1:   1000        -7849.750             0.158            0.016
Chain 1:   1100        -7843.166             0.058            0.015
Chain 1:   1200        -7765.127             0.025            0.012
Chain 1:   1300        -7711.790             0.010            0.012
Chain 1:   1400        -7735.802             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001553 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56756.100             1.000            1.000
Chain 1:    200       -17256.146             1.645            2.289
Chain 1:    300        -8611.441             1.431            1.004
Chain 1:    400        -8278.027             1.083            1.004
Chain 1:    500        -8091.246             0.871            1.000
Chain 1:    600        -8738.184             0.738            1.000
Chain 1:    700        -7967.252             0.647            0.097
Chain 1:    800        -7920.113             0.567            0.097
Chain 1:    900        -7924.950             0.504            0.074
Chain 1:   1000        -7596.689             0.458            0.074
Chain 1:   1100        -7740.324             0.360            0.043
Chain 1:   1200        -7494.450             0.134            0.040
Chain 1:   1300        -7506.720             0.034            0.033
Chain 1:   1400        -7548.705             0.030            0.023
Chain 1:   1500        -7515.256             0.028            0.019
Chain 1:   1600        -7660.507             0.023            0.019
Chain 1:   1700        -7422.427             0.016            0.019
Chain 1:   1800        -7482.217             0.017            0.019
Chain 1:   1900        -7459.278             0.017            0.019
Chain 1:   2000        -7485.101             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86666.035             1.000            1.000
Chain 1:    200       -13339.297             3.249            5.497
Chain 1:    300        -9713.512             2.290            1.000
Chain 1:    400       -10476.766             1.736            1.000
Chain 1:    500        -8697.311             1.430            0.373
Chain 1:    600        -8160.817             1.202            0.373
Chain 1:    700        -8300.899             1.033            0.205
Chain 1:    800        -9116.458             0.915            0.205
Chain 1:    900        -8575.130             0.820            0.089
Chain 1:   1000        -8176.196             0.743            0.089
Chain 1:   1100        -8569.838             0.648            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8178.380             0.103            0.066
Chain 1:   1300        -8341.872             0.067            0.063
Chain 1:   1400        -8394.041             0.061            0.049
Chain 1:   1500        -8284.239             0.042            0.048
Chain 1:   1600        -8390.518             0.036            0.046
Chain 1:   1700        -8480.543             0.036            0.046
Chain 1:   1800        -8069.479             0.032            0.046
Chain 1:   1900        -8165.511             0.027            0.020
Chain 1:   2000        -8138.297             0.022            0.013
Chain 1:   2100        -8260.246             0.019            0.013
Chain 1:   2200        -8080.786             0.017            0.013
Chain 1:   2300        -8161.479             0.016            0.013
Chain 1:   2400        -8230.152             0.016            0.013
Chain 1:   2500        -8175.580             0.015            0.012
Chain 1:   2600        -8174.356             0.014            0.011
Chain 1:   2700        -8091.656             0.014            0.010
Chain 1:   2800        -8056.312             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415915.292             1.000            1.000
Chain 1:    200     -1586709.277             2.652            4.304
Chain 1:    300      -890607.946             2.029            1.000
Chain 1:    400      -456981.841             1.759            1.000
Chain 1:    500      -356841.636             1.463            0.949
Chain 1:    600      -232070.854             1.309            0.949
Chain 1:    700      -118674.049             1.258            0.949
Chain 1:    800       -85979.498             1.149            0.949
Chain 1:    900       -66402.194             1.054            0.782
Chain 1:   1000       -51263.232             0.978            0.782
Chain 1:   1100       -38796.904             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37980.966             0.482            0.380
Chain 1:   1300       -25993.911             0.450            0.380
Chain 1:   1400       -25718.568             0.356            0.321
Chain 1:   1500       -22319.968             0.343            0.321
Chain 1:   1600       -21540.699             0.293            0.295
Chain 1:   1700       -20421.118             0.203            0.295
Chain 1:   1800       -20366.805             0.165            0.152
Chain 1:   1900       -20692.911             0.137            0.055
Chain 1:   2000       -19207.538             0.115            0.055
Chain 1:   2100       -19445.837             0.084            0.036
Chain 1:   2200       -19671.660             0.083            0.036
Chain 1:   2300       -19289.409             0.039            0.020
Chain 1:   2400       -19061.569             0.039            0.020
Chain 1:   2500       -18863.317             0.025            0.016
Chain 1:   2600       -18493.874             0.024            0.016
Chain 1:   2700       -18450.976             0.018            0.012
Chain 1:   2800       -18167.760             0.020            0.016
Chain 1:   2900       -18448.860             0.020            0.015
Chain 1:   3000       -18435.175             0.012            0.012
Chain 1:   3100       -18520.127             0.011            0.012
Chain 1:   3200       -18210.931             0.012            0.015
Chain 1:   3300       -18415.557             0.011            0.012
Chain 1:   3400       -17890.603             0.013            0.015
Chain 1:   3500       -18502.209             0.015            0.016
Chain 1:   3600       -17809.206             0.017            0.016
Chain 1:   3700       -18195.717             0.019            0.017
Chain 1:   3800       -17155.871             0.023            0.021
Chain 1:   3900       -17151.974             0.022            0.021
Chain 1:   4000       -17269.330             0.022            0.021
Chain 1:   4100       -17183.087             0.022            0.021
Chain 1:   4200       -16999.437             0.022            0.021
Chain 1:   4300       -17137.805             0.021            0.021
Chain 1:   4400       -17094.712             0.019            0.011
Chain 1:   4500       -16997.201             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13979.137             1.000            1.000
Chain 1:    200       -10692.386             0.654            1.000
Chain 1:    300        -8313.193             0.531            0.307
Chain 1:    400        -8558.147             0.406            0.307
Chain 1:    500        -8180.159             0.334            0.286
Chain 1:    600        -8466.511             0.284            0.286
Chain 1:    700        -8124.294             0.249            0.046
Chain 1:    800        -8227.232             0.220            0.046
Chain 1:    900        -8217.858             0.195            0.042
Chain 1:   1000        -8250.588             0.176            0.042
Chain 1:   1100        -8253.694             0.076            0.034
Chain 1:   1200        -8203.564             0.046            0.029
Chain 1:   1300        -8095.248             0.019            0.013
Chain 1:   1400        -8143.128             0.017            0.013
Chain 1:   1500        -8294.339             0.014            0.013
Chain 1:   1600        -8124.572             0.012            0.013
Chain 1:   1700        -8100.868             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59033.955             1.000            1.000
Chain 1:    200       -18693.940             1.579            2.158
Chain 1:    300        -9203.934             1.396            1.031
Chain 1:    400        -8089.615             1.082            1.031
Chain 1:    500        -8780.475             0.881            1.000
Chain 1:    600        -8959.912             0.738            1.000
Chain 1:    700        -8371.624             0.642            0.138
Chain 1:    800        -8367.234             0.562            0.138
Chain 1:    900        -8336.998             0.500            0.079
Chain 1:   1000        -7726.375             0.458            0.079
Chain 1:   1100        -7927.494             0.360            0.079
Chain 1:   1200        -7875.384             0.145            0.070
Chain 1:   1300        -7995.037             0.044            0.025
Chain 1:   1400        -8154.247             0.032            0.020
Chain 1:   1500        -7572.621             0.032            0.020
Chain 1:   1600        -7781.193             0.032            0.025
Chain 1:   1700        -7702.786             0.026            0.020
Chain 1:   1800        -7625.030             0.027            0.020
Chain 1:   1900        -7683.890             0.028            0.020
Chain 1:   2000        -7779.124             0.021            0.015
Chain 1:   2100        -7865.609             0.020            0.012
Chain 1:   2200        -7848.480             0.019            0.012
Chain 1:   2300        -7730.596             0.019            0.012
Chain 1:   2400        -7763.119             0.018            0.011
Chain 1:   2500        -7575.044             0.012            0.011
Chain 1:   2600        -7600.788             0.010            0.010
Chain 1:   2700        -7462.651             0.011            0.011
Chain 1:   2800        -7651.775             0.012            0.012
Chain 1:   2900        -7444.881             0.014            0.015
Chain 1:   3000        -7583.552             0.015            0.018
Chain 1:   3100        -7567.661             0.014            0.018
Chain 1:   3200        -7790.639             0.017            0.019
Chain 1:   3300        -7476.870             0.019            0.025
Chain 1:   3400        -7743.410             0.022            0.025
Chain 1:   3500        -7502.977             0.023            0.028
Chain 1:   3600        -7519.252             0.023            0.028
Chain 1:   3700        -7455.421             0.022            0.028
Chain 1:   3800        -7420.347             0.020            0.028
Chain 1:   3900        -7439.141             0.018            0.018
Chain 1:   4000        -7435.811             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003156 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87799.420             1.000            1.000
Chain 1:    200       -14469.974             3.034            5.068
Chain 1:    300       -10548.851             2.146            1.000
Chain 1:    400       -13061.594             1.658            1.000
Chain 1:    500       -10261.601             1.381            0.372
Chain 1:    600        -9006.453             1.174            0.372
Chain 1:    700        -9437.007             1.013            0.273
Chain 1:    800        -9062.865             0.891            0.273
Chain 1:    900        -9319.251             0.795            0.192
Chain 1:   1000        -8761.224             0.722            0.192
Chain 1:   1100        -9172.370             0.627            0.139   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8565.483             0.127            0.071
Chain 1:   1300        -9004.901             0.095            0.064
Chain 1:   1400        -8798.724             0.078            0.049
Chain 1:   1500        -8926.632             0.052            0.046
Chain 1:   1600        -8987.046             0.039            0.045
Chain 1:   1700        -9037.940             0.035            0.041
Chain 1:   1800        -8573.142             0.036            0.045
Chain 1:   1900        -8659.977             0.034            0.045
Chain 1:   2000        -8674.772             0.028            0.023
Chain 1:   2100        -8824.767             0.025            0.017
Chain 1:   2200        -8527.435             0.022            0.017
Chain 1:   2300        -8614.120             0.018            0.014
Chain 1:   2400        -8709.070             0.017            0.011
Chain 1:   2500        -8606.625             0.016            0.011
Chain 1:   2600        -8650.164             0.016            0.011
Chain 1:   2700        -8560.861             0.017            0.011
Chain 1:   2800        -8530.726             0.012            0.010
Chain 1:   2900        -8617.418             0.012            0.010
Chain 1:   3000        -8551.854             0.012            0.010
Chain 1:   3100        -8503.540             0.011            0.010
Chain 1:   3200        -8460.138             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002852 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8421080.721             1.000            1.000
Chain 1:    200     -1587655.112             2.652            4.304
Chain 1:    300      -890441.573             2.029            1.000
Chain 1:    400      -458073.476             1.758            1.000
Chain 1:    500      -358252.797             1.462            0.944
Chain 1:    600      -233440.190             1.307            0.944
Chain 1:    700      -119945.608             1.256            0.944
Chain 1:    800       -87238.895             1.146            0.944
Chain 1:    900       -67654.514             1.051            0.783
Chain 1:   1000       -52524.119             0.974            0.783
Chain 1:   1100       -40050.956             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39246.087             0.477            0.375
Chain 1:   1300       -27229.022             0.443            0.375
Chain 1:   1400       -26957.407             0.350            0.311
Chain 1:   1500       -23549.832             0.336            0.311
Chain 1:   1600       -22770.083             0.286            0.289
Chain 1:   1700       -21645.379             0.197            0.288
Chain 1:   1800       -21590.860             0.159            0.145
Chain 1:   1900       -21918.479             0.132            0.052
Chain 1:   2000       -20427.549             0.110            0.052
Chain 1:   2100       -20666.402             0.080            0.034
Chain 1:   2200       -20893.537             0.080            0.034
Chain 1:   2300       -20509.659             0.037            0.019
Chain 1:   2400       -20281.219             0.037            0.019
Chain 1:   2500       -20082.980             0.024            0.015
Chain 1:   2600       -19712.013             0.022            0.015
Chain 1:   2700       -19668.595             0.017            0.012
Chain 1:   2800       -19384.701             0.019            0.015
Chain 1:   2900       -19666.543             0.019            0.014
Chain 1:   3000       -19652.727             0.011            0.012
Chain 1:   3100       -19737.950             0.011            0.011
Chain 1:   3200       -19427.714             0.011            0.014
Chain 1:   3300       -19633.122             0.010            0.011
Chain 1:   3400       -19106.355             0.012            0.014
Chain 1:   3500       -19720.754             0.014            0.015
Chain 1:   3600       -19024.018             0.016            0.015
Chain 1:   3700       -19413.335             0.018            0.016
Chain 1:   3800       -18367.759             0.022            0.020
Chain 1:   3900       -18363.675             0.020            0.020
Chain 1:   4000       -18481.048             0.021            0.020
Chain 1:   4100       -18394.563             0.021            0.020
Chain 1:   4200       -18209.588             0.020            0.020
Chain 1:   4300       -18348.890             0.020            0.020
Chain 1:   4400       -18304.774             0.018            0.010
Chain 1:   4500       -18207.040             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49071.545             1.000            1.000
Chain 1:    200       -21276.952             1.153            1.306
Chain 1:    300       -14613.221             0.921            1.000
Chain 1:    400       -12580.415             0.731            1.000
Chain 1:    500       -19418.212             0.655            0.456
Chain 1:    600       -19127.922             0.549            0.456
Chain 1:    700       -14939.354             0.510            0.352
Chain 1:    800       -14886.543             0.447            0.352
Chain 1:    900       -10822.541             0.439            0.352
Chain 1:   1000       -12813.702             0.411            0.352
Chain 1:   1100       -14698.855             0.323            0.280
Chain 1:   1200       -11709.378             0.218            0.255
Chain 1:   1300       -10149.613             0.188            0.162
Chain 1:   1400       -10738.536             0.177            0.155
Chain 1:   1500       -11098.116             0.145            0.154
Chain 1:   1600       -11768.928             0.150            0.154
Chain 1:   1700        -9851.954             0.141            0.154
Chain 1:   1800        -9895.337             0.141            0.154
Chain 1:   1900       -13033.659             0.128            0.154
Chain 1:   2000       -13746.272             0.117            0.128
Chain 1:   2100       -10995.651             0.129            0.154
Chain 1:   2200       -10619.556             0.108            0.057
Chain 1:   2300        -9444.318             0.105            0.057
Chain 1:   2400       -12616.325             0.124            0.124
Chain 1:   2500        -9079.916             0.160            0.195
Chain 1:   2600       -10087.451             0.164            0.195
Chain 1:   2700       -10625.114             0.150            0.124
Chain 1:   2800       -17047.532             0.187            0.241
Chain 1:   2900       -12089.014             0.204            0.250
Chain 1:   3000       -12672.514             0.203            0.250
Chain 1:   3100       -10068.276             0.204            0.251
Chain 1:   3200       -16578.699             0.240            0.259
Chain 1:   3300        -9543.406             0.301            0.377
Chain 1:   3400        -8977.137             0.282            0.377
Chain 1:   3500        -9253.909             0.246            0.259
Chain 1:   3600        -9658.578             0.241            0.259
Chain 1:   3700        -8611.167             0.248            0.259
Chain 1:   3800       -13475.792             0.246            0.259
Chain 1:   3900        -9514.798             0.247            0.259
Chain 1:   4000       -10118.678             0.248            0.259
Chain 1:   4100       -10175.310             0.223            0.122
Chain 1:   4200       -13055.118             0.206            0.122
Chain 1:   4300        -8871.115             0.179            0.122
Chain 1:   4400       -14103.174             0.210            0.221
Chain 1:   4500       -14496.829             0.210            0.221
Chain 1:   4600        -8427.420             0.277            0.361
Chain 1:   4700       -11521.083             0.292            0.361
Chain 1:   4800        -8983.525             0.284            0.282
Chain 1:   4900       -10615.948             0.258            0.269
Chain 1:   5000       -13212.890             0.272            0.269
Chain 1:   5100       -12833.303             0.274            0.269
Chain 1:   5200        -9375.601             0.289            0.282
Chain 1:   5300       -16113.139             0.284            0.282
Chain 1:   5400        -9846.547             0.310            0.282
Chain 1:   5500        -9962.863             0.309            0.282
Chain 1:   5600        -9077.766             0.246            0.269
Chain 1:   5700        -8452.325             0.227            0.197
Chain 1:   5800       -11364.929             0.224            0.197
Chain 1:   5900        -8619.667             0.241            0.256
Chain 1:   6000        -9385.231             0.229            0.256
Chain 1:   6100        -8499.108             0.237            0.256
Chain 1:   6200       -10971.510             0.222            0.225
Chain 1:   6300       -14364.184             0.204            0.225
Chain 1:   6400       -11504.852             0.165            0.225
Chain 1:   6500       -12895.344             0.175            0.225
Chain 1:   6600       -10072.241             0.193            0.236
Chain 1:   6700        -8781.417             0.201            0.236
Chain 1:   6800       -11589.149             0.199            0.236
Chain 1:   6900        -8631.121             0.202            0.236
Chain 1:   7000        -9546.821             0.203            0.236
Chain 1:   7100       -12457.909             0.216            0.236
Chain 1:   7200       -10994.020             0.207            0.236
Chain 1:   7300        -8822.856             0.208            0.242
Chain 1:   7400        -8511.671             0.187            0.234
Chain 1:   7500       -10975.961             0.198            0.234
Chain 1:   7600        -9598.535             0.185            0.225
Chain 1:   7700        -9472.106             0.171            0.225
Chain 1:   7800        -9259.866             0.149            0.144
Chain 1:   7900        -8901.712             0.119            0.133
Chain 1:   8000        -8318.922             0.116            0.133
Chain 1:   8100        -8584.881             0.096            0.070
Chain 1:   8200        -8826.654             0.086            0.040
Chain 1:   8300        -8558.360             0.064            0.037
Chain 1:   8400       -11332.870             0.085            0.040
Chain 1:   8500        -8755.676             0.092            0.040
Chain 1:   8600       -11390.861             0.101            0.040
Chain 1:   8700        -9416.915             0.120            0.070
Chain 1:   8800        -9101.519             0.121            0.070
Chain 1:   8900       -10092.564             0.127            0.098
Chain 1:   9000        -8730.548             0.136            0.156
Chain 1:   9100        -8159.845             0.140            0.156
Chain 1:   9200       -10471.007             0.159            0.210
Chain 1:   9300       -11329.823             0.164            0.210
Chain 1:   9400       -11632.084             0.142            0.156
Chain 1:   9500        -9996.131             0.129            0.156
Chain 1:   9600        -8285.552             0.126            0.156
Chain 1:   9700        -9961.016             0.122            0.156
Chain 1:   9800        -9362.188             0.125            0.156
Chain 1:   9900       -10511.752             0.126            0.156
Chain 1:   10000       -10695.059             0.112            0.109
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45251.738             1.000            1.000
Chain 1:    200       -15393.788             1.470            1.940
Chain 1:    300        -8660.173             1.239            1.000
Chain 1:    400        -8565.393             0.932            1.000
Chain 1:    500        -8244.214             0.753            0.778
Chain 1:    600        -8780.315             0.638            0.778
Chain 1:    700        -8128.785             0.558            0.080
Chain 1:    800        -8029.455             0.490            0.080
Chain 1:    900        -7963.083             0.437            0.061
Chain 1:   1000        -7796.814             0.395            0.061
Chain 1:   1100        -7697.962             0.296            0.039
Chain 1:   1200        -7568.182             0.104            0.021
Chain 1:   1300        -7717.145             0.028            0.019
Chain 1:   1400        -7878.284             0.029            0.020
Chain 1:   1500        -7543.201             0.030            0.020
Chain 1:   1600        -7779.935             0.027            0.020
Chain 1:   1700        -7482.072             0.023            0.020
Chain 1:   1800        -7571.788             0.023            0.020
Chain 1:   1900        -7557.940             0.022            0.020
Chain 1:   2000        -7609.502             0.020            0.019
Chain 1:   2100        -7546.833             0.020            0.019
Chain 1:   2200        -7667.810             0.020            0.019
Chain 1:   2300        -7572.070             0.019            0.016
Chain 1:   2400        -7616.026             0.018            0.013
Chain 1:   2500        -7523.231             0.015            0.012
Chain 1:   2600        -7508.331             0.012            0.012
Chain 1:   2700        -7522.945             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003131 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85783.225             1.000            1.000
Chain 1:    200       -13543.929             3.167            5.334
Chain 1:    300        -9909.213             2.234            1.000
Chain 1:    400       -10895.279             1.698            1.000
Chain 1:    500        -8703.352             1.409            0.367
Chain 1:    600        -8369.741             1.180            0.367
Chain 1:    700        -8539.483             1.015            0.252
Chain 1:    800        -9262.727             0.898            0.252
Chain 1:    900        -8745.142             0.804            0.091
Chain 1:   1000        -8529.911             0.727            0.091
Chain 1:   1100        -8756.248             0.629            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8366.960             0.100            0.059
Chain 1:   1300        -8495.772             0.065            0.047
Chain 1:   1400        -8572.789             0.057            0.040
Chain 1:   1500        -8468.634             0.033            0.026
Chain 1:   1600        -8573.965             0.030            0.025
Chain 1:   1700        -8661.806             0.029            0.025
Chain 1:   1800        -8244.578             0.027            0.025
Chain 1:   1900        -8342.527             0.022            0.015
Chain 1:   2000        -8316.120             0.020            0.012
Chain 1:   2100        -8439.682             0.019            0.012
Chain 1:   2200        -8256.710             0.016            0.012
Chain 1:   2300        -8337.011             0.016            0.012
Chain 1:   2400        -8406.655             0.015            0.012
Chain 1:   2500        -8352.464             0.015            0.012
Chain 1:   2600        -8352.499             0.014            0.010
Chain 1:   2700        -8269.739             0.014            0.010
Chain 1:   2800        -8231.937             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003289 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393670.291             1.000            1.000
Chain 1:    200     -1586093.216             2.646            4.292
Chain 1:    300      -891897.259             2.023            1.000
Chain 1:    400      -458130.410             1.754            1.000
Chain 1:    500      -358395.731             1.459            0.947
Chain 1:    600      -233291.371             1.305            0.947
Chain 1:    700      -119400.349             1.255            0.947
Chain 1:    800       -86564.619             1.146            0.947
Chain 1:    900       -66889.353             1.051            0.778
Chain 1:   1000       -51673.495             0.975            0.778
Chain 1:   1100       -39135.797             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38311.163             0.480            0.379
Chain 1:   1300       -26255.676             0.448            0.379
Chain 1:   1400       -25973.640             0.355            0.320
Chain 1:   1500       -22557.122             0.342            0.320
Chain 1:   1600       -21772.348             0.292            0.294
Chain 1:   1700       -20644.909             0.202            0.294
Chain 1:   1800       -20588.804             0.165            0.151
Chain 1:   1900       -20914.904             0.137            0.055
Chain 1:   2000       -19425.270             0.115            0.055
Chain 1:   2100       -19663.839             0.084            0.036
Chain 1:   2200       -19890.264             0.083            0.036
Chain 1:   2300       -19507.486             0.039            0.020
Chain 1:   2400       -19279.543             0.039            0.020
Chain 1:   2500       -19081.523             0.025            0.016
Chain 1:   2600       -18711.774             0.023            0.016
Chain 1:   2700       -18668.769             0.018            0.012
Chain 1:   2800       -18385.556             0.020            0.015
Chain 1:   2900       -18666.853             0.019            0.015
Chain 1:   3000       -18653.095             0.012            0.012
Chain 1:   3100       -18738.052             0.011            0.012
Chain 1:   3200       -18428.759             0.012            0.015
Chain 1:   3300       -18633.467             0.011            0.012
Chain 1:   3400       -18108.373             0.012            0.015
Chain 1:   3500       -18720.264             0.015            0.015
Chain 1:   3600       -18026.973             0.017            0.015
Chain 1:   3700       -18413.709             0.018            0.017
Chain 1:   3800       -17373.448             0.023            0.021
Chain 1:   3900       -17369.587             0.021            0.021
Chain 1:   4000       -17486.904             0.022            0.021
Chain 1:   4100       -17400.618             0.022            0.021
Chain 1:   4200       -17216.900             0.021            0.021
Chain 1:   4300       -17355.292             0.021            0.021
Chain 1:   4400       -17312.118             0.019            0.011
Chain 1:   4500       -17214.644             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49544.409             1.000            1.000
Chain 1:    200       -15267.058             1.623            2.245
Chain 1:    300       -20176.066             1.163            1.000
Chain 1:    400       -20167.423             0.872            1.000
Chain 1:    500       -12284.629             0.826            0.642
Chain 1:    600       -12212.309             0.689            0.642
Chain 1:    700       -16676.717             0.629            0.268
Chain 1:    800       -14535.861             0.569            0.268
Chain 1:    900       -15692.932             0.514            0.243
Chain 1:   1000       -13172.342             0.482            0.243
Chain 1:   1100       -19617.879             0.415            0.243
Chain 1:   1200       -11477.917             0.261            0.243
Chain 1:   1300       -10724.314             0.244            0.191
Chain 1:   1400       -11307.535             0.249            0.191
Chain 1:   1500       -11085.299             0.187            0.147
Chain 1:   1600       -10063.085             0.196            0.147
Chain 1:   1700       -12199.666             0.187            0.147
Chain 1:   1800       -10064.742             0.193            0.175
Chain 1:   1900       -10399.790             0.189            0.175
Chain 1:   2000       -16152.889             0.206            0.175
Chain 1:   2100       -11581.030             0.212            0.175
Chain 1:   2200       -10563.201             0.151            0.102
Chain 1:   2300       -13234.255             0.164            0.175
Chain 1:   2400        -9241.104             0.202            0.202
Chain 1:   2500       -10004.433             0.208            0.202
Chain 1:   2600        -9958.373             0.198            0.202
Chain 1:   2700        -9549.334             0.185            0.202
Chain 1:   2800       -18573.564             0.212            0.202
Chain 1:   2900        -9722.365             0.300            0.356
Chain 1:   3000        -9159.623             0.271            0.202
Chain 1:   3100        -9198.267             0.232            0.096
Chain 1:   3200        -9172.997             0.222            0.076
Chain 1:   3300       -17842.384             0.251            0.076
Chain 1:   3400       -15742.190             0.221            0.076
Chain 1:   3500        -9557.688             0.278            0.133
Chain 1:   3600       -10445.114             0.286            0.133
Chain 1:   3700        -9216.660             0.295            0.133
Chain 1:   3800        -9159.360             0.247            0.133
Chain 1:   3900       -11344.275             0.175            0.133
Chain 1:   4000       -18636.461             0.208            0.133
Chain 1:   4100        -9206.933             0.310            0.193
Chain 1:   4200        -9286.597             0.311            0.193
Chain 1:   4300       -11204.811             0.279            0.171
Chain 1:   4400       -11918.994             0.272            0.171
Chain 1:   4500        -9069.768             0.239            0.171
Chain 1:   4600        -8775.018             0.234            0.171
Chain 1:   4700       -11958.069             0.247            0.193
Chain 1:   4800       -10125.170             0.264            0.193
Chain 1:   4900        -8784.479             0.260            0.181
Chain 1:   5000       -17221.957             0.270            0.181
Chain 1:   5100        -9122.261             0.257            0.181
Chain 1:   5200       -11633.209             0.277            0.216
Chain 1:   5300       -14434.564             0.280            0.216
Chain 1:   5400        -9029.302             0.333            0.266
Chain 1:   5500       -12711.321             0.331            0.266
Chain 1:   5600        -9166.286             0.366            0.290
Chain 1:   5700       -14324.248             0.376            0.360
Chain 1:   5800        -9371.151             0.410            0.387
Chain 1:   5900        -9926.380             0.401            0.387
Chain 1:   6000        -8532.757             0.368            0.360
Chain 1:   6100       -12290.382             0.310            0.306
Chain 1:   6200       -10144.833             0.309            0.306
Chain 1:   6300        -9540.683             0.296            0.306
Chain 1:   6400        -9202.051             0.240            0.290
Chain 1:   6500       -11723.700             0.233            0.215
Chain 1:   6600        -9595.349             0.216            0.215
Chain 1:   6700        -9071.671             0.186            0.211
Chain 1:   6800       -13194.353             0.164            0.211
Chain 1:   6900        -9339.221             0.200            0.215
Chain 1:   7000        -8817.236             0.190            0.215
Chain 1:   7100        -8355.346             0.165            0.211
Chain 1:   7200       -12007.285             0.174            0.215
Chain 1:   7300        -9697.349             0.191            0.222
Chain 1:   7400       -14178.581             0.219            0.238
Chain 1:   7500        -8352.536             0.268            0.304
Chain 1:   7600        -8668.485             0.249            0.304
Chain 1:   7700        -9947.967             0.256            0.304
Chain 1:   7800       -11875.383             0.241            0.238
Chain 1:   7900        -8583.250             0.238            0.238
Chain 1:   8000        -8373.089             0.235            0.238
Chain 1:   8100        -8208.336             0.231            0.238
Chain 1:   8200       -11454.898             0.229            0.238
Chain 1:   8300        -8921.262             0.234            0.283
Chain 1:   8400       -13583.276             0.236            0.283
Chain 1:   8500       -12767.831             0.173            0.162
Chain 1:   8600        -8873.014             0.213            0.283
Chain 1:   8700        -9304.221             0.205            0.283
Chain 1:   8800       -11034.501             0.205            0.283
Chain 1:   8900        -8992.371             0.189            0.227
Chain 1:   9000       -12981.604             0.217            0.283
Chain 1:   9100        -9450.542             0.252            0.284
Chain 1:   9200        -8400.655             0.237            0.284
Chain 1:   9300        -8781.857             0.213            0.227
Chain 1:   9400        -9037.851             0.181            0.157
Chain 1:   9500        -8221.469             0.185            0.157
Chain 1:   9600       -10644.215             0.163            0.157
Chain 1:   9700       -10373.952             0.161            0.157
Chain 1:   9800       -10140.170             0.148            0.125
Chain 1:   9900        -8281.455             0.148            0.125
Chain 1:   10000        -8275.245             0.117            0.099
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001371 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62091.766             1.000            1.000
Chain 1:    200       -18140.091             1.711            2.423
Chain 1:    300        -9024.870             1.478            1.010
Chain 1:    400        -9589.761             1.123            1.010
Chain 1:    500        -8110.047             0.935            1.000
Chain 1:    600        -9166.797             0.798            1.000
Chain 1:    700        -8128.916             0.702            0.182
Chain 1:    800        -8126.874             0.615            0.182
Chain 1:    900        -7864.128             0.550            0.128
Chain 1:   1000        -7882.117             0.495            0.128
Chain 1:   1100        -7727.838             0.397            0.115
Chain 1:   1200        -7739.541             0.155            0.059
Chain 1:   1300        -7888.906             0.056            0.033
Chain 1:   1400        -7992.534             0.051            0.020
Chain 1:   1500        -7618.271             0.038            0.020
Chain 1:   1600        -7794.384             0.029            0.020
Chain 1:   1700        -7710.784             0.017            0.019
Chain 1:   1800        -7642.830             0.018            0.019
Chain 1:   1900        -7626.014             0.015            0.013
Chain 1:   2000        -7736.417             0.016            0.014
Chain 1:   2100        -7637.186             0.015            0.013
Chain 1:   2200        -7767.101             0.017            0.014
Chain 1:   2300        -7627.403             0.017            0.014
Chain 1:   2400        -7742.045             0.017            0.015
Chain 1:   2500        -7495.812             0.015            0.015
Chain 1:   2600        -7580.805             0.014            0.014
Chain 1:   2700        -7593.252             0.013            0.014
Chain 1:   2800        -7677.770             0.014            0.014
Chain 1:   2900        -7443.136             0.017            0.015
Chain 1:   3000        -7579.660             0.017            0.017
Chain 1:   3100        -7579.260             0.016            0.017
Chain 1:   3200        -7784.026             0.017            0.018
Chain 1:   3300        -7506.799             0.018            0.018
Chain 1:   3400        -7727.510             0.020            0.026
Chain 1:   3500        -7490.869             0.020            0.026
Chain 1:   3600        -7557.634             0.019            0.026
Chain 1:   3700        -7506.607             0.020            0.026
Chain 1:   3800        -7504.171             0.019            0.026
Chain 1:   3900        -7472.373             0.016            0.018
Chain 1:   4000        -7465.562             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002637 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87091.314             1.000            1.000
Chain 1:    200       -13811.152             3.153            5.306
Chain 1:    300       -10079.083             2.225            1.000
Chain 1:    400       -11451.836             1.699            1.000
Chain 1:    500        -8642.034             1.424            0.370
Chain 1:    600        -8986.818             1.193            0.370
Chain 1:    700        -8642.002             1.028            0.325
Chain 1:    800        -9201.264             0.908            0.325
Chain 1:    900        -8752.956             0.812            0.120
Chain 1:   1000        -8927.102             0.733            0.120
Chain 1:   1100        -8673.537             0.636            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8423.438             0.108            0.051
Chain 1:   1300        -8684.319             0.074            0.040
Chain 1:   1400        -8704.171             0.063            0.038
Chain 1:   1500        -8571.108             0.032            0.030
Chain 1:   1600        -8683.179             0.029            0.030
Chain 1:   1700        -8744.590             0.026            0.029
Chain 1:   1800        -8301.658             0.025            0.029
Chain 1:   1900        -8408.542             0.021            0.020
Chain 1:   2000        -8392.749             0.019            0.016
Chain 1:   2100        -8519.169             0.018            0.015
Chain 1:   2200        -8307.339             0.018            0.015
Chain 1:   2300        -8402.276             0.016            0.013
Chain 1:   2400        -8469.375             0.016            0.013
Chain 1:   2500        -8417.271             0.015            0.013
Chain 1:   2600        -8429.609             0.014            0.011
Chain 1:   2700        -8337.994             0.015            0.011
Chain 1:   2800        -8286.240             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8436797.714             1.000            1.000
Chain 1:    200     -1590020.295             2.653            4.306
Chain 1:    300      -890930.843             2.030            1.000
Chain 1:    400      -457733.261             1.759            1.000
Chain 1:    500      -357649.117             1.463            0.946
Chain 1:    600      -232412.602             1.309            0.946
Chain 1:    700      -119023.409             1.258            0.946
Chain 1:    800       -86402.425             1.148            0.946
Chain 1:    900       -66832.406             1.053            0.785
Chain 1:   1000       -51724.652             0.977            0.785
Chain 1:   1100       -39282.550             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38473.875             0.480            0.378
Chain 1:   1300       -26491.600             0.447            0.378
Chain 1:   1400       -26219.729             0.353            0.317
Chain 1:   1500       -22823.811             0.340            0.317
Chain 1:   1600       -22046.824             0.290            0.293
Chain 1:   1700       -20926.666             0.200            0.292
Chain 1:   1800       -20872.883             0.163            0.149
Chain 1:   1900       -21199.628             0.135            0.054
Chain 1:   2000       -19713.488             0.113            0.054
Chain 1:   2100       -19951.472             0.083            0.035
Chain 1:   2200       -20177.978             0.082            0.035
Chain 1:   2300       -19795.049             0.038            0.019
Chain 1:   2400       -19567.047             0.039            0.019
Chain 1:   2500       -19368.996             0.025            0.015
Chain 1:   2600       -18998.639             0.023            0.015
Chain 1:   2700       -18955.552             0.018            0.012
Chain 1:   2800       -18672.146             0.019            0.015
Chain 1:   2900       -18953.568             0.019            0.015
Chain 1:   3000       -18939.664             0.012            0.012
Chain 1:   3100       -19024.755             0.011            0.012
Chain 1:   3200       -18715.090             0.011            0.015
Chain 1:   3300       -18920.139             0.011            0.012
Chain 1:   3400       -18394.414             0.012            0.015
Chain 1:   3500       -19007.169             0.015            0.015
Chain 1:   3600       -18312.697             0.016            0.015
Chain 1:   3700       -18700.293             0.018            0.017
Chain 1:   3800       -17658.191             0.023            0.021
Chain 1:   3900       -17654.300             0.021            0.021
Chain 1:   4000       -17771.600             0.022            0.021
Chain 1:   4100       -17685.269             0.022            0.021
Chain 1:   4200       -17501.159             0.021            0.021
Chain 1:   4300       -17639.798             0.021            0.021
Chain 1:   4400       -17596.270             0.018            0.011
Chain 1:   4500       -17498.772             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001186 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49151.430             1.000            1.000
Chain 1:    200       -16967.888             1.448            1.897
Chain 1:    300       -22320.644             1.046            1.000
Chain 1:    400       -15193.237             0.901            1.000
Chain 1:    500       -12142.248             0.771            0.469
Chain 1:    600       -16146.468             0.684            0.469
Chain 1:    700       -11709.115             0.641            0.379
Chain 1:    800       -15328.019             0.590            0.379
Chain 1:    900       -13714.011             0.538            0.251
Chain 1:   1000       -11148.048             0.507            0.251
Chain 1:   1100       -11681.959             0.411            0.248
Chain 1:   1200       -14653.918             0.242            0.240
Chain 1:   1300       -10192.654             0.262            0.248
Chain 1:   1400       -11734.441             0.228            0.236
Chain 1:   1500       -18264.968             0.239            0.236
Chain 1:   1600        -9817.814             0.300            0.236
Chain 1:   1700        -9935.402             0.263            0.230
Chain 1:   1800       -10054.525             0.241            0.203
Chain 1:   1900       -10764.847             0.236            0.203
Chain 1:   2000        -9853.497             0.222            0.131
Chain 1:   2100       -10380.087             0.222            0.131
Chain 1:   2200       -11155.290             0.209            0.092
Chain 1:   2300       -11459.755             0.168            0.069
Chain 1:   2400        -9243.612             0.179            0.069
Chain 1:   2500        -9894.764             0.149            0.066
Chain 1:   2600        -8977.888             0.074            0.066
Chain 1:   2700       -11268.145             0.093            0.069
Chain 1:   2800        -9353.192             0.112            0.092
Chain 1:   2900        -9753.157             0.110            0.092
Chain 1:   3000       -10934.197             0.111            0.102
Chain 1:   3100        -9119.750             0.126            0.108
Chain 1:   3200        -9817.810             0.126            0.108
Chain 1:   3300       -11205.932             0.136            0.124
Chain 1:   3400       -13809.551             0.131            0.124
Chain 1:   3500       -12027.985             0.139            0.148
Chain 1:   3600       -14279.254             0.145            0.158
Chain 1:   3700        -9113.999             0.181            0.158
Chain 1:   3800        -8787.801             0.164            0.148
Chain 1:   3900       -11885.423             0.186            0.158
Chain 1:   4000        -8806.492             0.210            0.189
Chain 1:   4100        -8940.941             0.192            0.158
Chain 1:   4200        -9567.971             0.191            0.158
Chain 1:   4300       -11492.003             0.196            0.167
Chain 1:   4400        -9200.559             0.202            0.167
Chain 1:   4500        -9858.922             0.194            0.167
Chain 1:   4600        -9168.017             0.185            0.167
Chain 1:   4700       -15861.969             0.171            0.167
Chain 1:   4800        -8911.180             0.245            0.249
Chain 1:   4900       -13618.504             0.254            0.249
Chain 1:   5000        -9358.726             0.264            0.249
Chain 1:   5100       -10353.776             0.272            0.249
Chain 1:   5200       -10113.367             0.268            0.249
Chain 1:   5300       -12394.625             0.270            0.249
Chain 1:   5400        -8775.716             0.286            0.346
Chain 1:   5500       -13681.546             0.315            0.359
Chain 1:   5600        -8574.989             0.367            0.412
Chain 1:   5700       -13542.476             0.362            0.367
Chain 1:   5800        -8895.993             0.336            0.367
Chain 1:   5900        -9254.980             0.305            0.367
Chain 1:   6000        -8521.420             0.268            0.359
Chain 1:   6100        -8475.937             0.259            0.359
Chain 1:   6200        -9633.767             0.269            0.359
Chain 1:   6300        -8603.464             0.263            0.359
Chain 1:   6400       -10420.967             0.239            0.174
Chain 1:   6500        -8697.990             0.223            0.174
Chain 1:   6600       -11574.784             0.188            0.174
Chain 1:   6700        -8414.169             0.189            0.174
Chain 1:   6800        -8514.562             0.138            0.120
Chain 1:   6900        -9669.779             0.146            0.120
Chain 1:   7000        -8484.770             0.151            0.140
Chain 1:   7100        -8265.738             0.153            0.140
Chain 1:   7200        -8343.955             0.142            0.140
Chain 1:   7300        -8340.857             0.130            0.140
Chain 1:   7400        -9023.713             0.121            0.119
Chain 1:   7500       -11984.976             0.125            0.119
Chain 1:   7600        -9915.569             0.121            0.119
Chain 1:   7700        -8927.730             0.095            0.111
Chain 1:   7800        -8703.890             0.096            0.111
Chain 1:   7900        -8379.426             0.088            0.076
Chain 1:   8000        -8421.849             0.075            0.039
Chain 1:   8100        -8237.762             0.074            0.039
Chain 1:   8200        -8127.070             0.075            0.039
Chain 1:   8300        -8189.899             0.076            0.039
Chain 1:   8400        -8846.548             0.075            0.039
Chain 1:   8500        -8704.317             0.052            0.026
Chain 1:   8600       -10225.621             0.046            0.026
Chain 1:   8700        -8354.119             0.058            0.026
Chain 1:   8800        -8205.436             0.057            0.022
Chain 1:   8900        -8520.317             0.057            0.022
Chain 1:   9000       -11061.203             0.079            0.037
Chain 1:   9100        -8409.116             0.108            0.074
Chain 1:   9200        -8020.727             0.112            0.074
Chain 1:   9300        -8382.234             0.116            0.074
Chain 1:   9400        -8356.782             0.108            0.048
Chain 1:   9500        -8678.794             0.110            0.048
Chain 1:   9600        -8235.138             0.101            0.048
Chain 1:   9700        -8832.009             0.085            0.048
Chain 1:   9800        -8531.236             0.087            0.048
Chain 1:   9900        -9885.931             0.097            0.054
Chain 1:   10000        -8225.538             0.094            0.054
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58028.217             1.000            1.000
Chain 1:    200       -17678.813             1.641            2.282
Chain 1:    300        -8726.707             1.436            1.026
Chain 1:    400        -8260.046             1.091            1.026
Chain 1:    500        -8217.654             0.874            1.000
Chain 1:    600        -8388.207             0.732            1.000
Chain 1:    700        -8340.946             0.628            0.056
Chain 1:    800        -8215.253             0.551            0.056
Chain 1:    900        -8093.594             0.492            0.020
Chain 1:   1000        -7790.728             0.447            0.039
Chain 1:   1100        -7729.850             0.347            0.020
Chain 1:   1200        -7681.580             0.120            0.015
Chain 1:   1300        -7694.782             0.017            0.015
Chain 1:   1400        -7697.734             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004962 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86812.851             1.000            1.000
Chain 1:    200       -13518.140             3.211            5.422
Chain 1:    300        -9866.781             2.264            1.000
Chain 1:    400       -10716.259             1.718            1.000
Chain 1:    500        -8854.114             1.416            0.370
Chain 1:    600        -8482.372             1.188            0.370
Chain 1:    700        -8229.715             1.022            0.210
Chain 1:    800        -8748.739             0.902            0.210
Chain 1:    900        -8701.946             0.802            0.079
Chain 1:   1000        -8343.663             0.726            0.079
Chain 1:   1100        -8724.565             0.631            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8311.813             0.094            0.050
Chain 1:   1300        -8466.704             0.058            0.044
Chain 1:   1400        -8525.124             0.051            0.044
Chain 1:   1500        -8424.930             0.031            0.043
Chain 1:   1600        -8530.862             0.028            0.031
Chain 1:   1700        -8617.513             0.026            0.018
Chain 1:   1800        -8198.768             0.025            0.018
Chain 1:   1900        -8297.525             0.026            0.018
Chain 1:   2000        -8271.379             0.022            0.012
Chain 1:   2100        -8395.410             0.019            0.012
Chain 1:   2200        -8208.843             0.016            0.012
Chain 1:   2300        -8292.061             0.015            0.012
Chain 1:   2400        -8361.565             0.016            0.012
Chain 1:   2500        -8307.497             0.015            0.012
Chain 1:   2600        -8307.830             0.014            0.010
Chain 1:   2700        -8224.992             0.014            0.010
Chain 1:   2800        -8186.569             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003034 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417193.636             1.000            1.000
Chain 1:    200     -1587129.464             2.652            4.303
Chain 1:    300      -890530.053             2.029            1.000
Chain 1:    400      -457092.287             1.758            1.000
Chain 1:    500      -357287.557             1.463            0.948
Chain 1:    600      -232457.565             1.308            0.948
Chain 1:    700      -119014.314             1.258            0.948
Chain 1:    800       -86252.966             1.148            0.948
Chain 1:    900       -66659.398             1.053            0.782
Chain 1:   1000       -51496.761             0.977            0.782
Chain 1:   1100       -39008.812             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38191.909             0.481            0.380
Chain 1:   1300       -26188.037             0.449            0.380
Chain 1:   1400       -25911.114             0.355            0.320
Chain 1:   1500       -22507.659             0.342            0.320
Chain 1:   1600       -21726.830             0.292            0.294
Chain 1:   1700       -20605.462             0.202            0.294
Chain 1:   1800       -20550.773             0.164            0.151
Chain 1:   1900       -20876.990             0.136            0.054
Chain 1:   2000       -19390.409             0.115            0.054
Chain 1:   2100       -19628.859             0.084            0.036
Chain 1:   2200       -19854.752             0.083            0.036
Chain 1:   2300       -19472.409             0.039            0.020
Chain 1:   2400       -19244.518             0.039            0.020
Chain 1:   2500       -19046.218             0.025            0.016
Chain 1:   2600       -18676.731             0.023            0.016
Chain 1:   2700       -18633.811             0.018            0.012
Chain 1:   2800       -18350.470             0.020            0.015
Chain 1:   2900       -18631.719             0.019            0.015
Chain 1:   3000       -18618.020             0.012            0.012
Chain 1:   3100       -18702.956             0.011            0.012
Chain 1:   3200       -18393.728             0.012            0.015
Chain 1:   3300       -18598.395             0.011            0.012
Chain 1:   3400       -18073.294             0.013            0.015
Chain 1:   3500       -18685.087             0.015            0.015
Chain 1:   3600       -17991.890             0.017            0.015
Chain 1:   3700       -18378.548             0.018            0.017
Chain 1:   3800       -17338.336             0.023            0.021
Chain 1:   3900       -17334.427             0.021            0.021
Chain 1:   4000       -17451.801             0.022            0.021
Chain 1:   4100       -17365.496             0.022            0.021
Chain 1:   4200       -17181.782             0.021            0.021
Chain 1:   4300       -17320.210             0.021            0.021
Chain 1:   4400       -17277.076             0.019            0.011
Chain 1:   4500       -17179.541             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00117 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48785.296             1.000            1.000
Chain 1:    200       -22832.511             1.068            1.137
Chain 1:    300       -14615.144             0.900            1.000
Chain 1:    400       -15075.575             0.682            1.000
Chain 1:    500       -13949.216             0.562            0.562
Chain 1:    600       -12737.314             0.484            0.562
Chain 1:    700       -14440.479             0.432            0.118
Chain 1:    800       -13759.522             0.384            0.118
Chain 1:    900       -12585.075             0.352            0.095
Chain 1:   1000       -30907.768             0.376            0.118
Chain 1:   1100       -11818.774             0.437            0.118
Chain 1:   1200       -12135.519             0.326            0.095
Chain 1:   1300       -12192.471             0.271            0.093
Chain 1:   1400        -9886.620             0.291            0.095
Chain 1:   1500       -10056.353             0.284            0.095
Chain 1:   1600       -10291.998             0.277            0.093
Chain 1:   1700       -17516.325             0.307            0.093
Chain 1:   1800       -11634.812             0.352            0.233
Chain 1:   1900       -11191.859             0.347            0.233
Chain 1:   2000       -11147.942             0.288            0.040
Chain 1:   2100       -16056.097             0.157            0.040
Chain 1:   2200        -9520.281             0.223            0.233
Chain 1:   2300       -16560.727             0.265            0.306
Chain 1:   2400        -9758.289             0.312            0.412
Chain 1:   2500       -10022.097             0.313            0.412
Chain 1:   2600        -9163.824             0.320            0.412
Chain 1:   2700        -9295.226             0.280            0.306
Chain 1:   2800        -8767.878             0.235            0.094
Chain 1:   2900        -9352.719             0.238            0.094
Chain 1:   3000        -9413.424             0.238            0.094
Chain 1:   3100        -9482.673             0.208            0.063
Chain 1:   3200        -8896.116             0.146            0.063
Chain 1:   3300        -9540.711             0.110            0.063
Chain 1:   3400       -10300.507             0.048            0.063
Chain 1:   3500        -9194.400             0.057            0.066
Chain 1:   3600        -8687.389             0.054            0.063
Chain 1:   3700        -8763.820             0.053            0.063
Chain 1:   3800       -11152.497             0.069            0.066
Chain 1:   3900        -8627.891             0.092            0.068
Chain 1:   4000        -9887.697             0.104            0.074
Chain 1:   4100       -14205.969             0.133            0.120
Chain 1:   4200       -12057.690             0.145            0.127
Chain 1:   4300        -8792.776             0.175            0.178
Chain 1:   4400        -8973.015             0.170            0.178
Chain 1:   4500       -15628.822             0.200            0.214
Chain 1:   4600        -9095.773             0.266            0.293
Chain 1:   4700        -8481.154             0.272            0.293
Chain 1:   4800        -8741.695             0.254            0.293
Chain 1:   4900        -9453.721             0.232            0.178
Chain 1:   5000        -8528.603             0.230            0.178
Chain 1:   5100        -8403.071             0.201            0.108
Chain 1:   5200       -15419.276             0.229            0.108
Chain 1:   5300       -13227.343             0.209            0.108
Chain 1:   5400       -11086.672             0.226            0.166
Chain 1:   5500       -12407.243             0.194            0.108
Chain 1:   5600       -13928.610             0.133            0.108
Chain 1:   5700       -12539.024             0.137            0.109
Chain 1:   5800        -8622.185             0.179            0.111
Chain 1:   5900       -12689.928             0.204            0.166
Chain 1:   6000        -8183.173             0.248            0.193
Chain 1:   6100        -9115.347             0.257            0.193
Chain 1:   6200        -8326.777             0.221            0.166
Chain 1:   6300        -8476.069             0.206            0.111
Chain 1:   6400       -10344.281             0.205            0.111
Chain 1:   6500       -12524.655             0.211            0.174
Chain 1:   6600        -9028.798             0.239            0.181
Chain 1:   6700       -10484.425             0.242            0.181
Chain 1:   6800        -8190.155             0.225            0.181
Chain 1:   6900       -10098.450             0.212            0.181
Chain 1:   7000        -8399.619             0.177            0.181
Chain 1:   7100        -8061.291             0.171            0.181
Chain 1:   7200        -8844.249             0.170            0.181
Chain 1:   7300        -8458.540             0.173            0.181
Chain 1:   7400       -10412.543             0.174            0.188
Chain 1:   7500        -9234.886             0.169            0.188
Chain 1:   7600        -8390.218             0.140            0.139
Chain 1:   7700        -8628.084             0.129            0.128
Chain 1:   7800        -8931.850             0.104            0.101
Chain 1:   7900        -8397.021             0.092            0.089
Chain 1:   8000        -9646.955             0.085            0.089
Chain 1:   8100        -9728.675             0.081            0.089
Chain 1:   8200        -8090.278             0.093            0.101
Chain 1:   8300       -10449.306             0.111            0.128
Chain 1:   8400        -8459.664             0.115            0.128
Chain 1:   8500        -8348.911             0.104            0.101
Chain 1:   8600       -11882.039             0.124            0.130
Chain 1:   8700        -8547.988             0.160            0.203
Chain 1:   8800        -9490.512             0.167            0.203
Chain 1:   8900       -10832.973             0.173            0.203
Chain 1:   9000        -9946.958             0.168            0.203
Chain 1:   9100        -8039.691             0.191            0.226
Chain 1:   9200        -9685.241             0.188            0.226
Chain 1:   9300        -8087.319             0.185            0.198
Chain 1:   9400        -8686.932             0.169            0.170
Chain 1:   9500        -8148.880             0.174            0.170
Chain 1:   9600        -8815.024             0.152            0.124
Chain 1:   9700        -8115.577             0.121            0.099
Chain 1:   9800        -8166.630             0.112            0.089
Chain 1:   9900       -10480.765             0.122            0.089
Chain 1:   10000       -10533.530             0.113            0.086
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56818.013             1.000            1.000
Chain 1:    200       -17354.078             1.637            2.274
Chain 1:    300        -8680.620             1.424            1.000
Chain 1:    400        -8354.453             1.078            1.000
Chain 1:    500        -8235.956             0.865            0.999
Chain 1:    600        -8089.495             0.724            0.999
Chain 1:    700        -8026.847             0.622            0.039
Chain 1:    800        -8098.634             0.545            0.039
Chain 1:    900        -7901.460             0.487            0.025
Chain 1:   1000        -7855.907             0.439            0.025
Chain 1:   1100        -7701.705             0.341            0.020
Chain 1:   1200        -7842.578             0.116            0.018
Chain 1:   1300        -7638.173             0.018            0.018
Chain 1:   1400        -7839.654             0.017            0.018
Chain 1:   1500        -7598.318             0.019            0.020
Chain 1:   1600        -7667.058             0.018            0.020
Chain 1:   1700        -7521.150             0.019            0.020
Chain 1:   1800        -7594.140             0.019            0.020
Chain 1:   1900        -7559.437             0.017            0.019
Chain 1:   2000        -7644.455             0.018            0.019
Chain 1:   2100        -7591.475             0.016            0.018
Chain 1:   2200        -7694.420             0.016            0.013
Chain 1:   2300        -7605.122             0.014            0.012
Chain 1:   2400        -7644.311             0.012            0.011
Chain 1:   2500        -7582.591             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003163 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86519.913             1.000            1.000
Chain 1:    200       -13398.754             3.229            5.457
Chain 1:    300        -9787.869             2.275            1.000
Chain 1:    400       -10738.713             1.729            1.000
Chain 1:    500        -8706.298             1.430            0.369
Chain 1:    600        -8292.311             1.200            0.369
Chain 1:    700        -8410.183             1.030            0.233
Chain 1:    800        -9112.326             0.911            0.233
Chain 1:    900        -8592.886             0.817            0.089
Chain 1:   1000        -8402.134             0.737            0.089
Chain 1:   1100        -8630.436             0.640            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8272.544             0.098            0.060
Chain 1:   1300        -8475.369             0.064            0.050
Chain 1:   1400        -8482.815             0.055            0.043
Chain 1:   1500        -8370.557             0.033            0.026
Chain 1:   1600        -8474.672             0.029            0.024
Chain 1:   1700        -8563.380             0.029            0.024
Chain 1:   1800        -8157.128             0.026            0.024
Chain 1:   1900        -8254.571             0.021            0.023
Chain 1:   2000        -8226.403             0.020            0.013
Chain 1:   2100        -8346.587             0.018            0.013
Chain 1:   2200        -8147.963             0.016            0.013
Chain 1:   2300        -8291.261             0.016            0.013
Chain 1:   2400        -8298.014             0.016            0.013
Chain 1:   2500        -8268.097             0.015            0.012
Chain 1:   2600        -8266.635             0.014            0.012
Chain 1:   2700        -8179.318             0.014            0.012
Chain 1:   2800        -8145.195             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003196 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393918.293             1.000            1.000
Chain 1:    200     -1583955.697             2.650            4.299
Chain 1:    300      -891069.822             2.026            1.000
Chain 1:    400      -457857.265             1.756            1.000
Chain 1:    500      -358299.513             1.460            0.946
Chain 1:    600      -233215.393             1.306            0.946
Chain 1:    700      -119274.753             1.256            0.946
Chain 1:    800       -86421.486             1.147            0.946
Chain 1:    900       -66739.739             1.052            0.778
Chain 1:   1000       -51511.584             0.976            0.778
Chain 1:   1100       -38966.699             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38139.095             0.481            0.380
Chain 1:   1300       -26081.900             0.449            0.380
Chain 1:   1400       -25798.607             0.356            0.322
Chain 1:   1500       -22381.909             0.343            0.322
Chain 1:   1600       -21596.531             0.293            0.296
Chain 1:   1700       -20469.251             0.203            0.295
Chain 1:   1800       -20412.906             0.165            0.153
Chain 1:   1900       -20738.870             0.138            0.055
Chain 1:   2000       -19249.512             0.116            0.055
Chain 1:   2100       -19488.086             0.085            0.036
Chain 1:   2200       -19714.393             0.084            0.036
Chain 1:   2300       -19331.723             0.039            0.020
Chain 1:   2400       -19103.872             0.040            0.020
Chain 1:   2500       -18905.808             0.025            0.016
Chain 1:   2600       -18536.323             0.024            0.016
Chain 1:   2700       -18493.316             0.018            0.012
Chain 1:   2800       -18210.245             0.020            0.016
Chain 1:   2900       -18491.412             0.020            0.015
Chain 1:   3000       -18477.670             0.012            0.012
Chain 1:   3100       -18562.624             0.011            0.012
Chain 1:   3200       -18253.439             0.012            0.015
Chain 1:   3300       -18458.024             0.011            0.012
Chain 1:   3400       -17933.188             0.013            0.015
Chain 1:   3500       -18544.714             0.015            0.016
Chain 1:   3600       -17851.842             0.017            0.016
Chain 1:   3700       -18238.341             0.019            0.017
Chain 1:   3800       -17198.719             0.023            0.021
Chain 1:   3900       -17194.850             0.022            0.021
Chain 1:   4000       -17312.171             0.022            0.021
Chain 1:   4100       -17225.981             0.022            0.021
Chain 1:   4200       -17042.337             0.022            0.021
Chain 1:   4300       -17180.665             0.021            0.021
Chain 1:   4400       -17137.631             0.019            0.011
Chain 1:   4500       -17040.155             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001358 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12677.997             1.000            1.000
Chain 1:    200        -9598.614             0.660            1.000
Chain 1:    300        -8103.526             0.502            0.321
Chain 1:    400        -8341.583             0.383            0.321
Chain 1:    500        -8238.064             0.309            0.184
Chain 1:    600        -8069.730             0.261            0.184
Chain 1:    700        -7971.094             0.226            0.029
Chain 1:    800        -8049.993             0.199            0.029
Chain 1:    900        -7936.899             0.178            0.021
Chain 1:   1000        -8119.662             0.163            0.023
Chain 1:   1100        -8304.467             0.065            0.022
Chain 1:   1200        -7994.504             0.037            0.022
Chain 1:   1300        -7966.455             0.019            0.021
Chain 1:   1400        -7955.086             0.016            0.014
Chain 1:   1500        -8051.399             0.016            0.014
Chain 1:   1600        -7995.436             0.014            0.012
Chain 1:   1700        -7945.585             0.014            0.012
Chain 1:   1800        -7918.781             0.013            0.012
Chain 1:   1900        -7941.370             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001594 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51243.051             1.000            1.000
Chain 1:    200       -16576.622             1.546            2.091
Chain 1:    300        -8867.257             1.320            1.000
Chain 1:    400       -10227.879             1.023            1.000
Chain 1:    500        -8653.894             0.855            0.869
Chain 1:    600        -8462.818             0.716            0.869
Chain 1:    700        -8172.695             0.619            0.182
Chain 1:    800        -8230.340             0.543            0.182
Chain 1:    900        -7557.947             0.492            0.133
Chain 1:   1000        -7767.815             0.446            0.133
Chain 1:   1100        -7790.965             0.346            0.089
Chain 1:   1200        -7766.536             0.137            0.035
Chain 1:   1300        -7687.923             0.051            0.027
Chain 1:   1400        -7762.839             0.039            0.023
Chain 1:   1500        -7571.295             0.023            0.023
Chain 1:   1600        -7798.260             0.024            0.025
Chain 1:   1700        -7667.283             0.022            0.017
Chain 1:   1800        -7554.559             0.023            0.017
Chain 1:   1900        -7656.056             0.015            0.015
Chain 1:   2000        -7589.813             0.013            0.013
Chain 1:   2100        -7551.224             0.014            0.013
Chain 1:   2200        -7749.929             0.016            0.015
Chain 1:   2300        -7565.681             0.017            0.017
Chain 1:   2400        -7624.929             0.017            0.017
Chain 1:   2500        -7552.760             0.016            0.015
Chain 1:   2600        -7496.838             0.013            0.013
Chain 1:   2700        -7494.609             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003664 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86629.647             1.000            1.000
Chain 1:    200       -13823.882             3.133            5.267
Chain 1:    300       -10076.553             2.213            1.000
Chain 1:    400       -11592.368             1.692            1.000
Chain 1:    500        -8797.409             1.417            0.372
Chain 1:    600        -8409.759             1.189            0.372
Chain 1:    700        -8660.703             1.023            0.318
Chain 1:    800        -9480.708             0.906            0.318
Chain 1:    900        -8773.285             0.814            0.131
Chain 1:   1000        -8446.124             0.737            0.131
Chain 1:   1100        -8837.520             0.641            0.086   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8412.281             0.120            0.081
Chain 1:   1300        -8795.334             0.087            0.051
Chain 1:   1400        -8558.815             0.076            0.046
Chain 1:   1500        -8593.498             0.045            0.044
Chain 1:   1600        -8691.565             0.042            0.044
Chain 1:   1700        -8751.253             0.039            0.044
Chain 1:   1800        -8313.388             0.036            0.044
Chain 1:   1900        -8417.158             0.029            0.039
Chain 1:   2000        -8397.763             0.026            0.028
Chain 1:   2100        -8521.248             0.023            0.014
Chain 1:   2200        -8315.629             0.020            0.014
Chain 1:   2300        -8409.211             0.017            0.012
Chain 1:   2400        -8476.406             0.015            0.011
Chain 1:   2500        -8425.185             0.015            0.011
Chain 1:   2600        -8437.385             0.014            0.011
Chain 1:   2700        -8345.900             0.014            0.011
Chain 1:   2800        -8294.433             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004042 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395948.553             1.000            1.000
Chain 1:    200     -1582709.730             2.652            4.305
Chain 1:    300      -892010.680             2.026            1.000
Chain 1:    400      -458539.326             1.756            1.000
Chain 1:    500      -359260.105             1.460            0.945
Chain 1:    600      -234019.716             1.306            0.945
Chain 1:    700      -119926.087             1.255            0.945
Chain 1:    800       -87039.143             1.146            0.945
Chain 1:    900       -67311.352             1.051            0.774
Chain 1:   1000       -52063.118             0.975            0.774
Chain 1:   1100       -39492.817             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38667.003             0.479            0.378
Chain 1:   1300       -26570.283             0.447            0.378
Chain 1:   1400       -26285.901             0.353            0.318
Chain 1:   1500       -22858.737             0.341            0.318
Chain 1:   1600       -22071.355             0.291            0.293
Chain 1:   1700       -20938.644             0.201            0.293
Chain 1:   1800       -20881.466             0.163            0.150
Chain 1:   1900       -21208.076             0.136            0.054
Chain 1:   2000       -19714.752             0.114            0.054
Chain 1:   2100       -19953.470             0.083            0.036
Chain 1:   2200       -20180.756             0.082            0.036
Chain 1:   2300       -19797.096             0.039            0.019
Chain 1:   2400       -19568.944             0.039            0.019
Chain 1:   2500       -19371.070             0.025            0.015
Chain 1:   2600       -19000.649             0.023            0.015
Chain 1:   2700       -18957.404             0.018            0.012
Chain 1:   2800       -18674.109             0.019            0.015
Chain 1:   2900       -18955.638             0.019            0.015
Chain 1:   3000       -18941.776             0.012            0.012
Chain 1:   3100       -19026.866             0.011            0.012
Chain 1:   3200       -18717.150             0.011            0.015
Chain 1:   3300       -18922.161             0.011            0.012
Chain 1:   3400       -18396.473             0.012            0.015
Chain 1:   3500       -19009.298             0.015            0.015
Chain 1:   3600       -18314.746             0.016            0.015
Chain 1:   3700       -18702.536             0.018            0.017
Chain 1:   3800       -17660.309             0.023            0.021
Chain 1:   3900       -17656.412             0.021            0.021
Chain 1:   4000       -17773.715             0.022            0.021
Chain 1:   4100       -17687.386             0.022            0.021
Chain 1:   4200       -17503.201             0.021            0.021
Chain 1:   4300       -17641.897             0.021            0.021
Chain 1:   4400       -17598.394             0.018            0.011
Chain 1:   4500       -17500.855             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001452 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12520.437             1.000            1.000
Chain 1:    200        -9396.844             0.666            1.000
Chain 1:    300        -8084.680             0.498            0.332
Chain 1:    400        -8267.508             0.379            0.332
Chain 1:    500        -8243.579             0.304            0.162
Chain 1:    600        -8028.592             0.258            0.162
Chain 1:    700        -7928.560             0.223            0.027
Chain 1:    800        -7962.844             0.195            0.027
Chain 1:    900        -8080.467             0.175            0.022
Chain 1:   1000        -7977.578             0.159            0.022
Chain 1:   1100        -8013.632             0.060            0.015
Chain 1:   1200        -7977.463             0.027            0.013
Chain 1:   1300        -7896.430             0.012            0.013
Chain 1:   1400        -7920.932             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002227 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 22.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46596.662             1.000            1.000
Chain 1:    200       -15712.851             1.483            1.966
Chain 1:    300        -8736.623             1.255            1.000
Chain 1:    400        -8614.190             0.945            1.000
Chain 1:    500        -8693.508             0.757            0.799
Chain 1:    600        -8298.251             0.639            0.799
Chain 1:    700        -7936.485             0.554            0.048
Chain 1:    800        -8129.397             0.488            0.048
Chain 1:    900        -7963.178             0.436            0.046
Chain 1:   1000        -7640.800             0.397            0.046
Chain 1:   1100        -7615.442             0.297            0.042
Chain 1:   1200        -7863.644             0.104            0.032
Chain 1:   1300        -7812.825             0.024            0.024
Chain 1:   1400        -7875.638             0.024            0.024
Chain 1:   1500        -7531.807             0.028            0.032
Chain 1:   1600        -7762.969             0.026            0.030
Chain 1:   1700        -7432.596             0.026            0.030
Chain 1:   1800        -7628.839             0.026            0.030
Chain 1:   1900        -7427.579             0.026            0.030
Chain 1:   2000        -7584.550             0.024            0.027
Chain 1:   2100        -7598.546             0.024            0.027
Chain 1:   2200        -7689.384             0.022            0.026
Chain 1:   2300        -7553.853             0.023            0.026
Chain 1:   2400        -7609.074             0.023            0.026
Chain 1:   2500        -7637.324             0.019            0.021
Chain 1:   2600        -7475.641             0.018            0.021
Chain 1:   2700        -7475.596             0.014            0.018
Chain 1:   2800        -7539.883             0.012            0.012
Chain 1:   2900        -7363.888             0.012            0.012
Chain 1:   3000        -7492.394             0.011            0.012
Chain 1:   3100        -7484.371             0.011            0.012
Chain 1:   3200        -7682.118             0.013            0.017
Chain 1:   3300        -7413.888             0.015            0.017
Chain 1:   3400        -7625.632             0.017            0.022
Chain 1:   3500        -7397.157             0.019            0.024
Chain 1:   3600        -7461.827             0.018            0.024
Chain 1:   3700        -7411.288             0.019            0.024
Chain 1:   3800        -7413.879             0.018            0.024
Chain 1:   3900        -7379.819             0.016            0.017
Chain 1:   4000        -7374.701             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003706 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86379.720             1.000            1.000
Chain 1:    200       -13621.678             3.171            5.341
Chain 1:    300        -9959.739             2.236            1.000
Chain 1:    400       -10791.810             1.697            1.000
Chain 1:    500        -8959.007             1.398            0.368
Chain 1:    600        -8408.033             1.176            0.368
Chain 1:    700        -8382.036             1.008            0.205
Chain 1:    800        -8748.797             0.888            0.205
Chain 1:    900        -8685.022             0.790            0.077
Chain 1:   1000        -8729.916             0.711            0.077
Chain 1:   1100        -8771.667             0.612            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8256.086             0.084            0.062
Chain 1:   1300        -8637.120             0.052            0.044
Chain 1:   1400        -8635.241             0.044            0.042
Chain 1:   1500        -8502.904             0.025            0.016
Chain 1:   1600        -8611.047             0.020            0.013
Chain 1:   1700        -8684.688             0.020            0.013
Chain 1:   1800        -8257.981             0.021            0.013
Chain 1:   1900        -8360.297             0.022            0.013
Chain 1:   2000        -8335.054             0.022            0.013
Chain 1:   2100        -8462.012             0.023            0.015
Chain 1:   2200        -8261.286             0.019            0.015
Chain 1:   2300        -8355.522             0.015            0.013
Chain 1:   2400        -8423.548             0.016            0.013
Chain 1:   2500        -8369.755             0.015            0.012
Chain 1:   2600        -8372.082             0.014            0.011
Chain 1:   2700        -8288.352             0.014            0.011
Chain 1:   2800        -8247.021             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003459 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396537.964             1.000            1.000
Chain 1:    200     -1583049.165             2.652            4.304
Chain 1:    300      -890780.286             2.027            1.000
Chain 1:    400      -457672.157             1.757            1.000
Chain 1:    500      -358150.279             1.461            0.946
Chain 1:    600      -233199.179             1.307            0.946
Chain 1:    700      -119402.215             1.256            0.946
Chain 1:    800       -86616.335             1.147            0.946
Chain 1:    900       -66951.765             1.052            0.777
Chain 1:   1000       -51744.198             0.976            0.777
Chain 1:   1100       -39214.425             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38391.177             0.480            0.379
Chain 1:   1300       -26336.236             0.448            0.379
Chain 1:   1400       -26055.553             0.354            0.320
Chain 1:   1500       -22639.497             0.342            0.320
Chain 1:   1600       -21855.231             0.292            0.294
Chain 1:   1700       -20727.298             0.202            0.294
Chain 1:   1800       -20671.184             0.164            0.151
Chain 1:   1900       -20997.484             0.136            0.054
Chain 1:   2000       -19507.380             0.115            0.054
Chain 1:   2100       -19745.940             0.084            0.036
Chain 1:   2200       -19972.626             0.083            0.036
Chain 1:   2300       -19589.570             0.039            0.020
Chain 1:   2400       -19361.522             0.039            0.020
Chain 1:   2500       -19163.616             0.025            0.016
Chain 1:   2600       -18793.660             0.023            0.016
Chain 1:   2700       -18750.542             0.018            0.012
Chain 1:   2800       -18467.363             0.019            0.015
Chain 1:   2900       -18748.681             0.019            0.015
Chain 1:   3000       -18734.891             0.012            0.012
Chain 1:   3100       -18819.902             0.011            0.012
Chain 1:   3200       -18510.489             0.012            0.015
Chain 1:   3300       -18715.268             0.011            0.012
Chain 1:   3400       -18190.042             0.012            0.015
Chain 1:   3500       -18802.201             0.015            0.015
Chain 1:   3600       -18108.472             0.017            0.015
Chain 1:   3700       -18495.578             0.018            0.017
Chain 1:   3800       -17454.724             0.023            0.021
Chain 1:   3900       -17450.837             0.021            0.021
Chain 1:   4000       -17568.150             0.022            0.021
Chain 1:   4100       -17481.890             0.022            0.021
Chain 1:   4200       -17297.982             0.021            0.021
Chain 1:   4300       -17436.495             0.021            0.021
Chain 1:   4400       -17393.197             0.018            0.011
Chain 1:   4500       -17295.691             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001357 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48790.108             1.000            1.000
Chain 1:    200       -26956.129             0.905            1.000
Chain 1:    300       -16629.369             0.810            0.810
Chain 1:    400       -20757.505             0.657            0.810
Chain 1:    500       -14174.359             0.619            0.621
Chain 1:    600       -13607.717             0.523            0.621
Chain 1:    700       -11187.376             0.479            0.464
Chain 1:    800       -14583.303             0.448            0.464
Chain 1:    900       -11124.986             0.433            0.311
Chain 1:   1000       -13180.747             0.405            0.311
Chain 1:   1100       -17284.947             0.329            0.237
Chain 1:   1200       -10793.911             0.308            0.237
Chain 1:   1300       -21921.679             0.297            0.237
Chain 1:   1400       -13176.373             0.343            0.311
Chain 1:   1500       -11431.880             0.312            0.237
Chain 1:   1600       -10308.799             0.319            0.237
Chain 1:   1700       -12546.242             0.315            0.237
Chain 1:   1800       -11196.702             0.304            0.237
Chain 1:   1900       -10334.562             0.281            0.178
Chain 1:   2000        -9416.485             0.275            0.178
Chain 1:   2100        -9801.620             0.255            0.153
Chain 1:   2200       -10068.948             0.198            0.121
Chain 1:   2300       -10443.444             0.151            0.109
Chain 1:   2400        -9634.780             0.093            0.097
Chain 1:   2500       -10735.205             0.088            0.097
Chain 1:   2600        -9910.112             0.085            0.084
Chain 1:   2700        -9411.240             0.073            0.083
Chain 1:   2800       -14296.892             0.095            0.083
Chain 1:   2900       -16133.126             0.098            0.084
Chain 1:   3000       -10839.210             0.137            0.084
Chain 1:   3100        -8675.859             0.158            0.103
Chain 1:   3200        -8645.909             0.156            0.103
Chain 1:   3300        -8979.405             0.156            0.103
Chain 1:   3400        -8580.441             0.152            0.103
Chain 1:   3500        -9558.461             0.152            0.102
Chain 1:   3600        -9202.373             0.147            0.102
Chain 1:   3700        -8523.144             0.150            0.102
Chain 1:   3800       -12501.776             0.148            0.102
Chain 1:   3900        -8441.827             0.184            0.102
Chain 1:   4000        -8685.281             0.138            0.080
Chain 1:   4100        -9207.661             0.119            0.057
Chain 1:   4200        -8697.672             0.125            0.059
Chain 1:   4300        -9643.658             0.131            0.080
Chain 1:   4400        -9640.221             0.126            0.080
Chain 1:   4500       -12481.245             0.139            0.080
Chain 1:   4600       -12721.687             0.137            0.080
Chain 1:   4700        -8707.921             0.175            0.098
Chain 1:   4800        -8728.941             0.143            0.059
Chain 1:   4900       -12308.936             0.124            0.059
Chain 1:   5000        -9157.786             0.156            0.098
Chain 1:   5100        -8440.565             0.159            0.098
Chain 1:   5200       -10239.696             0.170            0.176
Chain 1:   5300       -12512.204             0.179            0.182
Chain 1:   5400        -9814.028             0.206            0.228
Chain 1:   5500       -10437.210             0.189            0.182
Chain 1:   5600        -8970.579             0.204            0.182
Chain 1:   5700        -9275.856             0.161            0.176
Chain 1:   5800        -8682.620             0.168            0.176
Chain 1:   5900        -8945.699             0.142            0.163
Chain 1:   6000        -8557.644             0.112            0.085
Chain 1:   6100        -8171.148             0.108            0.068
Chain 1:   6200        -8213.657             0.091            0.060
Chain 1:   6300        -8199.565             0.073            0.047
Chain 1:   6400       -11637.127             0.075            0.047
Chain 1:   6500       -11416.927             0.071            0.045
Chain 1:   6600        -8434.610             0.090            0.045
Chain 1:   6700        -9580.198             0.099            0.047
Chain 1:   6800        -8592.000             0.103            0.047
Chain 1:   6900        -9839.986             0.113            0.115
Chain 1:   7000        -8643.332             0.122            0.120
Chain 1:   7100       -12572.696             0.149            0.127
Chain 1:   7200       -10580.363             0.167            0.138
Chain 1:   7300        -8364.483             0.193            0.188
Chain 1:   7400        -8546.662             0.166            0.138
Chain 1:   7500       -10667.201             0.184            0.188
Chain 1:   7600        -8312.743             0.177            0.188
Chain 1:   7700       -10876.581             0.189            0.199
Chain 1:   7800        -9660.629             0.190            0.199
Chain 1:   7900        -8311.843             0.193            0.199
Chain 1:   8000       -10631.867             0.201            0.218
Chain 1:   8100        -8476.670             0.195            0.218
Chain 1:   8200        -8693.239             0.179            0.218
Chain 1:   8300        -8070.659             0.160            0.199
Chain 1:   8400        -8209.529             0.160            0.199
Chain 1:   8500        -8166.302             0.140            0.162
Chain 1:   8600       -10974.241             0.138            0.162
Chain 1:   8700        -9623.426             0.128            0.140
Chain 1:   8800        -8434.431             0.130            0.141
Chain 1:   8900        -8321.825             0.115            0.140
Chain 1:   9000        -8366.789             0.093            0.077
Chain 1:   9100        -8012.690             0.072            0.044
Chain 1:   9200       -10144.763             0.091            0.077
Chain 1:   9300        -8236.323             0.106            0.140
Chain 1:   9400       -11477.211             0.133            0.141
Chain 1:   9500       -10499.546             0.142            0.141
Chain 1:   9600       -10186.541             0.119            0.140
Chain 1:   9700        -8775.359             0.121            0.141
Chain 1:   9800        -8709.895             0.108            0.093
Chain 1:   9900       -10350.546             0.122            0.159
Chain 1:   10000        -8096.943             0.150            0.161
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61742.055             1.000            1.000
Chain 1:    200       -17551.239             1.759            2.518
Chain 1:    300        -8731.019             1.509            1.010
Chain 1:    400        -9038.993             1.141            1.010
Chain 1:    500        -7896.588             0.941            1.000
Chain 1:    600        -8440.659             0.795            1.000
Chain 1:    700        -8235.254             0.685            0.145
Chain 1:    800        -8132.889             0.601            0.145
Chain 1:    900        -7863.450             0.538            0.064
Chain 1:   1000        -7845.089             0.485            0.064
Chain 1:   1100        -7694.603             0.386            0.034
Chain 1:   1200        -7644.298             0.135            0.034
Chain 1:   1300        -7707.348             0.035            0.025
Chain 1:   1400        -7888.274             0.034            0.023
Chain 1:   1500        -7622.350             0.023            0.023
Chain 1:   1600        -7547.701             0.018            0.020
Chain 1:   1700        -7557.420             0.015            0.013
Chain 1:   1800        -7580.931             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003457 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -84950.707             1.000            1.000
Chain 1:    200       -13196.191             3.219            5.438
Chain 1:    300        -9681.046             2.267            1.000
Chain 1:    400       -10501.856             1.720            1.000
Chain 1:    500        -8621.457             1.419            0.363
Chain 1:    600        -8228.781             1.191            0.363
Chain 1:    700        -8342.515             1.023            0.218
Chain 1:    800        -8964.696             0.903            0.218
Chain 1:    900        -8522.036             0.809            0.078
Chain 1:   1000        -8286.558             0.731            0.078
Chain 1:   1100        -8555.214             0.634            0.069   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8209.232             0.094            0.052
Chain 1:   1300        -8427.985             0.061            0.048
Chain 1:   1400        -8436.021             0.053            0.042
Chain 1:   1500        -8316.341             0.033            0.031
Chain 1:   1600        -8410.297             0.029            0.028
Chain 1:   1700        -8502.992             0.029            0.028
Chain 1:   1800        -8118.925             0.026            0.028
Chain 1:   1900        -8220.854             0.023            0.026
Chain 1:   2000        -8190.705             0.020            0.014
Chain 1:   2100        -8327.511             0.019            0.014
Chain 1:   2200        -8110.285             0.017            0.014
Chain 1:   2300        -8251.941             0.016            0.014
Chain 1:   2400        -8261.085             0.016            0.014
Chain 1:   2500        -8229.407             0.015            0.012
Chain 1:   2600        -8226.644             0.014            0.012
Chain 1:   2700        -8136.785             0.014            0.012
Chain 1:   2800        -8115.963             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003723 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402641.893             1.000            1.000
Chain 1:    200     -1582223.460             2.655            4.311
Chain 1:    300      -890018.110             2.029            1.000
Chain 1:    400      -457375.705             1.759            1.000
Chain 1:    500      -357849.876             1.462            0.946
Chain 1:    600      -232862.890             1.308            0.946
Chain 1:    700      -118991.309             1.258            0.946
Chain 1:    800       -86212.221             1.148            0.946
Chain 1:    900       -66524.317             1.054            0.778
Chain 1:   1000       -51295.940             0.978            0.778
Chain 1:   1100       -38760.173             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37928.373             0.481            0.380
Chain 1:   1300       -25876.223             0.450            0.380
Chain 1:   1400       -25592.161             0.357            0.323
Chain 1:   1500       -22178.773             0.344            0.323
Chain 1:   1600       -21394.680             0.294            0.297
Chain 1:   1700       -20267.678             0.204            0.296
Chain 1:   1800       -20211.512             0.166            0.154
Chain 1:   1900       -20537.044             0.138            0.056
Chain 1:   2000       -19049.425             0.117            0.056
Chain 1:   2100       -19287.395             0.085            0.037
Chain 1:   2200       -19513.671             0.084            0.037
Chain 1:   2300       -19131.270             0.040            0.020
Chain 1:   2400       -18903.602             0.040            0.020
Chain 1:   2500       -18705.865             0.026            0.016
Chain 1:   2600       -18336.383             0.024            0.016
Chain 1:   2700       -18293.512             0.019            0.012
Chain 1:   2800       -18010.736             0.020            0.016
Chain 1:   2900       -18291.748             0.020            0.015
Chain 1:   3000       -18277.874             0.012            0.012
Chain 1:   3100       -18362.773             0.011            0.012
Chain 1:   3200       -18053.813             0.012            0.015
Chain 1:   3300       -18258.296             0.011            0.012
Chain 1:   3400       -17733.932             0.013            0.015
Chain 1:   3500       -18344.750             0.015            0.016
Chain 1:   3600       -17652.860             0.017            0.016
Chain 1:   3700       -18038.603             0.019            0.017
Chain 1:   3800       -17000.554             0.023            0.021
Chain 1:   3900       -16996.820             0.022            0.021
Chain 1:   4000       -17114.065             0.022            0.021
Chain 1:   4100       -17027.938             0.022            0.021
Chain 1:   4200       -16844.719             0.022            0.021
Chain 1:   4300       -16982.696             0.022            0.021
Chain 1:   4400       -16939.900             0.019            0.011
Chain 1:   4500       -16842.583             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001299 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12423.729             1.000            1.000
Chain 1:    200        -9257.514             0.671            1.000
Chain 1:    300        -8168.459             0.492            0.342
Chain 1:    400        -8249.532             0.371            0.342
Chain 1:    500        -8231.579             0.297            0.133
Chain 1:    600        -8028.963             0.252            0.133
Chain 1:    700        -7922.009             0.218            0.025
Chain 1:    800        -7943.181             0.191            0.025
Chain 1:    900        -7914.281             0.170            0.014
Chain 1:   1000        -8093.543             0.155            0.022
Chain 1:   1100        -8030.336             0.056            0.014
Chain 1:   1200        -7945.893             0.023            0.011
Chain 1:   1300        -7914.372             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001642 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62011.158             1.000            1.000
Chain 1:    200       -17868.921             1.735            2.470
Chain 1:    300        -8890.004             1.493            1.010
Chain 1:    400        -8406.922             1.134            1.010
Chain 1:    500        -8498.713             0.910            1.000
Chain 1:    600        -8655.404             0.761            1.000
Chain 1:    700        -8176.983             0.661            0.059
Chain 1:    800        -8169.281             0.578            0.059
Chain 1:    900        -7904.820             0.518            0.057
Chain 1:   1000        -7893.333             0.466            0.057
Chain 1:   1100        -7876.015             0.366            0.033
Chain 1:   1200        -7611.947             0.123            0.033
Chain 1:   1300        -7805.817             0.024            0.025
Chain 1:   1400        -7640.393             0.021            0.022
Chain 1:   1500        -7570.683             0.021            0.022
Chain 1:   1600        -7758.325             0.021            0.024
Chain 1:   1700        -7484.505             0.019            0.024
Chain 1:   1800        -7633.472             0.021            0.024
Chain 1:   1900        -7629.485             0.017            0.022
Chain 1:   2000        -7581.061             0.018            0.022
Chain 1:   2100        -7570.698             0.018            0.022
Chain 1:   2200        -7698.811             0.016            0.020
Chain 1:   2300        -7588.655             0.015            0.017
Chain 1:   2400        -7629.852             0.013            0.015
Chain 1:   2500        -7560.047             0.013            0.015
Chain 1:   2600        -7534.282             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003528 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86198.441             1.000            1.000
Chain 1:    200       -13545.747             3.182            5.364
Chain 1:    300        -9932.796             2.242            1.000
Chain 1:    400       -10849.584             1.703            1.000
Chain 1:    500        -8891.005             1.406            0.364
Chain 1:    600        -8626.351             1.177            0.364
Chain 1:    700        -8446.803             1.012            0.220
Chain 1:    800        -8877.233             0.892            0.220
Chain 1:    900        -8694.271             0.795            0.084
Chain 1:   1000        -8457.086             0.718            0.084
Chain 1:   1100        -8696.175             0.621            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8418.390             0.088            0.033
Chain 1:   1300        -8626.175             0.054            0.031
Chain 1:   1400        -8629.659             0.045            0.028
Chain 1:   1500        -8484.206             0.025            0.027
Chain 1:   1600        -8597.406             0.023            0.024
Chain 1:   1700        -8681.616             0.022            0.024
Chain 1:   1800        -8269.553             0.022            0.024
Chain 1:   1900        -8365.563             0.021            0.024
Chain 1:   2000        -8338.782             0.019            0.017
Chain 1:   2100        -8461.101             0.018            0.014
Chain 1:   2200        -8280.912             0.017            0.014
Chain 1:   2300        -8360.724             0.015            0.013
Chain 1:   2400        -8430.320             0.016            0.013
Chain 1:   2500        -8375.633             0.015            0.011
Chain 1:   2600        -8374.905             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414803.887             1.000            1.000
Chain 1:    200     -1583152.936             2.658            4.315
Chain 1:    300      -889546.194             2.032            1.000
Chain 1:    400      -457539.080             1.760            1.000
Chain 1:    500      -357685.060             1.464            0.944
Chain 1:    600      -232799.816             1.309            0.944
Chain 1:    700      -119114.948             1.258            0.944
Chain 1:    800       -86400.576             1.148            0.944
Chain 1:    900       -66763.914             1.054            0.780
Chain 1:   1000       -51577.497             0.978            0.780
Chain 1:   1100       -39077.737             0.910            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38256.284             0.480            0.379
Chain 1:   1300       -26229.643             0.448            0.379
Chain 1:   1400       -25951.433             0.355            0.320
Chain 1:   1500       -22544.067             0.342            0.320
Chain 1:   1600       -21762.779             0.292            0.294
Chain 1:   1700       -20637.966             0.202            0.294
Chain 1:   1800       -20582.814             0.164            0.151
Chain 1:   1900       -20908.883             0.136            0.055
Chain 1:   2000       -19421.379             0.115            0.055
Chain 1:   2100       -19659.487             0.084            0.036
Chain 1:   2200       -19885.907             0.083            0.036
Chain 1:   2300       -19503.201             0.039            0.020
Chain 1:   2400       -19275.319             0.039            0.020
Chain 1:   2500       -19077.480             0.025            0.016
Chain 1:   2600       -18707.517             0.023            0.016
Chain 1:   2700       -18664.509             0.018            0.012
Chain 1:   2800       -18381.413             0.019            0.015
Chain 1:   2900       -18662.661             0.019            0.015
Chain 1:   3000       -18648.787             0.012            0.012
Chain 1:   3100       -18733.779             0.011            0.012
Chain 1:   3200       -18424.448             0.012            0.015
Chain 1:   3300       -18629.223             0.011            0.012
Chain 1:   3400       -18104.179             0.012            0.015
Chain 1:   3500       -18715.966             0.015            0.015
Chain 1:   3600       -18022.749             0.017            0.015
Chain 1:   3700       -18409.448             0.018            0.017
Chain 1:   3800       -17369.339             0.023            0.021
Chain 1:   3900       -17365.511             0.021            0.021
Chain 1:   4000       -17482.797             0.022            0.021
Chain 1:   4100       -17396.544             0.022            0.021
Chain 1:   4200       -17212.867             0.021            0.021
Chain 1:   4300       -17351.193             0.021            0.021
Chain 1:   4400       -17308.020             0.019            0.011
Chain 1:   4500       -17210.591             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001267 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49265.325             1.000            1.000
Chain 1:    200       -19494.679             1.264            1.527
Chain 1:    300       -18335.159             0.863            1.000
Chain 1:    400       -21229.105             0.682            1.000
Chain 1:    500       -13059.709             0.670            0.626
Chain 1:    600       -14629.267             0.577            0.626
Chain 1:    700       -13851.497             0.502            0.136
Chain 1:    800       -10991.270             0.472            0.260
Chain 1:    900       -14486.771             0.446            0.241
Chain 1:   1000       -10467.834             0.440            0.260
Chain 1:   1100       -11833.836             0.352            0.241
Chain 1:   1200       -10522.311             0.211            0.136
Chain 1:   1300       -11512.261             0.214            0.136
Chain 1:   1400       -20890.004             0.245            0.241
Chain 1:   1500       -11637.786             0.262            0.241
Chain 1:   1600       -12725.403             0.260            0.241
Chain 1:   1700       -17220.039             0.280            0.260
Chain 1:   1800       -10968.614             0.311            0.261
Chain 1:   1900       -10962.402             0.287            0.261
Chain 1:   2000       -21214.252             0.297            0.261
Chain 1:   2100       -10254.537             0.392            0.449
Chain 1:   2200       -10624.156             0.383            0.449
Chain 1:   2300       -11560.554             0.383            0.449
Chain 1:   2400        -9661.259             0.358            0.261
Chain 1:   2500        -9831.997             0.280            0.197
Chain 1:   2600        -9883.039             0.272            0.197
Chain 1:   2700       -12151.544             0.264            0.187
Chain 1:   2800       -10881.714             0.219            0.117
Chain 1:   2900        -9383.049             0.235            0.160
Chain 1:   3000        -9405.873             0.187            0.117
Chain 1:   3100       -10265.513             0.088            0.084
Chain 1:   3200       -12644.914             0.104            0.117
Chain 1:   3300       -13143.376             0.099            0.117
Chain 1:   3400       -10257.927             0.108            0.117
Chain 1:   3500        -9680.960             0.112            0.117
Chain 1:   3600       -10151.091             0.116            0.117
Chain 1:   3700       -11305.299             0.108            0.102
Chain 1:   3800        -9245.263             0.118            0.102
Chain 1:   3900       -15953.704             0.144            0.102
Chain 1:   4000       -14267.071             0.156            0.118
Chain 1:   4100        -9354.985             0.200            0.188
Chain 1:   4200        -9605.522             0.184            0.118
Chain 1:   4300       -10192.256             0.186            0.118
Chain 1:   4400       -13671.837             0.183            0.118
Chain 1:   4500        -9408.705             0.223            0.223
Chain 1:   4600        -9010.385             0.222            0.223
Chain 1:   4700        -8990.657             0.212            0.223
Chain 1:   4800        -8739.017             0.193            0.118
Chain 1:   4900        -8940.271             0.153            0.058
Chain 1:   5000       -13517.913             0.175            0.058
Chain 1:   5100       -10117.788             0.156            0.058
Chain 1:   5200       -18613.227             0.199            0.255
Chain 1:   5300        -9576.245             0.288            0.336
Chain 1:   5400       -10683.192             0.273            0.336
Chain 1:   5500       -13834.430             0.250            0.228
Chain 1:   5600        -9968.739             0.285            0.336
Chain 1:   5700       -15320.803             0.319            0.339
Chain 1:   5800        -8869.914             0.389            0.349
Chain 1:   5900       -12843.975             0.418            0.349
Chain 1:   6000        -8965.157             0.427            0.388
Chain 1:   6100       -10416.955             0.408            0.388
Chain 1:   6200        -8808.642             0.380            0.349
Chain 1:   6300        -8985.354             0.288            0.309
Chain 1:   6400        -8900.897             0.279            0.309
Chain 1:   6500        -9158.137             0.259            0.309
Chain 1:   6600        -8766.030             0.224            0.183
Chain 1:   6700        -8612.707             0.191            0.139
Chain 1:   6800       -11186.618             0.141            0.139
Chain 1:   6900        -9198.207             0.132            0.139
Chain 1:   7000       -13683.757             0.122            0.139
Chain 1:   7100        -8957.202             0.160            0.183
Chain 1:   7200        -8731.915             0.145            0.045
Chain 1:   7300        -8850.570             0.144            0.045
Chain 1:   7400        -8519.750             0.147            0.045
Chain 1:   7500        -9544.970             0.155            0.107
Chain 1:   7600        -8879.920             0.158            0.107
Chain 1:   7700        -8718.331             0.158            0.107
Chain 1:   7800        -9061.241             0.139            0.075
Chain 1:   7900       -11474.889             0.138            0.075
Chain 1:   8000        -8607.767             0.139            0.075
Chain 1:   8100        -8811.340             0.088            0.039
Chain 1:   8200        -8413.273             0.090            0.047
Chain 1:   8300        -8577.371             0.091            0.047
Chain 1:   8400       -16988.037             0.137            0.075
Chain 1:   8500        -8488.055             0.226            0.075
Chain 1:   8600       -10931.996             0.241            0.210
Chain 1:   8700       -10328.710             0.245            0.210
Chain 1:   8800       -11703.488             0.253            0.210
Chain 1:   8900        -9451.386             0.256            0.224
Chain 1:   9000       -11711.582             0.242            0.193
Chain 1:   9100        -8986.552             0.270            0.224
Chain 1:   9200        -8475.984             0.271            0.224
Chain 1:   9300        -8265.689             0.272            0.224
Chain 1:   9400       -12505.094             0.256            0.224
Chain 1:   9500        -8425.846             0.204            0.224
Chain 1:   9600       -10926.014             0.205            0.229
Chain 1:   9700       -12263.847             0.210            0.229
Chain 1:   9800        -8699.706             0.239            0.238
Chain 1:   9900        -8963.912             0.218            0.229
Chain 1:   10000        -8459.650             0.205            0.229
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00164 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59009.830             1.000            1.000
Chain 1:    200       -18343.658             1.608            2.217
Chain 1:    300        -9009.537             1.418            1.036
Chain 1:    400        -8244.453             1.086            1.036
Chain 1:    500        -8618.820             0.878            1.000
Chain 1:    600        -9274.394             0.743            1.000
Chain 1:    700        -8562.389             0.649            0.093
Chain 1:    800        -8446.613             0.570            0.093
Chain 1:    900        -7970.333             0.513            0.083
Chain 1:   1000        -7888.092             0.463            0.083
Chain 1:   1100        -7865.044             0.363            0.071
Chain 1:   1200        -7868.581             0.141            0.060
Chain 1:   1300        -7797.267             0.039            0.043
Chain 1:   1400        -7989.050             0.032            0.024
Chain 1:   1500        -7634.964             0.032            0.024
Chain 1:   1600        -7924.351             0.029            0.024
Chain 1:   1700        -7707.392             0.023            0.024
Chain 1:   1800        -7669.566             0.022            0.024
Chain 1:   1900        -7778.266             0.018            0.014
Chain 1:   2000        -7789.672             0.017            0.014
Chain 1:   2100        -7666.641             0.018            0.016
Chain 1:   2200        -7874.004             0.021            0.024
Chain 1:   2300        -7671.409             0.022            0.026
Chain 1:   2400        -7754.691             0.021            0.026
Chain 1:   2500        -7722.113             0.017            0.016
Chain 1:   2600        -7624.567             0.015            0.014
Chain 1:   2700        -7614.540             0.012            0.013
Chain 1:   2800        -7611.181             0.011            0.013
Chain 1:   2900        -7461.462             0.012            0.013
Chain 1:   3000        -7615.000             0.014            0.016
Chain 1:   3100        -7614.590             0.012            0.013
Chain 1:   3200        -7836.228             0.012            0.013
Chain 1:   3300        -7553.977             0.014            0.013
Chain 1:   3400        -7796.507             0.016            0.020
Chain 1:   3500        -7534.548             0.019            0.020
Chain 1:   3600        -7593.627             0.018            0.020
Chain 1:   3700        -7540.714             0.019            0.020
Chain 1:   3800        -7558.054             0.019            0.020
Chain 1:   3900        -7517.578             0.017            0.020
Chain 1:   4000        -7490.542             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003849 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86262.590             1.000            1.000
Chain 1:    200       -14047.983             3.070            5.141
Chain 1:    300       -10228.804             2.171            1.000
Chain 1:    400       -12421.759             1.673            1.000
Chain 1:    500        -8606.604             1.427            0.443
Chain 1:    600        -8885.247             1.194            0.443
Chain 1:    700        -8921.447             1.024            0.373
Chain 1:    800        -8972.411             0.897            0.373
Chain 1:    900        -8771.946             0.800            0.177
Chain 1:   1000        -8620.103             0.722            0.177
Chain 1:   1100        -8738.124             0.623            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8423.444             0.113            0.031
Chain 1:   1300        -8874.308             0.080            0.031
Chain 1:   1400        -8695.354             0.065            0.023
Chain 1:   1500        -8656.412             0.021            0.021
Chain 1:   1600        -8736.784             0.019            0.018
Chain 1:   1700        -8792.931             0.019            0.018
Chain 1:   1800        -8337.505             0.024            0.021
Chain 1:   1900        -8438.813             0.023            0.018
Chain 1:   2000        -8459.088             0.021            0.014
Chain 1:   2100        -8561.602             0.021            0.012
Chain 1:   2200        -8317.881             0.020            0.012
Chain 1:   2300        -8506.846             0.017            0.012
Chain 1:   2400        -8353.231             0.017            0.012
Chain 1:   2500        -8409.752             0.017            0.012
Chain 1:   2600        -8315.974             0.018            0.012
Chain 1:   2700        -8351.353             0.017            0.012
Chain 1:   2800        -8311.571             0.012            0.012
Chain 1:   2900        -8418.548             0.012            0.012
Chain 1:   3000        -8326.438             0.013            0.012
Chain 1:   3100        -8293.405             0.012            0.011
Chain 1:   3200        -8261.946             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393387.736             1.000            1.000
Chain 1:    200     -1579822.673             2.656            4.313
Chain 1:    300      -889923.483             2.029            1.000
Chain 1:    400      -458274.533             1.758            1.000
Chain 1:    500      -358989.842             1.461            0.942
Chain 1:    600      -234026.376             1.307            0.942
Chain 1:    700      -120046.697             1.256            0.942
Chain 1:    800       -87240.307             1.146            0.942
Chain 1:    900       -67539.295             1.051            0.775
Chain 1:   1000       -52317.164             0.975            0.775
Chain 1:   1100       -39765.540             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38946.356             0.477            0.376
Chain 1:   1300       -26848.363             0.445            0.376
Chain 1:   1400       -26567.176             0.352            0.316
Chain 1:   1500       -23140.539             0.339            0.316
Chain 1:   1600       -22354.952             0.289            0.292
Chain 1:   1700       -21220.365             0.199            0.291
Chain 1:   1800       -21163.443             0.162            0.148
Chain 1:   1900       -21490.647             0.134            0.053
Chain 1:   2000       -19995.981             0.113            0.053
Chain 1:   2100       -20234.484             0.082            0.035
Chain 1:   2200       -20462.533             0.081            0.035
Chain 1:   2300       -20078.112             0.038            0.019
Chain 1:   2400       -19849.761             0.038            0.019
Chain 1:   2500       -19652.167             0.024            0.015
Chain 1:   2600       -19280.868             0.023            0.015
Chain 1:   2700       -19237.434             0.018            0.012
Chain 1:   2800       -18954.005             0.019            0.015
Chain 1:   2900       -19235.803             0.019            0.015
Chain 1:   3000       -19221.797             0.012            0.012
Chain 1:   3100       -19306.998             0.011            0.012
Chain 1:   3200       -18996.892             0.011            0.015
Chain 1:   3300       -19202.250             0.010            0.012
Chain 1:   3400       -18675.929             0.012            0.015
Chain 1:   3500       -19289.804             0.014            0.015
Chain 1:   3600       -18593.891             0.016            0.015
Chain 1:   3700       -18982.667             0.018            0.016
Chain 1:   3800       -17938.452             0.022            0.020
Chain 1:   3900       -17934.569             0.021            0.020
Chain 1:   4000       -18051.810             0.021            0.020
Chain 1:   4100       -17965.417             0.021            0.020
Chain 1:   4200       -17780.804             0.021            0.020
Chain 1:   4300       -17919.761             0.021            0.020
Chain 1:   4400       -17875.864             0.018            0.010
Chain 1:   4500       -17778.331             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001382 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12490.906             1.000            1.000
Chain 1:    200        -9312.548             0.671            1.000
Chain 1:    300        -8124.501             0.496            0.341
Chain 1:    400        -8275.792             0.376            0.341
Chain 1:    500        -8262.306             0.301            0.146
Chain 1:    600        -8035.857             0.256            0.146
Chain 1:    700        -7934.641             0.221            0.028
Chain 1:    800        -7964.674             0.194            0.028
Chain 1:    900        -8070.242             0.174            0.018
Chain 1:   1000        -7984.299             0.158            0.018
Chain 1:   1100        -7977.599             0.058            0.013
Chain 1:   1200        -7953.094             0.024            0.013
Chain 1:   1300        -7907.501             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001645 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46098.047             1.000            1.000
Chain 1:    200       -15749.229             1.464            1.927
Chain 1:    300        -8789.533             1.240            1.000
Chain 1:    400        -8506.390             0.938            1.000
Chain 1:    500        -8720.405             0.755            0.792
Chain 1:    600        -8917.697             0.633            0.792
Chain 1:    700        -8023.019             0.559            0.112
Chain 1:    800        -8265.476             0.492            0.112
Chain 1:    900        -7703.431             0.446            0.073
Chain 1:   1000        -7908.244             0.404            0.073
Chain 1:   1100        -7799.005             0.305            0.033
Chain 1:   1200        -7851.159             0.113            0.029
Chain 1:   1300        -7819.008             0.034            0.026
Chain 1:   1400        -7900.702             0.032            0.025
Chain 1:   1500        -7623.697             0.033            0.026
Chain 1:   1600        -7659.267             0.032            0.026
Chain 1:   1700        -7587.859             0.021            0.014
Chain 1:   1800        -7616.289             0.019            0.010
Chain 1:   1900        -7624.032             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003303 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86372.676             1.000            1.000
Chain 1:    200       -13691.361             3.154            5.309
Chain 1:    300       -10001.837             2.226            1.000
Chain 1:    400       -11107.167             1.694            1.000
Chain 1:    500        -8994.440             1.402            0.369
Chain 1:    600        -8452.677             1.179            0.369
Chain 1:    700        -8670.749             1.014            0.235
Chain 1:    800        -9025.898             0.893            0.235
Chain 1:    900        -8798.185             0.796            0.100
Chain 1:   1000        -8748.119             0.717            0.100
Chain 1:   1100        -8585.545             0.619            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8380.587             0.091            0.039
Chain 1:   1300        -8686.688             0.057            0.035
Chain 1:   1400        -8634.234             0.048            0.026
Chain 1:   1500        -8519.074             0.026            0.025
Chain 1:   1600        -8625.775             0.021            0.024
Chain 1:   1700        -8699.774             0.019            0.019
Chain 1:   1800        -8267.074             0.020            0.019
Chain 1:   1900        -8371.171             0.019            0.014
Chain 1:   2000        -8346.574             0.019            0.014
Chain 1:   2100        -8325.285             0.017            0.012
Chain 1:   2200        -8289.576             0.015            0.012
Chain 1:   2300        -8418.532             0.013            0.012
Chain 1:   2400        -8273.431             0.014            0.012
Chain 1:   2500        -8341.956             0.014            0.012
Chain 1:   2600        -8261.310             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002965 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392547.310             1.000            1.000
Chain 1:    200     -1582440.623             2.652            4.304
Chain 1:    300      -891410.700             2.026            1.000
Chain 1:    400      -458485.767             1.756            1.000
Chain 1:    500      -358966.077             1.460            0.944
Chain 1:    600      -233716.786             1.306            0.944
Chain 1:    700      -119685.546             1.256            0.944
Chain 1:    800       -86845.299             1.146            0.944
Chain 1:    900       -67134.326             1.051            0.775
Chain 1:   1000       -51896.049             0.975            0.775
Chain 1:   1100       -39340.290             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38515.735             0.479            0.378
Chain 1:   1300       -26432.081             0.447            0.378
Chain 1:   1400       -26148.908             0.354            0.319
Chain 1:   1500       -22726.123             0.341            0.319
Chain 1:   1600       -21940.222             0.291            0.294
Chain 1:   1700       -20808.790             0.201            0.294
Chain 1:   1800       -20752.057             0.164            0.151
Chain 1:   1900       -21078.491             0.136            0.054
Chain 1:   2000       -19586.639             0.114            0.054
Chain 1:   2100       -19825.043             0.084            0.036
Chain 1:   2200       -20052.205             0.083            0.036
Chain 1:   2300       -19668.756             0.039            0.019
Chain 1:   2400       -19440.692             0.039            0.019
Chain 1:   2500       -19242.919             0.025            0.015
Chain 1:   2600       -18872.486             0.023            0.015
Chain 1:   2700       -18829.367             0.018            0.012
Chain 1:   2800       -18546.131             0.019            0.015
Chain 1:   2900       -18827.650             0.019            0.015
Chain 1:   3000       -18813.702             0.012            0.012
Chain 1:   3100       -18898.725             0.011            0.012
Chain 1:   3200       -18589.160             0.012            0.015
Chain 1:   3300       -18794.133             0.011            0.012
Chain 1:   3400       -18268.630             0.012            0.015
Chain 1:   3500       -18881.168             0.015            0.015
Chain 1:   3600       -18187.083             0.016            0.015
Chain 1:   3700       -18574.447             0.018            0.017
Chain 1:   3800       -17532.941             0.023            0.021
Chain 1:   3900       -17529.112             0.021            0.021
Chain 1:   4000       -17646.379             0.022            0.021
Chain 1:   4100       -17560.044             0.022            0.021
Chain 1:   4200       -17376.074             0.021            0.021
Chain 1:   4300       -17514.592             0.021            0.021
Chain 1:   4400       -17471.193             0.018            0.011
Chain 1:   4500       -17373.737             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001278 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13196.765             1.000            1.000
Chain 1:    200        -9888.373             0.667            1.000
Chain 1:    300        -8975.796             0.479            0.335
Chain 1:    400        -8773.671             0.365            0.335
Chain 1:    500        -8644.291             0.295            0.102
Chain 1:    600        -8274.856             0.253            0.102
Chain 1:    700        -8186.114             0.219            0.045
Chain 1:    800        -8200.912             0.191            0.045
Chain 1:    900        -8267.890             0.171            0.023
Chain 1:   1000        -8240.075             0.154            0.023
Chain 1:   1100        -8231.605             0.054            0.015
Chain 1:   1200        -8182.417             0.022            0.011
Chain 1:   1300        -8133.169             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001436 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51830.010             1.000            1.000
Chain 1:    200       -16999.256             1.524            2.049
Chain 1:    300        -9095.762             1.306            1.000
Chain 1:    400        -9045.157             0.981            1.000
Chain 1:    500        -8345.193             0.801            0.869
Chain 1:    600        -8315.061             0.668            0.869
Chain 1:    700        -8753.741             0.580            0.084
Chain 1:    800        -7812.479             0.523            0.120
Chain 1:    900        -7858.266             0.465            0.084
Chain 1:   1000        -7851.406             0.419            0.084
Chain 1:   1100        -7708.460             0.321            0.050
Chain 1:   1200        -7757.814             0.116            0.019
Chain 1:   1300        -7754.611             0.030            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003076 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87430.524             1.000            1.000
Chain 1:    200       -14223.318             3.073            5.147
Chain 1:    300       -10398.802             2.172            1.000
Chain 1:    400       -12250.068             1.666            1.000
Chain 1:    500        -9126.359             1.402            0.368
Chain 1:    600        -9890.840             1.181            0.368
Chain 1:    700        -8955.107             1.027            0.342
Chain 1:    800        -9052.208             0.900            0.342
Chain 1:    900        -9032.593             0.800            0.151
Chain 1:   1000        -9183.853             0.722            0.151
Chain 1:   1100        -8887.568             0.625            0.104   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8615.119             0.114            0.077
Chain 1:   1300        -9010.403             0.081            0.044
Chain 1:   1400        -8904.294             0.067            0.033
Chain 1:   1500        -8886.158             0.033            0.032
Chain 1:   1600        -8932.377             0.026            0.016
Chain 1:   1700        -8989.126             0.016            0.012
Chain 1:   1800        -8521.896             0.021            0.016
Chain 1:   1900        -8643.752             0.022            0.016
Chain 1:   2000        -8664.123             0.021            0.014
Chain 1:   2100        -8750.126             0.018            0.012
Chain 1:   2200        -8525.892             0.018            0.012
Chain 1:   2300        -8733.817             0.016            0.012
Chain 1:   2400        -8534.034             0.017            0.014
Chain 1:   2500        -8612.735             0.018            0.014
Chain 1:   2600        -8518.361             0.018            0.014
Chain 1:   2700        -8557.349             0.018            0.014
Chain 1:   2800        -8509.749             0.013            0.011
Chain 1:   2900        -8623.226             0.013            0.011
Chain 1:   3000        -8532.029             0.014            0.011
Chain 1:   3100        -8499.816             0.013            0.011
Chain 1:   3200        -8470.485             0.011            0.011
Chain 1:   3300        -8735.464             0.012            0.011
Chain 1:   3400        -8783.375             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00369 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426897.812             1.000            1.000
Chain 1:    200     -1587124.316             2.655            4.310
Chain 1:    300      -892745.081             2.029            1.000
Chain 1:    400      -459033.522             1.758            1.000
Chain 1:    500      -359135.442             1.462            0.945
Chain 1:    600      -233978.989             1.308            0.945
Chain 1:    700      -120097.729             1.256            0.945
Chain 1:    800       -87250.904             1.146            0.945
Chain 1:    900       -67578.203             1.051            0.778
Chain 1:   1000       -52375.714             0.975            0.778
Chain 1:   1100       -39845.218             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39028.971             0.478            0.376
Chain 1:   1300       -26969.742             0.445            0.376
Chain 1:   1400       -26691.073             0.351            0.314
Chain 1:   1500       -23272.919             0.338            0.314
Chain 1:   1600       -22488.901             0.288            0.291
Chain 1:   1700       -21360.428             0.199            0.290
Chain 1:   1800       -21304.515             0.161            0.147
Chain 1:   1900       -21631.527             0.134            0.053
Chain 1:   2000       -20139.567             0.112            0.053
Chain 1:   2100       -20378.371             0.082            0.035
Chain 1:   2200       -20605.428             0.081            0.035
Chain 1:   2300       -20221.844             0.038            0.019
Chain 1:   2400       -19993.584             0.038            0.019
Chain 1:   2500       -19795.427             0.024            0.015
Chain 1:   2600       -19424.816             0.023            0.015
Chain 1:   2700       -19381.558             0.018            0.012
Chain 1:   2800       -19097.911             0.019            0.015
Chain 1:   2900       -19379.634             0.019            0.015
Chain 1:   3000       -19365.801             0.011            0.012
Chain 1:   3100       -19450.909             0.011            0.011
Chain 1:   3200       -19140.976             0.011            0.015
Chain 1:   3300       -19346.186             0.010            0.011
Chain 1:   3400       -18819.960             0.012            0.015
Chain 1:   3500       -19433.450             0.014            0.015
Chain 1:   3600       -18738.043             0.016            0.015
Chain 1:   3700       -19126.411             0.018            0.016
Chain 1:   3800       -18082.740             0.022            0.020
Chain 1:   3900       -18078.758             0.021            0.020
Chain 1:   4000       -18196.124             0.021            0.020
Chain 1:   4100       -18109.653             0.021            0.020
Chain 1:   4200       -17925.180             0.021            0.020
Chain 1:   4300       -18064.117             0.020            0.020
Chain 1:   4400       -18020.370             0.018            0.010
Chain 1:   4500       -17922.733             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001447 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49551.658             1.000            1.000
Chain 1:    200       -22934.376             1.080            1.161
Chain 1:    300       -19946.109             0.770            1.000
Chain 1:    400       -36020.588             0.689            1.000
Chain 1:    500       -13715.574             0.877            1.000
Chain 1:    600       -19860.600             0.782            1.000
Chain 1:    700       -12621.770             0.752            0.574
Chain 1:    800       -13477.047             0.666            0.574
Chain 1:    900       -11036.596             0.617            0.446
Chain 1:   1000       -24656.944             0.610            0.552
Chain 1:   1100       -19341.178             0.538            0.446   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10612.357             0.504            0.446   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -22031.732             0.541            0.518   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -20123.513             0.506            0.518   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500       -10609.040             0.433            0.518   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1600       -12229.329             0.415            0.518   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1700        -9736.893             0.383            0.275
Chain 1:   1800       -12942.566             0.402            0.275
Chain 1:   1900       -11356.249             0.394            0.275
Chain 1:   2000       -10363.990             0.348            0.256
Chain 1:   2100       -10420.582             0.321            0.248
Chain 1:   2200       -14824.680             0.268            0.248
Chain 1:   2300       -10168.380             0.262            0.248
Chain 1:   2400        -9226.768             0.263            0.248
Chain 1:   2500        -9552.065             0.177            0.140
Chain 1:   2600        -9881.122             0.167            0.140
Chain 1:   2700        -9325.350             0.147            0.102
Chain 1:   2800        -9906.584             0.128            0.096
Chain 1:   2900       -10198.812             0.117            0.060
Chain 1:   3000       -18285.212             0.152            0.060
Chain 1:   3100        -9431.983             0.245            0.102
Chain 1:   3200        -8996.315             0.220            0.060
Chain 1:   3300       -16134.963             0.219            0.060
Chain 1:   3400        -9943.995             0.271            0.060
Chain 1:   3500        -9178.552             0.276            0.083
Chain 1:   3600       -10273.540             0.283            0.107
Chain 1:   3700        -9273.360             0.288            0.108
Chain 1:   3800        -9225.417             0.283            0.108
Chain 1:   3900        -9419.956             0.282            0.108
Chain 1:   4000        -8695.344             0.246            0.107
Chain 1:   4100        -8919.603             0.155            0.083
Chain 1:   4200        -9863.018             0.159            0.096
Chain 1:   4300       -13117.408             0.140            0.096
Chain 1:   4400        -9207.050             0.120            0.096
Chain 1:   4500        -8932.965             0.115            0.096
Chain 1:   4600        -8631.630             0.108            0.083
Chain 1:   4700        -9412.764             0.105            0.083
Chain 1:   4800       -12517.074             0.129            0.083
Chain 1:   4900       -10602.699             0.145            0.096
Chain 1:   5000       -10961.403             0.140            0.096
Chain 1:   5100       -15964.332             0.169            0.181
Chain 1:   5200       -10472.410             0.212            0.248
Chain 1:   5300       -10844.157             0.191            0.181
Chain 1:   5400       -14714.387             0.174            0.181
Chain 1:   5500       -10233.580             0.215            0.248
Chain 1:   5600       -14466.463             0.241            0.263
Chain 1:   5700       -10064.853             0.276            0.293
Chain 1:   5800        -9058.744             0.263            0.293
Chain 1:   5900       -15689.560             0.287            0.313
Chain 1:   6000       -10098.752             0.339            0.423
Chain 1:   6100       -12152.625             0.325            0.423
Chain 1:   6200        -8385.389             0.317            0.423
Chain 1:   6300       -10236.132             0.332            0.423
Chain 1:   6400        -9922.708             0.309            0.423
Chain 1:   6500        -8722.692             0.279            0.293
Chain 1:   6600        -8447.842             0.253            0.181
Chain 1:   6700       -11892.362             0.238            0.181
Chain 1:   6800        -9650.627             0.250            0.232
Chain 1:   6900        -8639.520             0.219            0.181
Chain 1:   7000       -13900.047             0.202            0.181
Chain 1:   7100        -8258.182             0.253            0.232
Chain 1:   7200        -8571.237             0.212            0.181
Chain 1:   7300        -8554.444             0.194            0.138
Chain 1:   7400        -8608.148             0.192            0.138
Chain 1:   7500        -8685.245             0.179            0.117
Chain 1:   7600        -8822.378             0.177            0.117
Chain 1:   7700        -9940.189             0.159            0.112
Chain 1:   7800        -8416.322             0.154            0.112
Chain 1:   7900       -11146.235             0.167            0.112
Chain 1:   8000        -9349.958             0.148            0.112
Chain 1:   8100        -8614.434             0.089            0.085
Chain 1:   8200        -9178.149             0.091            0.085
Chain 1:   8300        -8341.587             0.101            0.100
Chain 1:   8400        -8406.275             0.101            0.100
Chain 1:   8500        -9417.540             0.111            0.107
Chain 1:   8600       -10019.280             0.115            0.107
Chain 1:   8700        -8514.392             0.122            0.107
Chain 1:   8800        -9094.698             0.110            0.100
Chain 1:   8900        -9593.358             0.091            0.085
Chain 1:   9000       -11610.739             0.089            0.085
Chain 1:   9100        -9172.181             0.107            0.100
Chain 1:   9200        -8517.144             0.108            0.100
Chain 1:   9300        -8645.624             0.100            0.077
Chain 1:   9400        -8473.388             0.101            0.077
Chain 1:   9500        -8232.735             0.093            0.064
Chain 1:   9600        -8406.733             0.089            0.064
Chain 1:   9700        -8502.542             0.073            0.052
Chain 1:   9800        -9109.978             0.073            0.052
Chain 1:   9900        -9183.565             0.069            0.029
Chain 1:   10000       -10507.297             0.064            0.029
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58553.654             1.000            1.000
Chain 1:    200       -17897.106             1.636            2.272
Chain 1:    300        -8815.090             1.434            1.030
Chain 1:    400        -8192.184             1.095            1.030
Chain 1:    500        -8134.447             0.877            1.000
Chain 1:    600        -8434.401             0.737            1.000
Chain 1:    700        -8211.316             0.635            0.076
Chain 1:    800        -8319.196             0.558            0.076
Chain 1:    900        -8121.189             0.498            0.036
Chain 1:   1000        -7909.870             0.451            0.036
Chain 1:   1100        -7730.954             0.354            0.027
Chain 1:   1200        -7655.938             0.127            0.027
Chain 1:   1300        -7845.583             0.027            0.024
Chain 1:   1400        -7864.076             0.019            0.024
Chain 1:   1500        -7661.891             0.021            0.024
Chain 1:   1600        -7840.748             0.020            0.024
Chain 1:   1700        -7581.784             0.021            0.024
Chain 1:   1800        -7634.834             0.020            0.024
Chain 1:   1900        -7671.958             0.018            0.023
Chain 1:   2000        -7709.634             0.016            0.023
Chain 1:   2100        -7651.654             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003458 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86363.472             1.000            1.000
Chain 1:    200       -13705.237             3.151            5.301
Chain 1:    300       -10006.708             2.224            1.000
Chain 1:    400       -11059.301             1.692            1.000
Chain 1:    500        -8795.629             1.405            0.370
Chain 1:    600        -8344.747             1.180            0.370
Chain 1:    700        -8462.851             1.013            0.257
Chain 1:    800        -8733.755             0.890            0.257
Chain 1:    900        -8686.038             0.792            0.095
Chain 1:   1000        -8724.427             0.713            0.095
Chain 1:   1100        -8553.544             0.615            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8363.493             0.087            0.031
Chain 1:   1300        -8646.499             0.054            0.031
Chain 1:   1400        -8625.005             0.044            0.023
Chain 1:   1500        -8488.913             0.020            0.020
Chain 1:   1600        -8605.863             0.016            0.016
Chain 1:   1700        -8667.528             0.016            0.016
Chain 1:   1800        -8229.008             0.018            0.016
Chain 1:   1900        -8333.546             0.018            0.016
Chain 1:   2000        -8312.824             0.018            0.016
Chain 1:   2100        -8459.032             0.018            0.016
Chain 1:   2200        -8235.533             0.018            0.016
Chain 1:   2300        -8405.896             0.017            0.016
Chain 1:   2400        -8237.424             0.019            0.017
Chain 1:   2500        -8307.108             0.018            0.017
Chain 1:   2600        -8219.143             0.018            0.017
Chain 1:   2700        -8252.682             0.018            0.017
Chain 1:   2800        -8211.197             0.013            0.013
Chain 1:   2900        -8307.342             0.013            0.012
Chain 1:   3000        -8146.003             0.014            0.017
Chain 1:   3100        -8295.362             0.015            0.018
Chain 1:   3200        -8166.298             0.013            0.016
Chain 1:   3300        -8178.396             0.012            0.012
Chain 1:   3400        -8354.024             0.012            0.012
Chain 1:   3500        -8357.780             0.011            0.012
Chain 1:   3600        -8122.318             0.013            0.016
Chain 1:   3700        -8270.705             0.014            0.018
Chain 1:   3800        -8128.114             0.015            0.018
Chain 1:   3900        -8061.826             0.015            0.018
Chain 1:   4000        -8144.635             0.014            0.018
Chain 1:   4100        -8133.909             0.012            0.016
Chain 1:   4200        -8119.182             0.011            0.010
Chain 1:   4300        -8152.472             0.011            0.010
Chain 1:   4400        -8109.799             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003558 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431115.647             1.000            1.000
Chain 1:    200     -1588575.814             2.654            4.307
Chain 1:    300      -889350.980             2.031            1.000
Chain 1:    400      -456546.889             1.760            1.000
Chain 1:    500      -356690.568             1.464            0.948
Chain 1:    600      -231964.891             1.310            0.948
Chain 1:    700      -118826.450             1.259            0.948
Chain 1:    800       -86217.319             1.149            0.948
Chain 1:    900       -66687.355             1.054            0.786
Chain 1:   1000       -51586.178             0.978            0.786
Chain 1:   1100       -39154.717             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38347.488             0.481            0.378
Chain 1:   1300       -26383.376             0.447            0.378
Chain 1:   1400       -26112.600             0.354            0.317
Chain 1:   1500       -22720.346             0.341            0.317
Chain 1:   1600       -21944.175             0.290            0.293
Chain 1:   1700       -20826.556             0.200            0.293
Chain 1:   1800       -20773.286             0.163            0.149
Chain 1:   1900       -21099.967             0.135            0.054
Chain 1:   2000       -19614.894             0.113            0.054
Chain 1:   2100       -19853.069             0.083            0.035
Chain 1:   2200       -20079.143             0.082            0.035
Chain 1:   2300       -19696.536             0.039            0.019
Chain 1:   2400       -19468.532             0.039            0.019
Chain 1:   2500       -19270.270             0.025            0.015
Chain 1:   2600       -18900.249             0.023            0.015
Chain 1:   2700       -18857.203             0.018            0.012
Chain 1:   2800       -18573.699             0.019            0.015
Chain 1:   2900       -18855.070             0.019            0.015
Chain 1:   3000       -18841.284             0.012            0.012
Chain 1:   3100       -18926.341             0.011            0.012
Chain 1:   3200       -18616.789             0.012            0.015
Chain 1:   3300       -18821.726             0.011            0.012
Chain 1:   3400       -18296.080             0.012            0.015
Chain 1:   3500       -18908.683             0.015            0.015
Chain 1:   3600       -18214.353             0.016            0.015
Chain 1:   3700       -18601.822             0.018            0.017
Chain 1:   3800       -17559.934             0.023            0.021
Chain 1:   3900       -17555.999             0.021            0.021
Chain 1:   4000       -17673.352             0.022            0.021
Chain 1:   4100       -17586.995             0.022            0.021
Chain 1:   4200       -17402.926             0.021            0.021
Chain 1:   4300       -17541.591             0.021            0.021
Chain 1:   4400       -17498.112             0.018            0.011
Chain 1:   4500       -17400.558             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48871.449             1.000            1.000
Chain 1:    200       -15370.307             1.590            2.180
Chain 1:    300       -18907.050             1.122            1.000
Chain 1:    400       -18512.747             0.847            1.000
Chain 1:    500       -16623.035             0.700            0.187
Chain 1:    600       -15304.315             0.598            0.187
Chain 1:    700       -16182.077             0.520            0.114
Chain 1:    800       -13173.488             0.484            0.187
Chain 1:    900       -13913.544             0.436            0.114
Chain 1:   1000       -12070.569             0.408            0.153
Chain 1:   1100       -10141.108             0.327            0.153
Chain 1:   1200       -12822.482             0.130            0.153
Chain 1:   1300       -19318.612             0.145            0.153
Chain 1:   1400       -12998.503             0.191            0.190
Chain 1:   1500       -10880.780             0.199            0.195
Chain 1:   1600       -11760.160             0.198            0.195
Chain 1:   1700       -12102.983             0.195            0.195
Chain 1:   1800       -20065.885             0.212            0.195
Chain 1:   1900       -10801.385             0.293            0.209
Chain 1:   2000       -12924.260             0.294            0.209
Chain 1:   2100       -10026.463             0.304            0.289
Chain 1:   2200        -9484.393             0.289            0.289
Chain 1:   2300        -9803.615             0.258            0.195
Chain 1:   2400        -9620.829             0.211            0.164
Chain 1:   2500        -9890.624             0.195            0.075
Chain 1:   2600        -9845.624             0.188            0.057
Chain 1:   2700       -12800.422             0.208            0.164
Chain 1:   2800       -11296.124             0.182            0.133
Chain 1:   2900       -15786.836             0.124            0.133
Chain 1:   3000        -9027.392             0.183            0.133
Chain 1:   3100        -8917.094             0.155            0.057
Chain 1:   3200       -10919.527             0.168            0.133
Chain 1:   3300        -9387.933             0.181            0.163
Chain 1:   3400        -9248.146             0.180            0.163
Chain 1:   3500        -9860.323             0.184            0.163
Chain 1:   3600       -10283.548             0.187            0.163
Chain 1:   3700        -9456.070             0.173            0.133
Chain 1:   3800       -13762.487             0.191            0.163
Chain 1:   3900        -8806.398             0.219            0.163
Chain 1:   4000        -8770.490             0.144            0.088
Chain 1:   4100        -9251.530             0.148            0.088
Chain 1:   4200        -9682.440             0.135            0.062
Chain 1:   4300        -9832.065             0.120            0.052
Chain 1:   4400       -11053.134             0.129            0.062
Chain 1:   4500       -15135.208             0.150            0.088
Chain 1:   4600       -13345.184             0.159            0.110
Chain 1:   4700        -8839.932             0.202            0.134
Chain 1:   4800       -12846.739             0.201            0.134
Chain 1:   4900        -9548.258             0.180            0.134
Chain 1:   5000        -9575.463             0.180            0.134
Chain 1:   5100       -12031.560             0.195            0.204
Chain 1:   5200       -13026.641             0.198            0.204
Chain 1:   5300        -8659.042             0.247            0.270
Chain 1:   5400        -8875.057             0.238            0.270
Chain 1:   5500        -9146.690             0.214            0.204
Chain 1:   5600       -13218.052             0.232            0.308
Chain 1:   5700       -12868.592             0.183            0.204
Chain 1:   5800        -9013.330             0.195            0.204
Chain 1:   5900        -8410.336             0.168            0.076
Chain 1:   6000       -12458.047             0.200            0.204
Chain 1:   6100        -8832.958             0.220            0.308
Chain 1:   6200        -8444.614             0.217            0.308
Chain 1:   6300        -9108.475             0.174            0.073
Chain 1:   6400       -13283.214             0.203            0.308
Chain 1:   6500       -10701.908             0.224            0.308
Chain 1:   6600        -8951.628             0.213            0.241
Chain 1:   6700        -8805.522             0.212            0.241
Chain 1:   6800       -10972.968             0.189            0.198
Chain 1:   6900        -9324.115             0.200            0.198
Chain 1:   7000       -12277.701             0.191            0.198
Chain 1:   7100        -8303.813             0.198            0.198
Chain 1:   7200        -8995.045             0.201            0.198
Chain 1:   7300        -8430.817             0.200            0.198
Chain 1:   7400        -8822.259             0.173            0.196
Chain 1:   7500        -8654.178             0.151            0.177
Chain 1:   7600        -8442.110             0.134            0.077
Chain 1:   7700        -8285.513             0.135            0.077
Chain 1:   7800        -8731.198             0.120            0.067
Chain 1:   7900        -9346.855             0.109            0.066
Chain 1:   8000        -8312.196             0.097            0.066
Chain 1:   8100        -8780.528             0.055            0.053
Chain 1:   8200        -8674.527             0.048            0.051
Chain 1:   8300       -10520.591             0.059            0.051
Chain 1:   8400       -10360.838             0.056            0.051
Chain 1:   8500        -8435.989             0.077            0.053
Chain 1:   8600       -12888.634             0.109            0.066
Chain 1:   8700        -8341.752             0.162            0.124
Chain 1:   8800        -8691.931             0.161            0.124
Chain 1:   8900        -9736.585             0.165            0.124
Chain 1:   9000        -8385.100             0.168            0.161
Chain 1:   9100        -8925.053             0.169            0.161
Chain 1:   9200        -8372.952             0.174            0.161
Chain 1:   9300       -11107.336             0.182            0.161
Chain 1:   9400       -11485.461             0.183            0.161
Chain 1:   9500        -8449.332             0.196            0.161
Chain 1:   9600        -9976.612             0.177            0.153
Chain 1:   9700        -8369.846             0.142            0.153
Chain 1:   9800       -10522.908             0.158            0.161
Chain 1:   9900       -11086.179             0.153            0.161
Chain 1:   10000        -8271.426             0.171            0.192
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001525 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57001.043             1.000            1.000
Chain 1:    200       -17507.846             1.628            2.256
Chain 1:    300        -8755.176             1.418            1.000
Chain 1:    400        -8409.954             1.074            1.000
Chain 1:    500        -8141.657             0.866            1.000
Chain 1:    600        -8907.246             0.736            1.000
Chain 1:    700        -8041.596             0.646            0.108
Chain 1:    800        -7871.526             0.568            0.108
Chain 1:    900        -8165.822             0.509            0.086
Chain 1:   1000        -7874.610             0.462            0.086
Chain 1:   1100        -7801.045             0.363            0.041
Chain 1:   1200        -7661.047             0.139            0.037
Chain 1:   1300        -7689.925             0.039            0.036
Chain 1:   1400        -7847.418             0.037            0.033
Chain 1:   1500        -7646.490             0.037            0.026
Chain 1:   1600        -7791.503             0.030            0.022
Chain 1:   1700        -7547.412             0.022            0.022
Chain 1:   1800        -7597.034             0.021            0.020
Chain 1:   1900        -7633.598             0.018            0.019
Chain 1:   2000        -7666.290             0.014            0.018
Chain 1:   2100        -7697.215             0.014            0.018
Chain 1:   2200        -7740.234             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86668.781             1.000            1.000
Chain 1:    200       -13515.678             3.206            5.412
Chain 1:    300        -9902.667             2.259            1.000
Chain 1:    400       -10717.475             1.713            1.000
Chain 1:    500        -8860.080             1.413            0.365
Chain 1:    600        -8411.733             1.186            0.365
Chain 1:    700        -8662.337             1.021            0.210
Chain 1:    800        -9401.273             0.903            0.210
Chain 1:    900        -8774.067             0.811            0.079
Chain 1:   1000        -8492.335             0.733            0.079
Chain 1:   1100        -8697.708             0.635            0.076   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8270.279             0.099            0.071
Chain 1:   1300        -8584.368             0.066            0.053
Chain 1:   1400        -8600.403             0.059            0.052
Chain 1:   1500        -8482.993             0.039            0.037
Chain 1:   1600        -8590.473             0.035            0.033
Chain 1:   1700        -8675.850             0.033            0.033
Chain 1:   1800        -8268.486             0.030            0.033
Chain 1:   1900        -8365.055             0.024            0.024
Chain 1:   2000        -8337.399             0.021            0.014
Chain 1:   2100        -8458.266             0.020            0.014
Chain 1:   2200        -8281.751             0.017            0.014
Chain 1:   2300        -8403.408             0.015            0.014
Chain 1:   2400        -8414.098             0.015            0.014
Chain 1:   2500        -8376.247             0.014            0.013
Chain 1:   2600        -8375.733             0.013            0.012
Chain 1:   2700        -8290.733             0.013            0.012
Chain 1:   2800        -8255.531             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003128 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409672.474             1.000            1.000
Chain 1:    200     -1587468.522             2.649            4.298
Chain 1:    300      -891531.976             2.026            1.000
Chain 1:    400      -458007.601             1.756            1.000
Chain 1:    500      -358095.123             1.461            0.947
Chain 1:    600      -233013.429             1.307            0.947
Chain 1:    700      -119234.519             1.256            0.947
Chain 1:    800       -86450.735             1.147            0.947
Chain 1:    900       -66792.441             1.052            0.781
Chain 1:   1000       -51587.713             0.976            0.781
Chain 1:   1100       -39065.844             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38239.902             0.481            0.379
Chain 1:   1300       -26200.272             0.449            0.379
Chain 1:   1400       -25918.770             0.355            0.321
Chain 1:   1500       -22507.522             0.342            0.321
Chain 1:   1600       -21724.268             0.292            0.295
Chain 1:   1700       -20598.707             0.202            0.294
Chain 1:   1800       -20542.849             0.165            0.152
Chain 1:   1900       -20868.870             0.137            0.055
Chain 1:   2000       -19380.690             0.115            0.055
Chain 1:   2100       -19618.979             0.084            0.036
Chain 1:   2200       -19845.346             0.083            0.036
Chain 1:   2300       -19462.671             0.039            0.020
Chain 1:   2400       -19234.811             0.039            0.020
Chain 1:   2500       -19036.843             0.025            0.016
Chain 1:   2600       -18667.194             0.024            0.016
Chain 1:   2700       -18624.192             0.018            0.012
Chain 1:   2800       -18341.137             0.020            0.015
Chain 1:   2900       -18622.321             0.019            0.015
Chain 1:   3000       -18608.497             0.012            0.012
Chain 1:   3100       -18693.459             0.011            0.012
Chain 1:   3200       -18384.251             0.012            0.015
Chain 1:   3300       -18588.894             0.011            0.012
Chain 1:   3400       -18064.001             0.013            0.015
Chain 1:   3500       -18675.603             0.015            0.015
Chain 1:   3600       -17982.661             0.017            0.015
Chain 1:   3700       -18369.177             0.019            0.017
Chain 1:   3800       -17329.465             0.023            0.021
Chain 1:   3900       -17325.622             0.021            0.021
Chain 1:   4000       -17442.926             0.022            0.021
Chain 1:   4100       -17356.720             0.022            0.021
Chain 1:   4200       -17173.072             0.021            0.021
Chain 1:   4300       -17311.386             0.021            0.021
Chain 1:   4400       -17268.320             0.019            0.011
Chain 1:   4500       -17170.861             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12951.228             1.000            1.000
Chain 1:    200        -9540.310             0.679            1.000
Chain 1:    300        -8098.648             0.512            0.358
Chain 1:    400        -8270.125             0.389            0.358
Chain 1:    500        -8144.199             0.314            0.178
Chain 1:    600        -7900.078             0.267            0.178
Chain 1:    700        -8011.247             0.231            0.031
Chain 1:    800        -7809.343             0.205            0.031
Chain 1:    900        -7823.647             0.183            0.026
Chain 1:   1000        -7878.282             0.165            0.026
Chain 1:   1100        -7990.988             0.067            0.021
Chain 1:   1200        -7864.113             0.032            0.016
Chain 1:   1300        -7815.833             0.015            0.015
Chain 1:   1400        -7841.613             0.013            0.014
Chain 1:   1500        -7929.366             0.013            0.014
Chain 1:   1600        -7901.352             0.010            0.011
Chain 1:   1700        -7822.868             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56755.959             1.000            1.000
Chain 1:    200       -17273.972             1.643            2.286
Chain 1:    300        -8571.771             1.434            1.015
Chain 1:    400        -7880.483             1.097            1.015
Chain 1:    500        -8304.272             0.888            1.000
Chain 1:    600        -9185.264             0.756            1.000
Chain 1:    700        -7775.394             0.674            0.181
Chain 1:    800        -8072.655             0.594            0.181
Chain 1:    900        -7952.900             0.530            0.096
Chain 1:   1000        -7718.410             0.480            0.096
Chain 1:   1100        -7792.452             0.381            0.088
Chain 1:   1200        -7550.724             0.155            0.051
Chain 1:   1300        -7724.945             0.056            0.037
Chain 1:   1400        -7798.618             0.048            0.032
Chain 1:   1500        -7610.626             0.046            0.030
Chain 1:   1600        -7570.214             0.037            0.025
Chain 1:   1700        -7500.021             0.020            0.023
Chain 1:   1800        -7568.697             0.017            0.015
Chain 1:   1900        -7622.641             0.016            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86704.372             1.000            1.000
Chain 1:    200       -13348.234             3.248            5.496
Chain 1:    300        -9764.722             2.288            1.000
Chain 1:    400       -10465.454             1.732            1.000
Chain 1:    500        -8697.265             1.427            0.367
Chain 1:    600        -8280.619             1.197            0.367
Chain 1:    700        -8324.539             1.027            0.203
Chain 1:    800        -9124.075             0.910            0.203
Chain 1:    900        -8658.117             0.814            0.088
Chain 1:   1000        -8303.957             0.737            0.088
Chain 1:   1100        -8595.682             0.641            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8318.665             0.094            0.054
Chain 1:   1300        -8498.050             0.060            0.050
Chain 1:   1400        -8500.864             0.053            0.043
Chain 1:   1500        -8362.867             0.034            0.034
Chain 1:   1600        -8471.638             0.031            0.033
Chain 1:   1700        -8557.653             0.031            0.033
Chain 1:   1800        -8162.303             0.027            0.033
Chain 1:   1900        -8262.630             0.023            0.021
Chain 1:   2000        -8233.469             0.019            0.017
Chain 1:   2100        -8354.807             0.017            0.015
Chain 1:   2200        -8133.452             0.017            0.015
Chain 1:   2300        -8291.550             0.016            0.015
Chain 1:   2400        -8304.181             0.017            0.015
Chain 1:   2500        -8275.287             0.015            0.013
Chain 1:   2600        -8277.936             0.014            0.012
Chain 1:   2700        -8184.120             0.014            0.012
Chain 1:   2800        -8154.579             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424398.871             1.000            1.000
Chain 1:    200     -1587570.862             2.653            4.306
Chain 1:    300      -890995.456             2.029            1.000
Chain 1:    400      -457668.332             1.759            1.000
Chain 1:    500      -357523.498             1.463            0.947
Chain 1:    600      -232331.926             1.309            0.947
Chain 1:    700      -118768.931             1.259            0.947
Chain 1:    800       -86040.604             1.149            0.947
Chain 1:    900       -66429.447             1.054            0.782
Chain 1:   1000       -51266.998             0.978            0.782
Chain 1:   1100       -38791.591             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37970.118             0.482            0.380
Chain 1:   1300       -25983.014             0.450            0.380
Chain 1:   1400       -25705.816             0.356            0.322
Chain 1:   1500       -22308.032             0.343            0.322
Chain 1:   1600       -21528.583             0.293            0.296
Chain 1:   1700       -20409.576             0.203            0.295
Chain 1:   1800       -20355.213             0.165            0.152
Chain 1:   1900       -20680.992             0.137            0.055
Chain 1:   2000       -19196.627             0.115            0.055
Chain 1:   2100       -19434.738             0.085            0.036
Chain 1:   2200       -19660.343             0.083            0.036
Chain 1:   2300       -19278.379             0.039            0.020
Chain 1:   2400       -19050.668             0.039            0.020
Chain 1:   2500       -18852.434             0.025            0.016
Chain 1:   2600       -18483.219             0.024            0.016
Chain 1:   2700       -18440.394             0.018            0.012
Chain 1:   2800       -18157.307             0.020            0.016
Chain 1:   2900       -18438.301             0.020            0.015
Chain 1:   3000       -18424.579             0.012            0.012
Chain 1:   3100       -18509.496             0.011            0.012
Chain 1:   3200       -18200.476             0.012            0.015
Chain 1:   3300       -18404.969             0.011            0.012
Chain 1:   3400       -17880.332             0.013            0.015
Chain 1:   3500       -18491.438             0.015            0.016
Chain 1:   3600       -17799.094             0.017            0.016
Chain 1:   3700       -18185.119             0.019            0.017
Chain 1:   3800       -17146.275             0.023            0.021
Chain 1:   3900       -17142.412             0.022            0.021
Chain 1:   4000       -17259.762             0.022            0.021
Chain 1:   4100       -17173.568             0.022            0.021
Chain 1:   4200       -16990.135             0.022            0.021
Chain 1:   4300       -17128.333             0.021            0.021
Chain 1:   4400       -17085.419             0.019            0.011
Chain 1:   4500       -16987.959             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13015.433             1.000            1.000
Chain 1:    200       -10116.092             0.643            1.000
Chain 1:    300        -8703.474             0.483            0.287
Chain 1:    400        -8396.292             0.371            0.287
Chain 1:    500        -8267.878             0.300            0.162
Chain 1:    600        -8256.309             0.250            0.162
Chain 1:    700        -8275.206             0.215            0.037
Chain 1:    800        -8226.952             0.189            0.037
Chain 1:    900        -8176.546             0.169            0.016
Chain 1:   1000        -8256.053             0.153            0.016
Chain 1:   1100        -8303.388             0.053            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001433 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -52724.164             1.000            1.000
Chain 1:    200       -17174.804             1.535            2.070
Chain 1:    300        -9219.179             1.311            1.000
Chain 1:    400        -8643.046             1.000            1.000
Chain 1:    500        -9098.447             0.810            0.863
Chain 1:    600        -8362.405             0.690            0.863
Chain 1:    700        -7841.640             0.601            0.088
Chain 1:    800        -8601.280             0.537            0.088
Chain 1:    900        -8149.646             0.483            0.088
Chain 1:   1000        -7990.752             0.437            0.088
Chain 1:   1100        -7861.049             0.338            0.067
Chain 1:   1200        -7982.270             0.133            0.066
Chain 1:   1300        -7798.993             0.049            0.055
Chain 1:   1400        -7811.636             0.042            0.050
Chain 1:   1500        -7679.910             0.039            0.024
Chain 1:   1600        -7782.298             0.032            0.020
Chain 1:   1700        -7785.489             0.025            0.017
Chain 1:   1800        -7727.549             0.017            0.016
Chain 1:   1900        -7692.360             0.012            0.015
Chain 1:   2000        -7922.218             0.013            0.015
Chain 1:   2100        -7763.958             0.013            0.015
Chain 1:   2200        -8106.781             0.016            0.017
Chain 1:   2300        -7715.097             0.019            0.017
Chain 1:   2400        -7891.178             0.021            0.020
Chain 1:   2500        -7722.755             0.021            0.022
Chain 1:   2600        -7683.143             0.020            0.022
Chain 1:   2700        -7592.034             0.022            0.022
Chain 1:   2800        -7803.684             0.024            0.022
Chain 1:   2900        -7539.213             0.027            0.027
Chain 1:   3000        -7686.111             0.026            0.022
Chain 1:   3100        -7689.296             0.024            0.022
Chain 1:   3200        -7893.369             0.022            0.022
Chain 1:   3300        -7597.444             0.021            0.022
Chain 1:   3400        -7815.924             0.021            0.026
Chain 1:   3500        -7592.765             0.022            0.027
Chain 1:   3600        -7658.040             0.022            0.027
Chain 1:   3700        -7611.569             0.022            0.027
Chain 1:   3800        -7585.804             0.019            0.026
Chain 1:   3900        -7559.601             0.016            0.019
Chain 1:   4000        -7555.599             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003228 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86555.672             1.000            1.000
Chain 1:    200       -14235.423             3.040            5.080
Chain 1:    300       -10413.218             2.149            1.000
Chain 1:    400       -12535.058             1.654            1.000
Chain 1:    500        -8784.918             1.409            0.427
Chain 1:    600        -8770.927             1.174            0.427
Chain 1:    700        -9316.282             1.015            0.367
Chain 1:    800        -9198.542             0.890            0.367
Chain 1:    900        -9060.979             0.792            0.169
Chain 1:   1000        -9435.718             0.717            0.169
Chain 1:   1100        -9128.710             0.620            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8736.807             0.117            0.045
Chain 1:   1300        -8990.381             0.083            0.040
Chain 1:   1400        -8834.128             0.068            0.034
Chain 1:   1500        -8856.446             0.025            0.028
Chain 1:   1600        -8926.541             0.026            0.028
Chain 1:   1700        -8987.947             0.021            0.018
Chain 1:   1800        -8525.114             0.025            0.028
Chain 1:   1900        -8638.996             0.025            0.028
Chain 1:   2000        -8656.363             0.021            0.018
Chain 1:   2100        -8748.418             0.019            0.013
Chain 1:   2200        -8517.702             0.017            0.013
Chain 1:   2300        -8703.992             0.016            0.013
Chain 1:   2400        -8542.920             0.016            0.013
Chain 1:   2500        -8607.336             0.017            0.013
Chain 1:   2600        -8515.087             0.017            0.013
Chain 1:   2700        -8549.933             0.017            0.013
Chain 1:   2800        -8505.065             0.012            0.011
Chain 1:   2900        -8616.231             0.012            0.011
Chain 1:   3000        -8524.487             0.013            0.011
Chain 1:   3100        -8492.291             0.012            0.011
Chain 1:   3200        -8461.814             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003445 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8430167.934             1.000            1.000
Chain 1:    200     -1587811.308             2.655            4.309
Chain 1:    300      -892514.313             2.029            1.000
Chain 1:    400      -458881.777             1.758            1.000
Chain 1:    500      -359127.242             1.462            0.945
Chain 1:    600      -233870.345             1.308            0.945
Chain 1:    700      -120020.871             1.256            0.945
Chain 1:    800       -87243.431             1.146            0.945
Chain 1:    900       -67576.405             1.051            0.779
Chain 1:   1000       -52389.922             0.975            0.779
Chain 1:   1100       -39870.736             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39054.141             0.478            0.376
Chain 1:   1300       -26994.003             0.445            0.376
Chain 1:   1400       -26716.154             0.351            0.314
Chain 1:   1500       -23299.396             0.338            0.314
Chain 1:   1600       -22516.156             0.288            0.291
Chain 1:   1700       -21386.933             0.198            0.290
Chain 1:   1800       -21331.140             0.161            0.147
Chain 1:   1900       -21658.189             0.133            0.053
Chain 1:   2000       -20166.261             0.112            0.053
Chain 1:   2100       -20404.666             0.082            0.035
Chain 1:   2200       -20632.134             0.081            0.035
Chain 1:   2300       -20248.278             0.038            0.019
Chain 1:   2400       -20020.017             0.038            0.019
Chain 1:   2500       -19822.140             0.024            0.015
Chain 1:   2600       -19451.178             0.023            0.015
Chain 1:   2700       -19407.856             0.018            0.012
Chain 1:   2800       -19124.323             0.019            0.015
Chain 1:   2900       -19406.034             0.019            0.015
Chain 1:   3000       -19392.139             0.011            0.012
Chain 1:   3100       -19477.275             0.011            0.011
Chain 1:   3200       -19167.262             0.011            0.015
Chain 1:   3300       -19372.534             0.010            0.011
Chain 1:   3400       -18846.258             0.012            0.015
Chain 1:   3500       -19459.926             0.014            0.015
Chain 1:   3600       -18764.303             0.016            0.015
Chain 1:   3700       -19152.803             0.018            0.016
Chain 1:   3800       -18108.926             0.022            0.020
Chain 1:   3900       -18105.000             0.021            0.020
Chain 1:   4000       -18222.305             0.021            0.020
Chain 1:   4100       -18135.883             0.021            0.020
Chain 1:   4200       -17951.364             0.021            0.020
Chain 1:   4300       -18090.279             0.020            0.020
Chain 1:   4400       -18046.430             0.018            0.010
Chain 1:   4500       -17948.900             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48481.376             1.000            1.000
Chain 1:    200       -18766.727             1.292            1.583
Chain 1:    300       -29495.027             0.982            1.000
Chain 1:    400       -17512.015             0.908            1.000
Chain 1:    500       -16486.744             0.739            0.684
Chain 1:    600       -13558.504             0.652            0.684
Chain 1:    700       -11332.795             0.587            0.364
Chain 1:    800       -13431.633             0.533            0.364
Chain 1:    900       -21060.750             0.514            0.362
Chain 1:   1000       -11888.022             0.540            0.364
Chain 1:   1100       -15890.749             0.465            0.362
Chain 1:   1200       -12758.217             0.331            0.252
Chain 1:   1300       -11218.264             0.308            0.246
Chain 1:   1400       -10711.715             0.245            0.216
Chain 1:   1500       -12816.045             0.255            0.216
Chain 1:   1600       -12470.856             0.236            0.196
Chain 1:   1700        -9556.113             0.247            0.246
Chain 1:   1800       -11706.080             0.250            0.246
Chain 1:   1900        -9684.303             0.234            0.209
Chain 1:   2000       -18761.827             0.206            0.209
Chain 1:   2100       -10505.688             0.259            0.209
Chain 1:   2200       -11528.198             0.243            0.184
Chain 1:   2300       -11278.268             0.232            0.184
Chain 1:   2400        -9115.390             0.251            0.209
Chain 1:   2500        -9487.771             0.238            0.209
Chain 1:   2600        -9040.389             0.240            0.209
Chain 1:   2700       -10461.752             0.223            0.184
Chain 1:   2800       -10156.808             0.208            0.136
Chain 1:   2900        -9179.293             0.198            0.106
Chain 1:   3000       -12096.106             0.174            0.106
Chain 1:   3100       -10613.545             0.109            0.106
Chain 1:   3200        -8863.904             0.120            0.136
Chain 1:   3300        -9143.341             0.121            0.136
Chain 1:   3400        -8649.760             0.103            0.106
Chain 1:   3500       -14145.128             0.138            0.136
Chain 1:   3600        -8799.271             0.193            0.140
Chain 1:   3700       -15717.035             0.224            0.197
Chain 1:   3800        -8704.941             0.301            0.241
Chain 1:   3900        -9855.180             0.302            0.241
Chain 1:   4000        -8956.259             0.288            0.197
Chain 1:   4100        -9788.058             0.283            0.197
Chain 1:   4200       -12274.779             0.283            0.203
Chain 1:   4300        -8433.681             0.326            0.388
Chain 1:   4400       -12443.969             0.352            0.388
Chain 1:   4500        -8318.212             0.363            0.440
Chain 1:   4600       -15166.087             0.348            0.440
Chain 1:   4700       -14464.169             0.308            0.322
Chain 1:   4800        -8519.808             0.298            0.322
Chain 1:   4900       -14137.896             0.326            0.397
Chain 1:   5000       -12641.622             0.327            0.397
Chain 1:   5100        -8137.867             0.374            0.452
Chain 1:   5200       -13841.714             0.395            0.452
Chain 1:   5300       -12328.185             0.362            0.412
Chain 1:   5400       -14288.012             0.343            0.412
Chain 1:   5500        -8277.148             0.367            0.412
Chain 1:   5600        -9219.875             0.332            0.397
Chain 1:   5700       -12619.795             0.354            0.397
Chain 1:   5800        -9958.943             0.311            0.269
Chain 1:   5900       -14464.122             0.302            0.269
Chain 1:   6000        -8827.444             0.354            0.311
Chain 1:   6100        -8166.562             0.307            0.269
Chain 1:   6200        -8288.430             0.267            0.267
Chain 1:   6300       -13127.421             0.292            0.269
Chain 1:   6400       -11410.554             0.293            0.269
Chain 1:   6500       -10592.074             0.228            0.267
Chain 1:   6600        -9116.260             0.234            0.267
Chain 1:   6700       -11164.021             0.225            0.183
Chain 1:   6800        -9688.467             0.214            0.162
Chain 1:   6900       -12797.548             0.207            0.162
Chain 1:   7000       -11125.135             0.158            0.152
Chain 1:   7100        -8834.382             0.176            0.162
Chain 1:   7200        -8757.597             0.176            0.162
Chain 1:   7300        -9564.490             0.147            0.152
Chain 1:   7400       -11437.327             0.148            0.162
Chain 1:   7500        -8739.677             0.172            0.164
Chain 1:   7600        -8364.860             0.160            0.164
Chain 1:   7700        -8551.652             0.144            0.152
Chain 1:   7800       -11403.935             0.153            0.164
Chain 1:   7900        -8121.393             0.170            0.164
Chain 1:   8000        -9551.240             0.170            0.164
Chain 1:   8100        -7941.609             0.164            0.164
Chain 1:   8200        -9381.773             0.178            0.164
Chain 1:   8300        -9862.074             0.175            0.164
Chain 1:   8400        -8845.473             0.170            0.154
Chain 1:   8500        -7920.171             0.151            0.150
Chain 1:   8600        -9678.732             0.164            0.154
Chain 1:   8700        -9810.495             0.164            0.154
Chain 1:   8800        -7803.198             0.164            0.154
Chain 1:   8900        -9032.891             0.137            0.150
Chain 1:   9000        -9158.555             0.124            0.136
Chain 1:   9100        -7961.222             0.119            0.136
Chain 1:   9200        -8654.801             0.111            0.117
Chain 1:   9300        -7996.755             0.115            0.117
Chain 1:   9400       -11729.799             0.135            0.136
Chain 1:   9500       -11776.953             0.124            0.136
Chain 1:   9600        -8069.510             0.152            0.136
Chain 1:   9700        -8845.060             0.159            0.136
Chain 1:   9800        -9404.507             0.139            0.088
Chain 1:   9900       -10775.679             0.138            0.088
Chain 1:   10000       -10205.922             0.142            0.088
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62895.085             1.000            1.000
Chain 1:    200       -17690.869             1.778            2.555
Chain 1:    300        -8594.630             1.538            1.058
Chain 1:    400        -8142.271             1.167            1.058
Chain 1:    500        -8527.791             0.943            1.000
Chain 1:    600        -8553.229             0.786            1.000
Chain 1:    700        -8290.474             0.678            0.056
Chain 1:    800        -7929.284             0.599            0.056
Chain 1:    900        -7943.389             0.533            0.046
Chain 1:   1000        -7684.644             0.483            0.046
Chain 1:   1100        -7706.701             0.383            0.045
Chain 1:   1200        -7635.604             0.129            0.034
Chain 1:   1300        -7753.026             0.024            0.032
Chain 1:   1400        -7802.760             0.019            0.015
Chain 1:   1500        -7659.294             0.017            0.015
Chain 1:   1600        -7562.717             0.018            0.015
Chain 1:   1700        -7536.929             0.015            0.013
Chain 1:   1800        -7595.706             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003335 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85881.781             1.000            1.000
Chain 1:    200       -13016.423             3.299            5.598
Chain 1:    300        -9533.150             2.321            1.000
Chain 1:    400       -10266.590             1.759            1.000
Chain 1:    500        -8387.898             1.452            0.365
Chain 1:    600        -8660.776             1.215            0.365
Chain 1:    700        -8184.833             1.050            0.224
Chain 1:    800        -8624.160             0.925            0.224
Chain 1:    900        -8449.338             0.824            0.071
Chain 1:   1000        -8187.952             0.745            0.071
Chain 1:   1100        -8294.027             0.646            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8128.304             0.089            0.051
Chain 1:   1300        -8181.899             0.053            0.032
Chain 1:   1400        -8248.957             0.047            0.032
Chain 1:   1500        -8204.480             0.025            0.021
Chain 1:   1600        -8208.514             0.022            0.020
Chain 1:   1700        -8157.626             0.016            0.013
Chain 1:   1800        -8034.869             0.013            0.013
Chain 1:   1900        -8145.682             0.012            0.013
Chain 1:   2000        -8110.321             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003584 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.84 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402164.048             1.000            1.000
Chain 1:    200     -1586306.209             2.648            4.297
Chain 1:    300      -890783.019             2.026            1.000
Chain 1:    400      -457548.681             1.756            1.000
Chain 1:    500      -357655.826             1.461            0.947
Chain 1:    600      -232508.251             1.307            0.947
Chain 1:    700      -118663.744             1.257            0.947
Chain 1:    800       -85898.779             1.148            0.947
Chain 1:    900       -66227.734             1.053            0.781
Chain 1:   1000       -51013.323             0.978            0.781
Chain 1:   1100       -38496.059             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37662.927             0.483            0.381
Chain 1:   1300       -25639.267             0.452            0.381
Chain 1:   1400       -25356.211             0.358            0.325
Chain 1:   1500       -21950.267             0.346            0.325
Chain 1:   1600       -21167.655             0.296            0.298
Chain 1:   1700       -20044.431             0.205            0.297
Chain 1:   1800       -19988.757             0.167            0.155
Chain 1:   1900       -20313.934             0.139            0.056
Chain 1:   2000       -18828.644             0.117            0.056
Chain 1:   2100       -19066.718             0.086            0.037
Chain 1:   2200       -19292.398             0.085            0.037
Chain 1:   2300       -18910.507             0.040            0.020
Chain 1:   2400       -18682.939             0.040            0.020
Chain 1:   2500       -18485.089             0.026            0.016
Chain 1:   2600       -18116.270             0.024            0.016
Chain 1:   2700       -18073.439             0.019            0.012
Chain 1:   2800       -17790.830             0.020            0.016
Chain 1:   2900       -18071.566             0.020            0.016
Chain 1:   3000       -18057.771             0.012            0.012
Chain 1:   3100       -18142.674             0.011            0.012
Chain 1:   3200       -17833.995             0.012            0.016
Chain 1:   3300       -18038.180             0.011            0.012
Chain 1:   3400       -17514.361             0.013            0.016
Chain 1:   3500       -18124.433             0.015            0.016
Chain 1:   3600       -17433.347             0.017            0.016
Chain 1:   3700       -17818.524             0.019            0.017
Chain 1:   3800       -16781.849             0.024            0.022
Chain 1:   3900       -16778.067             0.022            0.022
Chain 1:   4000       -16895.344             0.023            0.022
Chain 1:   4100       -16809.363             0.023            0.022
Chain 1:   4200       -16626.317             0.022            0.022
Chain 1:   4300       -16764.180             0.022            0.022
Chain 1:   4400       -16721.632             0.019            0.011
Chain 1:   4500       -16624.277             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13019.383             1.000            1.000
Chain 1:    200        -9826.280             0.662            1.000
Chain 1:    300        -8529.427             0.492            0.325
Chain 1:    400        -8694.383             0.374            0.325
Chain 1:    500        -8642.282             0.300            0.152
Chain 1:    600        -8448.921             0.254            0.152
Chain 1:    700        -8380.451             0.219            0.023
Chain 1:    800        -8386.526             0.192            0.023
Chain 1:    900        -8322.128             0.171            0.019
Chain 1:   1000        -8504.286             0.156            0.021
Chain 1:   1100        -8489.534             0.056            0.019
Chain 1:   1200        -8394.046             0.025            0.011
Chain 1:   1300        -8297.961             0.011            0.011
Chain 1:   1400        -8313.455             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58883.116             1.000            1.000
Chain 1:    200       -18333.073             1.606            2.212
Chain 1:    300        -8987.335             1.417            1.040
Chain 1:    400        -8042.281             1.092            1.040
Chain 1:    500        -9300.009             0.901            1.000
Chain 1:    600        -8818.649             0.760            1.000
Chain 1:    700        -8937.429             0.653            0.135
Chain 1:    800        -8464.648             0.579            0.135
Chain 1:    900        -8122.728             0.519            0.118
Chain 1:   1000        -7882.925             0.470            0.118
Chain 1:   1100        -7787.657             0.371            0.056
Chain 1:   1200        -7607.488             0.152            0.055
Chain 1:   1300        -7732.981             0.050            0.042
Chain 1:   1400        -7764.207             0.039            0.030
Chain 1:   1500        -7536.883             0.028            0.030
Chain 1:   1600        -7749.770             0.026            0.027
Chain 1:   1700        -7606.208             0.026            0.027
Chain 1:   1800        -7547.740             0.021            0.024
Chain 1:   1900        -7592.438             0.018            0.019
Chain 1:   2000        -7635.093             0.015            0.016
Chain 1:   2100        -7572.334             0.015            0.016
Chain 1:   2200        -7853.266             0.016            0.016
Chain 1:   2300        -7608.654             0.018            0.019
Chain 1:   2400        -7612.711             0.017            0.019
Chain 1:   2500        -7589.442             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004033 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86701.933             1.000            1.000
Chain 1:    200       -14128.891             3.068            5.136
Chain 1:    300       -10425.181             2.164            1.000
Chain 1:    400       -11895.557             1.654            1.000
Chain 1:    500        -9381.478             1.377            0.355
Chain 1:    600        -8774.138             1.159            0.355
Chain 1:    700        -9432.073             1.003            0.268
Chain 1:    800        -9811.611             0.883            0.268
Chain 1:    900        -9102.458             0.793            0.124
Chain 1:   1000        -8752.323             0.718            0.124
Chain 1:   1100        -9165.476             0.622            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8774.374             0.113            0.070
Chain 1:   1300        -9128.157             0.082            0.069
Chain 1:   1400        -8907.880             0.072            0.045
Chain 1:   1500        -8935.273             0.045            0.045
Chain 1:   1600        -9043.373             0.039            0.040
Chain 1:   1700        -9111.013             0.033            0.039
Chain 1:   1800        -8671.656             0.034            0.040
Chain 1:   1900        -8776.829             0.028            0.039
Chain 1:   2000        -8755.781             0.024            0.025
Chain 1:   2100        -8896.011             0.021            0.016
Chain 1:   2200        -8683.934             0.019            0.016
Chain 1:   2300        -8843.306             0.017            0.016
Chain 1:   2400        -8680.913             0.016            0.016
Chain 1:   2500        -8752.392             0.017            0.016
Chain 1:   2600        -8664.244             0.017            0.016
Chain 1:   2700        -8697.921             0.016            0.016
Chain 1:   2800        -8657.583             0.012            0.012
Chain 1:   2900        -8751.576             0.012            0.011
Chain 1:   3000        -8586.790             0.013            0.016
Chain 1:   3100        -8740.349             0.014            0.018
Chain 1:   3200        -8612.056             0.013            0.015
Chain 1:   3300        -8621.254             0.011            0.011
Chain 1:   3400        -8784.368             0.011            0.011
Chain 1:   3500        -8796.344             0.010            0.011
Chain 1:   3600        -8568.406             0.012            0.015
Chain 1:   3700        -8715.335             0.013            0.017
Chain 1:   3800        -8574.643             0.014            0.017
Chain 1:   3900        -8508.868             0.014            0.017
Chain 1:   4000        -8586.609             0.013            0.016
Chain 1:   4100        -8580.233             0.011            0.015
Chain 1:   4200        -8564.726             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003304 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412306.651             1.000            1.000
Chain 1:    200     -1585847.309             2.652            4.305
Chain 1:    300      -891441.554             2.028            1.000
Chain 1:    400      -458453.657             1.757            1.000
Chain 1:    500      -358596.192             1.461            0.944
Chain 1:    600      -233515.326             1.307            0.944
Chain 1:    700      -119795.333             1.256            0.944
Chain 1:    800       -87048.479             1.146            0.944
Chain 1:    900       -67402.621             1.051            0.779
Chain 1:   1000       -52221.555             0.975            0.779
Chain 1:   1100       -39715.070             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38896.618             0.478            0.376
Chain 1:   1300       -26857.332             0.445            0.376
Chain 1:   1400       -26579.393             0.352            0.315
Chain 1:   1500       -23167.441             0.339            0.315
Chain 1:   1600       -22385.353             0.288            0.291
Chain 1:   1700       -21258.682             0.199            0.291
Chain 1:   1800       -21203.196             0.161            0.147
Chain 1:   1900       -21529.771             0.134            0.053
Chain 1:   2000       -20040.114             0.112            0.053
Chain 1:   2100       -20278.519             0.082            0.035
Chain 1:   2200       -20505.341             0.081            0.035
Chain 1:   2300       -20122.102             0.038            0.019
Chain 1:   2400       -19894.018             0.038            0.019
Chain 1:   2500       -19696.106             0.024            0.015
Chain 1:   2600       -19325.817             0.023            0.015
Chain 1:   2700       -19282.658             0.018            0.012
Chain 1:   2800       -18999.343             0.019            0.015
Chain 1:   2900       -19280.761             0.019            0.015
Chain 1:   3000       -19266.942             0.012            0.012
Chain 1:   3100       -19352.007             0.011            0.011
Chain 1:   3200       -19042.397             0.011            0.015
Chain 1:   3300       -19247.354             0.010            0.011
Chain 1:   3400       -18721.786             0.012            0.015
Chain 1:   3500       -19334.430             0.014            0.015
Chain 1:   3600       -18640.080             0.016            0.015
Chain 1:   3700       -19027.618             0.018            0.016
Chain 1:   3800       -17985.806             0.022            0.020
Chain 1:   3900       -17981.915             0.021            0.020
Chain 1:   4000       -18099.216             0.021            0.020
Chain 1:   4100       -18012.905             0.021            0.020
Chain 1:   4200       -17828.822             0.021            0.020
Chain 1:   4300       -17967.457             0.020            0.020
Chain 1:   4400       -17923.990             0.018            0.010
Chain 1:   4500       -17826.471             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001214 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12382.293             1.000            1.000
Chain 1:    200        -9284.616             0.667            1.000
Chain 1:    300        -7979.054             0.499            0.334
Chain 1:    400        -8248.339             0.382            0.334
Chain 1:    500        -8040.408             0.311            0.164
Chain 1:    600        -7964.194             0.261            0.164
Chain 1:    700        -7866.646             0.225            0.033
Chain 1:    800        -7871.563             0.197            0.033
Chain 1:    900        -7798.520             0.176            0.026
Chain 1:   1000        -7984.766             0.161            0.026
Chain 1:   1100        -8010.237             0.061            0.023
Chain 1:   1200        -7881.407             0.030            0.016
Chain 1:   1300        -7853.153             0.014            0.012
Chain 1:   1400        -7861.372             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001568 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61918.239             1.000            1.000
Chain 1:    200       -17910.492             1.729            2.457
Chain 1:    300        -8823.089             1.496            1.030
Chain 1:    400        -9406.741             1.137            1.030
Chain 1:    500        -8302.944             0.936            1.000
Chain 1:    600        -7866.090             0.790            1.000
Chain 1:    700        -7839.401             0.677            0.133
Chain 1:    800        -8184.103             0.598            0.133
Chain 1:    900        -7917.249             0.535            0.062
Chain 1:   1000        -7988.648             0.483            0.062
Chain 1:   1100        -7789.100             0.385            0.056
Chain 1:   1200        -7675.731             0.141            0.042
Chain 1:   1300        -7744.878             0.039            0.034
Chain 1:   1400        -7640.199             0.034            0.026
Chain 1:   1500        -7535.717             0.022            0.015
Chain 1:   1600        -7463.939             0.017            0.014
Chain 1:   1700        -7551.519             0.018            0.014
Chain 1:   1800        -7598.868             0.015            0.014
Chain 1:   1900        -7585.153             0.012            0.012
Chain 1:   2000        -7536.741             0.011            0.012
Chain 1:   2100        -7576.574             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003001 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86798.934             1.000            1.000
Chain 1:    200       -13533.220             3.207            5.414
Chain 1:    300        -9881.806             2.261            1.000
Chain 1:    400       -10810.230             1.717            1.000
Chain 1:    500        -8651.888             1.424            0.370
Chain 1:    600        -8285.301             1.194            0.370
Chain 1:    700        -8381.494             1.025            0.249
Chain 1:    800        -8832.736             0.903            0.249
Chain 1:    900        -8622.480             0.806            0.086
Chain 1:   1000        -8437.522             0.727            0.086
Chain 1:   1100        -8712.590             0.630            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8317.628             0.094            0.047
Chain 1:   1300        -8556.069             0.060            0.044
Chain 1:   1400        -8582.213             0.051            0.032
Chain 1:   1500        -8426.093             0.028            0.028
Chain 1:   1600        -8540.424             0.025            0.024
Chain 1:   1700        -8616.140             0.025            0.024
Chain 1:   1800        -8192.923             0.025            0.024
Chain 1:   1900        -8294.001             0.024            0.022
Chain 1:   2000        -8268.417             0.022            0.019
Chain 1:   2100        -8393.994             0.020            0.015
Chain 1:   2200        -8197.029             0.018            0.015
Chain 1:   2300        -8288.788             0.016            0.013
Chain 1:   2400        -8357.565             0.017            0.013
Chain 1:   2500        -8303.783             0.015            0.012
Chain 1:   2600        -8305.151             0.014            0.011
Chain 1:   2700        -8221.891             0.014            0.011
Chain 1:   2800        -8181.752             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00371 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402956.830             1.000            1.000
Chain 1:    200     -1584566.512             2.652            4.303
Chain 1:    300      -890013.444             2.028            1.000
Chain 1:    400      -456486.548             1.758            1.000
Chain 1:    500      -356913.715             1.462            0.950
Chain 1:    600      -231905.273             1.309            0.950
Chain 1:    700      -118730.601             1.258            0.950
Chain 1:    800       -86064.845             1.148            0.950
Chain 1:    900       -66521.947             1.053            0.780
Chain 1:   1000       -51414.008             0.977            0.780
Chain 1:   1100       -38971.387             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38163.763             0.481            0.380
Chain 1:   1300       -26193.083             0.449            0.380
Chain 1:   1400       -25920.935             0.355            0.319
Chain 1:   1500       -22525.846             0.342            0.319
Chain 1:   1600       -21748.548             0.291            0.294
Chain 1:   1700       -20630.289             0.202            0.294
Chain 1:   1800       -20576.767             0.164            0.151
Chain 1:   1900       -20903.101             0.136            0.054
Chain 1:   2000       -19418.240             0.114            0.054
Chain 1:   2100       -19656.512             0.084            0.036
Chain 1:   2200       -19882.277             0.083            0.036
Chain 1:   2300       -19500.036             0.039            0.020
Chain 1:   2400       -19272.137             0.039            0.020
Chain 1:   2500       -19073.824             0.025            0.016
Chain 1:   2600       -18704.213             0.023            0.016
Chain 1:   2700       -18661.360             0.018            0.012
Chain 1:   2800       -18377.951             0.019            0.015
Chain 1:   2900       -18659.188             0.019            0.015
Chain 1:   3000       -18645.448             0.012            0.012
Chain 1:   3100       -18730.420             0.011            0.012
Chain 1:   3200       -18421.134             0.012            0.015
Chain 1:   3300       -18625.904             0.011            0.012
Chain 1:   3400       -18100.683             0.013            0.015
Chain 1:   3500       -18712.657             0.015            0.015
Chain 1:   3600       -18019.217             0.017            0.015
Chain 1:   3700       -18405.986             0.018            0.017
Chain 1:   3800       -17365.489             0.023            0.021
Chain 1:   3900       -17361.606             0.021            0.021
Chain 1:   4000       -17478.933             0.022            0.021
Chain 1:   4100       -17392.609             0.022            0.021
Chain 1:   4200       -17208.912             0.021            0.021
Chain 1:   4300       -17347.339             0.021            0.021
Chain 1:   4400       -17304.120             0.019            0.011
Chain 1:   4500       -17206.611             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001343 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12120.377             1.000            1.000
Chain 1:    200        -9054.156             0.669            1.000
Chain 1:    300        -7867.590             0.496            0.339
Chain 1:    400        -8049.118             0.378            0.339
Chain 1:    500        -7951.404             0.305            0.151
Chain 1:    600        -7813.944             0.257            0.151
Chain 1:    700        -7736.916             0.222            0.023
Chain 1:    800        -7746.797             0.194            0.023
Chain 1:    900        -7655.047             0.174            0.018
Chain 1:   1000        -7764.017             0.158            0.018
Chain 1:   1100        -7777.090             0.058            0.014
Chain 1:   1200        -7745.935             0.025            0.012
Chain 1:   1300        -7754.993             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001471 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56627.847             1.000            1.000
Chain 1:    200       -17088.240             1.657            2.314
Chain 1:    300        -8569.976             1.436            1.000
Chain 1:    400        -7825.526             1.101            1.000
Chain 1:    500        -8322.842             0.893            0.994
Chain 1:    600        -8610.539             0.749            0.994
Chain 1:    700        -8228.179             0.649            0.095
Chain 1:    800        -8219.446             0.568            0.095
Chain 1:    900        -7729.083             0.512            0.063
Chain 1:   1000        -7724.756             0.461            0.063
Chain 1:   1100        -7611.294             0.362            0.060
Chain 1:   1200        -7552.061             0.132            0.046
Chain 1:   1300        -7600.463             0.033            0.033
Chain 1:   1400        -7753.650             0.025            0.020
Chain 1:   1500        -7547.348             0.022            0.020
Chain 1:   1600        -7509.339             0.019            0.015
Chain 1:   1700        -7478.915             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003503 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.03 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86207.637             1.000            1.000
Chain 1:    200       -13198.409             3.266            5.532
Chain 1:    300        -9632.304             2.301            1.000
Chain 1:    400       -10380.201             1.743            1.000
Chain 1:    500        -8530.520             1.438            0.370
Chain 1:    600        -8186.592             1.205            0.370
Chain 1:    700        -8344.261             1.036            0.217
Chain 1:    800        -8933.691             0.915            0.217
Chain 1:    900        -8509.256             0.819            0.072
Chain 1:   1000        -8198.625             0.741            0.072
Chain 1:   1100        -8532.965             0.644            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8059.651             0.097            0.059
Chain 1:   1300        -8234.525             0.062            0.050
Chain 1:   1400        -8364.403             0.057            0.042
Chain 1:   1500        -8252.529             0.036            0.039
Chain 1:   1600        -8361.683             0.033            0.038
Chain 1:   1700        -8442.387             0.032            0.038
Chain 1:   1800        -8049.985             0.031            0.038
Chain 1:   1900        -8152.943             0.027            0.021
Chain 1:   2000        -8122.850             0.024            0.016
Chain 1:   2100        -8249.892             0.021            0.015
Chain 1:   2200        -8036.060             0.018            0.015
Chain 1:   2300        -8181.478             0.018            0.015
Chain 1:   2400        -8196.788             0.016            0.014
Chain 1:   2500        -8163.273             0.015            0.013
Chain 1:   2600        -8165.019             0.014            0.013
Chain 1:   2700        -8072.036             0.014            0.013
Chain 1:   2800        -8045.435             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393288.755             1.000            1.000
Chain 1:    200     -1581194.945             2.654            4.308
Chain 1:    300      -889586.382             2.029            1.000
Chain 1:    400      -456617.357             1.758            1.000
Chain 1:    500      -357137.758             1.462            0.948
Chain 1:    600      -232168.245             1.308            0.948
Chain 1:    700      -118688.230             1.258            0.948
Chain 1:    800       -85956.222             1.148            0.948
Chain 1:    900       -66345.570             1.054            0.777
Chain 1:   1000       -51174.376             0.978            0.777
Chain 1:   1100       -38682.390             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37861.235             0.482            0.381
Chain 1:   1300       -25851.725             0.450            0.381
Chain 1:   1400       -25572.123             0.357            0.323
Chain 1:   1500       -22168.068             0.344            0.323
Chain 1:   1600       -21386.879             0.294            0.296
Chain 1:   1700       -20264.868             0.204            0.296
Chain 1:   1800       -20209.925             0.166            0.154
Chain 1:   1900       -20535.699             0.138            0.055
Chain 1:   2000       -19049.937             0.116            0.055
Chain 1:   2100       -19288.037             0.085            0.037
Chain 1:   2200       -19513.887             0.084            0.037
Chain 1:   2300       -19131.755             0.040            0.020
Chain 1:   2400       -18904.053             0.040            0.020
Chain 1:   2500       -18705.926             0.025            0.016
Chain 1:   2600       -18336.634             0.024            0.016
Chain 1:   2700       -18293.850             0.019            0.012
Chain 1:   2800       -18010.840             0.020            0.016
Chain 1:   2900       -18291.835             0.020            0.015
Chain 1:   3000       -18278.069             0.012            0.012
Chain 1:   3100       -18362.983             0.011            0.012
Chain 1:   3200       -18053.987             0.012            0.015
Chain 1:   3300       -18258.491             0.011            0.012
Chain 1:   3400       -17733.938             0.013            0.015
Chain 1:   3500       -18344.992             0.015            0.016
Chain 1:   3600       -17652.768             0.017            0.016
Chain 1:   3700       -18038.710             0.019            0.017
Chain 1:   3800       -17000.096             0.023            0.021
Chain 1:   3900       -16996.300             0.022            0.021
Chain 1:   4000       -17113.588             0.022            0.021
Chain 1:   4100       -17027.406             0.023            0.021
Chain 1:   4200       -16844.093             0.022            0.021
Chain 1:   4300       -16982.209             0.022            0.021
Chain 1:   4400       -16939.336             0.019            0.011
Chain 1:   4500       -16841.932             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48782.720             1.000            1.000
Chain 1:    200       -17841.537             1.367            1.734
Chain 1:    300       -19765.315             0.944            1.000
Chain 1:    400       -32434.736             0.806            1.000
Chain 1:    500       -18434.966             0.796            0.759
Chain 1:    600       -11725.223             0.759            0.759
Chain 1:    700       -15535.385             0.686            0.572
Chain 1:    800       -10800.244             0.655            0.572
Chain 1:    900       -10315.094             0.587            0.438
Chain 1:   1000       -11861.493             0.541            0.438
Chain 1:   1100       -27021.856             0.498            0.438
Chain 1:   1200       -10149.435             0.490            0.438
Chain 1:   1300       -21681.381             0.534            0.532   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -12247.520             0.572            0.561   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1500        -9737.580             0.522            0.532   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1600       -11625.656             0.481            0.438
Chain 1:   1700       -10126.466             0.471            0.438
Chain 1:   1800       -10137.493             0.427            0.258
Chain 1:   1900       -10355.138             0.425            0.258
Chain 1:   2000       -15714.053             0.446            0.341
Chain 1:   2100        -9956.025             0.447            0.341
Chain 1:   2200        -9654.976             0.284            0.258
Chain 1:   2300       -17321.393             0.275            0.258
Chain 1:   2400        -8893.233             0.293            0.258
Chain 1:   2500       -10193.949             0.280            0.162
Chain 1:   2600        -9827.168             0.268            0.148
Chain 1:   2700        -8940.765             0.263            0.128
Chain 1:   2800        -9404.563             0.268            0.128
Chain 1:   2900       -14834.861             0.302            0.341
Chain 1:   3000       -13641.661             0.277            0.128
Chain 1:   3100        -9610.993             0.261            0.128
Chain 1:   3200       -16501.581             0.299            0.366
Chain 1:   3300        -9226.349             0.334            0.366
Chain 1:   3400       -12182.651             0.264            0.243
Chain 1:   3500        -8917.290             0.287            0.366
Chain 1:   3600        -9711.236             0.292            0.366
Chain 1:   3700        -9997.131             0.285            0.366
Chain 1:   3800        -8672.080             0.295            0.366
Chain 1:   3900        -9674.606             0.269            0.243
Chain 1:   4000        -8563.471             0.273            0.243
Chain 1:   4100       -12871.316             0.265            0.243
Chain 1:   4200       -10619.755             0.244            0.212
Chain 1:   4300        -9736.487             0.174            0.153
Chain 1:   4400        -9030.039             0.158            0.130
Chain 1:   4500        -9326.486             0.124            0.104
Chain 1:   4600       -13172.104             0.145            0.130
Chain 1:   4700        -8780.296             0.193            0.153
Chain 1:   4800        -8791.551             0.177            0.130
Chain 1:   4900        -9070.649             0.170            0.130
Chain 1:   5000        -8502.777             0.164            0.091
Chain 1:   5100       -10701.237             0.151            0.091
Chain 1:   5200       -11914.502             0.140            0.091
Chain 1:   5300        -9262.428             0.159            0.102
Chain 1:   5400       -13542.084             0.183            0.205
Chain 1:   5500       -14571.800             0.187            0.205
Chain 1:   5600        -8831.150             0.223            0.205
Chain 1:   5700       -13860.680             0.209            0.205
Chain 1:   5800        -8587.379             0.270            0.286
Chain 1:   5900       -10992.326             0.289            0.286
Chain 1:   6000        -8382.899             0.314            0.311
Chain 1:   6100        -8840.376             0.298            0.311
Chain 1:   6200        -8355.957             0.294            0.311
Chain 1:   6300        -8560.499             0.268            0.311
Chain 1:   6400       -12544.367             0.268            0.311
Chain 1:   6500        -9053.843             0.299            0.318
Chain 1:   6600        -8446.252             0.242            0.311
Chain 1:   6700        -8289.740             0.207            0.219
Chain 1:   6800       -10006.869             0.163            0.172
Chain 1:   6900        -9917.796             0.142            0.072
Chain 1:   7000       -14698.693             0.143            0.072
Chain 1:   7100        -8181.269             0.218            0.172
Chain 1:   7200        -9009.605             0.221            0.172
Chain 1:   7300       -10382.906             0.232            0.172
Chain 1:   7400       -11217.330             0.208            0.132
Chain 1:   7500        -9036.173             0.193            0.132
Chain 1:   7600        -8352.671             0.194            0.132
Chain 1:   7700        -8701.705             0.196            0.132
Chain 1:   7800       -11738.117             0.205            0.132
Chain 1:   7900       -10472.861             0.216            0.132
Chain 1:   8000       -10339.553             0.185            0.121
Chain 1:   8100        -8202.108             0.131            0.121
Chain 1:   8200        -8110.774             0.123            0.121
Chain 1:   8300        -8333.322             0.113            0.082
Chain 1:   8400        -9695.331             0.119            0.121
Chain 1:   8500       -11115.663             0.108            0.121
Chain 1:   8600        -8064.766             0.138            0.128
Chain 1:   8700        -9601.767             0.150            0.140
Chain 1:   8800        -8221.627             0.141            0.140
Chain 1:   8900       -12941.262             0.165            0.160
Chain 1:   9000        -8464.767             0.217            0.168
Chain 1:   9100        -8141.876             0.195            0.160
Chain 1:   9200        -8526.298             0.198            0.160
Chain 1:   9300        -8909.395             0.200            0.160
Chain 1:   9400        -8891.575             0.186            0.160
Chain 1:   9500        -8449.523             0.178            0.160
Chain 1:   9600        -9235.030             0.149            0.085
Chain 1:   9700       -11065.108             0.149            0.085
Chain 1:   9800       -11402.361             0.136            0.052
Chain 1:   9900        -8331.411             0.136            0.052
Chain 1:   10000        -8127.314             0.086            0.045
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001868 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58113.534             1.000            1.000
Chain 1:    200       -17559.752             1.655            2.309
Chain 1:    300        -8610.699             1.450            1.039
Chain 1:    400        -8204.412             1.100            1.039
Chain 1:    500        -8092.295             0.882            1.000
Chain 1:    600        -8831.073             0.749            1.000
Chain 1:    700        -8135.386             0.654            0.086
Chain 1:    800        -8016.995             0.575            0.086
Chain 1:    900        -7888.296             0.512            0.084
Chain 1:   1000        -7733.720             0.463            0.084
Chain 1:   1100        -7882.122             0.365            0.050
Chain 1:   1200        -7782.074             0.135            0.020
Chain 1:   1300        -7763.149             0.032            0.019
Chain 1:   1400        -7884.894             0.028            0.016
Chain 1:   1500        -7579.056             0.031            0.019
Chain 1:   1600        -7769.293             0.025            0.019
Chain 1:   1700        -7522.652             0.020            0.019
Chain 1:   1800        -7608.690             0.019            0.019
Chain 1:   1900        -7626.440             0.018            0.019
Chain 1:   2000        -7598.585             0.016            0.015
Chain 1:   2100        -7619.368             0.015            0.013
Chain 1:   2200        -7693.793             0.015            0.011
Chain 1:   2300        -7567.491             0.016            0.015
Chain 1:   2400        -7634.106             0.015            0.011
Chain 1:   2500        -7501.534             0.013            0.011
Chain 1:   2600        -7543.392             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002751 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85832.653             1.000            1.000
Chain 1:    200       -13326.654             3.220            5.441
Chain 1:    300        -9766.338             2.268            1.000
Chain 1:    400       -10679.288             1.723            1.000
Chain 1:    500        -8682.668             1.424            0.365
Chain 1:    600        -8313.877             1.194            0.365
Chain 1:    700        -8381.250             1.025            0.230
Chain 1:    800        -8814.022             0.903            0.230
Chain 1:    900        -8574.176             0.806            0.085
Chain 1:   1000        -8344.856             0.728            0.085
Chain 1:   1100        -8690.947             0.632            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8208.416             0.094            0.049
Chain 1:   1300        -8362.018             0.059            0.044
Chain 1:   1400        -8439.169             0.051            0.040
Chain 1:   1500        -8368.064             0.029            0.028
Chain 1:   1600        -8367.237             0.025            0.027
Chain 1:   1700        -8292.549             0.025            0.027
Chain 1:   1800        -8181.854             0.021            0.018
Chain 1:   1900        -8299.769             0.020            0.014
Chain 1:   2000        -8260.103             0.018            0.014
Chain 1:   2100        -8388.888             0.015            0.014
Chain 1:   2200        -8179.992             0.012            0.014
Chain 1:   2300        -8322.366             0.012            0.014
Chain 1:   2400        -8336.462             0.011            0.014
Chain 1:   2500        -8304.291             0.011            0.014
Chain 1:   2600        -8303.390             0.011            0.014
Chain 1:   2700        -8211.493             0.011            0.014
Chain 1:   2800        -8186.570             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003412 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389641.587             1.000            1.000
Chain 1:    200     -1582577.584             2.651            4.301
Chain 1:    300      -890761.416             2.026            1.000
Chain 1:    400      -458183.408             1.756            1.000
Chain 1:    500      -358583.239             1.460            0.944
Chain 1:    600      -233438.038             1.306            0.944
Chain 1:    700      -119324.877             1.256            0.944
Chain 1:    800       -86459.185             1.147            0.944
Chain 1:    900       -66737.269             1.052            0.777
Chain 1:   1000       -51483.600             0.976            0.777
Chain 1:   1100       -38920.153             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38085.747             0.481            0.380
Chain 1:   1300       -26012.980             0.450            0.380
Chain 1:   1400       -25726.383             0.356            0.323
Chain 1:   1500       -22307.069             0.344            0.323
Chain 1:   1600       -21520.649             0.294            0.296
Chain 1:   1700       -20391.605             0.204            0.296
Chain 1:   1800       -20334.800             0.166            0.153
Chain 1:   1900       -20660.407             0.138            0.055
Chain 1:   2000       -19170.977             0.116            0.055
Chain 1:   2100       -19409.303             0.085            0.037
Chain 1:   2200       -19635.746             0.084            0.037
Chain 1:   2300       -19253.092             0.040            0.020
Chain 1:   2400       -19025.333             0.040            0.020
Chain 1:   2500       -18827.512             0.025            0.016
Chain 1:   2600       -18458.080             0.024            0.016
Chain 1:   2700       -18415.069             0.018            0.012
Chain 1:   2800       -18132.248             0.020            0.016
Chain 1:   2900       -18413.318             0.020            0.015
Chain 1:   3000       -18399.464             0.012            0.012
Chain 1:   3100       -18484.428             0.011            0.012
Chain 1:   3200       -18175.386             0.012            0.015
Chain 1:   3300       -18379.855             0.011            0.012
Chain 1:   3400       -17855.385             0.013            0.015
Chain 1:   3500       -18466.437             0.015            0.016
Chain 1:   3600       -17774.169             0.017            0.016
Chain 1:   3700       -18160.254             0.019            0.017
Chain 1:   3800       -17121.656             0.023            0.021
Chain 1:   3900       -17117.859             0.022            0.021
Chain 1:   4000       -17235.134             0.022            0.021
Chain 1:   4100       -17149.030             0.022            0.021
Chain 1:   4200       -16965.594             0.022            0.021
Chain 1:   4300       -17103.736             0.021            0.021
Chain 1:   4400       -17060.864             0.019            0.011
Chain 1:   4500       -16963.471             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001541 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12464.975             1.000            1.000
Chain 1:    200        -9388.913             0.664            1.000
Chain 1:    300        -8031.843             0.499            0.328
Chain 1:    400        -8213.078             0.380            0.328
Chain 1:    500        -8200.559             0.304            0.169
Chain 1:    600        -7981.155             0.258            0.169
Chain 1:    700        -7887.660             0.223            0.027
Chain 1:    800        -7918.553             0.195            0.027
Chain 1:    900        -8034.287             0.175            0.022
Chain 1:   1000        -7924.784             0.159            0.022
Chain 1:   1100        -7969.065             0.060            0.014
Chain 1:   1200        -7909.344             0.028            0.014
Chain 1:   1300        -7850.962             0.012            0.012
Chain 1:   1400        -7876.258             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58164.047             1.000            1.000
Chain 1:    200       -17763.243             1.637            2.274
Chain 1:    300        -8712.081             1.438            1.039
Chain 1:    400        -8148.160             1.096            1.039
Chain 1:    500        -8386.830             0.882            1.000
Chain 1:    600        -8711.074             0.741            1.000
Chain 1:    700        -7713.066             0.654            0.129
Chain 1:    800        -8237.565             0.580            0.129
Chain 1:    900        -8060.333             0.518            0.069
Chain 1:   1000        -7821.672             0.469            0.069
Chain 1:   1100        -7674.362             0.371            0.064
Chain 1:   1200        -7643.415             0.144            0.037
Chain 1:   1300        -7774.620             0.042            0.031
Chain 1:   1400        -7854.444             0.036            0.028
Chain 1:   1500        -7565.413             0.037            0.031
Chain 1:   1600        -7527.859             0.034            0.022
Chain 1:   1700        -7545.039             0.021            0.019
Chain 1:   1800        -7601.540             0.016            0.017
Chain 1:   1900        -7615.313             0.014            0.010
Chain 1:   2000        -7608.162             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003275 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87155.765             1.000            1.000
Chain 1:    200       -13578.782             3.209            5.419
Chain 1:    300        -9900.677             2.263            1.000
Chain 1:    400       -10939.439             1.721            1.000
Chain 1:    500        -8886.737             1.423            0.372
Chain 1:    600        -8347.953             1.197            0.372
Chain 1:    700        -8492.363             1.028            0.231
Chain 1:    800        -9021.871             0.907            0.231
Chain 1:    900        -8703.819             0.810            0.095
Chain 1:   1000        -8654.546             0.730            0.095
Chain 1:   1100        -8633.212             0.630            0.065   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8332.403             0.092            0.059
Chain 1:   1300        -8572.587             0.058            0.037
Chain 1:   1400        -8597.404             0.048            0.036
Chain 1:   1500        -8445.157             0.027            0.028
Chain 1:   1600        -8558.332             0.022            0.018
Chain 1:   1700        -8633.018             0.021            0.018
Chain 1:   1800        -8207.221             0.020            0.018
Chain 1:   1900        -8309.561             0.018            0.013
Chain 1:   2000        -8284.239             0.018            0.013
Chain 1:   2100        -8410.904             0.019            0.015
Chain 1:   2200        -8210.695             0.018            0.015
Chain 1:   2300        -8304.603             0.016            0.013
Chain 1:   2400        -8372.827             0.017            0.013
Chain 1:   2500        -8319.035             0.015            0.012
Chain 1:   2600        -8321.166             0.014            0.011
Chain 1:   2700        -8237.543             0.014            0.011
Chain 1:   2800        -8196.426             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003118 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8431299.533             1.000            1.000
Chain 1:    200     -1590997.367             2.650            4.299
Chain 1:    300      -891506.024             2.028            1.000
Chain 1:    400      -457470.265             1.758            1.000
Chain 1:    500      -357187.524             1.463            0.949
Chain 1:    600      -232322.938             1.308            0.949
Chain 1:    700      -118957.159             1.258            0.949
Chain 1:    800       -86215.279             1.148            0.949
Chain 1:    900       -66648.321             1.053            0.785
Chain 1:   1000       -51505.952             0.977            0.785
Chain 1:   1100       -39035.072             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38223.159             0.481            0.380
Chain 1:   1300       -26236.834             0.448            0.380
Chain 1:   1400       -25962.737             0.355            0.319
Chain 1:   1500       -22562.981             0.342            0.319
Chain 1:   1600       -21783.500             0.291            0.294
Chain 1:   1700       -20664.197             0.202            0.294
Chain 1:   1800       -20610.062             0.164            0.151
Chain 1:   1900       -20936.368             0.136            0.054
Chain 1:   2000       -19450.580             0.114            0.054
Chain 1:   2100       -19689.133             0.084            0.036
Chain 1:   2200       -19914.808             0.083            0.036
Chain 1:   2300       -19532.623             0.039            0.020
Chain 1:   2400       -19304.708             0.039            0.020
Chain 1:   2500       -19106.269             0.025            0.016
Chain 1:   2600       -18736.798             0.023            0.016
Chain 1:   2700       -18693.908             0.018            0.012
Chain 1:   2800       -18410.396             0.019            0.015
Chain 1:   2900       -18691.723             0.019            0.015
Chain 1:   3000       -18678.067             0.012            0.012
Chain 1:   3100       -18762.984             0.011            0.012
Chain 1:   3200       -18453.707             0.012            0.015
Chain 1:   3300       -18658.438             0.011            0.012
Chain 1:   3400       -18133.165             0.012            0.015
Chain 1:   3500       -18745.123             0.015            0.015
Chain 1:   3600       -18051.758             0.017            0.015
Chain 1:   3700       -18438.491             0.018            0.017
Chain 1:   3800       -17397.934             0.023            0.021
Chain 1:   3900       -17393.991             0.021            0.021
Chain 1:   4000       -17511.396             0.022            0.021
Chain 1:   4100       -17425.019             0.022            0.021
Chain 1:   4200       -17241.261             0.021            0.021
Chain 1:   4300       -17379.739             0.021            0.021
Chain 1:   4400       -17336.556             0.018            0.011
Chain 1:   4500       -17238.987             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00128 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49013.031             1.000            1.000
Chain 1:    200       -23526.726             1.042            1.083
Chain 1:    300       -14481.643             0.903            1.000
Chain 1:    400       -14803.079             0.682            1.000
Chain 1:    500       -16498.786             0.566            0.625
Chain 1:    600       -12701.758             0.522            0.625
Chain 1:    700       -12705.950             0.447            0.299
Chain 1:    800       -11555.478             0.404            0.299
Chain 1:    900       -12591.113             0.368            0.103
Chain 1:   1000       -30585.638             0.390            0.299
Chain 1:   1100       -10776.521             0.474            0.299
Chain 1:   1200       -14761.580             0.393            0.270
Chain 1:   1300       -12225.784             0.351            0.207
Chain 1:   1400       -10709.208             0.363            0.207
Chain 1:   1500       -12640.286             0.368            0.207
Chain 1:   1600       -10870.784             0.354            0.163
Chain 1:   1700       -11362.618             0.359            0.163
Chain 1:   1800       -13753.919             0.366            0.174
Chain 1:   1900       -10936.187             0.384            0.207
Chain 1:   2000       -10039.887             0.334            0.174
Chain 1:   2100       -10136.992             0.151            0.163
Chain 1:   2200       -11026.273             0.132            0.153
Chain 1:   2300       -10937.741             0.112            0.142
Chain 1:   2400       -11646.515             0.104            0.089
Chain 1:   2500        -9465.262             0.112            0.089
Chain 1:   2600        -9667.208             0.097            0.081
Chain 1:   2700       -17093.559             0.137            0.089
Chain 1:   2800        -9193.194             0.205            0.089
Chain 1:   2900       -14098.908             0.214            0.089
Chain 1:   3000       -11304.758             0.230            0.230
Chain 1:   3100       -10206.820             0.240            0.230
Chain 1:   3200        -9531.763             0.239            0.230
Chain 1:   3300        -9214.786             0.241            0.230
Chain 1:   3400       -20993.209             0.291            0.247
Chain 1:   3500        -9160.315             0.398            0.348
Chain 1:   3600        -9192.970             0.396            0.348
Chain 1:   3700       -20261.572             0.407            0.348
Chain 1:   3800       -10607.739             0.412            0.348
Chain 1:   3900        -9797.446             0.386            0.247
Chain 1:   4000        -8748.640             0.373            0.120
Chain 1:   4100       -12186.133             0.390            0.282
Chain 1:   4200        -9440.580             0.412            0.291
Chain 1:   4300       -12520.996             0.433            0.291
Chain 1:   4400        -8889.169             0.418            0.291
Chain 1:   4500       -10761.973             0.306            0.282
Chain 1:   4600        -9644.826             0.318            0.282
Chain 1:   4700       -10067.599             0.267            0.246
Chain 1:   4800        -9813.896             0.179            0.174
Chain 1:   4900        -9220.130             0.177            0.174
Chain 1:   5000       -15781.566             0.207            0.246
Chain 1:   5100        -8752.217             0.259            0.246
Chain 1:   5200       -16621.223             0.277            0.246
Chain 1:   5300        -8948.994             0.338            0.409
Chain 1:   5400        -8746.421             0.299            0.174
Chain 1:   5500       -11703.044             0.307            0.253
Chain 1:   5600        -8533.817             0.333            0.371
Chain 1:   5700        -9055.821             0.334            0.371
Chain 1:   5800       -12148.249             0.357            0.371
Chain 1:   5900       -14707.716             0.368            0.371
Chain 1:   6000       -11030.713             0.360            0.333
Chain 1:   6100       -10490.809             0.285            0.255
Chain 1:   6200       -12087.677             0.251            0.253
Chain 1:   6300        -8506.028             0.207            0.253
Chain 1:   6400       -12478.946             0.237            0.255
Chain 1:   6500        -8637.085             0.256            0.318
Chain 1:   6600        -8468.193             0.221            0.255
Chain 1:   6700        -8625.918             0.217            0.255
Chain 1:   6800        -8628.850             0.191            0.174
Chain 1:   6900        -8708.477             0.175            0.132
Chain 1:   7000       -12310.476             0.171            0.132
Chain 1:   7100        -8255.314             0.215            0.293
Chain 1:   7200        -9549.722             0.215            0.293
Chain 1:   7300        -8450.754             0.186            0.136
Chain 1:   7400        -8586.457             0.156            0.130
Chain 1:   7500       -10743.660             0.131            0.130
Chain 1:   7600        -8643.482             0.154            0.136
Chain 1:   7700        -8930.203             0.155            0.136
Chain 1:   7800        -9695.658             0.163            0.136
Chain 1:   7900        -8539.417             0.176            0.136
Chain 1:   8000        -8416.313             0.148            0.135
Chain 1:   8100        -9715.576             0.112            0.134
Chain 1:   8200        -9358.493             0.102            0.130
Chain 1:   8300        -8758.499             0.096            0.079
Chain 1:   8400        -8567.008             0.097            0.079
Chain 1:   8500        -8286.528             0.080            0.069
Chain 1:   8600       -12358.517             0.089            0.069
Chain 1:   8700        -8847.707             0.125            0.079
Chain 1:   8800        -8142.096             0.126            0.087
Chain 1:   8900        -9024.185             0.122            0.087
Chain 1:   9000       -11272.801             0.141            0.098
Chain 1:   9100        -8529.381             0.159            0.098
Chain 1:   9200        -8863.306             0.159            0.098
Chain 1:   9300        -8845.861             0.153            0.098
Chain 1:   9400       -10379.602             0.165            0.148
Chain 1:   9500        -8703.147             0.181            0.193
Chain 1:   9600        -8374.004             0.152            0.148
Chain 1:   9700        -8972.712             0.119            0.098
Chain 1:   9800       -11347.583             0.131            0.148
Chain 1:   9900        -9365.534             0.143            0.193
Chain 1:   10000       -10713.970             0.135            0.148
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001376 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57437.018             1.000            1.000
Chain 1:    200       -17434.385             1.647            2.294
Chain 1:    300        -8759.319             1.428            1.000
Chain 1:    400        -8145.167             1.090            1.000
Chain 1:    500        -8771.414             0.886            0.990
Chain 1:    600        -8351.022             0.747            0.990
Chain 1:    700        -7862.157             0.649            0.075
Chain 1:    800        -8501.968             0.577            0.075
Chain 1:    900        -8030.542             0.520            0.075
Chain 1:   1000        -7947.070             0.469            0.075
Chain 1:   1100        -7716.952             0.372            0.071
Chain 1:   1200        -7853.688             0.144            0.062
Chain 1:   1300        -7661.683             0.048            0.059
Chain 1:   1400        -7903.969             0.043            0.050
Chain 1:   1500        -7582.765             0.040            0.042
Chain 1:   1600        -7657.768             0.036            0.031
Chain 1:   1700        -7541.915             0.031            0.030
Chain 1:   1800        -7573.174             0.024            0.025
Chain 1:   1900        -7614.243             0.019            0.017
Chain 1:   2000        -7623.001             0.018            0.017
Chain 1:   2100        -7587.304             0.016            0.015
Chain 1:   2200        -7729.222             0.016            0.015
Chain 1:   2300        -7534.192             0.016            0.015
Chain 1:   2400        -7674.536             0.015            0.015
Chain 1:   2500        -7440.653             0.013            0.015
Chain 1:   2600        -7559.762             0.014            0.016
Chain 1:   2700        -7497.724             0.013            0.016
Chain 1:   2800        -7591.095             0.014            0.016
Chain 1:   2900        -7407.572             0.016            0.018
Chain 1:   3000        -7538.730             0.018            0.018
Chain 1:   3100        -7536.690             0.017            0.018
Chain 1:   3200        -7725.959             0.018            0.018
Chain 1:   3300        -7475.853             0.019            0.018
Chain 1:   3400        -7672.143             0.019            0.024
Chain 1:   3500        -7451.079             0.019            0.024
Chain 1:   3600        -7512.650             0.018            0.024
Chain 1:   3700        -7463.479             0.018            0.024
Chain 1:   3800        -7471.800             0.017            0.024
Chain 1:   3900        -7448.418             0.015            0.017
Chain 1:   4000        -7429.509             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003194 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86066.488             1.000            1.000
Chain 1:    200       -13590.159             3.167            5.333
Chain 1:    300        -9952.485             2.233            1.000
Chain 1:    400       -10881.506             1.696            1.000
Chain 1:    500        -8778.579             1.405            0.366
Chain 1:    600        -8407.186             1.178            0.366
Chain 1:    700        -8645.625             1.014            0.240
Chain 1:    800        -9237.702             0.895            0.240
Chain 1:    900        -8795.295             0.801            0.085
Chain 1:   1000        -8572.005             0.724            0.085
Chain 1:   1100        -8740.046             0.625            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8421.511             0.096            0.050
Chain 1:   1300        -8660.521             0.062            0.044
Chain 1:   1400        -8666.821             0.054            0.038
Chain 1:   1500        -8515.889             0.032            0.028
Chain 1:   1600        -8629.270             0.028            0.028
Chain 1:   1700        -8711.904             0.027            0.026
Chain 1:   1800        -8297.612             0.025            0.026
Chain 1:   1900        -8394.254             0.021            0.019
Chain 1:   2000        -8367.723             0.019            0.018
Chain 1:   2100        -8490.622             0.019            0.014
Chain 1:   2200        -8310.309             0.017            0.014
Chain 1:   2300        -8389.009             0.015            0.013
Chain 1:   2400        -8458.751             0.016            0.013
Chain 1:   2500        -8404.326             0.015            0.012
Chain 1:   2600        -8403.970             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003068 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392582.343             1.000            1.000
Chain 1:    200     -1584523.440             2.648            4.297
Chain 1:    300      -890970.252             2.025            1.000
Chain 1:    400      -457953.339             1.755            1.000
Chain 1:    500      -358209.430             1.460            0.946
Chain 1:    600      -233206.997             1.306            0.946
Chain 1:    700      -119355.123             1.256            0.946
Chain 1:    800       -86559.605             1.146            0.946
Chain 1:    900       -66893.514             1.051            0.778
Chain 1:   1000       -51686.452             0.976            0.778
Chain 1:   1100       -39160.521             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38336.855             0.480            0.379
Chain 1:   1300       -26288.080             0.448            0.379
Chain 1:   1400       -26007.157             0.355            0.320
Chain 1:   1500       -22592.778             0.342            0.320
Chain 1:   1600       -21809.122             0.292            0.294
Chain 1:   1700       -20682.050             0.202            0.294
Chain 1:   1800       -20626.138             0.164            0.151
Chain 1:   1900       -20952.251             0.136            0.054
Chain 1:   2000       -19463.033             0.115            0.054
Chain 1:   2100       -19701.461             0.084            0.036
Chain 1:   2200       -19927.969             0.083            0.036
Chain 1:   2300       -19545.092             0.039            0.020
Chain 1:   2400       -19317.161             0.039            0.020
Chain 1:   2500       -19119.251             0.025            0.016
Chain 1:   2600       -18749.409             0.023            0.016
Chain 1:   2700       -18706.388             0.018            0.012
Chain 1:   2800       -18423.258             0.019            0.015
Chain 1:   2900       -18704.513             0.019            0.015
Chain 1:   3000       -18690.694             0.012            0.012
Chain 1:   3100       -18775.686             0.011            0.012
Chain 1:   3200       -18466.385             0.012            0.015
Chain 1:   3300       -18671.106             0.011            0.012
Chain 1:   3400       -18146.067             0.012            0.015
Chain 1:   3500       -18757.936             0.015            0.015
Chain 1:   3600       -18064.610             0.017            0.015
Chain 1:   3700       -18451.392             0.018            0.017
Chain 1:   3800       -17411.160             0.023            0.021
Chain 1:   3900       -17407.312             0.021            0.021
Chain 1:   4000       -17524.597             0.022            0.021
Chain 1:   4100       -17438.356             0.022            0.021
Chain 1:   4200       -17254.630             0.021            0.021
Chain 1:   4300       -17393.007             0.021            0.021
Chain 1:   4400       -17349.830             0.018            0.011
Chain 1:   4500       -17252.368             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48946.085             1.000            1.000
Chain 1:    200       -18169.079             1.347            1.694
Chain 1:    300       -16965.898             0.922            1.000
Chain 1:    400       -13200.263             0.763            1.000
Chain 1:    500       -16329.265             0.648            0.285
Chain 1:    600       -19464.644             0.567            0.285
Chain 1:    700       -14969.957             0.529            0.285
Chain 1:    800       -10877.008             0.510            0.300
Chain 1:    900       -12925.369             0.471            0.285
Chain 1:   1000       -17026.837             0.448            0.285
Chain 1:   1100       -14426.978             0.366            0.241
Chain 1:   1200       -12296.139             0.214            0.192
Chain 1:   1300       -12238.722             0.207            0.192
Chain 1:   1400       -11254.919             0.187            0.180
Chain 1:   1500       -11103.771             0.170            0.173
Chain 1:   1600       -11817.153             0.160            0.173
Chain 1:   1700       -12589.061             0.136            0.158
Chain 1:   1800        -9655.416             0.128            0.158
Chain 1:   1900       -14768.616             0.147            0.173
Chain 1:   2000       -10188.695             0.168            0.173
Chain 1:   2100       -10497.756             0.153            0.087
Chain 1:   2200       -11124.514             0.141            0.061
Chain 1:   2300        -9196.082             0.162            0.087
Chain 1:   2400       -14495.984             0.190            0.210
Chain 1:   2500       -10169.297             0.231            0.304
Chain 1:   2600        -9767.449             0.229            0.304
Chain 1:   2700        -9826.960             0.223            0.304
Chain 1:   2800        -9002.997             0.202            0.210
Chain 1:   2900        -9779.857             0.175            0.092
Chain 1:   3000        -9590.101             0.132            0.079
Chain 1:   3100        -9408.547             0.131            0.079
Chain 1:   3200       -13648.065             0.157            0.092
Chain 1:   3300       -15515.468             0.148            0.092
Chain 1:   3400       -14528.011             0.118            0.079
Chain 1:   3500        -9475.883             0.129            0.079
Chain 1:   3600        -9741.543             0.128            0.079
Chain 1:   3700        -9564.058             0.129            0.079
Chain 1:   3800        -9622.285             0.120            0.068
Chain 1:   3900       -10144.516             0.117            0.051
Chain 1:   4000       -14347.139             0.145            0.068
Chain 1:   4100        -8763.560             0.207            0.120
Chain 1:   4200       -11042.622             0.196            0.120
Chain 1:   4300        -9701.513             0.198            0.138
Chain 1:   4400        -8776.542             0.202            0.138
Chain 1:   4500        -8613.318             0.150            0.105
Chain 1:   4600       -12360.297             0.178            0.138
Chain 1:   4700        -9662.379             0.204            0.206
Chain 1:   4800       -13300.255             0.231            0.274
Chain 1:   4900        -9313.793             0.268            0.279
Chain 1:   5000       -11454.715             0.258            0.274
Chain 1:   5100        -8680.145             0.226            0.274
Chain 1:   5200       -10146.806             0.220            0.274
Chain 1:   5300       -12775.975             0.227            0.274
Chain 1:   5400       -11595.101             0.226            0.274
Chain 1:   5500        -9808.039             0.242            0.274
Chain 1:   5600        -8366.871             0.229            0.206
Chain 1:   5700       -13223.475             0.238            0.206
Chain 1:   5800       -11614.610             0.225            0.187
Chain 1:   5900        -8245.833             0.223            0.187
Chain 1:   6000       -12661.104             0.239            0.206
Chain 1:   6100        -8058.665             0.264            0.206
Chain 1:   6200        -9224.635             0.262            0.206
Chain 1:   6300        -8478.636             0.250            0.182
Chain 1:   6400       -12005.184             0.270            0.294
Chain 1:   6500       -11237.577             0.258            0.294
Chain 1:   6600       -12655.075             0.252            0.294
Chain 1:   6700       -10165.838             0.240            0.245
Chain 1:   6800        -8994.056             0.239            0.245
Chain 1:   6900       -11531.367             0.220            0.220
Chain 1:   7000        -8257.859             0.225            0.220
Chain 1:   7100        -8107.020             0.170            0.130
Chain 1:   7200        -8363.781             0.160            0.130
Chain 1:   7300        -8039.140             0.156            0.130
Chain 1:   7400        -8356.364             0.130            0.112
Chain 1:   7500        -8042.785             0.127            0.112
Chain 1:   7600        -8440.983             0.121            0.047
Chain 1:   7700        -8402.450             0.097            0.040
Chain 1:   7800        -8537.951             0.085            0.039
Chain 1:   7900        -7977.130             0.070            0.039
Chain 1:   8000        -8275.538             0.034            0.038
Chain 1:   8100        -9372.448             0.044            0.039
Chain 1:   8200        -8617.341             0.050            0.040
Chain 1:   8300        -8249.113             0.050            0.045
Chain 1:   8400        -8172.909             0.047            0.045
Chain 1:   8500        -8257.326             0.044            0.045
Chain 1:   8600        -8301.039             0.040            0.036
Chain 1:   8700        -8293.419             0.040            0.036
Chain 1:   8800        -8343.341             0.039            0.036
Chain 1:   8900        -8394.746             0.032            0.010
Chain 1:   9000        -8409.370             0.029            0.009   MEDIAN ELBO CONVERGED
Chain 1: Informational Message: The ELBO at a previous iteration is larger than the ELBO upon convergence!
Chain 1: This variational approximation may not have converged to a good optimum.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001458 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63094.773             1.000            1.000
Chain 1:    200       -18104.251             1.743            2.485
Chain 1:    300        -8747.474             1.518            1.070
Chain 1:    400        -8560.205             1.144            1.070
Chain 1:    500        -8478.017             0.917            1.000
Chain 1:    600        -8238.914             0.769            1.000
Chain 1:    700        -7929.307             0.665            0.039
Chain 1:    800        -8018.131             0.583            0.039
Chain 1:    900        -7731.371             0.523            0.037
Chain 1:   1000        -7760.323             0.471            0.037
Chain 1:   1100        -7590.292             0.373            0.029
Chain 1:   1200        -7693.541             0.126            0.022
Chain 1:   1300        -7714.036             0.019            0.022
Chain 1:   1400        -7842.380             0.018            0.016
Chain 1:   1500        -7582.461             0.021            0.022
Chain 1:   1600        -7774.640             0.020            0.022
Chain 1:   1700        -7502.368             0.020            0.022
Chain 1:   1800        -7583.755             0.020            0.022
Chain 1:   1900        -7582.326             0.016            0.016
Chain 1:   2000        -7655.589             0.017            0.016
Chain 1:   2100        -7627.674             0.015            0.013
Chain 1:   2200        -7705.852             0.015            0.011
Chain 1:   2300        -7578.347             0.016            0.016
Chain 1:   2400        -7636.577             0.015            0.011
Chain 1:   2500        -7570.448             0.013            0.010
Chain 1:   2600        -7536.255             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003032 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86464.771             1.000            1.000
Chain 1:    200       -13402.590             3.226            5.451
Chain 1:    300        -9695.600             2.278            1.000
Chain 1:    400       -10960.472             1.737            1.000
Chain 1:    500        -8668.819             1.443            0.382
Chain 1:    600        -8091.393             1.214            0.382
Chain 1:    700        -8275.373             1.044            0.264
Chain 1:    800        -8455.287             0.916            0.264
Chain 1:    900        -8497.931             0.815            0.115
Chain 1:   1000        -8077.822             0.739            0.115
Chain 1:   1100        -8511.490             0.644            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8082.826             0.104            0.053
Chain 1:   1300        -8320.007             0.068            0.052
Chain 1:   1400        -8340.914             0.057            0.051
Chain 1:   1500        -8210.134             0.032            0.029
Chain 1:   1600        -8322.433             0.026            0.022
Chain 1:   1700        -8395.706             0.025            0.021
Chain 1:   1800        -7959.659             0.028            0.029
Chain 1:   1900        -8065.183             0.029            0.029
Chain 1:   2000        -8041.125             0.024            0.016
Chain 1:   2100        -8007.756             0.020            0.013
Chain 1:   2200        -7983.724             0.015            0.013
Chain 1:   2300        -8119.130             0.014            0.013
Chain 1:   2400        -7966.628             0.015            0.013
Chain 1:   2500        -8035.801             0.014            0.013
Chain 1:   2600        -7954.128             0.014            0.010
Chain 1:   2700        -7985.443             0.014            0.010
Chain 1:   2800        -7945.551             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8445482.975             1.000            1.000
Chain 1:    200     -1590563.944             2.655            4.310
Chain 1:    300      -891034.861             2.032            1.000
Chain 1:    400      -457267.841             1.761            1.000
Chain 1:    500      -357305.129             1.465            0.949
Chain 1:    600      -232340.959             1.310            0.949
Chain 1:    700      -118843.671             1.259            0.949
Chain 1:    800       -86120.913             1.150            0.949
Chain 1:    900       -66527.468             1.055            0.785
Chain 1:   1000       -51378.884             0.979            0.785
Chain 1:   1100       -38903.752             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38089.069             0.482            0.380
Chain 1:   1300       -26088.618             0.449            0.380
Chain 1:   1400       -25813.497             0.355            0.321
Chain 1:   1500       -22411.738             0.343            0.321
Chain 1:   1600       -21631.956             0.292            0.295
Chain 1:   1700       -20510.490             0.202            0.295
Chain 1:   1800       -20455.967             0.165            0.152
Chain 1:   1900       -20782.509             0.137            0.055
Chain 1:   2000       -19295.545             0.115            0.055
Chain 1:   2100       -19533.836             0.084            0.036
Chain 1:   2200       -19760.137             0.083            0.036
Chain 1:   2300       -19377.410             0.039            0.020
Chain 1:   2400       -19149.407             0.039            0.020
Chain 1:   2500       -18951.207             0.025            0.016
Chain 1:   2600       -18581.252             0.024            0.016
Chain 1:   2700       -18538.198             0.018            0.012
Chain 1:   2800       -18254.789             0.020            0.016
Chain 1:   2900       -18536.146             0.020            0.015
Chain 1:   3000       -18522.385             0.012            0.012
Chain 1:   3100       -18607.406             0.011            0.012
Chain 1:   3200       -18297.907             0.012            0.015
Chain 1:   3300       -18502.769             0.011            0.012
Chain 1:   3400       -17977.264             0.013            0.015
Chain 1:   3500       -18589.686             0.015            0.016
Chain 1:   3600       -17895.642             0.017            0.016
Chain 1:   3700       -18282.934             0.019            0.017
Chain 1:   3800       -17241.450             0.023            0.021
Chain 1:   3900       -17237.519             0.022            0.021
Chain 1:   4000       -17354.877             0.022            0.021
Chain 1:   4100       -17268.557             0.022            0.021
Chain 1:   4200       -17084.538             0.022            0.021
Chain 1:   4300       -17223.149             0.021            0.021
Chain 1:   4400       -17179.755             0.019            0.011
Chain 1:   4500       -17082.220             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001255 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13330.717             1.000            1.000
Chain 1:    200        -9693.207             0.688            1.000
Chain 1:    300        -8163.689             0.521            0.375
Chain 1:    400        -8498.784             0.401            0.375
Chain 1:    500        -8034.915             0.332            0.187
Chain 1:    600        -8159.653             0.279            0.187
Chain 1:    700        -8004.328             0.242            0.058
Chain 1:    800        -8035.781             0.212            0.058
Chain 1:    900        -8047.905             0.189            0.039
Chain 1:   1000        -8113.894             0.171            0.039
Chain 1:   1100        -7945.009             0.073            0.021
Chain 1:   1200        -8016.413             0.036            0.019
Chain 1:   1300        -7984.282             0.018            0.015
Chain 1:   1400        -8002.234             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001471 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56545.283             1.000            1.000
Chain 1:    200       -17956.041             1.575            2.149
Chain 1:    300        -9112.315             1.373            1.000
Chain 1:    400        -8403.919             1.051            1.000
Chain 1:    500        -8446.164             0.842            0.971
Chain 1:    600        -9080.923             0.713            0.971
Chain 1:    700        -8218.519             0.626            0.105
Chain 1:    800        -8254.682             0.549            0.105
Chain 1:    900        -7424.733             0.500            0.105
Chain 1:   1000        -8124.872             0.459            0.105
Chain 1:   1100        -7777.920             0.363            0.086
Chain 1:   1200        -7775.461             0.148            0.084
Chain 1:   1300        -7856.299             0.052            0.070
Chain 1:   1400        -8081.919             0.047            0.045
Chain 1:   1500        -7567.144             0.053            0.068
Chain 1:   1600        -7607.349             0.046            0.045
Chain 1:   1700        -7491.498             0.037            0.028
Chain 1:   1800        -7694.352             0.040            0.028
Chain 1:   1900        -7584.847             0.030            0.026
Chain 1:   2000        -7678.248             0.022            0.015
Chain 1:   2100        -7455.554             0.021            0.015
Chain 1:   2200        -7896.417             0.027            0.026
Chain 1:   2300        -7590.427             0.030            0.028
Chain 1:   2400        -7552.012             0.027            0.026
Chain 1:   2500        -7576.738             0.021            0.015
Chain 1:   2600        -7514.927             0.021            0.015
Chain 1:   2700        -7415.187             0.021            0.014
Chain 1:   2800        -7599.098             0.021            0.014
Chain 1:   2900        -7347.467             0.023            0.024
Chain 1:   3000        -7504.442             0.024            0.024
Chain 1:   3100        -7492.802             0.021            0.021
Chain 1:   3200        -7762.579             0.019            0.021
Chain 1:   3300        -7370.992             0.020            0.021
Chain 1:   3400        -7502.493             0.021            0.021
Chain 1:   3500        -7395.438             0.022            0.021
Chain 1:   3600        -7436.729             0.022            0.021
Chain 1:   3700        -7359.839             0.022            0.021
Chain 1:   3800        -7427.614             0.020            0.018
Chain 1:   3900        -7365.079             0.018            0.014
Chain 1:   4000        -7362.590             0.016            0.010
Chain 1:   4100        -7369.839             0.015            0.010
Chain 1:   4200        -7494.525             0.014            0.010
Chain 1:   4300        -7348.317             0.010            0.010
Chain 1:   4400        -7400.321             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002909 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -88117.654             1.000            1.000
Chain 1:    200       -14372.455             3.066            5.131
Chain 1:    300       -10433.579             2.170            1.000
Chain 1:    400       -13033.289             1.677            1.000
Chain 1:    500       -10093.452             1.400            0.378
Chain 1:    600        -8623.161             1.195            0.378
Chain 1:    700        -8567.248             1.025            0.291
Chain 1:    800        -9369.636             0.908            0.291
Chain 1:    900        -8972.627             0.812            0.199
Chain 1:   1000        -9595.658             0.737            0.199
Chain 1:   1100        -8998.844             0.644            0.171   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8419.301             0.138            0.086
Chain 1:   1300        -8942.402             0.106            0.069
Chain 1:   1400        -8796.190             0.087            0.066
Chain 1:   1500        -8724.332             0.059            0.065
Chain 1:   1600        -8728.621             0.042            0.058
Chain 1:   1700        -8904.032             0.043            0.058
Chain 1:   1800        -8413.625             0.041            0.058
Chain 1:   1900        -8521.808             0.037            0.058
Chain 1:   2000        -8531.522             0.031            0.020
Chain 1:   2100        -8691.572             0.026            0.018
Chain 1:   2200        -8379.659             0.023            0.018
Chain 1:   2300        -8463.938             0.018            0.017
Chain 1:   2400        -8558.956             0.018            0.013
Chain 1:   2500        -8449.838             0.018            0.013
Chain 1:   2600        -8501.743             0.019            0.013
Chain 1:   2700        -8411.061             0.018            0.013
Chain 1:   2800        -8381.772             0.012            0.011
Chain 1:   2900        -8471.529             0.012            0.011
Chain 1:   3000        -8407.766             0.013            0.011
Chain 1:   3100        -8357.256             0.012            0.011
Chain 1:   3200        -8310.961             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003559 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8420078.612             1.000            1.000
Chain 1:    200     -1585659.572             2.655            4.310
Chain 1:    300      -890296.121             2.030            1.000
Chain 1:    400      -457620.212             1.759            1.000
Chain 1:    500      -357712.488             1.463            0.945
Chain 1:    600      -232712.000             1.309            0.945
Chain 1:    700      -119564.530             1.257            0.945
Chain 1:    800       -86917.438             1.147            0.945
Chain 1:    900       -67390.913             1.052            0.781
Chain 1:   1000       -52305.023             0.975            0.781
Chain 1:   1100       -39875.635             0.906            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39080.756             0.478            0.376
Chain 1:   1300       -27104.212             0.444            0.376
Chain 1:   1400       -26838.605             0.350            0.312
Chain 1:   1500       -23440.496             0.337            0.312
Chain 1:   1600       -22664.003             0.286            0.290
Chain 1:   1700       -21543.837             0.197            0.288
Chain 1:   1800       -21490.856             0.160            0.145
Chain 1:   1900       -21818.485             0.132            0.052
Chain 1:   2000       -20329.945             0.111            0.052
Chain 1:   2100       -20568.690             0.081            0.034
Chain 1:   2200       -20795.365             0.080            0.034
Chain 1:   2300       -20411.940             0.037            0.019
Chain 1:   2400       -20183.491             0.037            0.019
Chain 1:   2500       -19985.029             0.024            0.015
Chain 1:   2600       -19614.063             0.022            0.015
Chain 1:   2700       -19570.865             0.017            0.012
Chain 1:   2800       -19286.708             0.019            0.015
Chain 1:   2900       -19568.607             0.019            0.014
Chain 1:   3000       -19554.866             0.011            0.012
Chain 1:   3100       -19640.000             0.011            0.011
Chain 1:   3200       -19329.799             0.011            0.014
Chain 1:   3300       -19535.280             0.010            0.011
Chain 1:   3400       -19008.334             0.012            0.014
Chain 1:   3500       -19622.831             0.014            0.015
Chain 1:   3600       -18926.092             0.016            0.015
Chain 1:   3700       -19315.261             0.018            0.016
Chain 1:   3800       -18269.551             0.022            0.020
Chain 1:   3900       -18265.477             0.021            0.020
Chain 1:   4000       -18382.873             0.021            0.020
Chain 1:   4100       -18296.231             0.021            0.020
Chain 1:   4200       -18111.392             0.021            0.020
Chain 1:   4300       -18250.646             0.020            0.020
Chain 1:   4400       -18206.486             0.018            0.010
Chain 1:   4500       -18108.764             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001267 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12007.673             1.000            1.000
Chain 1:    200        -8893.304             0.675            1.000
Chain 1:    300        -7834.667             0.495            0.350
Chain 1:    400        -7997.590             0.376            0.350
Chain 1:    500        -7856.611             0.305            0.135
Chain 1:    600        -7781.420             0.256            0.135
Chain 1:    700        -7706.890             0.220            0.020
Chain 1:    800        -7730.080             0.193            0.020
Chain 1:    900        -7747.216             0.172            0.018
Chain 1:   1000        -7755.698             0.155            0.018
Chain 1:   1100        -7824.816             0.056            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001491 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61338.303             1.000            1.000
Chain 1:    200       -17448.244             1.758            2.515
Chain 1:    300        -8675.049             1.509            1.011
Chain 1:    400        -8153.318             1.148            1.011
Chain 1:    500        -8264.830             0.921            1.000
Chain 1:    600        -7841.583             0.776            1.000
Chain 1:    700        -8026.723             0.669            0.064
Chain 1:    800        -8014.990             0.585            0.064
Chain 1:    900        -7832.941             0.523            0.054
Chain 1:   1000        -7645.685             0.473            0.054
Chain 1:   1100        -7580.182             0.374            0.024
Chain 1:   1200        -7730.835             0.124            0.023
Chain 1:   1300        -7542.379             0.026            0.023
Chain 1:   1400        -7773.634             0.022            0.023
Chain 1:   1500        -7563.436             0.024            0.024
Chain 1:   1600        -7467.875             0.020            0.023
Chain 1:   1700        -7459.590             0.017            0.023
Chain 1:   1800        -7490.013             0.018            0.023
Chain 1:   1900        -7546.810             0.016            0.019
Chain 1:   2000        -7530.464             0.014            0.013
Chain 1:   2100        -7606.596             0.014            0.013
Chain 1:   2200        -7642.760             0.012            0.010
Chain 1:   2300        -7533.620             0.011            0.010
Chain 1:   2400        -7553.295             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003181 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85984.567             1.000            1.000
Chain 1:    200       -13126.494             3.275            5.550
Chain 1:    300        -9580.630             2.307            1.000
Chain 1:    400       -10505.749             1.752            1.000
Chain 1:    500        -8508.343             1.449            0.370
Chain 1:    600        -8371.695             1.210            0.370
Chain 1:    700        -8209.032             1.040            0.235
Chain 1:    800        -8674.106             0.917            0.235
Chain 1:    900        -8402.551             0.818            0.088
Chain 1:   1000        -8151.534             0.740            0.088
Chain 1:   1100        -8475.591             0.643            0.054   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8148.634             0.092            0.040
Chain 1:   1300        -8318.038             0.057            0.038
Chain 1:   1400        -8304.217             0.049            0.032
Chain 1:   1500        -8212.742             0.026            0.031
Chain 1:   1600        -8309.051             0.026            0.031
Chain 1:   1700        -8397.367             0.025            0.031
Chain 1:   1800        -8012.018             0.024            0.031
Chain 1:   1900        -8114.401             0.023            0.020
Chain 1:   2000        -8084.337             0.020            0.013
Chain 1:   2100        -8217.885             0.018            0.013
Chain 1:   2200        -8002.910             0.016            0.013
Chain 1:   2300        -8144.194             0.016            0.013
Chain 1:   2400        -8155.756             0.016            0.013
Chain 1:   2500        -8123.993             0.015            0.013
Chain 1:   2600        -8122.647             0.014            0.013
Chain 1:   2700        -8031.530             0.014            0.013
Chain 1:   2800        -8008.742             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003021 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8432789.836             1.000            1.000
Chain 1:    200     -1590386.155             2.651            4.302
Chain 1:    300      -891920.145             2.028            1.000
Chain 1:    400      -457542.748             1.759            1.000
Chain 1:    500      -357109.098             1.463            0.949
Chain 1:    600      -232069.362             1.309            0.949
Chain 1:    700      -118538.484             1.259            0.949
Chain 1:    800       -85806.246             1.149            0.949
Chain 1:    900       -66204.683             1.054            0.783
Chain 1:   1000       -51043.391             0.979            0.783
Chain 1:   1100       -38564.561             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37743.497             0.483            0.381
Chain 1:   1300       -25754.171             0.451            0.381
Chain 1:   1400       -25476.907             0.357            0.324
Chain 1:   1500       -22078.371             0.345            0.324
Chain 1:   1600       -21298.637             0.294            0.297
Chain 1:   1700       -20179.340             0.204            0.296
Chain 1:   1800       -20124.933             0.166            0.154
Chain 1:   1900       -20450.489             0.138            0.055
Chain 1:   2000       -18966.481             0.116            0.055
Chain 1:   2100       -19204.554             0.085            0.037
Chain 1:   2200       -19429.994             0.084            0.037
Chain 1:   2300       -19048.282             0.040            0.020
Chain 1:   2400       -18820.636             0.040            0.020
Chain 1:   2500       -18622.453             0.026            0.016
Chain 1:   2600       -18253.369             0.024            0.016
Chain 1:   2700       -18210.647             0.019            0.012
Chain 1:   2800       -17927.578             0.020            0.016
Chain 1:   2900       -18208.569             0.020            0.015
Chain 1:   3000       -18194.870             0.012            0.012
Chain 1:   3100       -18279.710             0.011            0.012
Chain 1:   3200       -17970.817             0.012            0.015
Chain 1:   3300       -18175.262             0.011            0.012
Chain 1:   3400       -17650.809             0.013            0.015
Chain 1:   3500       -18261.577             0.015            0.016
Chain 1:   3600       -17569.778             0.017            0.016
Chain 1:   3700       -17955.348             0.019            0.017
Chain 1:   3800       -16917.300             0.023            0.021
Chain 1:   3900       -16913.486             0.022            0.021
Chain 1:   4000       -17030.828             0.023            0.021
Chain 1:   4100       -16944.602             0.023            0.021
Chain 1:   4200       -16761.421             0.022            0.021
Chain 1:   4300       -16899.443             0.022            0.021
Chain 1:   4400       -16856.663             0.019            0.011
Chain 1:   4500       -16759.265             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001301 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48472.996             1.000            1.000
Chain 1:    200       -17155.307             1.413            1.826
Chain 1:    300       -17129.485             0.942            1.000
Chain 1:    400       -12865.358             0.790            1.000
Chain 1:    500       -19492.689             0.700            0.340
Chain 1:    600       -13878.990             0.650            0.404
Chain 1:    700       -13872.527             0.558            0.340
Chain 1:    800       -12423.384             0.503            0.340
Chain 1:    900       -19535.120             0.487            0.340
Chain 1:   1000       -12151.054             0.499            0.364
Chain 1:   1100       -10972.797             0.410            0.340
Chain 1:   1200       -11902.951             0.235            0.331
Chain 1:   1300       -11263.642             0.241            0.331
Chain 1:   1400       -11565.820             0.210            0.117
Chain 1:   1500       -10231.182             0.189            0.117
Chain 1:   1600       -15753.149             0.184            0.117
Chain 1:   1700       -13118.126             0.204            0.130
Chain 1:   1800       -21661.769             0.232            0.201
Chain 1:   1900        -9593.884             0.321            0.201
Chain 1:   2000       -11080.668             0.274            0.134
Chain 1:   2100       -10625.572             0.267            0.134
Chain 1:   2200       -10743.290             0.260            0.134
Chain 1:   2300       -10016.116             0.262            0.134
Chain 1:   2400       -10994.075             0.268            0.134
Chain 1:   2500       -14445.342             0.279            0.201
Chain 1:   2600        -9262.016             0.300            0.201
Chain 1:   2700       -10298.932             0.290            0.134
Chain 1:   2800       -10016.319             0.253            0.101
Chain 1:   2900        -9375.410             0.135            0.089
Chain 1:   3000        -9113.867             0.124            0.073
Chain 1:   3100       -14598.132             0.157            0.089
Chain 1:   3200        -9748.488             0.206            0.101
Chain 1:   3300        -9844.000             0.200            0.101
Chain 1:   3400        -8912.770             0.201            0.104
Chain 1:   3500        -9401.384             0.182            0.101
Chain 1:   3600       -10478.209             0.137            0.101
Chain 1:   3700        -8672.198             0.148            0.103
Chain 1:   3800       -15338.196             0.188            0.104
Chain 1:   3900        -8537.469             0.261            0.208
Chain 1:   4000        -9426.294             0.268            0.208
Chain 1:   4100        -8517.998             0.241            0.107
Chain 1:   4200       -10066.477             0.206            0.107
Chain 1:   4300       -11858.218             0.220            0.151
Chain 1:   4400        -8760.494             0.245            0.154
Chain 1:   4500        -9995.180             0.253            0.154
Chain 1:   4600        -9949.183             0.243            0.154
Chain 1:   4700        -8366.447             0.241            0.154
Chain 1:   4800        -8706.220             0.201            0.151
Chain 1:   4900        -8709.191             0.122            0.124
Chain 1:   5000       -13949.972             0.150            0.151
Chain 1:   5100        -8718.609             0.199            0.154
Chain 1:   5200       -10069.811             0.197            0.151
Chain 1:   5300       -14405.675             0.212            0.189
Chain 1:   5400        -8460.266             0.247            0.189
Chain 1:   5500        -8402.292             0.235            0.189
Chain 1:   5600        -8699.926             0.238            0.189
Chain 1:   5700       -14628.287             0.260            0.301
Chain 1:   5800        -8943.466             0.320            0.376
Chain 1:   5900        -8871.215             0.320            0.376
Chain 1:   6000       -10305.605             0.297            0.301
Chain 1:   6100        -9319.440             0.247            0.139
Chain 1:   6200        -8298.501             0.246            0.139
Chain 1:   6300       -12471.990             0.250            0.139
Chain 1:   6400       -12337.227             0.180            0.123
Chain 1:   6500       -10739.496             0.195            0.139
Chain 1:   6600        -8515.895             0.217            0.149
Chain 1:   6700        -8437.250             0.178            0.139
Chain 1:   6800        -8148.385             0.118            0.123
Chain 1:   6900       -11587.776             0.147            0.139
Chain 1:   7000        -8765.565             0.165            0.149
Chain 1:   7100        -8073.529             0.163            0.149
Chain 1:   7200        -8205.390             0.152            0.149
Chain 1:   7300       -11861.503             0.149            0.149
Chain 1:   7400       -10604.533             0.160            0.149
Chain 1:   7500       -11768.675             0.155            0.119
Chain 1:   7600        -9134.742             0.158            0.119
Chain 1:   7700        -8256.115             0.168            0.119
Chain 1:   7800        -8275.478             0.164            0.119
Chain 1:   7900        -8648.039             0.139            0.106
Chain 1:   8000        -8139.422             0.113            0.099
Chain 1:   8100        -8360.722             0.107            0.099
Chain 1:   8200        -8521.718             0.107            0.099
Chain 1:   8300        -8158.111             0.081            0.062
Chain 1:   8400       -11011.295             0.095            0.062
Chain 1:   8500       -10972.164             0.086            0.045
Chain 1:   8600        -8756.380             0.082            0.045
Chain 1:   8700        -8145.296             0.079            0.045
Chain 1:   8800        -8207.314             0.079            0.045
Chain 1:   8900        -8826.939             0.082            0.062
Chain 1:   9000        -8649.394             0.078            0.045
Chain 1:   9100       -10749.723             0.095            0.070
Chain 1:   9200        -8690.030             0.117            0.075
Chain 1:   9300        -9776.873             0.123            0.111
Chain 1:   9400        -9507.288             0.100            0.075
Chain 1:   9500        -8885.447             0.107            0.075
Chain 1:   9600        -8640.411             0.084            0.070
Chain 1:   9700        -8223.459             0.082            0.070
Chain 1:   9800       -10237.314             0.101            0.070
Chain 1:   9900       -10868.059             0.100            0.070
Chain 1:   10000        -8070.750             0.132            0.111
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56857.627             1.000            1.000
Chain 1:    200       -17232.992             1.650            2.299
Chain 1:    300        -8665.018             1.429            1.000
Chain 1:    400        -8032.629             1.092            1.000
Chain 1:    500        -8034.815             0.873            0.989
Chain 1:    600        -7968.748             0.729            0.989
Chain 1:    700        -7756.577             0.629            0.079
Chain 1:    800        -8251.043             0.558            0.079
Chain 1:    900        -8011.713             0.499            0.060
Chain 1:   1000        -7902.611             0.451            0.060
Chain 1:   1100        -7729.836             0.353            0.030
Chain 1:   1200        -7651.128             0.124            0.027
Chain 1:   1300        -7776.939             0.027            0.022
Chain 1:   1400        -7696.011             0.020            0.016
Chain 1:   1500        -7640.780             0.021            0.016
Chain 1:   1600        -7571.579             0.021            0.016
Chain 1:   1700        -7530.868             0.018            0.014
Chain 1:   1800        -7600.089             0.013            0.011
Chain 1:   1900        -7618.079             0.011            0.010
Chain 1:   2000        -7615.263             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002536 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85890.699             1.000            1.000
Chain 1:    200       -13319.120             3.224            5.449
Chain 1:    300        -9772.573             2.271            1.000
Chain 1:    400       -10549.222             1.721            1.000
Chain 1:    500        -8708.968             1.419            0.363
Chain 1:    600        -8324.789             1.190            0.363
Chain 1:    700        -8425.519             1.022            0.211
Chain 1:    800        -8613.771             0.897            0.211
Chain 1:    900        -8609.662             0.797            0.074
Chain 1:   1000        -8419.926             0.720            0.074
Chain 1:   1100        -8599.125             0.622            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8279.740             0.081            0.039
Chain 1:   1300        -8510.810             0.047            0.027
Chain 1:   1400        -8509.259             0.040            0.023
Chain 1:   1500        -8399.073             0.020            0.022
Chain 1:   1600        -8494.170             0.017            0.021
Chain 1:   1700        -8584.381             0.017            0.021
Chain 1:   1800        -8194.976             0.019            0.021
Chain 1:   1900        -8297.436             0.020            0.021
Chain 1:   2000        -8267.337             0.019            0.013
Chain 1:   2100        -8398.199             0.018            0.013
Chain 1:   2200        -8184.381             0.017            0.013
Chain 1:   2300        -8326.504             0.016            0.013
Chain 1:   2400        -8339.292             0.016            0.013
Chain 1:   2500        -8307.180             0.015            0.012
Chain 1:   2600        -8307.391             0.014            0.012
Chain 1:   2700        -8215.359             0.014            0.012
Chain 1:   2800        -8190.934             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002713 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392942.489             1.000            1.000
Chain 1:    200     -1579508.349             2.657            4.314
Chain 1:    300      -889327.154             2.030            1.000
Chain 1:    400      -456697.483             1.759            1.000
Chain 1:    500      -357333.652             1.463            0.947
Chain 1:    600      -232525.934             1.309            0.947
Chain 1:    700      -118938.255             1.258            0.947
Chain 1:    800       -86190.646             1.148            0.947
Chain 1:    900       -66552.438             1.054            0.776
Chain 1:   1000       -51354.912             0.978            0.776
Chain 1:   1100       -38842.380             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38017.326             0.481            0.380
Chain 1:   1300       -25984.778             0.449            0.380
Chain 1:   1400       -25703.311             0.356            0.322
Chain 1:   1500       -22293.654             0.343            0.322
Chain 1:   1600       -21511.030             0.293            0.296
Chain 1:   1700       -20386.044             0.203            0.295
Chain 1:   1800       -20330.405             0.166            0.153
Chain 1:   1900       -20656.136             0.138            0.055
Chain 1:   2000       -19169.039             0.116            0.055
Chain 1:   2100       -19407.177             0.085            0.036
Chain 1:   2200       -19633.301             0.084            0.036
Chain 1:   2300       -19250.940             0.040            0.020
Chain 1:   2400       -19023.222             0.040            0.020
Chain 1:   2500       -18825.304             0.025            0.016
Chain 1:   2600       -18455.907             0.024            0.016
Chain 1:   2700       -18413.056             0.018            0.012
Chain 1:   2800       -18130.171             0.020            0.016
Chain 1:   2900       -18411.165             0.020            0.015
Chain 1:   3000       -18397.361             0.012            0.012
Chain 1:   3100       -18482.284             0.011            0.012
Chain 1:   3200       -18173.277             0.012            0.015
Chain 1:   3300       -18377.787             0.011            0.012
Chain 1:   3400       -17853.304             0.013            0.015
Chain 1:   3500       -18464.302             0.015            0.016
Chain 1:   3600       -17772.133             0.017            0.016
Chain 1:   3700       -18158.080             0.019            0.017
Chain 1:   3800       -17119.611             0.023            0.021
Chain 1:   3900       -17115.832             0.022            0.021
Chain 1:   4000       -17233.096             0.022            0.021
Chain 1:   4100       -17146.951             0.022            0.021
Chain 1:   4200       -16963.640             0.022            0.021
Chain 1:   4300       -17101.727             0.021            0.021
Chain 1:   4400       -17058.877             0.019            0.011
Chain 1:   4500       -16961.488             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001301 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12201.584             1.000            1.000
Chain 1:    200        -9144.723             0.667            1.000
Chain 1:    300        -7906.211             0.497            0.334
Chain 1:    400        -8116.075             0.379            0.334
Chain 1:    500        -7988.586             0.307            0.157
Chain 1:    600        -7859.214             0.258            0.157
Chain 1:    700        -7770.397             0.223            0.026
Chain 1:    800        -7779.265             0.195            0.026
Chain 1:    900        -7700.412             0.175            0.016
Chain 1:   1000        -7877.832             0.159            0.023
Chain 1:   1100        -7906.217             0.060            0.016
Chain 1:   1200        -7802.195             0.028            0.016
Chain 1:   1300        -7748.374             0.013            0.013
Chain 1:   1400        -7765.785             0.010            0.011
Chain 1:   1500        -7852.371             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001443 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61653.658             1.000            1.000
Chain 1:    200       -17725.412             1.739            2.478
Chain 1:    300        -8749.680             1.501            1.026
Chain 1:    400        -8141.766             1.145            1.026
Chain 1:    500        -8265.602             0.919            1.000
Chain 1:    600        -8501.200             0.770            1.000
Chain 1:    700        -7698.113             0.675            0.104
Chain 1:    800        -8017.605             0.596            0.104
Chain 1:    900        -7794.445             0.533            0.075
Chain 1:   1000        -7658.166             0.481            0.075
Chain 1:   1100        -7667.785             0.381            0.040
Chain 1:   1200        -7489.804             0.136            0.029
Chain 1:   1300        -7709.114             0.036            0.028
Chain 1:   1400        -7787.172             0.030            0.028
Chain 1:   1500        -7487.476             0.032            0.028
Chain 1:   1600        -7573.499             0.031            0.028
Chain 1:   1700        -7459.568             0.022            0.024
Chain 1:   1800        -7473.532             0.018            0.018
Chain 1:   1900        -7502.495             0.015            0.015
Chain 1:   2000        -7509.329             0.014            0.011
Chain 1:   2100        -7503.354             0.014            0.011
Chain 1:   2200        -7612.311             0.013            0.011
Chain 1:   2300        -7504.502             0.011            0.011
Chain 1:   2400        -7565.880             0.011            0.011
Chain 1:   2500        -7508.082             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85993.937             1.000            1.000
Chain 1:    200       -13336.300             3.224            5.448
Chain 1:    300        -9722.441             2.273            1.000
Chain 1:    400       -10562.442             1.725            1.000
Chain 1:    500        -8689.151             1.423            0.372
Chain 1:    600        -8211.513             1.196            0.372
Chain 1:    700        -8269.812             1.026            0.216
Chain 1:    800        -8860.577             0.906            0.216
Chain 1:    900        -8571.858             0.809            0.080
Chain 1:   1000        -8321.752             0.731            0.080
Chain 1:   1100        -8536.017             0.634            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8279.220             0.092            0.058
Chain 1:   1300        -8435.777             0.057            0.034
Chain 1:   1400        -8440.110             0.049            0.031
Chain 1:   1500        -8298.282             0.029            0.030
Chain 1:   1600        -8411.220             0.024            0.025
Chain 1:   1700        -8497.772             0.025            0.025
Chain 1:   1800        -8091.824             0.023            0.025
Chain 1:   1900        -8188.493             0.021            0.019
Chain 1:   2000        -8160.584             0.018            0.017
Chain 1:   2100        -8281.072             0.017            0.015
Chain 1:   2200        -8091.227             0.016            0.015
Chain 1:   2300        -8228.142             0.016            0.015
Chain 1:   2400        -8235.151             0.016            0.015
Chain 1:   2500        -8201.797             0.015            0.013
Chain 1:   2600        -8199.781             0.014            0.012
Chain 1:   2700        -8113.781             0.014            0.012
Chain 1:   2800        -8079.009             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003228 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397495.079             1.000            1.000
Chain 1:    200     -1585413.973             2.648            4.297
Chain 1:    300      -891681.689             2.025            1.000
Chain 1:    400      -457831.095             1.756            1.000
Chain 1:    500      -358104.317             1.460            0.948
Chain 1:    600      -233034.706             1.306            0.948
Chain 1:    700      -119157.405             1.256            0.948
Chain 1:    800       -86320.217             1.147            0.948
Chain 1:    900       -66644.994             1.052            0.778
Chain 1:   1000       -51431.399             0.976            0.778
Chain 1:   1100       -38894.800             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38071.111             0.481            0.380
Chain 1:   1300       -26020.472             0.450            0.380
Chain 1:   1400       -25738.252             0.356            0.322
Chain 1:   1500       -22323.455             0.343            0.322
Chain 1:   1600       -21538.984             0.293            0.296
Chain 1:   1700       -20412.271             0.203            0.295
Chain 1:   1800       -20356.308             0.166            0.153
Chain 1:   1900       -20682.235             0.138            0.055
Chain 1:   2000       -19193.524             0.116            0.055
Chain 1:   2100       -19431.882             0.085            0.036
Chain 1:   2200       -19658.155             0.084            0.036
Chain 1:   2300       -19275.626             0.040            0.020
Chain 1:   2400       -19047.798             0.040            0.020
Chain 1:   2500       -18849.762             0.025            0.016
Chain 1:   2600       -18480.115             0.024            0.016
Chain 1:   2700       -18437.211             0.018            0.012
Chain 1:   2800       -18154.051             0.020            0.016
Chain 1:   2900       -18435.317             0.020            0.015
Chain 1:   3000       -18421.535             0.012            0.012
Chain 1:   3100       -18506.439             0.011            0.012
Chain 1:   3200       -18197.249             0.012            0.015
Chain 1:   3300       -18401.919             0.011            0.012
Chain 1:   3400       -17877.001             0.013            0.015
Chain 1:   3500       -18488.544             0.015            0.016
Chain 1:   3600       -17795.786             0.017            0.016
Chain 1:   3700       -18182.130             0.019            0.017
Chain 1:   3800       -17142.582             0.023            0.021
Chain 1:   3900       -17138.765             0.022            0.021
Chain 1:   4000       -17256.085             0.022            0.021
Chain 1:   4100       -17169.795             0.022            0.021
Chain 1:   4200       -16986.292             0.022            0.021
Chain 1:   4300       -17124.521             0.021            0.021
Chain 1:   4400       -17081.482             0.019            0.011
Chain 1:   4500       -16984.077             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001625 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48251.694             1.000            1.000
Chain 1:    200       -15225.281             1.585            2.169
Chain 1:    300       -12013.418             1.146            1.000
Chain 1:    400       -21161.159             0.967            1.000
Chain 1:    500       -12171.036             0.921            0.739
Chain 1:    600       -20598.828             0.836            0.739
Chain 1:    700       -14340.083             0.779            0.436
Chain 1:    800       -11464.046             0.713            0.436
Chain 1:    900       -23660.172             0.691            0.436
Chain 1:   1000       -24487.165             0.625            0.436
Chain 1:   1100       -10499.720             0.659            0.436   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12221.519             0.456            0.432
Chain 1:   1300       -11608.716             0.434            0.432
Chain 1:   1400       -10624.763             0.400            0.409
Chain 1:   1500       -12696.245             0.343            0.251
Chain 1:   1600       -10400.637             0.324            0.221
Chain 1:   1700       -10929.998             0.285            0.163
Chain 1:   1800        -9821.027             0.271            0.141
Chain 1:   1900        -9576.513             0.222            0.113
Chain 1:   2000       -11014.196             0.232            0.131
Chain 1:   2100        -9338.089             0.117            0.131
Chain 1:   2200        -9686.718             0.106            0.113
Chain 1:   2300        -9740.718             0.101            0.113
Chain 1:   2400        -9737.051             0.092            0.113
Chain 1:   2500       -11185.349             0.089            0.113
Chain 1:   2600        -9031.387             0.091            0.113
Chain 1:   2700       -11906.527             0.110            0.129
Chain 1:   2800        -9132.456             0.129            0.131
Chain 1:   2900        -9094.799             0.127            0.131
Chain 1:   3000       -16178.272             0.158            0.179
Chain 1:   3100        -9561.755             0.209            0.238
Chain 1:   3200        -8622.161             0.216            0.238
Chain 1:   3300        -9147.606             0.221            0.238
Chain 1:   3400       -12471.453             0.248            0.241
Chain 1:   3500        -8809.815             0.277            0.267
Chain 1:   3600       -10214.718             0.267            0.267
Chain 1:   3700        -9056.150             0.255            0.267
Chain 1:   3800       -12348.921             0.251            0.267
Chain 1:   3900        -8832.506             0.291            0.267
Chain 1:   4000        -8705.197             0.249            0.267
Chain 1:   4100        -9172.084             0.184            0.138
Chain 1:   4200       -11347.735             0.193            0.192
Chain 1:   4300       -15075.649             0.212            0.247
Chain 1:   4400       -12319.583             0.207            0.224
Chain 1:   4500        -8402.402             0.212            0.224
Chain 1:   4600       -12862.716             0.233            0.247
Chain 1:   4700       -11469.076             0.233            0.247
Chain 1:   4800        -8302.522             0.244            0.247
Chain 1:   4900        -8991.588             0.212            0.224
Chain 1:   5000        -9002.454             0.211            0.224
Chain 1:   5100       -11169.800             0.225            0.224
Chain 1:   5200        -8566.521             0.236            0.247
Chain 1:   5300       -13207.418             0.247            0.304
Chain 1:   5400        -8767.420             0.275            0.347
Chain 1:   5500        -8720.051             0.229            0.304
Chain 1:   5600       -15309.780             0.237            0.304
Chain 1:   5700        -8940.067             0.296            0.351
Chain 1:   5800       -12761.737             0.288            0.304
Chain 1:   5900        -8248.275             0.335            0.351
Chain 1:   6000        -8548.959             0.339            0.351
Chain 1:   6100        -8655.214             0.320            0.351
Chain 1:   6200        -7905.218             0.300            0.351
Chain 1:   6300        -9082.650             0.277            0.299
Chain 1:   6400        -8690.009             0.231            0.130
Chain 1:   6500        -9219.357             0.236            0.130
Chain 1:   6600       -10242.267             0.203            0.100
Chain 1:   6700        -8054.975             0.159            0.100
Chain 1:   6800        -8853.513             0.138            0.095
Chain 1:   6900       -12832.545             0.115            0.095
Chain 1:   7000        -8287.952             0.166            0.100
Chain 1:   7100        -8236.159             0.165            0.100
Chain 1:   7200       -10264.897             0.176            0.130
Chain 1:   7300       -10935.203             0.169            0.100
Chain 1:   7400        -8373.778             0.195            0.198
Chain 1:   7500        -7996.986             0.194            0.198
Chain 1:   7600       -11231.447             0.213            0.272
Chain 1:   7700        -8222.862             0.222            0.288
Chain 1:   7800        -7930.640             0.217            0.288
Chain 1:   7900        -7962.410             0.186            0.198
Chain 1:   8000       -10607.997             0.156            0.198
Chain 1:   8100        -8134.342             0.186            0.249
Chain 1:   8200        -7975.224             0.168            0.249
Chain 1:   8300        -8116.493             0.164            0.249
Chain 1:   8400        -8223.522             0.135            0.047
Chain 1:   8500        -9888.857             0.147            0.168
Chain 1:   8600        -8170.675             0.139            0.168
Chain 1:   8700        -7916.530             0.106            0.037
Chain 1:   8800       -10001.067             0.123            0.168
Chain 1:   8900        -8190.617             0.144            0.208
Chain 1:   9000       -10067.043             0.138            0.186
Chain 1:   9100        -8149.148             0.131            0.186
Chain 1:   9200        -7973.615             0.131            0.186
Chain 1:   9300        -9038.628             0.141            0.186
Chain 1:   9400       -11393.283             0.161            0.207
Chain 1:   9500        -8975.225             0.171            0.208
Chain 1:   9600        -9641.952             0.157            0.207
Chain 1:   9700        -8672.969             0.165            0.207
Chain 1:   9800       -10348.932             0.160            0.186
Chain 1:   9900        -8479.205             0.160            0.186
Chain 1:   10000        -8468.757             0.142            0.162
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001736 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56335.317             1.000            1.000
Chain 1:    200       -16981.199             1.659            2.318
Chain 1:    300        -8505.273             1.438            1.000
Chain 1:    400        -8862.564             1.089            1.000
Chain 1:    500        -8472.629             0.880            0.997
Chain 1:    600        -8998.724             0.743            0.997
Chain 1:    700        -7663.478             0.662            0.174
Chain 1:    800        -7962.381             0.584            0.174
Chain 1:    900        -7812.624             0.521            0.058
Chain 1:   1000        -7666.070             0.471            0.058
Chain 1:   1100        -7555.247             0.372            0.046
Chain 1:   1200        -7541.092             0.141            0.040
Chain 1:   1300        -7639.764             0.042            0.038
Chain 1:   1400        -7713.238             0.039            0.019
Chain 1:   1500        -7543.689             0.037            0.019
Chain 1:   1600        -7477.571             0.032            0.019
Chain 1:   1700        -7427.306             0.015            0.015
Chain 1:   1800        -7474.428             0.012            0.013
Chain 1:   1900        -7532.851             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85733.576             1.000            1.000
Chain 1:    200       -13073.254             3.279            5.558
Chain 1:    300        -9515.692             2.311            1.000
Chain 1:    400       -10253.156             1.751            1.000
Chain 1:    500        -8425.976             1.444            0.374
Chain 1:    600        -8183.425             1.208            0.374
Chain 1:    700        -8257.948             1.037            0.217
Chain 1:    800        -8519.843             0.911            0.217
Chain 1:    900        -8397.748             0.812            0.072
Chain 1:   1000        -8137.696             0.734            0.072
Chain 1:   1100        -8383.740             0.637            0.032   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8125.426             0.084            0.032
Chain 1:   1300        -8239.147             0.048            0.031
Chain 1:   1400        -8260.094             0.041            0.030
Chain 1:   1500        -8150.243             0.021            0.029
Chain 1:   1600        -8248.211             0.019            0.015
Chain 1:   1700        -8336.620             0.019            0.015
Chain 1:   1800        -7948.745             0.021            0.015
Chain 1:   1900        -8051.335             0.021            0.014
Chain 1:   2000        -8021.093             0.018            0.013
Chain 1:   2100        -8152.949             0.017            0.013
Chain 1:   2200        -7938.795             0.016            0.013
Chain 1:   2300        -8080.527             0.016            0.013
Chain 1:   2400        -8092.821             0.016            0.013
Chain 1:   2500        -8060.894             0.015            0.013
Chain 1:   2600        -8060.602             0.014            0.013
Chain 1:   2700        -7968.874             0.014            0.013
Chain 1:   2800        -7944.942             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003116 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372009.864             1.000            1.000
Chain 1:    200     -1580827.554             2.648            4.296
Chain 1:    300      -890628.001             2.024            1.000
Chain 1:    400      -457253.579             1.755            1.000
Chain 1:    500      -357680.829             1.459            0.948
Chain 1:    600      -232858.256             1.306            0.948
Chain 1:    700      -118991.684             1.256            0.948
Chain 1:    800       -86109.228             1.146            0.948
Chain 1:    900       -66434.181             1.052            0.775
Chain 1:   1000       -51197.918             0.977            0.775
Chain 1:   1100       -38642.448             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37814.069             0.482            0.382
Chain 1:   1300       -25751.936             0.451            0.382
Chain 1:   1400       -25467.195             0.357            0.325
Chain 1:   1500       -22048.043             0.345            0.325
Chain 1:   1600       -21261.557             0.295            0.298
Chain 1:   1700       -20134.014             0.205            0.296
Chain 1:   1800       -20077.463             0.167            0.155
Chain 1:   1900       -20403.067             0.139            0.056
Chain 1:   2000       -18914.026             0.117            0.056
Chain 1:   2100       -19152.682             0.086            0.037
Chain 1:   2200       -19378.671             0.085            0.037
Chain 1:   2300       -18996.372             0.040            0.020
Chain 1:   2400       -18768.619             0.040            0.020
Chain 1:   2500       -18570.511             0.026            0.016
Chain 1:   2600       -18201.409             0.024            0.016
Chain 1:   2700       -18158.583             0.019            0.012
Chain 1:   2800       -17875.566             0.020            0.016
Chain 1:   2900       -18156.634             0.020            0.015
Chain 1:   3000       -18142.980             0.012            0.012
Chain 1:   3100       -18227.813             0.011            0.012
Chain 1:   3200       -17918.906             0.012            0.015
Chain 1:   3300       -18123.308             0.011            0.012
Chain 1:   3400       -17598.858             0.013            0.015
Chain 1:   3500       -18209.782             0.015            0.016
Chain 1:   3600       -17517.794             0.017            0.016
Chain 1:   3700       -17903.586             0.019            0.017
Chain 1:   3800       -16865.281             0.024            0.022
Chain 1:   3900       -16861.462             0.022            0.022
Chain 1:   4000       -16978.789             0.023            0.022
Chain 1:   4100       -16892.586             0.023            0.022
Chain 1:   4200       -16709.306             0.022            0.022
Chain 1:   4300       -16847.408             0.022            0.022
Chain 1:   4400       -16804.619             0.019            0.011
Chain 1:   4500       -16707.185             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001533 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12420.474             1.000            1.000
Chain 1:    200        -9345.352             0.665            1.000
Chain 1:    300        -8084.135             0.495            0.329
Chain 1:    400        -8180.746             0.374            0.329
Chain 1:    500        -8119.937             0.301            0.156
Chain 1:    600        -7979.484             0.254            0.156
Chain 1:    700        -7895.722             0.219            0.018
Chain 1:    800        -7906.887             0.192            0.018
Chain 1:    900        -7859.963             0.171            0.012
Chain 1:   1000        -7962.333             0.155            0.013
Chain 1:   1100        -8040.648             0.056            0.012
Chain 1:   1200        -7905.574             0.025            0.012
Chain 1:   1300        -7863.728             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001742 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58187.918             1.000            1.000
Chain 1:    200       -17723.605             1.642            2.283
Chain 1:    300        -8704.302             1.440            1.036
Chain 1:    400        -8217.457             1.095            1.036
Chain 1:    500        -8111.295             0.878            1.000
Chain 1:    600        -8660.023             0.742            1.000
Chain 1:    700        -7962.457             0.649            0.088
Chain 1:    800        -8085.671             0.570            0.088
Chain 1:    900        -7920.630             0.509            0.063
Chain 1:   1000        -8132.212             0.460            0.063
Chain 1:   1100        -7719.200             0.366            0.059
Chain 1:   1200        -7600.615             0.139            0.054
Chain 1:   1300        -7787.307             0.038            0.026
Chain 1:   1400        -7911.774             0.033            0.024
Chain 1:   1500        -7629.481             0.036            0.026
Chain 1:   1600        -7703.983             0.031            0.024
Chain 1:   1700        -7560.456             0.024            0.021
Chain 1:   1800        -7579.899             0.022            0.021
Chain 1:   1900        -7611.791             0.021            0.019
Chain 1:   2000        -7652.836             0.019            0.016
Chain 1:   2100        -7644.196             0.013            0.016
Chain 1:   2200        -7707.105             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004597 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 45.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85461.493             1.000            1.000
Chain 1:    200       -13499.400             3.165            5.331
Chain 1:    300        -9874.976             2.233            1.000
Chain 1:    400       -10695.604             1.694            1.000
Chain 1:    500        -8859.692             1.396            0.367
Chain 1:    600        -8459.931             1.171            0.367
Chain 1:    700        -8426.801             1.005            0.207
Chain 1:    800        -8954.799             0.886            0.207
Chain 1:    900        -8652.822             0.792            0.077
Chain 1:   1000        -8488.595             0.715            0.077
Chain 1:   1100        -8725.755             0.617            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8240.970             0.090            0.059
Chain 1:   1300        -8571.710             0.057            0.047
Chain 1:   1400        -8571.433             0.050            0.039
Chain 1:   1500        -8445.002             0.030            0.035
Chain 1:   1600        -8552.462             0.027            0.027
Chain 1:   1700        -8635.835             0.028            0.027
Chain 1:   1800        -8222.007             0.027            0.027
Chain 1:   1900        -8318.286             0.024            0.019
Chain 1:   2000        -8291.568             0.023            0.015
Chain 1:   2100        -8414.337             0.021            0.015
Chain 1:   2200        -8234.399             0.018            0.015
Chain 1:   2300        -8313.085             0.015            0.013
Chain 1:   2400        -8382.803             0.016            0.013
Chain 1:   2500        -8328.284             0.015            0.012
Chain 1:   2600        -8327.805             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388868.194             1.000            1.000
Chain 1:    200     -1582636.244             2.650            4.301
Chain 1:    300      -890937.536             2.026            1.000
Chain 1:    400      -457702.155             1.756            1.000
Chain 1:    500      -358203.460             1.460            0.947
Chain 1:    600      -233290.950             1.306            0.947
Chain 1:    700      -119390.578             1.256            0.947
Chain 1:    800       -86553.576             1.146            0.947
Chain 1:    900       -66873.305             1.052            0.776
Chain 1:   1000       -51648.857             0.976            0.776
Chain 1:   1100       -39103.550             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38278.081             0.480            0.379
Chain 1:   1300       -26215.913             0.448            0.379
Chain 1:   1400       -25932.967             0.355            0.321
Chain 1:   1500       -22515.165             0.342            0.321
Chain 1:   1600       -21729.925             0.292            0.295
Chain 1:   1700       -20601.681             0.202            0.294
Chain 1:   1800       -20545.373             0.165            0.152
Chain 1:   1900       -20871.447             0.137            0.055
Chain 1:   2000       -19381.498             0.115            0.055
Chain 1:   2100       -19619.993             0.084            0.036
Chain 1:   2200       -19846.563             0.083            0.036
Chain 1:   2300       -19463.692             0.039            0.020
Chain 1:   2400       -19235.786             0.039            0.020
Chain 1:   2500       -19037.809             0.025            0.016
Chain 1:   2600       -18668.049             0.024            0.016
Chain 1:   2700       -18624.987             0.018            0.012
Chain 1:   2800       -18341.868             0.020            0.015
Chain 1:   2900       -18623.140             0.020            0.015
Chain 1:   3000       -18609.352             0.012            0.012
Chain 1:   3100       -18694.331             0.011            0.012
Chain 1:   3200       -18385.014             0.012            0.015
Chain 1:   3300       -18589.726             0.011            0.012
Chain 1:   3400       -18064.678             0.013            0.015
Chain 1:   3500       -18676.500             0.015            0.015
Chain 1:   3600       -17983.284             0.017            0.015
Chain 1:   3700       -18370.038             0.019            0.017
Chain 1:   3800       -17329.866             0.023            0.021
Chain 1:   3900       -17326.018             0.021            0.021
Chain 1:   4000       -17443.334             0.022            0.021
Chain 1:   4100       -17357.090             0.022            0.021
Chain 1:   4200       -17173.369             0.022            0.021
Chain 1:   4300       -17311.746             0.021            0.021
Chain 1:   4400       -17268.602             0.019            0.011
Chain 1:   4500       -17171.147             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001591 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13802.217             1.000            1.000
Chain 1:    200       -10258.953             0.673            1.000
Chain 1:    300        -8857.284             0.501            0.345
Chain 1:    400        -8623.238             0.383            0.345
Chain 1:    500        -8362.705             0.312            0.158
Chain 1:    600        -8393.547             0.261            0.158
Chain 1:    700        -8237.084             0.226            0.031
Chain 1:    800        -8241.079             0.198            0.031
Chain 1:    900        -8346.724             0.178            0.027
Chain 1:   1000        -8326.008             0.160            0.027
Chain 1:   1100        -8398.576             0.061            0.019
Chain 1:   1200        -8283.183             0.028            0.014
Chain 1:   1300        -8195.842             0.013            0.013
Chain 1:   1400        -8226.557             0.011            0.011
Chain 1:   1500        -8324.629             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001807 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -59113.655             1.000            1.000
Chain 1:    200       -18575.603             1.591            2.182
Chain 1:    300        -9084.113             1.409            1.045
Chain 1:    400        -8067.701             1.088            1.045
Chain 1:    500        -8715.471             0.885            1.000
Chain 1:    600        -9905.416             0.758            1.000
Chain 1:    700        -7893.570             0.686            0.255
Chain 1:    800        -8398.901             0.608            0.255
Chain 1:    900        -7756.788             0.549            0.126
Chain 1:   1000        -7923.002             0.497            0.126
Chain 1:   1100        -7759.897             0.399            0.120
Chain 1:   1200        -7660.376             0.182            0.083
Chain 1:   1300        -7722.315             0.078            0.074
Chain 1:   1400        -7642.098             0.067            0.060
Chain 1:   1500        -7564.492             0.060            0.021
Chain 1:   1600        -7666.475             0.049            0.021
Chain 1:   1700        -7695.309             0.024            0.013
Chain 1:   1800        -7562.314             0.020            0.013
Chain 1:   1900        -7763.592             0.014            0.013
Chain 1:   2000        -7752.272             0.012            0.013
Chain 1:   2100        -7644.035             0.012            0.013
Chain 1:   2200        -7760.552             0.012            0.013
Chain 1:   2300        -7611.050             0.013            0.014
Chain 1:   2400        -7762.383             0.014            0.015
Chain 1:   2500        -7675.044             0.014            0.015
Chain 1:   2600        -7536.359             0.015            0.018
Chain 1:   2700        -7530.883             0.014            0.018
Chain 1:   2800        -7638.584             0.014            0.015
Chain 1:   2900        -7394.157             0.015            0.015
Chain 1:   3000        -7527.878             0.016            0.018
Chain 1:   3100        -7528.338             0.015            0.018
Chain 1:   3200        -7654.109             0.015            0.018
Chain 1:   3300        -7397.177             0.017            0.018
Chain 1:   3400        -7687.608             0.018            0.018
Chain 1:   3500        -7441.352             0.021            0.018
Chain 1:   3600        -7509.711             0.020            0.018
Chain 1:   3700        -7465.300             0.020            0.018
Chain 1:   3800        -7436.258             0.019            0.018
Chain 1:   3900        -7463.834             0.016            0.016
Chain 1:   4000        -7396.549             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003341 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87494.985             1.000            1.000
Chain 1:    200       -14283.041             3.063            5.126
Chain 1:    300       -10482.976             2.163            1.000
Chain 1:    400       -12421.733             1.661            1.000
Chain 1:    500        -8882.958             1.409            0.398
Chain 1:    600        -9380.399             1.183            0.398
Chain 1:    700        -8957.531             1.020            0.362
Chain 1:    800        -8815.825             0.895            0.362
Chain 1:    900        -8757.142             0.796            0.156
Chain 1:   1000        -9143.030             0.721            0.156
Chain 1:   1100        -9219.845             0.622            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8697.951             0.115            0.053
Chain 1:   1300        -9061.070             0.083            0.047
Chain 1:   1400        -9008.564             0.068            0.042
Chain 1:   1500        -8912.353             0.029            0.040
Chain 1:   1600        -8976.652             0.024            0.016
Chain 1:   1700        -9052.343             0.021            0.011
Chain 1:   1800        -8607.455             0.024            0.011
Chain 1:   1900        -8703.170             0.025            0.011
Chain 1:   2000        -8725.109             0.021            0.011
Chain 1:   2100        -8815.339             0.021            0.011
Chain 1:   2200        -8585.025             0.017            0.011
Chain 1:   2300        -8783.286             0.016            0.011
Chain 1:   2400        -8606.762             0.017            0.011
Chain 1:   2500        -8674.479             0.017            0.011
Chain 1:   2600        -8582.717             0.017            0.011
Chain 1:   2700        -8617.150             0.017            0.011
Chain 1:   2800        -8570.055             0.012            0.011
Chain 1:   2900        -8683.291             0.012            0.011
Chain 1:   3000        -8591.666             0.013            0.011
Chain 1:   3100        -8559.606             0.013            0.011
Chain 1:   3200        -8529.682             0.010            0.011
Chain 1:   3300        -8797.625             0.011            0.011
Chain 1:   3400        -8847.929             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005061 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 50.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8444102.022             1.000            1.000
Chain 1:    200     -1588735.607             2.657            4.315
Chain 1:    300      -890316.692             2.033            1.000
Chain 1:    400      -458242.477             1.761            1.000
Chain 1:    500      -357925.686             1.465            0.943
Chain 1:    600      -233052.888             1.310            0.943
Chain 1:    700      -119603.129             1.258            0.943
Chain 1:    800       -86941.018             1.148            0.943
Chain 1:    900       -67361.904             1.053            0.784
Chain 1:   1000       -52247.408             0.976            0.784
Chain 1:   1100       -39799.115             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38991.272             0.478            0.376
Chain 1:   1300       -27001.465             0.444            0.376
Chain 1:   1400       -26730.740             0.351            0.313
Chain 1:   1500       -23331.521             0.337            0.313
Chain 1:   1600       -22553.939             0.287            0.291
Chain 1:   1700       -21432.410             0.198            0.289
Chain 1:   1800       -21378.515             0.160            0.146
Chain 1:   1900       -21705.640             0.133            0.052
Chain 1:   2000       -20217.692             0.111            0.052
Chain 1:   2100       -20456.032             0.081            0.034
Chain 1:   2200       -20682.776             0.080            0.034
Chain 1:   2300       -20299.477             0.038            0.019
Chain 1:   2400       -20071.226             0.038            0.019
Chain 1:   2500       -19873.113             0.024            0.015
Chain 1:   2600       -19502.420             0.023            0.015
Chain 1:   2700       -19459.203             0.018            0.012
Chain 1:   2800       -19175.561             0.019            0.015
Chain 1:   2900       -19457.151             0.019            0.014
Chain 1:   3000       -19443.330             0.011            0.012
Chain 1:   3100       -19528.470             0.011            0.011
Chain 1:   3200       -19218.574             0.011            0.014
Chain 1:   3300       -19423.750             0.010            0.011
Chain 1:   3400       -18897.559             0.012            0.014
Chain 1:   3500       -19511.087             0.014            0.015
Chain 1:   3600       -18815.532             0.016            0.015
Chain 1:   3700       -19203.915             0.018            0.016
Chain 1:   3800       -18160.211             0.022            0.020
Chain 1:   3900       -18156.229             0.021            0.020
Chain 1:   4000       -18273.576             0.021            0.020
Chain 1:   4100       -18187.161             0.021            0.020
Chain 1:   4200       -18002.648             0.021            0.020
Chain 1:   4300       -18141.601             0.020            0.020
Chain 1:   4400       -18097.781             0.018            0.010
Chain 1:   4500       -18000.178             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12611.031             1.000            1.000
Chain 1:    200        -9525.943             0.662            1.000
Chain 1:    300        -8179.427             0.496            0.324
Chain 1:    400        -8362.527             0.378            0.324
Chain 1:    500        -8341.939             0.303            0.165
Chain 1:    600        -8133.286             0.256            0.165
Chain 1:    700        -8058.971             0.221            0.026
Chain 1:    800        -8071.491             0.194            0.026
Chain 1:    900        -8005.475             0.173            0.022
Chain 1:   1000        -8062.569             0.156            0.022
Chain 1:   1100        -8137.297             0.057            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58618.344             1.000            1.000
Chain 1:    200       -17944.109             1.633            2.267
Chain 1:    300        -8819.764             1.434            1.035
Chain 1:    400        -8233.967             1.093            1.035
Chain 1:    500        -8521.621             0.881            1.000
Chain 1:    600        -8789.167             0.739            1.000
Chain 1:    700        -8043.439             0.647            0.093
Chain 1:    800        -8442.150             0.572            0.093
Chain 1:    900        -7732.370             0.519            0.092
Chain 1:   1000        -7731.540             0.467            0.092
Chain 1:   1100        -7916.957             0.369            0.071
Chain 1:   1200        -7938.175             0.143            0.047
Chain 1:   1300        -7827.388             0.041            0.034
Chain 1:   1400        -7733.095             0.035            0.030
Chain 1:   1500        -7605.072             0.033            0.023
Chain 1:   1600        -7793.068             0.033            0.023
Chain 1:   1700        -7576.493             0.026            0.023
Chain 1:   1800        -7683.683             0.023            0.017
Chain 1:   1900        -7718.268             0.014            0.014
Chain 1:   2000        -7711.612             0.014            0.014
Chain 1:   2100        -7666.384             0.012            0.014
Chain 1:   2200        -7769.670             0.013            0.014
Chain 1:   2300        -7609.804             0.014            0.014
Chain 1:   2400        -7723.571             0.014            0.015
Chain 1:   2500        -7696.812             0.013            0.014
Chain 1:   2600        -7585.265             0.012            0.014
Chain 1:   2700        -7596.341             0.009            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003941 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86502.645             1.000            1.000
Chain 1:    200       -13714.557             3.154            5.307
Chain 1:    300       -10053.118             2.224            1.000
Chain 1:    400       -11105.381             1.692            1.000
Chain 1:    500        -9034.375             1.399            0.364
Chain 1:    600        -8512.556             1.176            0.364
Chain 1:    700        -8476.107             1.009            0.229
Chain 1:    800        -9313.846             0.894            0.229
Chain 1:    900        -8914.088             0.800            0.095
Chain 1:   1000        -8572.393             0.724            0.095
Chain 1:   1100        -8871.654             0.627            0.090   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8431.415             0.101            0.061
Chain 1:   1300        -8767.839             0.069            0.052
Chain 1:   1400        -8740.867             0.060            0.045
Chain 1:   1500        -8587.765             0.039            0.040
Chain 1:   1600        -8703.788             0.034            0.038
Chain 1:   1700        -8779.644             0.034            0.038
Chain 1:   1800        -8353.043             0.030            0.038
Chain 1:   1900        -8455.610             0.027            0.034
Chain 1:   2000        -8430.391             0.023            0.018
Chain 1:   2100        -8557.032             0.021            0.015
Chain 1:   2200        -8356.639             0.019            0.015
Chain 1:   2300        -8450.704             0.016            0.013
Chain 1:   2400        -8518.849             0.016            0.013
Chain 1:   2500        -8465.094             0.015            0.012
Chain 1:   2600        -8467.300             0.014            0.011
Chain 1:   2700        -8383.619             0.014            0.011
Chain 1:   2800        -8342.427             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419173.351             1.000            1.000
Chain 1:    200     -1587284.288             2.652            4.304
Chain 1:    300      -889890.127             2.029            1.000
Chain 1:    400      -457241.353             1.759            1.000
Chain 1:    500      -357357.266             1.463            0.946
Chain 1:    600      -232579.286             1.308            0.946
Chain 1:    700      -119116.303             1.258            0.946
Chain 1:    800       -86422.881             1.148            0.946
Chain 1:    900       -66832.927             1.053            0.784
Chain 1:   1000       -51686.735             0.977            0.784
Chain 1:   1100       -39210.569             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38394.312             0.480            0.378
Chain 1:   1300       -26393.842             0.447            0.378
Chain 1:   1400       -26117.935             0.354            0.318
Chain 1:   1500       -22716.701             0.341            0.318
Chain 1:   1600       -21937.043             0.291            0.293
Chain 1:   1700       -20815.514             0.201            0.293
Chain 1:   1800       -20760.989             0.163            0.150
Chain 1:   1900       -21087.300             0.135            0.054
Chain 1:   2000       -19600.632             0.114            0.054
Chain 1:   2100       -19838.958             0.083            0.036
Chain 1:   2200       -20065.166             0.082            0.036
Chain 1:   2300       -19682.531             0.039            0.019
Chain 1:   2400       -19454.595             0.039            0.019
Chain 1:   2500       -19256.489             0.025            0.015
Chain 1:   2600       -18886.734             0.023            0.015
Chain 1:   2700       -18843.652             0.018            0.012
Chain 1:   2800       -18560.408             0.019            0.015
Chain 1:   2900       -18841.647             0.019            0.015
Chain 1:   3000       -18827.844             0.012            0.012
Chain 1:   3100       -18912.906             0.011            0.012
Chain 1:   3200       -18603.504             0.012            0.015
Chain 1:   3300       -18808.261             0.011            0.012
Chain 1:   3400       -18283.065             0.012            0.015
Chain 1:   3500       -18895.110             0.015            0.015
Chain 1:   3600       -18201.462             0.016            0.015
Chain 1:   3700       -18588.507             0.018            0.017
Chain 1:   3800       -17547.755             0.023            0.021
Chain 1:   3900       -17543.843             0.021            0.021
Chain 1:   4000       -17661.174             0.022            0.021
Chain 1:   4100       -17574.947             0.022            0.021
Chain 1:   4200       -17391.039             0.021            0.021
Chain 1:   4300       -17529.556             0.021            0.021
Chain 1:   4400       -17486.295             0.018            0.011
Chain 1:   4500       -17388.768             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001329 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49820.029             1.000            1.000
Chain 1:    200       -13619.948             1.829            2.658
Chain 1:    300       -18441.748             1.306            1.000
Chain 1:    400       -15895.639             1.020            1.000
Chain 1:    500       -13314.517             0.855            0.261
Chain 1:    600       -15949.330             0.740            0.261
Chain 1:    700       -15127.167             0.642            0.194
Chain 1:    800       -14218.342             0.570            0.194
Chain 1:    900       -13101.453             0.516            0.165
Chain 1:   1000       -11843.583             0.475            0.165
Chain 1:   1100       -11159.835             0.381            0.160
Chain 1:   1200       -21730.248             0.164            0.160
Chain 1:   1300       -17811.967             0.160            0.160
Chain 1:   1400       -12966.282             0.181            0.165
Chain 1:   1500       -10878.355             0.181            0.165
Chain 1:   1600       -10406.459             0.169            0.106
Chain 1:   1700       -11459.751             0.173            0.106
Chain 1:   1800       -12518.540             0.175            0.106
Chain 1:   1900       -10603.824             0.184            0.181
Chain 1:   2000       -12121.518             0.186            0.181
Chain 1:   2100       -11008.442             0.190            0.181
Chain 1:   2200       -11387.601             0.145            0.125
Chain 1:   2300       -10212.763             0.134            0.115
Chain 1:   2400       -10475.220             0.099            0.101
Chain 1:   2500       -10122.665             0.084            0.092
Chain 1:   2600       -10590.896             0.084            0.092
Chain 1:   2700       -11900.352             0.085            0.101
Chain 1:   2800       -18476.650             0.113            0.110
Chain 1:   2900       -15520.892             0.114            0.110
Chain 1:   3000       -10239.003             0.153            0.110
Chain 1:   3100       -10273.221             0.143            0.110
Chain 1:   3200        -9757.823             0.145            0.110
Chain 1:   3300       -10398.793             0.139            0.062
Chain 1:   3400        -9887.085             0.142            0.062
Chain 1:   3500       -15258.229             0.174            0.110
Chain 1:   3600       -11719.260             0.200            0.190
Chain 1:   3700       -14138.599             0.206            0.190
Chain 1:   3800        -9740.392             0.215            0.190
Chain 1:   3900        -9891.375             0.198            0.171
Chain 1:   4000       -11480.978             0.160            0.138
Chain 1:   4100       -10380.060             0.170            0.138
Chain 1:   4200       -16988.722             0.204            0.171
Chain 1:   4300        -9604.189             0.275            0.302
Chain 1:   4400       -14320.409             0.302            0.329
Chain 1:   4500       -11634.692             0.290            0.302
Chain 1:   4600        -9349.583             0.284            0.244
Chain 1:   4700       -10789.403             0.281            0.244
Chain 1:   4800        -9427.037             0.250            0.231
Chain 1:   4900        -9333.051             0.250            0.231
Chain 1:   5000       -10493.774             0.247            0.231
Chain 1:   5100       -10439.722             0.237            0.231
Chain 1:   5200        -8961.806             0.214            0.165
Chain 1:   5300       -10920.126             0.155            0.165
Chain 1:   5400        -9116.556             0.142            0.165
Chain 1:   5500       -14851.561             0.158            0.165
Chain 1:   5600       -14586.271             0.135            0.145
Chain 1:   5700       -10039.615             0.167            0.165
Chain 1:   5800       -10625.231             0.158            0.165
Chain 1:   5900        -9266.728             0.172            0.165
Chain 1:   6000       -14972.883             0.199            0.179
Chain 1:   6100       -14213.172             0.204            0.179
Chain 1:   6200        -8933.529             0.246            0.198
Chain 1:   6300       -10100.481             0.240            0.198
Chain 1:   6400        -8889.892             0.234            0.147
Chain 1:   6500        -9795.557             0.204            0.136
Chain 1:   6600       -12212.816             0.222            0.147
Chain 1:   6700       -13270.666             0.185            0.136
Chain 1:   6800       -11476.340             0.195            0.147
Chain 1:   6900        -9291.680             0.204            0.156
Chain 1:   7000       -14433.420             0.201            0.156
Chain 1:   7100        -9607.194             0.246            0.198
Chain 1:   7200        -9092.189             0.193            0.156
Chain 1:   7300        -9555.047             0.186            0.156
Chain 1:   7400        -9528.577             0.173            0.156
Chain 1:   7500        -9191.382             0.167            0.156
Chain 1:   7600        -9289.101             0.148            0.080
Chain 1:   7700        -9047.917             0.143            0.057
Chain 1:   7800       -13455.957             0.160            0.057
Chain 1:   7900        -9085.089             0.185            0.057
Chain 1:   8000       -14139.780             0.185            0.057
Chain 1:   8100       -11188.222             0.161            0.057
Chain 1:   8200        -9070.761             0.179            0.233
Chain 1:   8300        -8864.871             0.176            0.233
Chain 1:   8400        -9942.922             0.187            0.233
Chain 1:   8500        -9150.258             0.192            0.233
Chain 1:   8600        -9006.486             0.192            0.233
Chain 1:   8700       -10842.856             0.207            0.233
Chain 1:   8800        -8850.549             0.196            0.225
Chain 1:   8900       -10869.743             0.167            0.186
Chain 1:   9000       -13043.595             0.148            0.169
Chain 1:   9100       -12065.618             0.130            0.167
Chain 1:   9200        -9030.760             0.140            0.167
Chain 1:   9300        -8718.373             0.141            0.167
Chain 1:   9400       -13065.070             0.164            0.169
Chain 1:   9500        -8991.390             0.200            0.186
Chain 1:   9600        -9339.111             0.202            0.186
Chain 1:   9700       -11168.624             0.202            0.186
Chain 1:   9800       -10616.322             0.184            0.167
Chain 1:   9900       -11504.282             0.174            0.164
Chain 1:   10000        -9918.705             0.173            0.160
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001707 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58224.211             1.000            1.000
Chain 1:    200       -18158.020             1.603            2.207
Chain 1:    300        -9151.872             1.397            1.000
Chain 1:    400        -8348.872             1.072            1.000
Chain 1:    500        -8586.259             0.863            0.984
Chain 1:    600        -8390.825             0.723            0.984
Chain 1:    700        -7807.365             0.630            0.096
Chain 1:    800        -8564.081             0.563            0.096
Chain 1:    900        -7831.904             0.510            0.093
Chain 1:   1000        -8001.297             0.462            0.093
Chain 1:   1100        -7947.822             0.362            0.088
Chain 1:   1200        -7747.157             0.144            0.075
Chain 1:   1300        -7654.642             0.047            0.028
Chain 1:   1400        -7895.140             0.040            0.028
Chain 1:   1500        -7561.861             0.042            0.030
Chain 1:   1600        -7752.646             0.042            0.030
Chain 1:   1700        -7692.690             0.035            0.026
Chain 1:   1800        -7794.912             0.028            0.025
Chain 1:   1900        -7652.613             0.020            0.021
Chain 1:   2000        -7740.893             0.019            0.019
Chain 1:   2100        -7595.507             0.021            0.019
Chain 1:   2200        -8017.557             0.023            0.019
Chain 1:   2300        -7767.747             0.025            0.025
Chain 1:   2400        -7659.849             0.024            0.019
Chain 1:   2500        -7572.802             0.021            0.019
Chain 1:   2600        -7578.851             0.018            0.014
Chain 1:   2700        -7595.881             0.018            0.014
Chain 1:   2800        -7702.013             0.018            0.014
Chain 1:   2900        -7416.226             0.020            0.014
Chain 1:   3000        -7566.826             0.020            0.019
Chain 1:   3100        -7567.373             0.019            0.014
Chain 1:   3200        -7812.853             0.016            0.014
Chain 1:   3300        -7446.005             0.018            0.014
Chain 1:   3400        -7736.299             0.021            0.020
Chain 1:   3500        -7496.404             0.023            0.031
Chain 1:   3600        -7544.482             0.023            0.031
Chain 1:   3700        -7491.117             0.024            0.031
Chain 1:   3800        -7476.562             0.022            0.031
Chain 1:   3900        -7460.474             0.019            0.020
Chain 1:   4000        -7440.496             0.017            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003887 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87221.501             1.000            1.000
Chain 1:    200       -14342.026             3.041            5.082
Chain 1:    300       -10584.992             2.145            1.000
Chain 1:    400       -12228.908             1.643            1.000
Chain 1:    500        -9206.460             1.380            0.355
Chain 1:    600        -9138.146             1.151            0.355
Chain 1:    700        -9379.792             0.990            0.328
Chain 1:    800        -9837.712             0.872            0.328
Chain 1:    900        -9374.603             0.781            0.134
Chain 1:   1000        -9358.398             0.703            0.134
Chain 1:   1100        -9367.535             0.603            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8963.834             0.099            0.047
Chain 1:   1300        -9227.143             0.067            0.045
Chain 1:   1400        -9043.554             0.055            0.029
Chain 1:   1500        -9074.919             0.023            0.026
Chain 1:   1600        -9183.881             0.023            0.026
Chain 1:   1700        -9240.040             0.021            0.020
Chain 1:   1800        -8793.215             0.022            0.020
Chain 1:   1900        -8900.043             0.018            0.012
Chain 1:   2000        -8883.863             0.018            0.012
Chain 1:   2100        -9022.421             0.020            0.015
Chain 1:   2200        -8794.689             0.018            0.015
Chain 1:   2300        -8890.970             0.016            0.012
Chain 1:   2400        -8965.602             0.015            0.012
Chain 1:   2500        -8907.641             0.015            0.012
Chain 1:   2600        -8924.689             0.014            0.011
Chain 1:   2700        -8830.697             0.014            0.011
Chain 1:   2800        -8775.276             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003796 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389870.294             1.000            1.000
Chain 1:    200     -1580712.621             2.654            4.308
Chain 1:    300      -890803.329             2.027            1.000
Chain 1:    400      -458222.303             1.757            1.000
Chain 1:    500      -358909.565             1.461            0.944
Chain 1:    600      -234009.822             1.306            0.944
Chain 1:    700      -120197.987             1.255            0.944
Chain 1:    800       -87395.193             1.145            0.944
Chain 1:    900       -67723.918             1.050            0.774
Chain 1:   1000       -52510.840             0.974            0.774
Chain 1:   1100       -39972.961             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39155.806             0.477            0.375
Chain 1:   1300       -27082.659             0.444            0.375
Chain 1:   1400       -26803.227             0.350            0.314
Chain 1:   1500       -23381.407             0.337            0.314
Chain 1:   1600       -22596.708             0.287            0.290
Chain 1:   1700       -21465.750             0.198            0.290
Chain 1:   1800       -21409.495             0.161            0.146
Chain 1:   1900       -21736.225             0.133            0.053
Chain 1:   2000       -20243.845             0.112            0.053
Chain 1:   2100       -20482.608             0.081            0.035
Chain 1:   2200       -20709.751             0.080            0.035
Chain 1:   2300       -20326.129             0.038            0.019
Chain 1:   2400       -20097.924             0.038            0.019
Chain 1:   2500       -19900.063             0.024            0.015
Chain 1:   2600       -19529.577             0.023            0.015
Chain 1:   2700       -19486.333             0.018            0.012
Chain 1:   2800       -19202.951             0.019            0.015
Chain 1:   2900       -19484.511             0.019            0.014
Chain 1:   3000       -19470.662             0.011            0.012
Chain 1:   3100       -19555.770             0.011            0.011
Chain 1:   3200       -19246.005             0.011            0.014
Chain 1:   3300       -19451.078             0.010            0.011
Chain 1:   3400       -18925.300             0.012            0.014
Chain 1:   3500       -19538.297             0.014            0.015
Chain 1:   3600       -18843.461             0.016            0.015
Chain 1:   3700       -19231.412             0.018            0.016
Chain 1:   3800       -18188.866             0.022            0.020
Chain 1:   3900       -18184.947             0.021            0.020
Chain 1:   4000       -18302.234             0.021            0.020
Chain 1:   4100       -18215.888             0.021            0.020
Chain 1:   4200       -18031.652             0.021            0.020
Chain 1:   4300       -18170.396             0.020            0.020
Chain 1:   4400       -18126.819             0.018            0.010
Chain 1:   4500       -18029.264             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49176.813             1.000            1.000
Chain 1:    200       -16825.497             1.461            1.923
Chain 1:    300       -16178.961             0.988            1.000
Chain 1:    400       -19861.285             0.787            1.000
Chain 1:    500       -15503.702             0.686            0.281
Chain 1:    600       -17000.497             0.586            0.281
Chain 1:    700       -16464.107             0.507            0.185
Chain 1:    800       -15000.608             0.456            0.185
Chain 1:    900       -10848.956             0.448            0.185
Chain 1:   1000       -10909.001             0.404            0.185
Chain 1:   1100       -10583.856             0.307            0.098
Chain 1:   1200       -10455.407             0.116            0.088
Chain 1:   1300       -12754.377             0.130            0.098
Chain 1:   1400       -11209.540             0.125            0.098
Chain 1:   1500       -13122.424             0.111            0.098
Chain 1:   1600       -10258.598             0.130            0.138
Chain 1:   1700       -10302.730             0.128            0.138
Chain 1:   1800       -10655.403             0.121            0.138
Chain 1:   1900       -10860.527             0.085            0.033
Chain 1:   2000        -9568.184             0.098            0.135
Chain 1:   2100        -9625.912             0.095            0.135
Chain 1:   2200       -16602.706             0.136            0.138
Chain 1:   2300        -9310.163             0.196            0.138
Chain 1:   2400        -9781.856             0.187            0.135
Chain 1:   2500       -10778.090             0.182            0.092
Chain 1:   2600        -9565.768             0.167            0.092
Chain 1:   2700        -9368.549             0.168            0.092
Chain 1:   2800       -12740.321             0.192            0.127
Chain 1:   2900       -12425.775             0.192            0.127
Chain 1:   3000        -8835.160             0.219            0.127
Chain 1:   3100        -9337.925             0.224            0.127
Chain 1:   3200       -13208.955             0.211            0.127
Chain 1:   3300        -9506.520             0.172            0.127
Chain 1:   3400        -9279.255             0.170            0.127
Chain 1:   3500        -9607.412             0.164            0.127
Chain 1:   3600        -9468.395             0.153            0.054
Chain 1:   3700        -9847.340             0.154            0.054
Chain 1:   3800        -8796.666             0.140            0.054
Chain 1:   3900       -13292.043             0.171            0.119
Chain 1:   4000        -9597.189             0.169            0.119
Chain 1:   4100        -9059.815             0.170            0.119
Chain 1:   4200        -9217.915             0.142            0.059
Chain 1:   4300        -9945.022             0.110            0.059
Chain 1:   4400       -11013.960             0.118            0.073
Chain 1:   4500       -10429.415             0.120            0.073
Chain 1:   4600       -10013.215             0.123            0.073
Chain 1:   4700       -10210.334             0.121            0.073
Chain 1:   4800       -12339.619             0.126            0.073
Chain 1:   4900        -9127.093             0.127            0.073
Chain 1:   5000        -9722.056             0.095            0.061
Chain 1:   5100       -10612.686             0.097            0.073
Chain 1:   5200       -11930.263             0.107            0.084
Chain 1:   5300       -12465.815             0.104            0.084
Chain 1:   5400        -8558.576             0.140            0.084
Chain 1:   5500        -8488.192             0.135            0.084
Chain 1:   5600        -9289.842             0.139            0.086
Chain 1:   5700        -9945.805             0.144            0.086
Chain 1:   5800        -9454.674             0.132            0.084
Chain 1:   5900        -8513.246             0.108            0.084
Chain 1:   6000       -11743.354             0.129            0.086
Chain 1:   6100        -8747.418             0.155            0.110
Chain 1:   6200        -8749.542             0.144            0.086
Chain 1:   6300       -11846.758             0.166            0.111
Chain 1:   6400        -9203.655             0.149            0.111
Chain 1:   6500        -9087.555             0.149            0.111
Chain 1:   6600       -11379.898             0.161            0.201
Chain 1:   6700       -13847.165             0.172            0.201
Chain 1:   6800        -9929.691             0.206            0.261
Chain 1:   6900       -10262.594             0.199            0.261
Chain 1:   7000        -9610.502             0.178            0.201
Chain 1:   7100        -8548.248             0.156            0.178
Chain 1:   7200        -8568.627             0.156            0.178
Chain 1:   7300        -9222.335             0.137            0.124
Chain 1:   7400        -8716.772             0.114            0.071
Chain 1:   7500       -10804.020             0.132            0.124
Chain 1:   7600        -8750.570             0.136            0.124
Chain 1:   7700        -8782.204             0.118            0.071
Chain 1:   7800        -8547.390             0.081            0.068
Chain 1:   7900        -9211.560             0.085            0.071
Chain 1:   8000        -8585.223             0.086            0.072
Chain 1:   8100        -8342.497             0.076            0.071
Chain 1:   8200       -10740.914             0.099            0.072
Chain 1:   8300       -13151.570             0.110            0.073
Chain 1:   8400       -12220.011             0.112            0.076
Chain 1:   8500        -8425.597             0.137            0.076
Chain 1:   8600        -8839.893             0.119            0.073
Chain 1:   8700        -8220.553             0.126            0.075
Chain 1:   8800        -8946.253             0.131            0.076
Chain 1:   8900       -12314.809             0.151            0.081
Chain 1:   9000       -11293.133             0.153            0.090
Chain 1:   9100        -8871.997             0.177            0.183
Chain 1:   9200        -9745.435             0.164            0.090
Chain 1:   9300        -8363.099             0.162            0.090
Chain 1:   9400        -8685.740             0.158            0.090
Chain 1:   9500        -8237.656             0.119            0.090
Chain 1:   9600        -9911.904             0.131            0.090
Chain 1:   9700        -8773.467             0.136            0.130
Chain 1:   9800        -8461.420             0.132            0.130
Chain 1:   9900       -10624.741             0.125            0.130
Chain 1:   10000        -8256.753             0.145            0.165
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001521 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57043.469             1.000            1.000
Chain 1:    200       -17498.829             1.630            2.260
Chain 1:    300        -8664.610             1.426            1.020
Chain 1:    400        -7879.860             1.095            1.020
Chain 1:    500        -8648.853             0.894            1.000
Chain 1:    600        -8780.647             0.747            1.000
Chain 1:    700        -7793.165             0.659            0.127
Chain 1:    800        -8103.121             0.581            0.127
Chain 1:    900        -7812.982             0.521            0.100
Chain 1:   1000        -7740.306             0.469            0.100
Chain 1:   1100        -7521.507             0.372            0.089
Chain 1:   1200        -7918.713             0.151            0.050
Chain 1:   1300        -7721.232             0.052            0.038
Chain 1:   1400        -7765.220             0.043            0.037
Chain 1:   1500        -7569.877             0.036            0.029
Chain 1:   1600        -7731.133             0.037            0.029
Chain 1:   1700        -7513.855             0.027            0.029
Chain 1:   1800        -7554.271             0.024            0.026
Chain 1:   1900        -7572.939             0.020            0.026
Chain 1:   2000        -7636.844             0.020            0.026
Chain 1:   2100        -7564.066             0.018            0.021
Chain 1:   2200        -7694.743             0.015            0.017
Chain 1:   2300        -7508.367             0.015            0.017
Chain 1:   2400        -7636.352             0.016            0.017
Chain 1:   2500        -7616.676             0.014            0.017
Chain 1:   2600        -7518.640             0.013            0.013
Chain 1:   2700        -7440.728             0.011            0.010
Chain 1:   2800        -7562.060             0.012            0.013
Chain 1:   2900        -7394.522             0.014            0.016
Chain 1:   3000        -7523.304             0.015            0.017
Chain 1:   3100        -7516.147             0.014            0.017
Chain 1:   3200        -7704.214             0.015            0.017
Chain 1:   3300        -7452.884             0.016            0.017
Chain 1:   3400        -7652.663             0.017            0.017
Chain 1:   3500        -7428.745             0.019            0.023
Chain 1:   3600        -7491.080             0.019            0.023
Chain 1:   3700        -7441.366             0.019            0.023
Chain 1:   3800        -7448.559             0.017            0.023
Chain 1:   3900        -7413.817             0.015            0.017
Chain 1:   4000        -7407.307             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002502 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86417.531             1.000            1.000
Chain 1:    200       -13602.639             3.176            5.353
Chain 1:    300        -9992.222             2.238            1.000
Chain 1:    400       -10769.215             1.697            1.000
Chain 1:    500        -8967.621             1.397            0.361
Chain 1:    600        -8483.898             1.174            0.361
Chain 1:    700        -8559.634             1.008            0.201
Chain 1:    800        -9167.397             0.890            0.201
Chain 1:    900        -8819.454             0.795            0.072
Chain 1:   1000        -8647.782             0.718            0.072
Chain 1:   1100        -8790.388             0.620            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8370.712             0.089            0.057
Chain 1:   1300        -8664.605             0.056            0.050
Chain 1:   1400        -8701.474             0.050            0.039
Chain 1:   1500        -8578.221             0.031            0.034
Chain 1:   1600        -8690.137             0.027            0.020
Chain 1:   1700        -8769.960             0.027            0.020
Chain 1:   1800        -8363.311             0.025            0.020
Chain 1:   1900        -8460.778             0.022            0.016
Chain 1:   2000        -8432.891             0.020            0.014
Chain 1:   2100        -8553.350             0.020            0.014
Chain 1:   2200        -8362.462             0.017            0.014
Chain 1:   2300        -8500.248             0.016            0.014
Chain 1:   2400        -8507.300             0.015            0.014
Chain 1:   2500        -8474.172             0.014            0.013
Chain 1:   2600        -8472.232             0.013            0.012
Chain 1:   2700        -8386.052             0.013            0.012
Chain 1:   2800        -8351.387             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419626.226             1.000            1.000
Chain 1:    200     -1586512.597             2.654            4.307
Chain 1:    300      -890755.754             2.029            1.000
Chain 1:    400      -457979.366             1.758            1.000
Chain 1:    500      -358199.196             1.462            0.945
Chain 1:    600      -233000.892             1.308            0.945
Chain 1:    700      -119239.113             1.258            0.945
Chain 1:    800       -86470.312             1.148            0.945
Chain 1:    900       -66819.445             1.053            0.781
Chain 1:   1000       -51626.713             0.977            0.781
Chain 1:   1100       -39120.838             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38296.250             0.480            0.379
Chain 1:   1300       -26273.943             0.448            0.379
Chain 1:   1400       -25993.929             0.355            0.320
Chain 1:   1500       -22587.307             0.342            0.320
Chain 1:   1600       -21805.511             0.292            0.294
Chain 1:   1700       -20681.918             0.202            0.294
Chain 1:   1800       -20626.571             0.164            0.151
Chain 1:   1900       -20952.580             0.136            0.054
Chain 1:   2000       -19465.501             0.115            0.054
Chain 1:   2100       -19703.712             0.084            0.036
Chain 1:   2200       -19929.924             0.083            0.036
Chain 1:   2300       -19547.354             0.039            0.020
Chain 1:   2400       -19319.533             0.039            0.020
Chain 1:   2500       -19121.517             0.025            0.016
Chain 1:   2600       -18751.957             0.023            0.016
Chain 1:   2700       -18708.959             0.018            0.012
Chain 1:   2800       -18425.909             0.019            0.015
Chain 1:   2900       -18707.013             0.019            0.015
Chain 1:   3000       -18693.208             0.012            0.012
Chain 1:   3100       -18778.209             0.011            0.012
Chain 1:   3200       -18469.018             0.012            0.015
Chain 1:   3300       -18673.610             0.011            0.012
Chain 1:   3400       -18148.788             0.012            0.015
Chain 1:   3500       -18760.309             0.015            0.015
Chain 1:   3600       -18067.375             0.017            0.015
Chain 1:   3700       -18453.909             0.018            0.017
Chain 1:   3800       -17414.259             0.023            0.021
Chain 1:   3900       -17410.394             0.021            0.021
Chain 1:   4000       -17527.701             0.022            0.021
Chain 1:   4100       -17441.550             0.022            0.021
Chain 1:   4200       -17257.875             0.021            0.021
Chain 1:   4300       -17396.201             0.021            0.021
Chain 1:   4400       -17353.150             0.018            0.011
Chain 1:   4500       -17255.683             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001332 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -14274.546             1.000            1.000
Chain 1:    200       -10883.808             0.656            1.000
Chain 1:    300        -9320.575             0.493            0.312
Chain 1:    400        -8911.243             0.381            0.312
Chain 1:    500        -9035.887             0.308            0.168
Chain 1:    600        -8790.860             0.261            0.168
Chain 1:    700        -8678.736             0.226            0.046
Chain 1:    800        -8706.592             0.198            0.046
Chain 1:    900        -8752.965             0.176            0.028
Chain 1:   1000        -8764.662             0.159            0.028
Chain 1:   1100        -8774.864             0.059            0.014
Chain 1:   1200        -8709.804             0.029            0.013
Chain 1:   1300        -8622.916             0.013            0.010
Chain 1:   1400        -8656.353             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61917.166             1.000            1.000
Chain 1:    200       -19241.154             1.609            2.218
Chain 1:    300        -9420.447             1.420            1.042
Chain 1:    400        -8575.155             1.090            1.042
Chain 1:    500        -9279.858             0.887            1.000
Chain 1:    600        -8997.075             0.744            1.000
Chain 1:    700        -8295.124             0.650            0.099
Chain 1:    800        -9014.448             0.579            0.099
Chain 1:    900        -7609.207             0.535            0.099
Chain 1:   1000        -8157.595             0.488            0.099
Chain 1:   1100        -7710.022             0.394            0.085
Chain 1:   1200        -7915.711             0.175            0.080
Chain 1:   1300        -7923.302             0.071            0.076
Chain 1:   1400        -8194.326             0.064            0.067
Chain 1:   1500        -7824.998             0.061            0.058
Chain 1:   1600        -8113.629             0.062            0.058
Chain 1:   1700        -7799.961             0.057            0.047
Chain 1:   1800        -7794.405             0.049            0.040
Chain 1:   1900        -7773.323             0.031            0.036
Chain 1:   2000        -7886.402             0.026            0.033
Chain 1:   2100        -7691.489             0.023            0.026
Chain 1:   2200        -8123.926             0.025            0.033
Chain 1:   2300        -7708.433             0.031            0.036
Chain 1:   2400        -7879.003             0.029            0.036
Chain 1:   2500        -7742.656             0.027            0.025
Chain 1:   2600        -7688.570             0.024            0.022
Chain 1:   2700        -7615.382             0.021            0.018
Chain 1:   2800        -7547.636             0.021            0.018
Chain 1:   2900        -7537.075             0.021            0.018
Chain 1:   3000        -7677.226             0.022            0.018
Chain 1:   3100        -7670.738             0.019            0.018
Chain 1:   3200        -7985.495             0.018            0.018
Chain 1:   3300        -7534.695             0.018            0.018
Chain 1:   3400        -7912.103             0.021            0.018
Chain 1:   3500        -7620.042             0.023            0.018
Chain 1:   3600        -7737.678             0.024            0.018
Chain 1:   3700        -7529.736             0.026            0.028
Chain 1:   3800        -7765.828             0.028            0.030
Chain 1:   3900        -7533.747             0.031            0.031
Chain 1:   4000        -7518.562             0.029            0.031
Chain 1:   4100        -7533.869             0.029            0.031
Chain 1:   4200        -7687.982             0.027            0.030
Chain 1:   4300        -7511.990             0.024            0.028
Chain 1:   4400        -7565.794             0.020            0.023
Chain 1:   4500        -7733.714             0.018            0.022
Chain 1:   4600        -7590.031             0.018            0.022
Chain 1:   4700        -7576.194             0.016            0.020
Chain 1:   4800        -7508.792             0.014            0.019
Chain 1:   4900        -7816.047             0.015            0.019
Chain 1:   5000        -7739.224             0.015            0.019
Chain 1:   5100        -7618.770             0.017            0.019
Chain 1:   5200        -7564.967             0.015            0.016
Chain 1:   5300        -7627.961             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002582 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87554.310             1.000            1.000
Chain 1:    200       -14736.157             2.971            4.941
Chain 1:    300       -10914.294             2.097            1.000
Chain 1:    400       -12952.313             1.612            1.000
Chain 1:    500        -9292.659             1.369            0.394
Chain 1:    600        -9283.635             1.141            0.394
Chain 1:    700        -9181.143             0.979            0.350
Chain 1:    800       -10128.580             0.869            0.350
Chain 1:    900        -9498.850             0.779            0.157
Chain 1:   1000        -9909.688             0.706            0.157
Chain 1:   1100        -9656.525             0.608            0.094   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9079.902             0.120            0.066
Chain 1:   1300        -9486.407             0.090            0.064
Chain 1:   1400        -9349.266             0.075            0.043
Chain 1:   1500        -9350.453             0.036            0.041
Chain 1:   1600        -9435.121             0.037            0.041
Chain 1:   1700        -9491.466             0.036            0.041
Chain 1:   1800        -9027.885             0.032            0.041
Chain 1:   1900        -9144.064             0.027            0.026
Chain 1:   2000        -9164.903             0.023            0.015
Chain 1:   2100        -9255.627             0.021            0.013
Chain 1:   2200        -9024.696             0.017            0.013
Chain 1:   2300        -9222.947             0.015            0.013
Chain 1:   2400        -9047.471             0.016            0.013
Chain 1:   2500        -9114.542             0.016            0.013
Chain 1:   2600        -9022.623             0.017            0.013
Chain 1:   2700        -9057.125             0.016            0.013
Chain 1:   2800        -9010.437             0.012            0.010
Chain 1:   2900        -9123.340             0.012            0.010
Chain 1:   3000        -9031.597             0.013            0.010
Chain 1:   3100        -8999.440             0.012            0.010
Chain 1:   3200        -8969.538             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002971 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8385965.286             1.000            1.000
Chain 1:    200     -1579788.872             2.654            4.308
Chain 1:    300      -890825.984             2.027            1.000
Chain 1:    400      -458052.977             1.757            1.000
Chain 1:    500      -359047.091             1.460            0.945
Chain 1:    600      -234110.810             1.306            0.945
Chain 1:    700      -120488.225             1.254            0.943
Chain 1:    800       -87717.046             1.144            0.943
Chain 1:    900       -68072.828             1.049            0.773
Chain 1:   1000       -52885.647             0.973            0.773
Chain 1:   1100       -40363.619             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39554.098             0.475            0.374
Chain 1:   1300       -27491.230             0.442            0.374
Chain 1:   1400       -27214.334             0.348            0.310
Chain 1:   1500       -23794.749             0.335            0.310
Chain 1:   1600       -23011.183             0.285            0.289
Chain 1:   1700       -21881.111             0.196            0.287
Chain 1:   1800       -21825.461             0.159            0.144
Chain 1:   1900       -22152.561             0.131            0.052
Chain 1:   2000       -20660.012             0.110            0.052
Chain 1:   2100       -20898.759             0.080            0.034
Chain 1:   2200       -21126.058             0.079            0.034
Chain 1:   2300       -20742.263             0.037            0.019
Chain 1:   2400       -20513.962             0.037            0.019
Chain 1:   2500       -20315.970             0.024            0.015
Chain 1:   2600       -19945.151             0.022            0.015
Chain 1:   2700       -19901.889             0.017            0.011
Chain 1:   2800       -19618.238             0.018            0.014
Chain 1:   2900       -19900.026             0.018            0.014
Chain 1:   3000       -19886.126             0.011            0.011
Chain 1:   3100       -19971.256             0.010            0.011
Chain 1:   3200       -19661.273             0.011            0.014
Chain 1:   3300       -19866.564             0.010            0.011
Chain 1:   3400       -19340.294             0.012            0.014
Chain 1:   3500       -19953.948             0.014            0.014
Chain 1:   3600       -19258.341             0.016            0.014
Chain 1:   3700       -19646.831             0.017            0.016
Chain 1:   3800       -18602.971             0.022            0.020
Chain 1:   3900       -18599.028             0.020            0.020
Chain 1:   4000       -18716.335             0.021            0.020
Chain 1:   4100       -18629.870             0.021            0.020
Chain 1:   4200       -18445.401             0.020            0.020
Chain 1:   4300       -18584.322             0.020            0.020
Chain 1:   4400       -18540.517             0.017            0.010
Chain 1:   4500       -18442.936             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001189 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12762.610             1.000            1.000
Chain 1:    200        -9254.676             0.690            1.000
Chain 1:    300        -7994.276             0.512            0.379
Chain 1:    400        -7994.372             0.384            0.379
Chain 1:    500        -7902.302             0.310            0.158
Chain 1:    600        -7810.484             0.260            0.158
Chain 1:    700        -7739.155             0.224            0.012
Chain 1:    800        -7719.748             0.196            0.012
Chain 1:    900        -7885.574             0.177            0.012
Chain 1:   1000        -7818.604             0.160            0.012
Chain 1:   1100        -7853.011             0.061            0.012
Chain 1:   1200        -7762.893             0.024            0.012
Chain 1:   1300        -7737.527             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001424 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57732.569             1.000            1.000
Chain 1:    200       -17287.044             1.670            2.340
Chain 1:    300        -8532.261             1.455            1.026
Chain 1:    400        -8095.537             1.105            1.026
Chain 1:    500        -8287.623             0.889            1.000
Chain 1:    600        -8754.843             0.749            1.000
Chain 1:    700        -7772.098             0.660            0.126
Chain 1:    800        -8095.321             0.583            0.126
Chain 1:    900        -7791.236             0.522            0.054
Chain 1:   1000        -7606.191             0.473            0.054
Chain 1:   1100        -7720.408             0.374            0.053
Chain 1:   1200        -7593.676             0.142            0.040
Chain 1:   1300        -7596.211             0.039            0.039
Chain 1:   1400        -7645.956             0.034            0.024
Chain 1:   1500        -7609.686             0.033            0.024
Chain 1:   1600        -7511.092             0.029            0.017
Chain 1:   1700        -7498.453             0.016            0.015
Chain 1:   1800        -7534.823             0.013            0.013
Chain 1:   1900        -7587.184             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002486 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85561.563             1.000            1.000
Chain 1:    200       -13177.078             3.247            5.493
Chain 1:    300        -9620.656             2.288            1.000
Chain 1:    400       -10159.084             1.729            1.000
Chain 1:    500        -8581.346             1.420            0.370
Chain 1:    600        -8139.451             1.192            0.370
Chain 1:    700        -8486.013             1.028            0.184
Chain 1:    800        -9065.145             0.907            0.184
Chain 1:    900        -8469.451             0.814            0.070
Chain 1:   1000        -8197.311             0.736            0.070
Chain 1:   1100        -8454.649             0.639            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8144.514             0.094            0.054
Chain 1:   1300        -8343.834             0.059            0.053
Chain 1:   1400        -8360.639             0.054            0.041
Chain 1:   1500        -8260.090             0.037            0.038
Chain 1:   1600        -8353.602             0.033            0.033
Chain 1:   1700        -8447.245             0.030            0.030
Chain 1:   1800        -8061.989             0.028            0.030
Chain 1:   1900        -8164.347             0.022            0.024
Chain 1:   2000        -8134.073             0.019            0.013
Chain 1:   2100        -8269.083             0.018            0.013
Chain 1:   2200        -8053.158             0.017            0.013
Chain 1:   2300        -8194.424             0.016            0.013
Chain 1:   2400        -8205.118             0.016            0.013
Chain 1:   2500        -8173.466             0.015            0.013
Chain 1:   2600        -8171.365             0.014            0.013
Chain 1:   2700        -8080.777             0.014            0.013
Chain 1:   2800        -8059.318             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002561 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397963.737             1.000            1.000
Chain 1:    200     -1585179.590             2.649            4.298
Chain 1:    300      -890488.310             2.026            1.000
Chain 1:    400      -457304.687             1.756            1.000
Chain 1:    500      -357442.334             1.461            0.947
Chain 1:    600      -232453.631             1.307            0.947
Chain 1:    700      -118761.692             1.257            0.947
Chain 1:    800       -86008.008             1.148            0.947
Chain 1:    900       -66363.489             1.053            0.780
Chain 1:   1000       -51170.939             0.977            0.780
Chain 1:   1100       -38663.064             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37835.002             0.482            0.381
Chain 1:   1300       -25814.160             0.451            0.381
Chain 1:   1400       -25533.002             0.357            0.324
Chain 1:   1500       -22126.785             0.344            0.324
Chain 1:   1600       -21344.565             0.294            0.297
Chain 1:   1700       -20221.581             0.204            0.296
Chain 1:   1800       -20166.227             0.166            0.154
Chain 1:   1900       -20491.800             0.138            0.056
Chain 1:   2000       -19005.750             0.117            0.056
Chain 1:   2100       -19243.884             0.085            0.037
Chain 1:   2200       -19469.753             0.084            0.037
Chain 1:   2300       -19087.624             0.040            0.020
Chain 1:   2400       -18859.951             0.040            0.020
Chain 1:   2500       -18661.963             0.026            0.016
Chain 1:   2600       -18292.782             0.024            0.016
Chain 1:   2700       -18249.941             0.019            0.012
Chain 1:   2800       -17967.055             0.020            0.016
Chain 1:   2900       -18247.985             0.020            0.015
Chain 1:   3000       -18234.234             0.012            0.012
Chain 1:   3100       -18319.140             0.011            0.012
Chain 1:   3200       -18010.228             0.012            0.015
Chain 1:   3300       -18214.623             0.011            0.012
Chain 1:   3400       -17690.266             0.013            0.015
Chain 1:   3500       -18301.067             0.015            0.016
Chain 1:   3600       -17609.129             0.017            0.016
Chain 1:   3700       -17994.885             0.019            0.017
Chain 1:   3800       -16956.769             0.023            0.021
Chain 1:   3900       -16952.967             0.022            0.021
Chain 1:   4000       -17070.261             0.023            0.021
Chain 1:   4100       -16984.144             0.023            0.021
Chain 1:   4200       -16800.862             0.022            0.021
Chain 1:   4300       -16938.923             0.022            0.021
Chain 1:   4400       -16896.126             0.019            0.011
Chain 1:   4500       -16798.738             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12433.521             1.000            1.000
Chain 1:    200        -9368.807             0.664            1.000
Chain 1:    300        -8159.876             0.492            0.327
Chain 1:    400        -8288.026             0.373            0.327
Chain 1:    500        -8233.195             0.299            0.148
Chain 1:    600        -8100.546             0.252            0.148
Chain 1:    700        -8030.060             0.218            0.016
Chain 1:    800        -8033.689             0.190            0.016
Chain 1:    900        -8024.992             0.169            0.015
Chain 1:   1000        -8095.296             0.153            0.015
Chain 1:   1100        -8115.121             0.054            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62250.418             1.000            1.000
Chain 1:    200       -17899.743             1.739            2.478
Chain 1:    300        -8908.698             1.496            1.009
Chain 1:    400        -9433.903             1.136            1.009
Chain 1:    500        -8008.084             0.944            1.000
Chain 1:    600        -8122.361             0.789            1.000
Chain 1:    700        -8009.725             0.678            0.178
Chain 1:    800        -8151.016             0.596            0.178
Chain 1:    900        -7987.218             0.532            0.056
Chain 1:   1000        -8150.313             0.481            0.056
Chain 1:   1100        -8216.068             0.381            0.021
Chain 1:   1200        -7802.099             0.139            0.021
Chain 1:   1300        -7788.354             0.038            0.020
Chain 1:   1400        -7706.806             0.034            0.017
Chain 1:   1500        -7640.565             0.017            0.014
Chain 1:   1600        -7698.235             0.016            0.014
Chain 1:   1700        -7586.666             0.016            0.015
Chain 1:   1800        -7656.351             0.015            0.011
Chain 1:   1900        -7654.587             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86222.532             1.000            1.000
Chain 1:    200       -13543.059             3.183            5.367
Chain 1:    300        -9962.711             2.242            1.000
Chain 1:    400       -10980.666             1.705            1.000
Chain 1:    500        -8909.373             1.410            0.359
Chain 1:    600        -8519.376             1.183            0.359
Chain 1:    700        -8538.944             1.014            0.232
Chain 1:    800        -8864.326             0.892            0.232
Chain 1:    900        -8773.216             0.794            0.093
Chain 1:   1000        -8544.516             0.717            0.093
Chain 1:   1100        -8678.224             0.619            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8463.839             0.085            0.037
Chain 1:   1300        -8668.331             0.051            0.027
Chain 1:   1400        -8665.220             0.042            0.025
Chain 1:   1500        -8562.299             0.020            0.024
Chain 1:   1600        -8664.589             0.016            0.015
Chain 1:   1700        -8752.455             0.017            0.015
Chain 1:   1800        -8352.674             0.018            0.015
Chain 1:   1900        -8453.262             0.019            0.015
Chain 1:   2000        -8424.209             0.016            0.012
Chain 1:   2100        -8544.623             0.016            0.012
Chain 1:   2200        -8321.190             0.016            0.012
Chain 1:   2300        -8482.493             0.016            0.012
Chain 1:   2400        -8496.673             0.016            0.012
Chain 1:   2500        -8463.048             0.015            0.012
Chain 1:   2600        -8468.831             0.014            0.012
Chain 1:   2700        -8374.463             0.014            0.012
Chain 1:   2800        -8344.890             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002481 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403194.991             1.000            1.000
Chain 1:    200     -1583483.864             2.653            4.307
Chain 1:    300      -890911.597             2.028            1.000
Chain 1:    400      -458370.553             1.757            1.000
Chain 1:    500      -358795.085             1.461            0.944
Chain 1:    600      -233474.433             1.307            0.944
Chain 1:    700      -119420.407             1.257            0.944
Chain 1:    800       -86608.204             1.147            0.944
Chain 1:    900       -66899.962             1.052            0.777
Chain 1:   1000       -51665.693             0.977            0.777
Chain 1:   1100       -39122.814             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38291.120             0.480            0.379
Chain 1:   1300       -26232.111             0.448            0.379
Chain 1:   1400       -25947.317             0.355            0.321
Chain 1:   1500       -22532.440             0.342            0.321
Chain 1:   1600       -21747.974             0.292            0.295
Chain 1:   1700       -20620.110             0.202            0.295
Chain 1:   1800       -20563.735             0.165            0.152
Chain 1:   1900       -20889.550             0.137            0.055
Chain 1:   2000       -19400.765             0.115            0.055
Chain 1:   2100       -19638.868             0.084            0.036
Chain 1:   2200       -19865.455             0.083            0.036
Chain 1:   2300       -19482.642             0.039            0.020
Chain 1:   2400       -19254.874             0.039            0.020
Chain 1:   2500       -19057.097             0.025            0.016
Chain 1:   2600       -18687.398             0.023            0.016
Chain 1:   2700       -18644.334             0.018            0.012
Chain 1:   2800       -18361.506             0.020            0.015
Chain 1:   2900       -18642.595             0.019            0.015
Chain 1:   3000       -18628.672             0.012            0.012
Chain 1:   3100       -18713.701             0.011            0.012
Chain 1:   3200       -18404.510             0.012            0.015
Chain 1:   3300       -18609.110             0.011            0.012
Chain 1:   3400       -18084.437             0.013            0.015
Chain 1:   3500       -18695.787             0.015            0.015
Chain 1:   3600       -18003.092             0.017            0.015
Chain 1:   3700       -18389.508             0.018            0.017
Chain 1:   3800       -17350.277             0.023            0.021
Chain 1:   3900       -17346.485             0.021            0.021
Chain 1:   4000       -17463.735             0.022            0.021
Chain 1:   4100       -17377.639             0.022            0.021
Chain 1:   4200       -17194.064             0.021            0.021
Chain 1:   4300       -17332.285             0.021            0.021
Chain 1:   4400       -17289.289             0.019            0.011
Chain 1:   4500       -17191.904             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001334 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49478.775             1.000            1.000
Chain 1:    200       -19944.319             1.240            1.481
Chain 1:    300       -20660.244             0.838            1.000
Chain 1:    400       -19506.562             0.644            1.000
Chain 1:    500       -13446.356             0.605            0.451
Chain 1:    600       -14392.135             0.515            0.451
Chain 1:    700       -16495.202             0.460            0.127
Chain 1:    800       -15563.595             0.410            0.127
Chain 1:    900       -12211.670             0.395            0.127
Chain 1:   1000       -11472.468             0.362            0.127
Chain 1:   1100       -11870.151             0.265            0.066
Chain 1:   1200       -17527.980             0.149            0.066
Chain 1:   1300       -15081.627             0.162            0.127
Chain 1:   1400       -11082.624             0.192            0.162
Chain 1:   1500       -26748.948             0.206            0.162
Chain 1:   1600       -18468.170             0.244            0.274
Chain 1:   1700       -11733.589             0.289            0.323
Chain 1:   1800       -12671.473             0.290            0.323
Chain 1:   1900       -10793.579             0.280            0.323
Chain 1:   2000       -12310.508             0.286            0.323
Chain 1:   2100       -20549.033             0.323            0.361
Chain 1:   2200       -13419.699             0.343            0.401
Chain 1:   2300       -12307.508             0.336            0.401
Chain 1:   2400        -9679.632             0.327            0.401
Chain 1:   2500        -9743.007             0.269            0.271
Chain 1:   2600        -9951.864             0.227            0.174
Chain 1:   2700       -12575.447             0.190            0.174
Chain 1:   2800       -10881.556             0.198            0.174
Chain 1:   2900       -11217.736             0.184            0.156
Chain 1:   3000       -14135.856             0.192            0.206
Chain 1:   3100       -17739.178             0.172            0.203
Chain 1:   3200       -13713.430             0.149            0.203
Chain 1:   3300       -16087.027             0.154            0.203
Chain 1:   3400       -11630.049             0.166            0.203
Chain 1:   3500       -14724.157             0.186            0.206
Chain 1:   3600       -10630.462             0.222            0.209
Chain 1:   3700       -10477.177             0.203            0.206
Chain 1:   3800        -9204.818             0.201            0.206
Chain 1:   3900       -13225.128             0.229            0.210
Chain 1:   4000       -13097.920             0.209            0.210
Chain 1:   4100        -9288.403             0.230            0.294
Chain 1:   4200        -9130.529             0.202            0.210
Chain 1:   4300       -11286.267             0.206            0.210
Chain 1:   4400       -12486.079             0.178            0.191
Chain 1:   4500       -10711.543             0.173            0.166
Chain 1:   4600        -9132.754             0.152            0.166
Chain 1:   4700       -10396.962             0.163            0.166
Chain 1:   4800        -9393.618             0.160            0.166
Chain 1:   4900        -9807.247             0.133            0.122
Chain 1:   5000       -14658.734             0.165            0.166
Chain 1:   5100        -9836.968             0.173            0.166
Chain 1:   5200        -9192.224             0.179            0.166
Chain 1:   5300       -13792.640             0.193            0.166
Chain 1:   5400       -10874.349             0.210            0.173
Chain 1:   5500       -13931.268             0.216            0.219
Chain 1:   5600       -13533.195             0.201            0.219
Chain 1:   5700       -15446.974             0.201            0.219
Chain 1:   5800       -18810.055             0.209            0.219
Chain 1:   5900        -9485.954             0.303            0.268
Chain 1:   6000       -10611.716             0.280            0.219
Chain 1:   6100       -10199.604             0.235            0.179
Chain 1:   6200       -12002.415             0.243            0.179
Chain 1:   6300        -8869.834             0.245            0.179
Chain 1:   6400        -8707.024             0.220            0.150
Chain 1:   6500       -10304.958             0.214            0.150
Chain 1:   6600       -11743.863             0.223            0.150
Chain 1:   6700        -9746.213             0.231            0.155
Chain 1:   6800        -9029.484             0.221            0.150
Chain 1:   6900        -8950.298             0.124            0.123
Chain 1:   7000        -9125.194             0.115            0.123
Chain 1:   7100        -8793.447             0.115            0.123
Chain 1:   7200       -12874.174             0.132            0.123
Chain 1:   7300        -8778.043             0.143            0.123
Chain 1:   7400        -9662.489             0.150            0.123
Chain 1:   7500        -9239.920             0.139            0.092
Chain 1:   7600       -11863.709             0.149            0.092
Chain 1:   7700        -9116.075             0.159            0.092
Chain 1:   7800       -10087.989             0.161            0.096
Chain 1:   7900        -8957.494             0.172            0.126
Chain 1:   8000        -8734.941             0.173            0.126
Chain 1:   8100       -10633.893             0.187            0.179
Chain 1:   8200       -12106.588             0.167            0.126
Chain 1:   8300       -11406.718             0.127            0.122
Chain 1:   8400        -9016.163             0.144            0.126
Chain 1:   8500        -8913.900             0.141            0.126
Chain 1:   8600        -8654.339             0.122            0.122
Chain 1:   8700        -8627.237             0.092            0.096
Chain 1:   8800        -9151.280             0.088            0.061
Chain 1:   8900       -12700.850             0.103            0.061
Chain 1:   9000       -11976.064             0.107            0.061
Chain 1:   9100        -9267.666             0.118            0.061
Chain 1:   9200       -11027.467             0.122            0.061
Chain 1:   9300        -9309.327             0.134            0.160
Chain 1:   9400        -9150.917             0.110            0.061
Chain 1:   9500        -9525.019             0.112            0.061
Chain 1:   9600        -8841.221             0.117            0.077
Chain 1:   9700        -8795.826             0.117            0.077
Chain 1:   9800       -12008.427             0.138            0.160
Chain 1:   9900       -11385.671             0.116            0.077
Chain 1:   10000        -8652.741             0.141            0.160
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001455 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62623.467             1.000            1.000
Chain 1:    200       -18523.191             1.690            2.381
Chain 1:    300        -9174.538             1.467            1.019
Chain 1:    400        -8906.997             1.107            1.019
Chain 1:    500        -8733.771             0.890            1.000
Chain 1:    600        -9481.739             0.755            1.000
Chain 1:    700        -8302.295             0.667            0.142
Chain 1:    800        -8029.890             0.588            0.142
Chain 1:    900        -8377.987             0.527            0.079
Chain 1:   1000        -7914.861             0.480            0.079
Chain 1:   1100        -7811.312             0.382            0.059
Chain 1:   1200        -7843.932             0.144            0.042
Chain 1:   1300        -7672.250             0.044            0.034
Chain 1:   1400        -7912.386             0.044            0.034
Chain 1:   1500        -7596.716             0.047            0.042
Chain 1:   1600        -7856.497             0.042            0.034
Chain 1:   1700        -7635.316             0.031            0.033
Chain 1:   1800        -7684.803             0.028            0.030
Chain 1:   1900        -7645.844             0.024            0.029
Chain 1:   2000        -7758.165             0.020            0.022
Chain 1:   2100        -7604.707             0.021            0.022
Chain 1:   2200        -7815.037             0.023            0.027
Chain 1:   2300        -7631.115             0.023            0.027
Chain 1:   2400        -7777.778             0.022            0.024
Chain 1:   2500        -7669.631             0.019            0.020
Chain 1:   2600        -7568.316             0.017            0.019
Chain 1:   2700        -7556.992             0.015            0.014
Chain 1:   2800        -7557.025             0.014            0.014
Chain 1:   2900        -7408.114             0.015            0.019
Chain 1:   3000        -7563.962             0.016            0.020
Chain 1:   3100        -7561.780             0.014            0.019
Chain 1:   3200        -7786.840             0.014            0.019
Chain 1:   3300        -7501.559             0.016            0.019
Chain 1:   3400        -7754.547             0.017            0.020
Chain 1:   3500        -7475.376             0.019            0.021
Chain 1:   3600        -7538.993             0.019            0.021
Chain 1:   3700        -7490.748             0.019            0.021
Chain 1:   3800        -7473.549             0.020            0.021
Chain 1:   3900        -7442.642             0.018            0.021
Chain 1:   4000        -7437.762             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002608 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86543.806             1.000            1.000
Chain 1:    200       -14130.081             3.062            5.125
Chain 1:    300       -10456.839             2.159            1.000
Chain 1:    400       -11618.109             1.644            1.000
Chain 1:    500        -9445.397             1.361            0.351
Chain 1:    600        -9228.951             1.138            0.351
Chain 1:    700        -9019.205             0.979            0.230
Chain 1:    800        -9455.161             0.862            0.230
Chain 1:    900        -9267.811             0.769            0.100
Chain 1:   1000        -8902.064             0.696            0.100
Chain 1:   1100        -9277.511             0.600            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8892.394             0.092            0.043
Chain 1:   1300        -9168.117             0.060            0.041
Chain 1:   1400        -9163.640             0.050            0.040
Chain 1:   1500        -9007.117             0.029            0.030
Chain 1:   1600        -9119.085             0.027            0.030
Chain 1:   1700        -9198.673             0.026            0.030
Chain 1:   1800        -8773.491             0.026            0.030
Chain 1:   1900        -8875.240             0.025            0.030
Chain 1:   2000        -8850.058             0.022            0.017
Chain 1:   2100        -8975.949             0.019            0.014
Chain 1:   2200        -8777.034             0.017            0.014
Chain 1:   2300        -8870.216             0.015            0.012
Chain 1:   2400        -8938.722             0.016            0.012
Chain 1:   2500        -8885.052             0.014            0.011
Chain 1:   2600        -8886.843             0.013            0.011
Chain 1:   2700        -8803.311             0.013            0.011
Chain 1:   2800        -8762.650             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00251 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401959.792             1.000            1.000
Chain 1:    200     -1583752.225             2.653            4.305
Chain 1:    300      -891933.944             2.027            1.000
Chain 1:    400      -458787.470             1.756            1.000
Chain 1:    500      -359254.836             1.460            0.944
Chain 1:    600      -234062.460             1.306            0.944
Chain 1:    700      -120064.188             1.255            0.944
Chain 1:    800       -87245.208             1.145            0.944
Chain 1:    900       -67542.085             1.050            0.776
Chain 1:   1000       -52310.000             0.975            0.776
Chain 1:   1100       -39759.153             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38933.912             0.478            0.376
Chain 1:   1300       -26854.841             0.445            0.376
Chain 1:   1400       -26571.990             0.352            0.316
Chain 1:   1500       -23150.214             0.339            0.316
Chain 1:   1600       -22364.734             0.289            0.292
Chain 1:   1700       -21233.679             0.199            0.291
Chain 1:   1800       -21176.910             0.162            0.148
Chain 1:   1900       -21503.221             0.134            0.053
Chain 1:   2000       -20011.802             0.113            0.053
Chain 1:   2100       -20250.253             0.082            0.035
Chain 1:   2200       -20477.293             0.081            0.035
Chain 1:   2300       -20093.951             0.038            0.019
Chain 1:   2400       -19865.942             0.038            0.019
Chain 1:   2500       -19668.228             0.024            0.015
Chain 1:   2600       -19298.057             0.023            0.015
Chain 1:   2700       -19254.885             0.018            0.012
Chain 1:   2800       -18971.800             0.019            0.015
Chain 1:   2900       -19253.161             0.019            0.015
Chain 1:   3000       -19239.274             0.012            0.012
Chain 1:   3100       -19324.321             0.011            0.011
Chain 1:   3200       -19014.833             0.011            0.015
Chain 1:   3300       -19219.694             0.010            0.011
Chain 1:   3400       -18694.453             0.012            0.015
Chain 1:   3500       -19306.653             0.014            0.015
Chain 1:   3600       -18612.902             0.016            0.015
Chain 1:   3700       -19000.073             0.018            0.016
Chain 1:   3800       -17959.203             0.022            0.020
Chain 1:   3900       -17955.363             0.021            0.020
Chain 1:   4000       -18072.623             0.021            0.020
Chain 1:   4100       -17986.395             0.021            0.020
Chain 1:   4200       -17802.502             0.021            0.020
Chain 1:   4300       -17940.959             0.020            0.020
Chain 1:   4400       -17897.674             0.018            0.010
Chain 1:   4500       -17800.222             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48891.832             1.000            1.000
Chain 1:    200       -21801.668             1.121            1.243
Chain 1:    300       -24400.871             0.783            1.000
Chain 1:    400       -38310.252             0.678            1.000
Chain 1:    500       -14098.437             0.886            1.000
Chain 1:    600       -14356.346             0.741            1.000
Chain 1:    700       -11593.534             0.669            0.363
Chain 1:    800       -14668.762             0.612            0.363
Chain 1:    900       -11812.325             0.571            0.242
Chain 1:   1000       -11274.674             0.518            0.242
Chain 1:   1100        -9779.337             0.434            0.238
Chain 1:   1200       -10929.048             0.320            0.210
Chain 1:   1300       -10165.027             0.317            0.210
Chain 1:   1400       -12679.725             0.300            0.198
Chain 1:   1500       -12079.218             0.134            0.153
Chain 1:   1600       -12293.254             0.134            0.153
Chain 1:   1700       -12101.031             0.111            0.105
Chain 1:   1800       -11401.379             0.097            0.075
Chain 1:   1900        -9865.210             0.088            0.075
Chain 1:   2000       -14079.067             0.113            0.105
Chain 1:   2100       -13166.838             0.105            0.075
Chain 1:   2200       -10127.810             0.124            0.075
Chain 1:   2300       -12164.355             0.133            0.156
Chain 1:   2400       -19453.708             0.151            0.156
Chain 1:   2500        -9077.002             0.260            0.167
Chain 1:   2600       -12430.334             0.286            0.270
Chain 1:   2700       -11653.191             0.291            0.270
Chain 1:   2800        -9000.183             0.314            0.295
Chain 1:   2900       -13341.456             0.331            0.299
Chain 1:   3000        -8722.905             0.354            0.300
Chain 1:   3100        -8994.287             0.350            0.300
Chain 1:   3200        -9705.557             0.327            0.295
Chain 1:   3300        -9846.339             0.312            0.295
Chain 1:   3400        -9139.301             0.282            0.270
Chain 1:   3500        -9175.235             0.169            0.077
Chain 1:   3600        -9303.178             0.143            0.073
Chain 1:   3700        -8750.217             0.143            0.073
Chain 1:   3800        -8733.714             0.113            0.063
Chain 1:   3900        -9047.416             0.084            0.035
Chain 1:   4000        -8679.292             0.035            0.035
Chain 1:   4100        -9368.596             0.040            0.042
Chain 1:   4200        -9165.222             0.035            0.035
Chain 1:   4300       -10626.499             0.047            0.042
Chain 1:   4400        -8819.615             0.060            0.042
Chain 1:   4500        -9420.235             0.066            0.063
Chain 1:   4600        -8813.680             0.071            0.064
Chain 1:   4700        -9966.900             0.077            0.069
Chain 1:   4800        -8716.879             0.091            0.074
Chain 1:   4900        -8461.591             0.090            0.074
Chain 1:   5000        -9872.287             0.100            0.116
Chain 1:   5100        -8540.687             0.109            0.138
Chain 1:   5200       -12720.206             0.139            0.143
Chain 1:   5300       -14031.218             0.135            0.143
Chain 1:   5400        -8850.160             0.173            0.143
Chain 1:   5500        -9806.741             0.176            0.143
Chain 1:   5600        -8409.558             0.186            0.143
Chain 1:   5700        -8871.553             0.180            0.143
Chain 1:   5800        -9379.766             0.171            0.143
Chain 1:   5900       -12804.683             0.194            0.156
Chain 1:   6000       -10891.805             0.198            0.166
Chain 1:   6100        -8332.806             0.213            0.176
Chain 1:   6200        -8255.914             0.181            0.166
Chain 1:   6300        -9523.963             0.185            0.166
Chain 1:   6400       -11535.790             0.144            0.166
Chain 1:   6500        -9131.670             0.160            0.174
Chain 1:   6600        -8544.897             0.151            0.174
Chain 1:   6700        -9446.642             0.155            0.174
Chain 1:   6800       -13006.647             0.177            0.176
Chain 1:   6900        -8146.554             0.210            0.176
Chain 1:   7000        -8509.314             0.196            0.174
Chain 1:   7100        -8139.078             0.170            0.133
Chain 1:   7200       -10577.224             0.192            0.174
Chain 1:   7300       -10834.875             0.181            0.174
Chain 1:   7400        -9274.305             0.181            0.168
Chain 1:   7500        -8642.803             0.162            0.095
Chain 1:   7600       -10939.021             0.176            0.168
Chain 1:   7700        -8811.868             0.191            0.210
Chain 1:   7800        -9492.663             0.170            0.168
Chain 1:   7900        -9246.155             0.113            0.073
Chain 1:   8000        -8924.393             0.113            0.073
Chain 1:   8100        -9295.948             0.112            0.073
Chain 1:   8200        -8782.570             0.095            0.072
Chain 1:   8300        -8311.419             0.098            0.072
Chain 1:   8400        -8144.678             0.083            0.058
Chain 1:   8500        -8110.902             0.077            0.057
Chain 1:   8600        -8689.020             0.062            0.057
Chain 1:   8700       -10995.527             0.059            0.057
Chain 1:   8800        -8097.391             0.088            0.057
Chain 1:   8900       -10735.617             0.110            0.058
Chain 1:   9000        -9949.902             0.114            0.067
Chain 1:   9100        -9283.842             0.117            0.072
Chain 1:   9200       -11734.113             0.132            0.079
Chain 1:   9300        -8871.278             0.159            0.209
Chain 1:   9400        -9311.068             0.161            0.209
Chain 1:   9500       -10346.223             0.171            0.209
Chain 1:   9600       -10248.873             0.165            0.209
Chain 1:   9700        -8279.708             0.168            0.209
Chain 1:   9800        -8538.059             0.135            0.100
Chain 1:   9900        -9234.375             0.118            0.079
Chain 1:   10000        -8211.585             0.123            0.100
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001375 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56864.177             1.000            1.000
Chain 1:    200       -17333.201             1.640            2.281
Chain 1:    300        -8680.386             1.426            1.000
Chain 1:    400        -8339.907             1.080            1.000
Chain 1:    500        -8290.669             0.865            0.997
Chain 1:    600        -8247.779             0.722            0.997
Chain 1:    700        -7967.456             0.624            0.041
Chain 1:    800        -8154.776             0.548            0.041
Chain 1:    900        -7793.654             0.493            0.041
Chain 1:   1000        -7754.773             0.444            0.041
Chain 1:   1100        -7746.479             0.344            0.035
Chain 1:   1200        -7578.123             0.118            0.023
Chain 1:   1300        -7492.280             0.020            0.022
Chain 1:   1400        -7612.348             0.017            0.016
Chain 1:   1500        -7589.228             0.017            0.016
Chain 1:   1600        -7533.851             0.017            0.016
Chain 1:   1700        -7499.462             0.014            0.011
Chain 1:   1800        -7566.707             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002613 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86172.286             1.000            1.000
Chain 1:    200       -13408.958             3.213            5.426
Chain 1:    300        -9809.436             2.264            1.000
Chain 1:    400       -10692.927             1.719            1.000
Chain 1:    500        -8772.949             1.419            0.367
Chain 1:    600        -8319.479             1.192            0.367
Chain 1:    700        -8328.522             1.021            0.219
Chain 1:    800        -8610.641             0.898            0.219
Chain 1:    900        -8644.353             0.799            0.083
Chain 1:   1000        -8382.794             0.722            0.083
Chain 1:   1100        -8654.049             0.625            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8300.518             0.087            0.043
Chain 1:   1300        -8525.928             0.053            0.033
Chain 1:   1400        -8523.496             0.044            0.031
Chain 1:   1500        -8420.710             0.024            0.031
Chain 1:   1600        -8521.141             0.019            0.026
Chain 1:   1700        -8610.393             0.020            0.026
Chain 1:   1800        -8209.895             0.022            0.026
Chain 1:   1900        -8310.568             0.023            0.026
Chain 1:   2000        -8281.446             0.020            0.012
Chain 1:   2100        -8401.871             0.018            0.012
Chain 1:   2200        -8178.473             0.017            0.012
Chain 1:   2300        -8339.785             0.016            0.012
Chain 1:   2400        -8221.610             0.017            0.014
Chain 1:   2500        -8285.781             0.017            0.014
Chain 1:   2600        -8306.661             0.016            0.014
Chain 1:   2700        -8226.224             0.016            0.014
Chain 1:   2800        -8201.001             0.011            0.012
Chain 1:   2900        -8255.664             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002502 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401498.896             1.000            1.000
Chain 1:    200     -1585168.770             2.650            4.300
Chain 1:    300      -891472.746             2.026            1.000
Chain 1:    400      -457640.044             1.757            1.000
Chain 1:    500      -358008.454             1.461            0.948
Chain 1:    600      -232929.064             1.307            0.948
Chain 1:    700      -119144.781             1.257            0.948
Chain 1:    800       -86337.371             1.147            0.948
Chain 1:    900       -66680.133             1.052            0.778
Chain 1:   1000       -51471.282             0.977            0.778
Chain 1:   1100       -38944.664             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38117.549             0.481            0.380
Chain 1:   1300       -26077.664             0.449            0.380
Chain 1:   1400       -25795.283             0.356            0.322
Chain 1:   1500       -22383.290             0.343            0.322
Chain 1:   1600       -21599.375             0.293            0.295
Chain 1:   1700       -20474.125             0.203            0.295
Chain 1:   1800       -20418.210             0.165            0.152
Chain 1:   1900       -20744.058             0.137            0.055
Chain 1:   2000       -19256.016             0.116            0.055
Chain 1:   2100       -19494.461             0.085            0.036
Chain 1:   2200       -19720.603             0.084            0.036
Chain 1:   2300       -19338.116             0.039            0.020
Chain 1:   2400       -19110.319             0.039            0.020
Chain 1:   2500       -18912.251             0.025            0.016
Chain 1:   2600       -18542.920             0.024            0.016
Chain 1:   2700       -18499.950             0.018            0.012
Chain 1:   2800       -18216.942             0.020            0.016
Chain 1:   2900       -18497.986             0.020            0.015
Chain 1:   3000       -18484.267             0.012            0.012
Chain 1:   3100       -18569.227             0.011            0.012
Chain 1:   3200       -18260.129             0.012            0.015
Chain 1:   3300       -18464.633             0.011            0.012
Chain 1:   3400       -17939.961             0.013            0.015
Chain 1:   3500       -18551.270             0.015            0.016
Chain 1:   3600       -17858.638             0.017            0.016
Chain 1:   3700       -18244.939             0.019            0.017
Chain 1:   3800       -17205.768             0.023            0.021
Chain 1:   3900       -17201.909             0.022            0.021
Chain 1:   4000       -17319.216             0.022            0.021
Chain 1:   4100       -17233.075             0.022            0.021
Chain 1:   4200       -17049.518             0.022            0.021
Chain 1:   4300       -17187.790             0.021            0.021
Chain 1:   4400       -17144.822             0.019            0.011
Chain 1:   4500       -17047.359             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12489.493             1.000            1.000
Chain 1:    200        -9408.082             0.664            1.000
Chain 1:    300        -8127.255             0.495            0.328
Chain 1:    400        -8215.479             0.374            0.328
Chain 1:    500        -8170.172             0.300            0.158
Chain 1:    600        -7996.075             0.254            0.158
Chain 1:    700        -7958.577             0.218            0.022
Chain 1:    800        -7970.566             0.191            0.022
Chain 1:    900        -7939.334             0.170            0.011
Chain 1:   1000        -8028.540             0.154            0.011
Chain 1:   1100        -8080.497             0.055            0.011
Chain 1:   1200        -7975.976             0.024            0.011
Chain 1:   1300        -7927.229             0.009            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58157.192             1.000            1.000
Chain 1:    200       -17734.061             1.640            2.279
Chain 1:    300        -8739.955             1.436            1.029
Chain 1:    400        -8254.674             1.092            1.029
Chain 1:    500        -8417.031             0.877            1.000
Chain 1:    600        -8414.463             0.731            1.000
Chain 1:    700        -7982.283             0.634            0.059
Chain 1:    800        -8235.476             0.559            0.059
Chain 1:    900        -7935.551             0.501            0.054
Chain 1:   1000        -7876.753             0.452            0.054
Chain 1:   1100        -7793.824             0.353            0.038
Chain 1:   1200        -7622.670             0.127            0.031
Chain 1:   1300        -7803.949             0.026            0.023
Chain 1:   1400        -7902.715             0.022            0.022
Chain 1:   1500        -7668.178             0.023            0.023
Chain 1:   1600        -7802.006             0.025            0.023
Chain 1:   1700        -7563.877             0.022            0.023
Chain 1:   1800        -7660.411             0.021            0.022
Chain 1:   1900        -7603.815             0.018            0.017
Chain 1:   2000        -7679.054             0.018            0.017
Chain 1:   2100        -7697.378             0.017            0.017
Chain 1:   2200        -7755.938             0.015            0.013
Chain 1:   2300        -7655.004             0.014            0.013
Chain 1:   2400        -7712.634             0.014            0.013
Chain 1:   2500        -7628.735             0.012            0.011
Chain 1:   2600        -7605.713             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003137 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86356.703             1.000            1.000
Chain 1:    200       -13555.545             3.185            5.371
Chain 1:    300        -9931.779             2.245            1.000
Chain 1:    400       -10809.353             1.704            1.000
Chain 1:    500        -8898.056             1.406            0.365
Chain 1:    600        -8362.434             1.183            0.365
Chain 1:    700        -8531.061             1.016            0.215
Chain 1:    800        -9217.211             0.899            0.215
Chain 1:    900        -8727.570             0.805            0.081
Chain 1:   1000        -8558.694             0.727            0.081
Chain 1:   1100        -8692.233             0.628            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8464.301             0.094            0.064
Chain 1:   1300        -8574.710             0.059            0.056
Chain 1:   1400        -8609.991             0.051            0.027
Chain 1:   1500        -8501.822             0.031            0.020
Chain 1:   1600        -8608.078             0.025            0.020
Chain 1:   1700        -8696.033             0.024            0.015
Chain 1:   1800        -8286.299             0.022            0.015
Chain 1:   1900        -8382.234             0.018            0.013
Chain 1:   2000        -8354.936             0.016            0.013
Chain 1:   2100        -8476.626             0.016            0.013
Chain 1:   2200        -8318.565             0.015            0.013
Chain 1:   2300        -8379.675             0.014            0.012
Chain 1:   2400        -8446.849             0.015            0.012
Chain 1:   2500        -8392.670             0.014            0.011
Chain 1:   2600        -8390.976             0.013            0.010
Chain 1:   2700        -8308.175             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003406 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408319.755             1.000            1.000
Chain 1:    200     -1584308.905             2.654            4.307
Chain 1:    300      -890246.392             2.029            1.000
Chain 1:    400      -457715.296             1.758            1.000
Chain 1:    500      -358106.395             1.462            0.945
Chain 1:    600      -233146.936             1.308            0.945
Chain 1:    700      -119311.867             1.257            0.945
Chain 1:    800       -86512.196             1.147            0.945
Chain 1:    900       -66845.520             1.053            0.780
Chain 1:   1000       -51635.786             0.977            0.780
Chain 1:   1100       -39109.234             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38283.129             0.480            0.379
Chain 1:   1300       -26239.512             0.448            0.379
Chain 1:   1400       -25957.466             0.355            0.320
Chain 1:   1500       -22545.522             0.342            0.320
Chain 1:   1600       -21761.954             0.292            0.295
Chain 1:   1700       -20635.908             0.202            0.294
Chain 1:   1800       -20580.007             0.165            0.151
Chain 1:   1900       -20906.071             0.137            0.055
Chain 1:   2000       -19417.489             0.115            0.055
Chain 1:   2100       -19655.806             0.084            0.036
Chain 1:   2200       -19882.279             0.083            0.036
Chain 1:   2300       -19499.470             0.039            0.020
Chain 1:   2400       -19271.616             0.039            0.020
Chain 1:   2500       -19073.657             0.025            0.016
Chain 1:   2600       -18703.973             0.023            0.016
Chain 1:   2700       -18660.869             0.018            0.012
Chain 1:   2800       -18377.855             0.020            0.015
Chain 1:   2900       -18659.029             0.019            0.015
Chain 1:   3000       -18645.181             0.012            0.012
Chain 1:   3100       -18730.214             0.011            0.012
Chain 1:   3200       -18420.926             0.012            0.015
Chain 1:   3300       -18625.578             0.011            0.012
Chain 1:   3400       -18100.657             0.012            0.015
Chain 1:   3500       -18712.333             0.015            0.015
Chain 1:   3600       -18019.191             0.017            0.015
Chain 1:   3700       -18405.942             0.018            0.017
Chain 1:   3800       -17365.950             0.023            0.021
Chain 1:   3900       -17362.087             0.021            0.021
Chain 1:   4000       -17479.392             0.022            0.021
Chain 1:   4100       -17393.234             0.022            0.021
Chain 1:   4200       -17209.474             0.021            0.021
Chain 1:   4300       -17347.866             0.021            0.021
Chain 1:   4400       -17304.751             0.019            0.011
Chain 1:   4500       -17207.284             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001402 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49170.424             1.000            1.000
Chain 1:    200       -23141.351             1.062            1.125
Chain 1:    300       -19074.570             0.779            1.000
Chain 1:    400       -13826.679             0.679            1.000
Chain 1:    500       -22460.984             0.620            0.384
Chain 1:    600       -12960.242             0.639            0.733
Chain 1:    700       -15121.171             0.568            0.384
Chain 1:    800       -14662.266             0.501            0.384
Chain 1:    900       -14277.636             0.448            0.380
Chain 1:   1000       -12447.289             0.418            0.380
Chain 1:   1100       -11173.756             0.330            0.213
Chain 1:   1200       -11457.764             0.220            0.147
Chain 1:   1300       -13966.041             0.216            0.147
Chain 1:   1400       -15029.777             0.185            0.143
Chain 1:   1500       -11222.506             0.181            0.143
Chain 1:   1600       -12878.006             0.121            0.129
Chain 1:   1700        -9711.591             0.139            0.129
Chain 1:   1800       -10508.780             0.143            0.129
Chain 1:   1900       -11188.017             0.147            0.129
Chain 1:   2000       -17947.709             0.170            0.129
Chain 1:   2100       -16577.854             0.166            0.129
Chain 1:   2200       -10491.406             0.222            0.180
Chain 1:   2300       -10254.279             0.206            0.129
Chain 1:   2400        -9423.568             0.208            0.129
Chain 1:   2500        -9568.771             0.176            0.088
Chain 1:   2600        -9856.584             0.166            0.083
Chain 1:   2700       -11501.762             0.147            0.083
Chain 1:   2800       -10594.134             0.148            0.086
Chain 1:   2900       -10019.360             0.148            0.086
Chain 1:   3000       -10145.046             0.112            0.083
Chain 1:   3100       -15934.456             0.140            0.086
Chain 1:   3200       -10236.747             0.137            0.086
Chain 1:   3300       -10006.838             0.137            0.086
Chain 1:   3400        -9487.966             0.134            0.057
Chain 1:   3500        -9479.075             0.133            0.057
Chain 1:   3600       -18162.593             0.178            0.086
Chain 1:   3700       -14633.113             0.187            0.086
Chain 1:   3800       -13039.691             0.191            0.122
Chain 1:   3900        -9718.932             0.219            0.241
Chain 1:   4000        -9041.745             0.226            0.241
Chain 1:   4100        -9413.575             0.193            0.122
Chain 1:   4200       -17357.337             0.183            0.122
Chain 1:   4300        -8926.531             0.276            0.241
Chain 1:   4400        -9400.725             0.275            0.241
Chain 1:   4500        -9033.292             0.279            0.241
Chain 1:   4600        -9452.456             0.236            0.122
Chain 1:   4700        -8725.596             0.220            0.083
Chain 1:   4800        -9033.254             0.211            0.075
Chain 1:   4900        -9174.260             0.178            0.050
Chain 1:   5000        -9844.616             0.178            0.050
Chain 1:   5100        -8945.072             0.184            0.068
Chain 1:   5200       -10333.437             0.152            0.068
Chain 1:   5300       -13829.220             0.082            0.068
Chain 1:   5400        -9271.468             0.127            0.083
Chain 1:   5500       -14377.570             0.158            0.101
Chain 1:   5600        -9407.279             0.206            0.134
Chain 1:   5700       -13460.224             0.228            0.253
Chain 1:   5800        -9273.256             0.270            0.301
Chain 1:   5900        -9252.988             0.269            0.301
Chain 1:   6000       -11001.601             0.278            0.301
Chain 1:   6100        -8815.085             0.292            0.301
Chain 1:   6200       -11673.391             0.303            0.301
Chain 1:   6300       -12799.629             0.287            0.301
Chain 1:   6400        -9207.419             0.277            0.301
Chain 1:   6500        -9162.816             0.242            0.248
Chain 1:   6600        -8705.645             0.194            0.245
Chain 1:   6700        -9380.950             0.171            0.159
Chain 1:   6800        -8518.139             0.136            0.101
Chain 1:   6900       -12514.387             0.168            0.159
Chain 1:   7000        -9151.902             0.189            0.245
Chain 1:   7100       -12521.113             0.191            0.245
Chain 1:   7200       -11915.140             0.172            0.101
Chain 1:   7300       -10882.767             0.172            0.101
Chain 1:   7400        -9215.122             0.151            0.101
Chain 1:   7500        -9031.919             0.153            0.101
Chain 1:   7600       -13365.320             0.180            0.181
Chain 1:   7700        -8522.577             0.230            0.269
Chain 1:   7800       -11662.122             0.246            0.269
Chain 1:   7900       -10110.504             0.230            0.269
Chain 1:   8000        -8828.006             0.208            0.181
Chain 1:   8100       -11938.673             0.207            0.181
Chain 1:   8200       -10053.894             0.220            0.187
Chain 1:   8300        -8536.637             0.229            0.187
Chain 1:   8400        -8561.253             0.211            0.187
Chain 1:   8500        -8479.467             0.210            0.187
Chain 1:   8600        -8834.450             0.181            0.178
Chain 1:   8700        -8653.242             0.127            0.153
Chain 1:   8800       -12450.242             0.130            0.153
Chain 1:   8900       -10218.730             0.137            0.178
Chain 1:   9000        -9462.246             0.130            0.178
Chain 1:   9100        -8652.458             0.114            0.094
Chain 1:   9200        -8651.066             0.095            0.080
Chain 1:   9300        -8479.938             0.079            0.040
Chain 1:   9400        -8553.557             0.080            0.040
Chain 1:   9500        -8454.555             0.080            0.040
Chain 1:   9600       -10367.225             0.094            0.080
Chain 1:   9700        -8348.894             0.116            0.094
Chain 1:   9800       -12444.263             0.119            0.094
Chain 1:   9900        -8649.865             0.141            0.094
Chain 1:   10000        -8755.596             0.134            0.094
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57635.895             1.000            1.000
Chain 1:    200       -17863.942             1.613            2.226
Chain 1:    300        -8929.563             1.409            1.001
Chain 1:    400        -8256.226             1.077            1.001
Chain 1:    500        -9152.620             0.881            1.000
Chain 1:    600        -8471.122             0.748            1.000
Chain 1:    700        -8144.853             0.647            0.098
Chain 1:    800        -8724.316             0.574            0.098
Chain 1:    900        -8130.316             0.518            0.082
Chain 1:   1000        -7735.779             0.472            0.082
Chain 1:   1100        -7693.837             0.372            0.080
Chain 1:   1200        -7649.363             0.150            0.073
Chain 1:   1300        -7659.251             0.050            0.066
Chain 1:   1400        -8073.353             0.047            0.051
Chain 1:   1500        -7667.455             0.043            0.051
Chain 1:   1600        -7871.211             0.037            0.051
Chain 1:   1700        -7625.265             0.037            0.051
Chain 1:   1800        -7673.155             0.031            0.032
Chain 1:   1900        -7675.605             0.023            0.026
Chain 1:   2000        -7741.894             0.019            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002633 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85509.757             1.000            1.000
Chain 1:    200       -13871.791             3.082            5.164
Chain 1:    300       -10189.562             2.175            1.000
Chain 1:    400       -11226.587             1.655            1.000
Chain 1:    500        -9003.202             1.373            0.361
Chain 1:    600        -9515.329             1.153            0.361
Chain 1:    700        -8649.620             1.003            0.247
Chain 1:    800        -9406.242             0.887            0.247
Chain 1:    900        -8919.107             0.795            0.100
Chain 1:   1000        -9029.956             0.717            0.100
Chain 1:   1100        -8764.039             0.620            0.092   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8554.043             0.106            0.080
Chain 1:   1300        -8862.335             0.073            0.055
Chain 1:   1400        -8815.503             0.064            0.054
Chain 1:   1500        -8700.229             0.041            0.035
Chain 1:   1600        -8807.158             0.037            0.030
Chain 1:   1700        -8879.128             0.028            0.025
Chain 1:   1800        -8445.322             0.025            0.025
Chain 1:   1900        -8549.578             0.020            0.013
Chain 1:   2000        -8525.070             0.019            0.013
Chain 1:   2100        -8473.176             0.017            0.012
Chain 1:   2200        -8467.135             0.015            0.012
Chain 1:   2300        -8603.695             0.013            0.012
Chain 1:   2400        -8450.954             0.014            0.012
Chain 1:   2500        -8519.792             0.014            0.012
Chain 1:   2600        -8438.614             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002565 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8376427.667             1.000            1.000
Chain 1:    200     -1581074.100             2.649            4.298
Chain 1:    300      -891525.243             2.024            1.000
Chain 1:    400      -458619.648             1.754            1.000
Chain 1:    500      -359451.049             1.458            0.944
Chain 1:    600      -234344.331             1.304            0.944
Chain 1:    700      -120080.868             1.254            0.944
Chain 1:    800       -87213.384             1.144            0.944
Chain 1:    900       -67457.062             1.050            0.773
Chain 1:   1000       -52190.440             0.974            0.773
Chain 1:   1100       -39601.975             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38771.379             0.478            0.377
Chain 1:   1300       -26648.169             0.446            0.377
Chain 1:   1400       -26361.813             0.353            0.318
Chain 1:   1500       -22928.677             0.340            0.318
Chain 1:   1600       -22139.849             0.290            0.293
Chain 1:   1700       -21003.260             0.201            0.293
Chain 1:   1800       -20945.290             0.163            0.150
Chain 1:   1900       -21271.772             0.136            0.054
Chain 1:   2000       -19776.985             0.114            0.054
Chain 1:   2100       -20015.620             0.083            0.036
Chain 1:   2200       -20243.324             0.082            0.036
Chain 1:   2300       -19859.332             0.039            0.019
Chain 1:   2400       -19631.161             0.039            0.019
Chain 1:   2500       -19433.680             0.025            0.015
Chain 1:   2600       -19063.089             0.023            0.015
Chain 1:   2700       -19019.772             0.018            0.012
Chain 1:   2800       -18736.712             0.019            0.015
Chain 1:   2900       -19018.182             0.019            0.015
Chain 1:   3000       -19004.270             0.012            0.012
Chain 1:   3100       -19089.370             0.011            0.012
Chain 1:   3200       -18779.708             0.011            0.015
Chain 1:   3300       -18984.665             0.011            0.012
Chain 1:   3400       -18459.205             0.012            0.015
Chain 1:   3500       -19071.890             0.014            0.015
Chain 1:   3600       -18377.490             0.016            0.015
Chain 1:   3700       -18765.160             0.018            0.016
Chain 1:   3800       -17723.402             0.023            0.021
Chain 1:   3900       -17719.567             0.021            0.021
Chain 1:   4000       -17836.792             0.022            0.021
Chain 1:   4100       -17750.561             0.022            0.021
Chain 1:   4200       -17566.440             0.021            0.021
Chain 1:   4300       -17705.035             0.021            0.021
Chain 1:   4400       -17661.566             0.018            0.010
Chain 1:   4500       -17564.112             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13153.057             1.000            1.000
Chain 1:    200        -9534.503             0.690            1.000
Chain 1:    300        -8245.000             0.512            0.380
Chain 1:    400        -8291.430             0.385            0.380
Chain 1:    500        -8097.802             0.313            0.156
Chain 1:    600        -8195.840             0.263            0.156
Chain 1:    700        -8063.396             0.228            0.024
Chain 1:    800        -8099.588             0.200            0.024
Chain 1:    900        -8171.017             0.179            0.016
Chain 1:   1000        -8180.432             0.161            0.016
Chain 1:   1100        -8127.269             0.061            0.012
Chain 1:   1200        -8067.946             0.024            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001448 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50778.838             1.000            1.000
Chain 1:    200       -16913.600             1.501            2.002
Chain 1:    300        -9104.612             1.287            1.000
Chain 1:    400        -8534.510             0.982            1.000
Chain 1:    500        -8879.343             0.793            0.858
Chain 1:    600        -8505.505             0.668            0.858
Chain 1:    700        -8415.489             0.574            0.067
Chain 1:    800        -8434.991             0.503            0.067
Chain 1:    900        -7855.284             0.455            0.067
Chain 1:   1000        -7749.259             0.411            0.067
Chain 1:   1100        -7909.568             0.313            0.044
Chain 1:   1200        -7696.489             0.116            0.039
Chain 1:   1300        -7579.238             0.031            0.028
Chain 1:   1400        -8378.550             0.034            0.028
Chain 1:   1500        -7525.162             0.042            0.028
Chain 1:   1600        -7850.850             0.041            0.028
Chain 1:   1700        -7744.553             0.042            0.028
Chain 1:   1800        -7536.806             0.044            0.028
Chain 1:   1900        -7561.029             0.037            0.028
Chain 1:   2000        -7602.438             0.036            0.028
Chain 1:   2100        -7542.126             0.035            0.028
Chain 1:   2200        -7835.550             0.036            0.028
Chain 1:   2300        -7630.390             0.037            0.028
Chain 1:   2400        -7641.807             0.028            0.027
Chain 1:   2500        -7544.246             0.018            0.014
Chain 1:   2600        -7492.681             0.014            0.013
Chain 1:   2700        -7432.884             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002933 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86724.095             1.000            1.000
Chain 1:    200       -14233.281             3.047            5.093
Chain 1:    300       -10363.867             2.155            1.000
Chain 1:    400       -12537.183             1.660            1.000
Chain 1:    500        -8851.467             1.411            0.416
Chain 1:    600        -8646.792             1.180            0.416
Chain 1:    700        -8844.563             1.015            0.373
Chain 1:    800        -9825.750             0.900            0.373
Chain 1:    900        -9152.122             0.808            0.173
Chain 1:   1000        -8825.392             0.731            0.173
Chain 1:   1100        -9096.607             0.634            0.100   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8672.342             0.130            0.074
Chain 1:   1300        -8881.707             0.095            0.049
Chain 1:   1400        -8774.702             0.079            0.037
Chain 1:   1500        -8787.510             0.037            0.030
Chain 1:   1600        -8856.346             0.036            0.030
Chain 1:   1700        -8896.754             0.034            0.030
Chain 1:   1800        -8419.186             0.030            0.030
Chain 1:   1900        -8537.813             0.024            0.024
Chain 1:   2000        -8547.245             0.020            0.014
Chain 1:   2100        -8666.252             0.018            0.014
Chain 1:   2200        -8409.006             0.017            0.014
Chain 1:   2300        -8501.076             0.015            0.012
Chain 1:   2400        -8589.432             0.015            0.011
Chain 1:   2500        -8507.216             0.016            0.011
Chain 1:   2600        -8534.103             0.015            0.011
Chain 1:   2700        -8447.212             0.016            0.011
Chain 1:   2800        -8411.343             0.011            0.010
Chain 1:   2900        -8507.036             0.011            0.010
Chain 1:   3000        -8426.435             0.011            0.010
Chain 1:   3100        -8385.204             0.010            0.010
Chain 1:   3200        -8348.824             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003496 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412302.809             1.000            1.000
Chain 1:    200     -1586491.381             2.651            4.302
Chain 1:    300      -892919.302             2.026            1.000
Chain 1:    400      -458837.836             1.756            1.000
Chain 1:    500      -359003.345             1.461            0.946
Chain 1:    600      -233783.655             1.306            0.946
Chain 1:    700      -120026.429             1.255            0.946
Chain 1:    800       -87219.970             1.145            0.946
Chain 1:    900       -67570.130             1.050            0.777
Chain 1:   1000       -52389.930             0.974            0.777
Chain 1:   1100       -39873.829             0.906            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39064.343             0.478            0.376
Chain 1:   1300       -27007.980             0.445            0.376
Chain 1:   1400       -26731.996             0.351            0.314
Chain 1:   1500       -23314.372             0.338            0.314
Chain 1:   1600       -22531.449             0.288            0.291
Chain 1:   1700       -21402.468             0.198            0.290
Chain 1:   1800       -21346.901             0.161            0.147
Chain 1:   1900       -21674.230             0.133            0.053
Chain 1:   2000       -20181.684             0.112            0.053
Chain 1:   2100       -20420.435             0.081            0.035
Chain 1:   2200       -20647.820             0.081            0.035
Chain 1:   2300       -20263.926             0.038            0.019
Chain 1:   2400       -20035.551             0.038            0.019
Chain 1:   2500       -19837.503             0.024            0.015
Chain 1:   2600       -19466.486             0.023            0.015
Chain 1:   2700       -19423.166             0.018            0.012
Chain 1:   2800       -19139.365             0.019            0.015
Chain 1:   2900       -19421.271             0.019            0.015
Chain 1:   3000       -19407.388             0.011            0.012
Chain 1:   3100       -19492.522             0.011            0.011
Chain 1:   3200       -19182.400             0.011            0.015
Chain 1:   3300       -19387.811             0.010            0.011
Chain 1:   3400       -18861.195             0.012            0.015
Chain 1:   3500       -19475.261             0.014            0.015
Chain 1:   3600       -18779.166             0.016            0.015
Chain 1:   3700       -19168.000             0.018            0.016
Chain 1:   3800       -18123.282             0.022            0.020
Chain 1:   3900       -18119.309             0.021            0.020
Chain 1:   4000       -18236.656             0.021            0.020
Chain 1:   4100       -18150.101             0.021            0.020
Chain 1:   4200       -17965.449             0.021            0.020
Chain 1:   4300       -18104.500             0.020            0.020
Chain 1:   4400       -18060.546             0.018            0.010
Chain 1:   4500       -17962.919             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001177 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48771.228             1.000            1.000
Chain 1:    200       -22963.585             1.062            1.124
Chain 1:    300       -13191.593             0.955            1.000
Chain 1:    400       -12679.817             0.726            1.000
Chain 1:    500       -20298.171             0.656            0.741
Chain 1:    600       -11934.648             0.664            0.741
Chain 1:    700       -11968.889             0.569            0.701
Chain 1:    800       -13951.366             0.516            0.701
Chain 1:    900       -12363.471             0.473            0.375
Chain 1:   1000       -30316.985             0.485            0.592
Chain 1:   1100       -25885.975             0.402            0.375
Chain 1:   1200       -12969.316             0.389            0.375
Chain 1:   1300       -12989.764             0.315            0.171
Chain 1:   1400       -16708.132             0.333            0.223
Chain 1:   1500       -12122.657             0.334            0.223
Chain 1:   1600       -12188.645             0.264            0.171
Chain 1:   1700       -14892.832             0.282            0.182
Chain 1:   1800       -10092.776             0.315            0.223
Chain 1:   1900        -9582.696             0.308            0.223
Chain 1:   2000        -9510.916             0.249            0.182
Chain 1:   2100       -10802.787             0.244            0.182
Chain 1:   2200        -9497.670             0.158            0.137
Chain 1:   2300       -15482.199             0.197            0.182
Chain 1:   2400        -9322.010             0.241            0.182
Chain 1:   2500        -9129.714             0.205            0.137
Chain 1:   2600        -9867.447             0.212            0.137
Chain 1:   2700        -9576.567             0.197            0.120
Chain 1:   2800        -9631.253             0.150            0.075
Chain 1:   2900       -11985.622             0.164            0.120
Chain 1:   3000        -9068.747             0.195            0.137
Chain 1:   3100        -9374.778             0.187            0.137
Chain 1:   3200        -9176.148             0.175            0.075
Chain 1:   3300        -9055.321             0.138            0.033
Chain 1:   3400       -14953.027             0.111            0.033
Chain 1:   3500       -10755.074             0.148            0.075
Chain 1:   3600        -9473.394             0.154            0.135
Chain 1:   3700        -8725.370             0.160            0.135
Chain 1:   3800        -9039.745             0.163            0.135
Chain 1:   3900       -11666.398             0.165            0.135
Chain 1:   4000        -8626.074             0.169            0.135
Chain 1:   4100        -8955.851             0.169            0.135
Chain 1:   4200       -11209.462             0.187            0.201
Chain 1:   4300       -12260.378             0.194            0.201
Chain 1:   4400       -12209.164             0.155            0.135
Chain 1:   4500        -8840.296             0.154            0.135
Chain 1:   4600       -11750.697             0.165            0.201
Chain 1:   4700       -14159.375             0.174            0.201
Chain 1:   4800        -8753.763             0.232            0.225
Chain 1:   4900        -9214.565             0.215            0.201
Chain 1:   5000        -9497.614             0.182            0.170
Chain 1:   5100       -11345.672             0.195            0.170
Chain 1:   5200       -16445.022             0.206            0.170
Chain 1:   5300       -10827.887             0.249            0.248
Chain 1:   5400       -13608.703             0.269            0.248
Chain 1:   5500        -8835.133             0.285            0.248
Chain 1:   5600        -8403.321             0.266            0.204
Chain 1:   5700        -8989.808             0.255            0.204
Chain 1:   5800        -8518.874             0.199            0.163
Chain 1:   5900        -9019.170             0.199            0.163
Chain 1:   6000        -8847.419             0.198            0.163
Chain 1:   6100        -8375.840             0.188            0.065
Chain 1:   6200        -8126.683             0.160            0.056
Chain 1:   6300       -15454.686             0.155            0.056
Chain 1:   6400       -13921.178             0.146            0.056
Chain 1:   6500        -9766.471             0.134            0.056
Chain 1:   6600        -8259.381             0.147            0.065
Chain 1:   6700       -10375.467             0.161            0.110
Chain 1:   6800        -9866.262             0.161            0.110
Chain 1:   6900       -12269.873             0.175            0.182
Chain 1:   7000        -8184.371             0.223            0.196
Chain 1:   7100        -8933.344             0.226            0.196
Chain 1:   7200        -8393.939             0.229            0.196
Chain 1:   7300        -9697.750             0.195            0.182
Chain 1:   7400       -12540.648             0.207            0.196
Chain 1:   7500       -10817.611             0.180            0.182
Chain 1:   7600        -8735.053             0.186            0.196
Chain 1:   7700        -8223.874             0.172            0.159
Chain 1:   7800       -10692.395             0.190            0.196
Chain 1:   7900        -8207.075             0.200            0.227
Chain 1:   8000        -8269.022             0.151            0.159
Chain 1:   8100        -8850.813             0.149            0.159
Chain 1:   8200        -8673.872             0.145            0.159
Chain 1:   8300        -8165.217             0.138            0.159
Chain 1:   8400        -9524.067             0.129            0.143
Chain 1:   8500       -10392.646             0.122            0.084
Chain 1:   8600       -11446.910             0.107            0.084
Chain 1:   8700        -7997.722             0.144            0.092
Chain 1:   8800        -8115.009             0.122            0.084
Chain 1:   8900        -8515.601             0.097            0.066
Chain 1:   9000        -9364.592             0.105            0.084
Chain 1:   9100        -7989.732             0.116            0.091
Chain 1:   9200       -10141.490             0.135            0.092
Chain 1:   9300        -8576.405             0.147            0.143
Chain 1:   9400        -7957.572             0.140            0.092
Chain 1:   9500        -8085.213             0.134            0.092
Chain 1:   9600        -8194.210             0.126            0.091
Chain 1:   9700        -8405.921             0.085            0.078
Chain 1:   9800        -8465.194             0.084            0.078
Chain 1:   9900       -10822.221             0.101            0.091
Chain 1:   10000        -8330.855             0.122            0.172
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001377 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58147.838             1.000            1.000
Chain 1:    200       -17623.264             1.650            2.299
Chain 1:    300        -8609.952             1.449            1.047
Chain 1:    400        -8171.981             1.100            1.047
Chain 1:    500        -8314.276             0.883            1.000
Chain 1:    600        -8350.595             0.737            1.000
Chain 1:    700        -7875.625             0.640            0.060
Chain 1:    800        -8131.068             0.564            0.060
Chain 1:    900        -7745.866             0.507            0.054
Chain 1:   1000        -7874.924             0.458            0.054
Chain 1:   1100        -7678.941             0.360            0.050
Chain 1:   1200        -7609.698             0.131            0.031
Chain 1:   1300        -7602.171             0.027            0.026
Chain 1:   1400        -7577.129             0.022            0.017
Chain 1:   1500        -7542.747             0.021            0.016
Chain 1:   1600        -7716.602             0.022            0.023
Chain 1:   1700        -7456.393             0.020            0.023
Chain 1:   1800        -7592.593             0.018            0.018
Chain 1:   1900        -7570.301             0.014            0.016
Chain 1:   2000        -7589.905             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003046 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86276.329             1.000            1.000
Chain 1:    200       -13386.319             3.223            5.445
Chain 1:    300        -9762.832             2.272            1.000
Chain 1:    400       -10863.312             1.729            1.000
Chain 1:    500        -8643.982             1.435            0.371
Chain 1:    600        -8393.532             1.201            0.371
Chain 1:    700        -8483.942             1.031            0.257
Chain 1:    800        -9130.479             0.911            0.257
Chain 1:    900        -8557.397             0.817            0.101
Chain 1:   1000        -8416.516             0.737            0.101
Chain 1:   1100        -8546.580             0.638            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8124.558             0.099            0.067
Chain 1:   1300        -8445.824             0.066            0.052
Chain 1:   1400        -8476.258             0.056            0.038
Chain 1:   1500        -8330.731             0.032            0.030
Chain 1:   1600        -8449.781             0.031            0.017
Chain 1:   1700        -8531.230             0.030            0.017
Chain 1:   1800        -8121.344             0.028            0.017
Chain 1:   1900        -8217.269             0.023            0.017
Chain 1:   2000        -8190.087             0.022            0.015
Chain 1:   2100        -8311.878             0.021            0.015
Chain 1:   2200        -8152.869             0.018            0.015
Chain 1:   2300        -8214.813             0.015            0.014
Chain 1:   2400        -8281.999             0.016            0.014
Chain 1:   2500        -8227.803             0.015            0.012
Chain 1:   2600        -8226.127             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004359 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8404817.796             1.000            1.000
Chain 1:    200     -1585003.568             2.651            4.303
Chain 1:    300      -890247.601             2.028            1.000
Chain 1:    400      -457461.986             1.757            1.000
Chain 1:    500      -357752.560             1.462            0.946
Chain 1:    600      -232826.297             1.307            0.946
Chain 1:    700      -119080.393             1.257            0.946
Chain 1:    800       -86295.765             1.147            0.946
Chain 1:    900       -66647.382             1.053            0.780
Chain 1:   1000       -51447.664             0.977            0.780
Chain 1:   1100       -38928.906             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38105.328             0.481            0.380
Chain 1:   1300       -26069.779             0.449            0.380
Chain 1:   1400       -25788.982             0.356            0.322
Chain 1:   1500       -22378.454             0.343            0.322
Chain 1:   1600       -21595.401             0.293            0.295
Chain 1:   1700       -20470.227             0.203            0.295
Chain 1:   1800       -20414.653             0.165            0.152
Chain 1:   1900       -20740.692             0.137            0.055
Chain 1:   2000       -19252.518             0.116            0.055
Chain 1:   2100       -19490.934             0.085            0.036
Chain 1:   2200       -19717.212             0.084            0.036
Chain 1:   2300       -19334.574             0.039            0.020
Chain 1:   2400       -19106.705             0.039            0.020
Chain 1:   2500       -18908.671             0.025            0.016
Chain 1:   2600       -18539.038             0.024            0.016
Chain 1:   2700       -18496.023             0.018            0.012
Chain 1:   2800       -18212.895             0.020            0.016
Chain 1:   2900       -18494.106             0.020            0.015
Chain 1:   3000       -18480.298             0.012            0.012
Chain 1:   3100       -18565.289             0.011            0.012
Chain 1:   3200       -18256.044             0.012            0.015
Chain 1:   3300       -18460.702             0.011            0.012
Chain 1:   3400       -17935.748             0.013            0.015
Chain 1:   3500       -18547.436             0.015            0.016
Chain 1:   3600       -17854.342             0.017            0.016
Chain 1:   3700       -18240.977             0.019            0.017
Chain 1:   3800       -17201.039             0.023            0.021
Chain 1:   3900       -17197.179             0.022            0.021
Chain 1:   4000       -17314.488             0.022            0.021
Chain 1:   4100       -17228.265             0.022            0.021
Chain 1:   4200       -17044.577             0.022            0.021
Chain 1:   4300       -17182.938             0.021            0.021
Chain 1:   4400       -17139.831             0.019            0.011
Chain 1:   4500       -17042.354             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001264 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12193.357             1.000            1.000
Chain 1:    200        -9088.474             0.671            1.000
Chain 1:    300        -7911.045             0.497            0.342
Chain 1:    400        -8007.452             0.376            0.342
Chain 1:    500        -7875.838             0.304            0.149
Chain 1:    600        -7790.141             0.255            0.149
Chain 1:    700        -7704.158             0.220            0.017
Chain 1:    800        -7712.126             0.193            0.017
Chain 1:    900        -7613.199             0.173            0.013
Chain 1:   1000        -7756.116             0.157            0.017
Chain 1:   1100        -7718.610             0.058            0.013
Chain 1:   1200        -7743.921             0.024            0.012
Chain 1:   1300        -7668.955             0.010            0.011
Chain 1:   1400        -7696.400             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001417 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49126.441             1.000            1.000
Chain 1:    200       -15717.778             1.563            2.126
Chain 1:    300        -8585.804             1.319            1.000
Chain 1:    400        -8316.164             0.997            1.000
Chain 1:    500        -7830.728             0.810            0.831
Chain 1:    600        -8932.462             0.696            0.831
Chain 1:    700        -8161.370             0.610            0.123
Chain 1:    800        -7803.384             0.539            0.123
Chain 1:    900        -8043.559             0.483            0.094
Chain 1:   1000        -7954.908             0.436            0.094
Chain 1:   1100        -7610.002             0.340            0.062
Chain 1:   1200        -7580.232             0.128            0.046
Chain 1:   1300        -7732.783             0.047            0.045
Chain 1:   1400        -7771.622             0.044            0.045
Chain 1:   1500        -7564.875             0.041            0.030
Chain 1:   1600        -7524.145             0.029            0.027
Chain 1:   1700        -7473.459             0.020            0.020
Chain 1:   1800        -7511.219             0.016            0.011
Chain 1:   1900        -7525.434             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002533 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86360.086             1.000            1.000
Chain 1:    200       -13291.021             3.249            5.498
Chain 1:    300        -9664.668             2.291            1.000
Chain 1:    400       -10397.062             1.736            1.000
Chain 1:    500        -8640.617             1.429            0.375
Chain 1:    600        -8118.722             1.202            0.375
Chain 1:    700        -8377.706             1.035            0.203
Chain 1:    800        -9078.980             0.915            0.203
Chain 1:    900        -8428.205             0.822            0.077
Chain 1:   1000        -8284.819             0.741            0.077
Chain 1:   1100        -8500.873             0.644            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8034.188             0.100            0.070
Chain 1:   1300        -8247.402             0.065            0.064
Chain 1:   1400        -8371.948             0.059            0.058
Chain 1:   1500        -8233.931             0.041            0.031
Chain 1:   1600        -8346.425             0.036            0.026
Chain 1:   1700        -8428.813             0.034            0.025
Chain 1:   1800        -8018.550             0.031            0.025
Chain 1:   1900        -8114.658             0.024            0.017
Chain 1:   2000        -8087.380             0.023            0.017
Chain 1:   2100        -8209.174             0.022            0.015
Chain 1:   2200        -8047.591             0.018            0.015
Chain 1:   2300        -8111.982             0.016            0.015
Chain 1:   2400        -8179.299             0.016            0.013
Chain 1:   2500        -8125.079             0.015            0.012
Chain 1:   2600        -8123.436             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003168 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402625.421             1.000            1.000
Chain 1:    200     -1585653.489             2.650            4.299
Chain 1:    300      -891796.335             2.026            1.000
Chain 1:    400      -458228.132             1.756            1.000
Chain 1:    500      -358443.445             1.460            0.946
Chain 1:    600      -232975.823             1.307            0.946
Chain 1:    700      -119086.995             1.257            0.946
Chain 1:    800       -86247.994             1.147            0.946
Chain 1:    900       -66572.180             1.053            0.778
Chain 1:   1000       -51361.835             0.977            0.778
Chain 1:   1100       -38834.528             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38008.148             0.481            0.381
Chain 1:   1300       -25969.991             0.450            0.381
Chain 1:   1400       -25687.559             0.356            0.323
Chain 1:   1500       -22276.598             0.344            0.323
Chain 1:   1600       -21492.896             0.294            0.296
Chain 1:   1700       -20368.153             0.204            0.296
Chain 1:   1800       -20312.418             0.166            0.153
Chain 1:   1900       -20638.372             0.138            0.055
Chain 1:   2000       -19150.442             0.116            0.055
Chain 1:   2100       -19388.746             0.085            0.036
Chain 1:   2200       -19614.969             0.084            0.036
Chain 1:   2300       -19232.413             0.040            0.020
Chain 1:   2400       -19004.601             0.040            0.020
Chain 1:   2500       -18806.437             0.025            0.016
Chain 1:   2600       -18436.888             0.024            0.016
Chain 1:   2700       -18393.936             0.018            0.012
Chain 1:   2800       -18110.805             0.020            0.016
Chain 1:   2900       -18391.974             0.020            0.015
Chain 1:   3000       -18378.195             0.012            0.012
Chain 1:   3100       -18463.150             0.011            0.012
Chain 1:   3200       -18153.944             0.012            0.015
Chain 1:   3300       -18358.566             0.011            0.012
Chain 1:   3400       -17833.631             0.013            0.015
Chain 1:   3500       -18445.246             0.015            0.016
Chain 1:   3600       -17752.296             0.017            0.016
Chain 1:   3700       -18138.814             0.019            0.017
Chain 1:   3800       -17099.013             0.023            0.021
Chain 1:   3900       -17095.158             0.022            0.021
Chain 1:   4000       -17212.483             0.022            0.021
Chain 1:   4100       -17126.266             0.022            0.021
Chain 1:   4200       -16942.616             0.022            0.021
Chain 1:   4300       -17080.951             0.021            0.021
Chain 1:   4400       -17037.873             0.019            0.011
Chain 1:   4500       -16940.420             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001341 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12343.227             1.000            1.000
Chain 1:    200        -9158.693             0.674            1.000
Chain 1:    300        -8115.314             0.492            0.348
Chain 1:    400        -8225.362             0.372            0.348
Chain 1:    500        -8113.857             0.301            0.129
Chain 1:    600        -8037.635             0.252            0.129
Chain 1:    700        -7959.788             0.218            0.014
Chain 1:    800        -7972.174             0.191            0.014
Chain 1:    900        -8087.628             0.171            0.014
Chain 1:   1000        -8022.339             0.155            0.014
Chain 1:   1100        -8091.973             0.056            0.013
Chain 1:   1200        -7970.468             0.022            0.013
Chain 1:   1300        -7946.527             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57073.000             1.000            1.000
Chain 1:    200       -17373.311             1.643            2.285
Chain 1:    300        -8772.343             1.422            1.000
Chain 1:    400        -8424.539             1.077            1.000
Chain 1:    500        -8861.858             0.871            0.980
Chain 1:    600        -8306.655             0.737            0.980
Chain 1:    700        -8015.343             0.637            0.067
Chain 1:    800        -8128.533             0.559            0.067
Chain 1:    900        -8120.684             0.497            0.049
Chain 1:   1000        -7721.617             0.453            0.052
Chain 1:   1100        -7796.446             0.354            0.049
Chain 1:   1200        -7730.180             0.126            0.041
Chain 1:   1300        -7763.468             0.028            0.036
Chain 1:   1400        -7990.288             0.027            0.028
Chain 1:   1500        -7683.406             0.026            0.028
Chain 1:   1600        -7709.475             0.020            0.014
Chain 1:   1700        -7607.989             0.017            0.013
Chain 1:   1800        -7672.106             0.017            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002522 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85906.460             1.000            1.000
Chain 1:    200       -13445.880             3.195            5.389
Chain 1:    300        -9874.555             2.250            1.000
Chain 1:    400       -10760.042             1.708            1.000
Chain 1:    500        -8820.299             1.411            0.362
Chain 1:    600        -8734.946             1.177            0.362
Chain 1:    700        -8542.542             1.012            0.220
Chain 1:    800        -8679.405             0.888            0.220
Chain 1:    900        -8646.086             0.789            0.082
Chain 1:   1000        -8456.066             0.713            0.082
Chain 1:   1100        -8726.855             0.616            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8443.616             0.080            0.031
Chain 1:   1300        -8613.046             0.046            0.023
Chain 1:   1400        -8612.187             0.038            0.022
Chain 1:   1500        -8476.312             0.017            0.020
Chain 1:   1600        -8584.665             0.018            0.020
Chain 1:   1700        -8670.967             0.017            0.016
Chain 1:   1800        -8276.150             0.020            0.020
Chain 1:   1900        -8376.699             0.021            0.020
Chain 1:   2000        -8347.500             0.019            0.016
Chain 1:   2100        -8469.368             0.017            0.014
Chain 1:   2200        -8249.393             0.016            0.014
Chain 1:   2300        -8405.570             0.016            0.014
Chain 1:   2400        -8419.147             0.016            0.014
Chain 1:   2500        -8389.063             0.015            0.013
Chain 1:   2600        -8391.750             0.014            0.012
Chain 1:   2700        -8297.965             0.014            0.012
Chain 1:   2800        -8268.801             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003319 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418143.304             1.000            1.000
Chain 1:    200     -1583638.732             2.658            4.316
Chain 1:    300      -890598.092             2.031            1.000
Chain 1:    400      -457935.722             1.760            1.000
Chain 1:    500      -358099.260             1.463            0.945
Chain 1:    600      -232981.295             1.309            0.945
Chain 1:    700      -119148.644             1.259            0.945
Chain 1:    800       -86386.904             1.149            0.945
Chain 1:    900       -66717.361             1.054            0.778
Chain 1:   1000       -51511.474             0.978            0.778
Chain 1:   1100       -38993.785             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38165.429             0.481            0.379
Chain 1:   1300       -26125.785             0.449            0.379
Chain 1:   1400       -25843.939             0.355            0.321
Chain 1:   1500       -22433.523             0.343            0.321
Chain 1:   1600       -21650.704             0.293            0.295
Chain 1:   1700       -20524.833             0.203            0.295
Chain 1:   1800       -20469.018             0.165            0.152
Chain 1:   1900       -20794.817             0.137            0.055
Chain 1:   2000       -19307.250             0.115            0.055
Chain 1:   2100       -19545.253             0.084            0.036
Chain 1:   2200       -19771.634             0.083            0.036
Chain 1:   2300       -19389.045             0.039            0.020
Chain 1:   2400       -19161.289             0.039            0.020
Chain 1:   2500       -18963.475             0.025            0.016
Chain 1:   2600       -18593.788             0.024            0.016
Chain 1:   2700       -18550.860             0.018            0.012
Chain 1:   2800       -18267.977             0.020            0.015
Chain 1:   2900       -18549.024             0.020            0.015
Chain 1:   3000       -18535.183             0.012            0.012
Chain 1:   3100       -18620.143             0.011            0.012
Chain 1:   3200       -18311.009             0.012            0.015
Chain 1:   3300       -18515.600             0.011            0.012
Chain 1:   3400       -17990.948             0.013            0.015
Chain 1:   3500       -18602.219             0.015            0.015
Chain 1:   3600       -17909.691             0.017            0.015
Chain 1:   3700       -18295.898             0.019            0.017
Chain 1:   3800       -17256.895             0.023            0.021
Chain 1:   3900       -17253.117             0.022            0.021
Chain 1:   4000       -17370.372             0.022            0.021
Chain 1:   4100       -17284.223             0.022            0.021
Chain 1:   4200       -17100.770             0.022            0.021
Chain 1:   4300       -17238.925             0.021            0.021
Chain 1:   4400       -17195.959             0.019            0.011
Chain 1:   4500       -17098.581             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001278 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12331.556             1.000            1.000
Chain 1:    200        -8965.088             0.688            1.000
Chain 1:    300        -7930.780             0.502            0.376
Chain 1:    400        -8097.689             0.382            0.376
Chain 1:    500        -8065.896             0.306            0.130
Chain 1:    600        -7870.274             0.259            0.130
Chain 1:    700        -7914.067             0.223            0.025
Chain 1:    800        -7807.379             0.197            0.025
Chain 1:    900        -7888.878             0.176            0.021
Chain 1:   1000        -7856.444             0.159            0.021
Chain 1:   1100        -7916.715             0.060            0.014
Chain 1:   1200        -7818.535             0.023            0.013
Chain 1:   1300        -7841.112             0.011            0.010
Chain 1:   1400        -7798.823             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62806.572             1.000            1.000
Chain 1:    200       -17972.972             1.747            2.495
Chain 1:    300        -8664.332             1.523            1.074
Chain 1:    400        -8314.953             1.153            1.074
Chain 1:    500        -8447.457             0.925            1.000
Chain 1:    600        -8848.666             0.779            1.000
Chain 1:    700        -8323.597             0.676            0.063
Chain 1:    800        -7874.663             0.599            0.063
Chain 1:    900        -7753.685             0.534            0.057
Chain 1:   1000        -7764.207             0.481            0.057
Chain 1:   1100        -7621.633             0.383            0.045
Chain 1:   1200        -7599.514             0.134            0.042
Chain 1:   1300        -7539.089             0.027            0.019
Chain 1:   1400        -7868.506             0.027            0.019
Chain 1:   1500        -7571.043             0.029            0.039
Chain 1:   1600        -7473.853             0.026            0.019
Chain 1:   1700        -7469.342             0.020            0.016
Chain 1:   1800        -7568.181             0.015            0.013
Chain 1:   1900        -7574.132             0.014            0.013
Chain 1:   2000        -7566.460             0.014            0.013
Chain 1:   2100        -7569.183             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003292 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85811.892             1.000            1.000
Chain 1:    200       -13277.408             3.232            5.463
Chain 1:    300        -9713.781             2.277            1.000
Chain 1:    400       -10443.312             1.725            1.000
Chain 1:    500        -8620.278             1.422            0.367
Chain 1:    600        -8302.776             1.192            0.367
Chain 1:    700        -8622.659             1.027            0.211
Chain 1:    800        -8583.613             0.899            0.211
Chain 1:    900        -8555.150             0.799            0.070
Chain 1:   1000        -8373.522             0.722            0.070
Chain 1:   1100        -8580.259             0.624            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8246.869             0.082            0.038
Chain 1:   1300        -8429.836             0.047            0.037
Chain 1:   1400        -8429.218             0.040            0.024
Chain 1:   1500        -8328.076             0.020            0.022
Chain 1:   1600        -8427.644             0.018            0.022
Chain 1:   1700        -8515.114             0.015            0.012
Chain 1:   1800        -8122.520             0.019            0.022
Chain 1:   1900        -8224.581             0.020            0.022
Chain 1:   2000        -8194.869             0.018            0.012
Chain 1:   2100        -8320.832             0.018            0.012
Chain 1:   2200        -8106.356             0.016            0.012
Chain 1:   2300        -8253.324             0.016            0.012
Chain 1:   2400        -8268.775             0.016            0.012
Chain 1:   2500        -8235.443             0.015            0.012
Chain 1:   2600        -8237.404             0.014            0.012
Chain 1:   2700        -8144.251             0.014            0.012
Chain 1:   2800        -8117.150             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419708.778             1.000            1.000
Chain 1:    200     -1586064.897             2.654            4.309
Chain 1:    300      -890928.198             2.030            1.000
Chain 1:    400      -457683.273             1.759            1.000
Chain 1:    500      -357645.474             1.463            0.947
Chain 1:    600      -232673.472             1.309            0.947
Chain 1:    700      -118917.381             1.258            0.947
Chain 1:    800       -86134.515             1.149            0.947
Chain 1:    900       -66487.082             1.054            0.780
Chain 1:   1000       -51293.019             0.978            0.780
Chain 1:   1100       -38783.359             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37957.158             0.482            0.381
Chain 1:   1300       -25936.053             0.450            0.381
Chain 1:   1400       -25655.548             0.356            0.323
Chain 1:   1500       -22248.988             0.344            0.323
Chain 1:   1600       -21466.857             0.294            0.296
Chain 1:   1700       -20343.871             0.204            0.296
Chain 1:   1800       -20288.549             0.166            0.153
Chain 1:   1900       -20614.252             0.138            0.055
Chain 1:   2000       -19127.963             0.116            0.055
Chain 1:   2100       -19366.140             0.085            0.036
Chain 1:   2200       -19592.020             0.084            0.036
Chain 1:   2300       -19209.876             0.040            0.020
Chain 1:   2400       -18982.152             0.040            0.020
Chain 1:   2500       -18784.078             0.025            0.016
Chain 1:   2600       -18414.747             0.024            0.016
Chain 1:   2700       -18371.909             0.018            0.012
Chain 1:   2800       -18088.887             0.020            0.016
Chain 1:   2900       -18369.939             0.020            0.015
Chain 1:   3000       -18356.182             0.012            0.012
Chain 1:   3100       -18441.073             0.011            0.012
Chain 1:   3200       -18132.056             0.012            0.015
Chain 1:   3300       -18336.560             0.011            0.012
Chain 1:   3400       -17811.972             0.013            0.015
Chain 1:   3500       -18423.040             0.015            0.016
Chain 1:   3600       -17730.811             0.017            0.016
Chain 1:   3700       -18116.760             0.019            0.017
Chain 1:   3800       -17078.088             0.023            0.021
Chain 1:   3900       -17074.267             0.022            0.021
Chain 1:   4000       -17191.590             0.022            0.021
Chain 1:   4100       -17105.394             0.022            0.021
Chain 1:   4200       -16922.022             0.022            0.021
Chain 1:   4300       -17060.153             0.021            0.021
Chain 1:   4400       -17017.270             0.019            0.011
Chain 1:   4500       -16919.856             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001442 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11959.651             1.000            1.000
Chain 1:    200        -8904.938             0.672            1.000
Chain 1:    300        -7733.487             0.498            0.343
Chain 1:    400        -7911.954             0.379            0.343
Chain 1:    500        -7954.925             0.304            0.151
Chain 1:    600        -7811.393             0.257            0.151
Chain 1:    700        -7601.833             0.224            0.028
Chain 1:    800        -7620.132             0.196            0.028
Chain 1:    900        -7578.550             0.175            0.023
Chain 1:   1000        -7654.008             0.159            0.023
Chain 1:   1100        -7721.495             0.059            0.018
Chain 1:   1200        -7610.895             0.027            0.015
Chain 1:   1300        -7636.008             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001388 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -45968.437             1.000            1.000
Chain 1:    200       -15127.219             1.519            2.039
Chain 1:    300        -8470.176             1.275            1.000
Chain 1:    400        -8325.955             0.961            1.000
Chain 1:    500        -8129.377             0.773            0.786
Chain 1:    600        -7785.524             0.652            0.786
Chain 1:    700        -8238.468             0.566            0.055
Chain 1:    800        -8169.382             0.497            0.055
Chain 1:    900        -7816.902             0.447            0.045
Chain 1:   1000        -7790.323             0.402            0.045
Chain 1:   1100        -7592.038             0.305            0.044
Chain 1:   1200        -7686.821             0.102            0.026
Chain 1:   1300        -7623.366             0.024            0.024
Chain 1:   1400        -7605.098             0.023            0.024
Chain 1:   1500        -7566.165             0.021            0.012
Chain 1:   1600        -7482.889             0.018            0.011
Chain 1:   1700        -7481.854             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85932.404             1.000            1.000
Chain 1:    200       -13048.643             3.293            5.586
Chain 1:    300        -9482.312             2.321            1.000
Chain 1:    400       -10311.089             1.761            1.000
Chain 1:    500        -8415.583             1.453            0.376
Chain 1:    600        -8032.948             1.219            0.376
Chain 1:    700        -8338.094             1.050            0.225
Chain 1:    800        -8469.815             0.921            0.225
Chain 1:    900        -8388.539             0.820            0.080
Chain 1:   1000        -8066.158             0.742            0.080
Chain 1:   1100        -8382.891             0.645            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8035.295             0.091            0.043
Chain 1:   1300        -8117.027             0.055            0.040
Chain 1:   1400        -8262.250             0.048            0.038
Chain 1:   1500        -8103.693             0.028            0.037
Chain 1:   1600        -8214.672             0.024            0.020
Chain 1:   1700        -8295.419             0.022            0.018
Chain 1:   1800        -7905.003             0.025            0.020
Chain 1:   1900        -8008.333             0.025            0.020
Chain 1:   2000        -7977.834             0.022            0.018
Chain 1:   2100        -8107.105             0.020            0.016
Chain 1:   2200        -7893.691             0.018            0.016
Chain 1:   2300        -8036.845             0.019            0.018
Chain 1:   2400        -8050.800             0.017            0.016
Chain 1:   2500        -8018.003             0.016            0.014
Chain 1:   2600        -8019.039             0.014            0.013
Chain 1:   2700        -7926.512             0.014            0.013
Chain 1:   2800        -7901.061             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002531 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8377178.433             1.000            1.000
Chain 1:    200     -1579989.473             2.651            4.302
Chain 1:    300      -889870.950             2.026            1.000
Chain 1:    400      -457146.052             1.756            1.000
Chain 1:    500      -357831.927             1.460            0.947
Chain 1:    600      -232862.969             1.306            0.947
Chain 1:    700      -118950.103             1.257            0.947
Chain 1:    800       -86103.943             1.147            0.947
Chain 1:    900       -66410.339             1.053            0.776
Chain 1:   1000       -51175.584             0.977            0.776
Chain 1:   1100       -38622.510             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37793.666             0.482            0.381
Chain 1:   1300       -25728.657             0.451            0.381
Chain 1:   1400       -25443.811             0.357            0.325
Chain 1:   1500       -22025.368             0.345            0.325
Chain 1:   1600       -21239.388             0.295            0.298
Chain 1:   1700       -20111.102             0.205            0.297
Chain 1:   1800       -20054.583             0.167            0.155
Chain 1:   1900       -20380.284             0.139            0.056
Chain 1:   2000       -18891.169             0.117            0.056
Chain 1:   2100       -19129.570             0.086            0.037
Chain 1:   2200       -19355.812             0.085            0.037
Chain 1:   2300       -18973.342             0.040            0.020
Chain 1:   2400       -18745.565             0.040            0.020
Chain 1:   2500       -18547.642             0.026            0.016
Chain 1:   2600       -18178.236             0.024            0.016
Chain 1:   2700       -18135.383             0.019            0.012
Chain 1:   2800       -17852.416             0.020            0.016
Chain 1:   2900       -18133.566             0.020            0.016
Chain 1:   3000       -18119.736             0.012            0.012
Chain 1:   3100       -18204.623             0.011            0.012
Chain 1:   3200       -17895.652             0.012            0.016
Chain 1:   3300       -18100.138             0.011            0.012
Chain 1:   3400       -17575.629             0.013            0.016
Chain 1:   3500       -18186.674             0.015            0.016
Chain 1:   3600       -17494.530             0.017            0.016
Chain 1:   3700       -17880.457             0.019            0.017
Chain 1:   3800       -16841.938             0.024            0.022
Chain 1:   3900       -16838.154             0.022            0.022
Chain 1:   4000       -16955.444             0.023            0.022
Chain 1:   4100       -16869.249             0.023            0.022
Chain 1:   4200       -16685.925             0.022            0.022
Chain 1:   4300       -16824.016             0.022            0.022
Chain 1:   4400       -16781.167             0.019            0.011
Chain 1:   4500       -16683.771             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001406 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12896.666             1.000            1.000
Chain 1:    200        -9773.187             0.660            1.000
Chain 1:    300        -8443.890             0.492            0.320
Chain 1:    400        -8612.393             0.374            0.320
Chain 1:    500        -8592.927             0.300            0.157
Chain 1:    600        -8367.205             0.254            0.157
Chain 1:    700        -8448.625             0.219            0.027
Chain 1:    800        -8274.410             0.195            0.027
Chain 1:    900        -8399.505             0.175            0.021
Chain 1:   1000        -8375.973             0.157            0.021
Chain 1:   1100        -8401.752             0.058            0.020
Chain 1:   1200        -8304.344             0.027            0.015
Chain 1:   1300        -8391.403             0.012            0.012
Chain 1:   1400        -8283.684             0.012            0.012
Chain 1:   1500        -8388.188             0.013            0.012
Chain 1:   1600        -8313.123             0.011            0.012
Chain 1:   1700        -8265.409             0.010            0.012
Chain 1:   1800        -8239.737             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001404 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56701.544             1.000            1.000
Chain 1:    200       -17753.129             1.597            2.194
Chain 1:    300        -8919.232             1.395            1.000
Chain 1:    400        -8347.419             1.063            1.000
Chain 1:    500        -8789.820             0.861            0.990
Chain 1:    600        -9114.681             0.723            0.990
Chain 1:    700        -8014.449             0.639            0.137
Chain 1:    800        -8106.425             0.561            0.137
Chain 1:    900        -7961.890             0.501            0.069
Chain 1:   1000        -8014.324             0.451            0.069
Chain 1:   1100        -7802.554             0.354            0.050
Chain 1:   1200        -7678.233             0.136            0.036
Chain 1:   1300        -7646.835             0.038            0.027
Chain 1:   1400        -7791.847             0.033            0.019
Chain 1:   1500        -7557.354             0.031            0.019
Chain 1:   1600        -7629.681             0.028            0.018
Chain 1:   1700        -7624.840             0.014            0.016
Chain 1:   1800        -7551.525             0.014            0.016
Chain 1:   1900        -7594.060             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002538 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86580.763             1.000            1.000
Chain 1:    200       -14063.169             3.078            5.157
Chain 1:    300       -10359.806             2.171            1.000
Chain 1:    400       -11553.965             1.654            1.000
Chain 1:    500        -9345.330             1.371            0.357
Chain 1:    600        -8945.776             1.150            0.357
Chain 1:    700        -8783.712             0.988            0.236
Chain 1:    800        -9339.151             0.872            0.236
Chain 1:    900        -9100.245             0.778            0.103
Chain 1:   1000        -8910.439             0.702            0.103
Chain 1:   1100        -9120.990             0.605            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8627.583             0.095            0.057
Chain 1:   1300        -8862.696             0.062            0.045
Chain 1:   1400        -8975.805             0.053            0.027
Chain 1:   1500        -8887.993             0.030            0.026
Chain 1:   1600        -8998.110             0.027            0.023
Chain 1:   1700        -9056.732             0.026            0.023
Chain 1:   1800        -8622.554             0.025            0.023
Chain 1:   1900        -8726.792             0.023            0.021
Chain 1:   2000        -8702.052             0.021            0.013
Chain 1:   2100        -8670.418             0.019            0.012
Chain 1:   2200        -8645.077             0.014            0.012
Chain 1:   2300        -8780.549             0.013            0.012
Chain 1:   2400        -8627.499             0.013            0.012
Chain 1:   2500        -8696.769             0.013            0.012
Chain 1:   2600        -8614.911             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002595 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393494.247             1.000            1.000
Chain 1:    200     -1584941.882             2.648            4.296
Chain 1:    300      -891708.669             2.024            1.000
Chain 1:    400      -458443.859             1.755            1.000
Chain 1:    500      -358775.947             1.459            0.945
Chain 1:    600      -233681.528             1.305            0.945
Chain 1:    700      -119864.243             1.254            0.945
Chain 1:    800       -87066.599             1.145            0.945
Chain 1:    900       -67402.628             1.050            0.777
Chain 1:   1000       -52197.316             0.974            0.777
Chain 1:   1100       -39666.396             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38847.849             0.478            0.377
Chain 1:   1300       -26788.899             0.445            0.377
Chain 1:   1400       -26508.985             0.352            0.316
Chain 1:   1500       -23092.070             0.339            0.316
Chain 1:   1600       -22307.944             0.289            0.292
Chain 1:   1700       -21179.467             0.199            0.291
Chain 1:   1800       -21123.500             0.162            0.148
Chain 1:   1900       -21449.994             0.134            0.053
Chain 1:   2000       -19959.412             0.113            0.053
Chain 1:   2100       -20197.878             0.082            0.035
Chain 1:   2200       -20424.759             0.081            0.035
Chain 1:   2300       -20041.528             0.038            0.019
Chain 1:   2400       -19813.473             0.038            0.019
Chain 1:   2500       -19615.548             0.024            0.015
Chain 1:   2600       -19245.281             0.023            0.015
Chain 1:   2700       -19202.179             0.018            0.012
Chain 1:   2800       -18918.853             0.019            0.015
Chain 1:   2900       -19200.354             0.019            0.015
Chain 1:   3000       -19186.487             0.012            0.012
Chain 1:   3100       -19271.496             0.011            0.012
Chain 1:   3200       -18961.937             0.011            0.015
Chain 1:   3300       -19166.893             0.010            0.012
Chain 1:   3400       -18641.355             0.012            0.015
Chain 1:   3500       -19253.885             0.014            0.015
Chain 1:   3600       -18559.798             0.016            0.015
Chain 1:   3700       -18947.157             0.018            0.016
Chain 1:   3800       -17905.594             0.022            0.020
Chain 1:   3900       -17901.730             0.021            0.020
Chain 1:   4000       -18019.039             0.021            0.020
Chain 1:   4100       -17932.686             0.021            0.020
Chain 1:   4200       -17748.705             0.021            0.020
Chain 1:   4300       -17887.249             0.021            0.020
Chain 1:   4400       -17843.848             0.018            0.010
Chain 1:   4500       -17746.366             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001253 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48676.708             1.000            1.000
Chain 1:    200       -16979.975             1.433            1.867
Chain 1:    300       -13020.513             1.057            1.000
Chain 1:    400       -18748.355             0.869            1.000
Chain 1:    500       -13203.468             0.779            0.420
Chain 1:    600       -15825.010             0.677            0.420
Chain 1:    700       -14099.523             0.598            0.306
Chain 1:    800       -13521.491             0.528            0.306
Chain 1:    900       -15283.978             0.482            0.304
Chain 1:   1000       -20728.602             0.461            0.304
Chain 1:   1100       -10136.848             0.465            0.304
Chain 1:   1200       -19473.280             0.326            0.304
Chain 1:   1300       -11329.829             0.368            0.306
Chain 1:   1400        -9795.132             0.353            0.263
Chain 1:   1500        -9993.968             0.313            0.166
Chain 1:   1600       -12064.656             0.313            0.172
Chain 1:   1700        -9754.746             0.325            0.237
Chain 1:   1800       -11958.676             0.339            0.237
Chain 1:   1900       -11086.069             0.335            0.237
Chain 1:   2000       -10819.137             0.312            0.184
Chain 1:   2100        -9654.973             0.219            0.172
Chain 1:   2200       -10351.403             0.178            0.157
Chain 1:   2300       -11926.693             0.119            0.132
Chain 1:   2400        -9234.265             0.133            0.132
Chain 1:   2500        -9532.362             0.134            0.132
Chain 1:   2600        -9204.444             0.120            0.121
Chain 1:   2700        -9120.623             0.098            0.079
Chain 1:   2800        -9539.362             0.083            0.067
Chain 1:   2900        -9721.449             0.077            0.044
Chain 1:   3000        -9297.046             0.080            0.046
Chain 1:   3100        -9640.434             0.071            0.044
Chain 1:   3200        -8740.948             0.075            0.044
Chain 1:   3300        -9521.616             0.070            0.044
Chain 1:   3400       -10383.817             0.049            0.044
Chain 1:   3500        -9026.610             0.061            0.046
Chain 1:   3600        -9912.389             0.066            0.082
Chain 1:   3700        -8796.545             0.078            0.083
Chain 1:   3800       -10263.382             0.088            0.089
Chain 1:   3900        -8850.485             0.102            0.103
Chain 1:   4000        -9101.907             0.100            0.103
Chain 1:   4100        -8861.281             0.099            0.103
Chain 1:   4200       -11784.489             0.114            0.127
Chain 1:   4300        -9322.198             0.132            0.143
Chain 1:   4400        -8620.681             0.132            0.143
Chain 1:   4500        -9173.670             0.123            0.127
Chain 1:   4600       -10408.055             0.126            0.127
Chain 1:   4700        -8916.812             0.130            0.143
Chain 1:   4800        -8612.765             0.119            0.119
Chain 1:   4900       -13619.456             0.140            0.119
Chain 1:   5000       -11052.460             0.160            0.167
Chain 1:   5100        -8908.399             0.182            0.232
Chain 1:   5200       -14448.724             0.195            0.232
Chain 1:   5300       -13936.967             0.172            0.167
Chain 1:   5400        -8483.710             0.228            0.232
Chain 1:   5500        -8417.294             0.223            0.232
Chain 1:   5600        -8512.270             0.213            0.232
Chain 1:   5700       -10377.926             0.214            0.232
Chain 1:   5800        -8503.713             0.232            0.232
Chain 1:   5900       -16585.030             0.244            0.232
Chain 1:   6000        -9031.503             0.305            0.241
Chain 1:   6100        -8890.589             0.282            0.220
Chain 1:   6200        -8954.073             0.245            0.180
Chain 1:   6300       -13108.043             0.273            0.220
Chain 1:   6400        -9616.120             0.245            0.220
Chain 1:   6500        -9381.664             0.246            0.220
Chain 1:   6600       -10410.593             0.255            0.220
Chain 1:   6700        -8764.281             0.256            0.220
Chain 1:   6800        -8386.639             0.238            0.188
Chain 1:   6900        -9079.064             0.197            0.099
Chain 1:   7000        -8441.446             0.121            0.076
Chain 1:   7100        -8568.189             0.121            0.076
Chain 1:   7200        -9312.427             0.128            0.080
Chain 1:   7300        -8363.087             0.108            0.080
Chain 1:   7400        -8600.526             0.074            0.076
Chain 1:   7500        -9169.179             0.078            0.076
Chain 1:   7600        -8426.849             0.077            0.076
Chain 1:   7700        -8577.834             0.060            0.076
Chain 1:   7800       -10298.422             0.072            0.076
Chain 1:   7900        -8264.886             0.089            0.080
Chain 1:   8000        -9165.352             0.091            0.088
Chain 1:   8100       -12914.854             0.119            0.098
Chain 1:   8200        -8258.604             0.167            0.114
Chain 1:   8300       -12472.265             0.190            0.167
Chain 1:   8400       -10422.553             0.207            0.197
Chain 1:   8500       -12613.396             0.218            0.197
Chain 1:   8600        -8788.817             0.253            0.246
Chain 1:   8700        -9719.817             0.260            0.246
Chain 1:   8800        -8976.449             0.252            0.246
Chain 1:   8900        -8672.759             0.231            0.197
Chain 1:   9000        -9237.332             0.227            0.197
Chain 1:   9100        -8574.130             0.206            0.174
Chain 1:   9200        -9873.282             0.163            0.132
Chain 1:   9300        -9693.678             0.131            0.096
Chain 1:   9400        -9503.921             0.113            0.083
Chain 1:   9500        -8355.144             0.109            0.083
Chain 1:   9600        -9027.533             0.073            0.077
Chain 1:   9700        -8470.316             0.070            0.074
Chain 1:   9800        -9131.897             0.069            0.072
Chain 1:   9900        -8551.594             0.073            0.072
Chain 1:   10000        -8295.369             0.070            0.072
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56961.448             1.000            1.000
Chain 1:    200       -17283.471             1.648            2.296
Chain 1:    300        -8657.571             1.431            1.000
Chain 1:    400        -8354.595             1.082            1.000
Chain 1:    500        -8540.017             0.870            0.996
Chain 1:    600        -8842.776             0.731            0.996
Chain 1:    700        -8347.774             0.635            0.059
Chain 1:    800        -7969.139             0.561            0.059
Chain 1:    900        -7837.734             0.501            0.048
Chain 1:   1000        -7869.797             0.451            0.048
Chain 1:   1100        -7620.084             0.354            0.036
Chain 1:   1200        -7734.625             0.126            0.034
Chain 1:   1300        -7728.201             0.027            0.033
Chain 1:   1400        -7667.763             0.024            0.022
Chain 1:   1500        -7582.353             0.023            0.017
Chain 1:   1600        -7758.655             0.022            0.017
Chain 1:   1700        -7491.591             0.019            0.017
Chain 1:   1800        -7585.572             0.016            0.015
Chain 1:   1900        -7616.018             0.015            0.012
Chain 1:   2000        -7643.665             0.015            0.012
Chain 1:   2100        -7601.964             0.012            0.011
Chain 1:   2200        -7685.550             0.011            0.011
Chain 1:   2300        -7591.698             0.013            0.011
Chain 1:   2400        -7630.190             0.012            0.011
Chain 1:   2500        -7566.629             0.012            0.011
Chain 1:   2600        -7514.412             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002997 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86312.179             1.000            1.000
Chain 1:    200       -13405.906             3.219            5.438
Chain 1:    300        -9849.817             2.266            1.000
Chain 1:    400       -10692.472             1.720            1.000
Chain 1:    500        -8714.128             1.421            0.361
Chain 1:    600        -8553.829             1.187            0.361
Chain 1:    700        -8576.390             1.018            0.227
Chain 1:    800        -8782.636             0.894            0.227
Chain 1:    900        -8670.259             0.796            0.079
Chain 1:   1000        -8447.156             0.719            0.079
Chain 1:   1100        -8714.606             0.622            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8454.950             0.081            0.031
Chain 1:   1300        -8568.715             0.046            0.026
Chain 1:   1400        -8574.321             0.039            0.023
Chain 1:   1500        -8447.312             0.017            0.019
Chain 1:   1600        -8554.319             0.017            0.015
Chain 1:   1700        -8639.616             0.018            0.015
Chain 1:   1800        -8248.195             0.020            0.015
Chain 1:   1900        -8349.536             0.020            0.015
Chain 1:   2000        -8320.047             0.018            0.013
Chain 1:   2100        -8445.184             0.016            0.013
Chain 1:   2200        -8230.017             0.016            0.013
Chain 1:   2300        -8378.376             0.016            0.015
Chain 1:   2400        -8393.654             0.016            0.015
Chain 1:   2500        -8360.799             0.015            0.013
Chain 1:   2600        -8362.892             0.014            0.012
Chain 1:   2700        -8269.592             0.014            0.012
Chain 1:   2800        -8242.131             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003007 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401566.255             1.000            1.000
Chain 1:    200     -1583625.342             2.653            4.305
Chain 1:    300      -889790.641             2.028            1.000
Chain 1:    400      -456615.034             1.758            1.000
Chain 1:    500      -357108.412             1.462            0.949
Chain 1:    600      -232210.686             1.308            0.949
Chain 1:    700      -118799.347             1.258            0.949
Chain 1:    800       -86114.279             1.148            0.949
Chain 1:    900       -66520.084             1.053            0.780
Chain 1:   1000       -51360.559             0.977            0.780
Chain 1:   1100       -38878.841             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38056.888             0.481            0.380
Chain 1:   1300       -26054.185             0.449            0.380
Chain 1:   1400       -25775.474             0.355            0.321
Chain 1:   1500       -22374.114             0.343            0.321
Chain 1:   1600       -21593.731             0.293            0.295
Chain 1:   1700       -20472.495             0.203            0.295
Chain 1:   1800       -20417.617             0.165            0.152
Chain 1:   1900       -20743.479             0.137            0.055
Chain 1:   2000       -19257.983             0.115            0.055
Chain 1:   2100       -19496.059             0.084            0.036
Chain 1:   2200       -19721.999             0.083            0.036
Chain 1:   2300       -19339.747             0.039            0.020
Chain 1:   2400       -19112.034             0.039            0.020
Chain 1:   2500       -18913.972             0.025            0.016
Chain 1:   2600       -18544.680             0.024            0.016
Chain 1:   2700       -18501.789             0.018            0.012
Chain 1:   2800       -18218.861             0.020            0.016
Chain 1:   2900       -18499.799             0.020            0.015
Chain 1:   3000       -18486.035             0.012            0.012
Chain 1:   3100       -18570.991             0.011            0.012
Chain 1:   3200       -18261.955             0.012            0.015
Chain 1:   3300       -18466.445             0.011            0.012
Chain 1:   3400       -17941.887             0.013            0.015
Chain 1:   3500       -18552.988             0.015            0.016
Chain 1:   3600       -17860.621             0.017            0.016
Chain 1:   3700       -18246.724             0.019            0.017
Chain 1:   3800       -17207.945             0.023            0.021
Chain 1:   3900       -17204.120             0.022            0.021
Chain 1:   4000       -17321.414             0.022            0.021
Chain 1:   4100       -17235.302             0.022            0.021
Chain 1:   4200       -17051.858             0.022            0.021
Chain 1:   4300       -17190.039             0.021            0.021
Chain 1:   4400       -17147.129             0.019            0.011
Chain 1:   4500       -17049.705             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001509 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12901.149             1.000            1.000
Chain 1:    200        -9748.897             0.662            1.000
Chain 1:    300        -8324.739             0.498            0.323
Chain 1:    400        -8552.903             0.380            0.323
Chain 1:    500        -8451.229             0.307            0.171
Chain 1:    600        -8279.859             0.259            0.171
Chain 1:    700        -8177.726             0.224            0.027
Chain 1:    800        -8245.688             0.197            0.027
Chain 1:    900        -8088.297             0.177            0.021
Chain 1:   1000        -8305.349             0.162            0.026
Chain 1:   1100        -8335.030             0.062            0.021
Chain 1:   1200        -8225.081             0.031            0.019
Chain 1:   1300        -8159.726             0.015            0.013
Chain 1:   1400        -8173.511             0.013            0.012
Chain 1:   1500        -8265.698             0.012            0.012
Chain 1:   1600        -8192.296             0.011            0.011
Chain 1:   1700        -8152.893             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00144 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50621.417             1.000            1.000
Chain 1:    200       -16600.298             1.525            2.049
Chain 1:    300        -9031.118             1.296            1.000
Chain 1:    400        -8300.280             0.994            1.000
Chain 1:    500        -8656.401             0.803            0.838
Chain 1:    600        -8880.166             0.674            0.838
Chain 1:    700        -8391.717             0.586            0.088
Chain 1:    800        -8027.268             0.518            0.088
Chain 1:    900        -8300.257             0.464            0.058
Chain 1:   1000        -7953.451             0.422            0.058
Chain 1:   1100        -7937.355             0.322            0.045
Chain 1:   1200        -8058.131             0.119            0.044
Chain 1:   1300        -7859.137             0.038            0.041
Chain 1:   1400        -7819.338             0.029            0.033
Chain 1:   1500        -7706.842             0.027            0.025
Chain 1:   1600        -7842.980             0.026            0.025
Chain 1:   1700        -7694.797             0.022            0.019
Chain 1:   1800        -7767.029             0.018            0.017
Chain 1:   1900        -7713.456             0.016            0.015
Chain 1:   2000        -7834.352             0.013            0.015
Chain 1:   2100        -7685.827             0.015            0.015
Chain 1:   2200        -7853.794             0.015            0.017
Chain 1:   2300        -7678.853             0.015            0.017
Chain 1:   2400        -7729.841             0.015            0.017
Chain 1:   2500        -7565.892             0.016            0.019
Chain 1:   2600        -7644.538             0.015            0.019
Chain 1:   2700        -7632.495             0.014            0.015
Chain 1:   2800        -7655.936             0.013            0.015
Chain 1:   2900        -7494.860             0.014            0.019
Chain 1:   3000        -7646.842             0.015            0.020
Chain 1:   3100        -7644.722             0.013            0.020
Chain 1:   3200        -7867.288             0.014            0.020
Chain 1:   3300        -7551.164             0.015            0.020
Chain 1:   3400        -7796.086             0.018            0.021
Chain 1:   3500        -7555.932             0.019            0.021
Chain 1:   3600        -7622.924             0.019            0.021
Chain 1:   3700        -7572.311             0.019            0.021
Chain 1:   3800        -7573.047             0.019            0.021
Chain 1:   3900        -7532.067             0.017            0.020
Chain 1:   4000        -7523.836             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002544 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86653.663             1.000            1.000
Chain 1:    200       -14046.298             3.085            5.169
Chain 1:    300       -10305.484             2.177            1.000
Chain 1:    400       -11717.358             1.663            1.000
Chain 1:    500        -9230.598             1.384            0.363
Chain 1:    600        -9027.732             1.157            0.363
Chain 1:    700        -9696.424             1.002            0.269
Chain 1:    800        -8768.608             0.890            0.269
Chain 1:    900        -8628.528             0.793            0.120
Chain 1:   1000        -9321.183             0.721            0.120
Chain 1:   1100        -8796.425             0.627            0.106   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9254.348             0.115            0.074
Chain 1:   1300        -8679.403             0.085            0.069
Chain 1:   1400        -8826.462             0.075            0.066
Chain 1:   1500        -8710.779             0.049            0.060
Chain 1:   1600        -8717.707             0.047            0.060
Chain 1:   1700        -8604.305             0.042            0.049
Chain 1:   1800        -8663.067             0.032            0.017
Chain 1:   1900        -8547.276             0.031            0.017
Chain 1:   2000        -8606.375             0.025            0.014
Chain 1:   2100        -8761.323             0.020            0.014
Chain 1:   2200        -8540.615             0.018            0.014
Chain 1:   2300        -8678.151             0.013            0.014
Chain 1:   2400        -8537.227             0.013            0.014
Chain 1:   2500        -8606.673             0.013            0.014
Chain 1:   2600        -8520.023             0.013            0.014
Chain 1:   2700        -8551.458             0.012            0.014
Chain 1:   2800        -8508.005             0.012            0.014
Chain 1:   2900        -8608.923             0.012            0.012
Chain 1:   3000        -8457.715             0.013            0.016
Chain 1:   3100        -8594.101             0.013            0.016
Chain 1:   3200        -8464.046             0.012            0.015
Chain 1:   3300        -8485.171             0.011            0.012
Chain 1:   3400        -8680.337             0.011            0.012
Chain 1:   3500        -8638.954             0.011            0.012
Chain 1:   3600        -8419.371             0.013            0.015
Chain 1:   3700        -8570.741             0.014            0.016
Chain 1:   3800        -8425.000             0.015            0.017
Chain 1:   3900        -8357.671             0.015            0.017
Chain 1:   4000        -8452.765             0.014            0.016
Chain 1:   4100        -8431.178             0.013            0.015
Chain 1:   4200        -8416.912             0.011            0.011
Chain 1:   4300        -8450.118             0.012            0.011
Chain 1:   4400        -8406.858             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002619 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396196.607             1.000            1.000
Chain 1:    200     -1581141.028             2.655            4.310
Chain 1:    300      -890966.436             2.028            1.000
Chain 1:    400      -458080.463             1.757            1.000
Chain 1:    500      -358747.443             1.461            0.945
Chain 1:    600      -233693.796             1.307            0.945
Chain 1:    700      -119870.910             1.256            0.945
Chain 1:    800       -87056.515             1.146            0.945
Chain 1:    900       -67385.613             1.051            0.775
Chain 1:   1000       -52183.472             0.975            0.775
Chain 1:   1100       -39652.824             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38834.353             0.478            0.377
Chain 1:   1300       -26773.160             0.445            0.377
Chain 1:   1400       -26493.812             0.352            0.316
Chain 1:   1500       -23075.491             0.339            0.316
Chain 1:   1600       -22291.014             0.289            0.292
Chain 1:   1700       -21162.007             0.199            0.291
Chain 1:   1800       -21106.037             0.162            0.148
Chain 1:   1900       -21432.670             0.134            0.053
Chain 1:   2000       -19941.307             0.113            0.053
Chain 1:   2100       -20179.957             0.082            0.035
Chain 1:   2200       -20406.928             0.081            0.035
Chain 1:   2300       -20023.543             0.038            0.019
Chain 1:   2400       -19795.393             0.038            0.019
Chain 1:   2500       -19597.401             0.024            0.015
Chain 1:   2600       -19227.066             0.023            0.015
Chain 1:   2700       -19183.899             0.018            0.012
Chain 1:   2800       -18900.498             0.019            0.015
Chain 1:   2900       -19181.998             0.019            0.015
Chain 1:   3000       -19168.201             0.012            0.012
Chain 1:   3100       -19253.256             0.011            0.012
Chain 1:   3200       -18943.583             0.011            0.015
Chain 1:   3300       -19148.569             0.010            0.012
Chain 1:   3400       -18622.871             0.012            0.015
Chain 1:   3500       -19235.720             0.014            0.015
Chain 1:   3600       -18541.127             0.016            0.015
Chain 1:   3700       -18928.865             0.018            0.016
Chain 1:   3800       -17886.615             0.022            0.020
Chain 1:   3900       -17882.696             0.021            0.020
Chain 1:   4000       -18000.006             0.021            0.020
Chain 1:   4100       -17913.665             0.021            0.020
Chain 1:   4200       -17729.489             0.021            0.020
Chain 1:   4300       -17868.194             0.021            0.020
Chain 1:   4400       -17824.657             0.018            0.010
Chain 1:   4500       -17727.126             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001343 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12219.665             1.000            1.000
Chain 1:    200        -9101.333             0.671            1.000
Chain 1:    300        -7884.928             0.499            0.343
Chain 1:    400        -7993.591             0.378            0.343
Chain 1:    500        -7937.294             0.304            0.154
Chain 1:    600        -7806.918             0.256            0.154
Chain 1:    700        -7978.158             0.222            0.021
Chain 1:    800        -7766.407             0.198            0.027
Chain 1:    900        -7726.809             0.176            0.021
Chain 1:   1000        -7800.513             0.160            0.021
Chain 1:   1100        -7847.796             0.060            0.017
Chain 1:   1200        -7766.024             0.027            0.014
Chain 1:   1300        -7724.808             0.012            0.011
Chain 1:   1400        -7732.351             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57660.202             1.000            1.000
Chain 1:    200       -17416.523             1.655            2.311
Chain 1:    300        -8533.749             1.451            1.041
Chain 1:    400        -8061.914             1.103            1.041
Chain 1:    500        -8367.378             0.889            1.000
Chain 1:    600        -8114.396             0.746            1.000
Chain 1:    700        -8074.468             0.640            0.059
Chain 1:    800        -8019.934             0.561            0.059
Chain 1:    900        -7928.894             0.500            0.037
Chain 1:   1000        -7862.666             0.451            0.037
Chain 1:   1100        -7779.677             0.352            0.031
Chain 1:   1200        -7786.613             0.121            0.011
Chain 1:   1300        -7747.993             0.017            0.011
Chain 1:   1400        -7613.508             0.013            0.011
Chain 1:   1500        -7519.864             0.011            0.011
Chain 1:   1600        -7500.068             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003049 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85954.504             1.000            1.000
Chain 1:    200       -13271.029             3.238            5.477
Chain 1:    300        -9667.270             2.283            1.000
Chain 1:    400       -10621.241             1.735            1.000
Chain 1:    500        -8612.007             1.435            0.373
Chain 1:    600        -8262.961             1.202            0.373
Chain 1:    700        -8335.133             1.032            0.233
Chain 1:    800        -8732.229             0.909            0.233
Chain 1:    900        -8545.919             0.810            0.090
Chain 1:   1000        -8226.561             0.733            0.090
Chain 1:   1100        -8550.790             0.637            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8155.568             0.094            0.045
Chain 1:   1300        -8374.347             0.059            0.042
Chain 1:   1400        -8363.051             0.050            0.039
Chain 1:   1500        -8265.486             0.028            0.038
Chain 1:   1600        -8371.128             0.025            0.026
Chain 1:   1700        -8460.386             0.025            0.026
Chain 1:   1800        -8057.486             0.026            0.026
Chain 1:   1900        -8156.552             0.025            0.026
Chain 1:   2000        -8127.811             0.021            0.013
Chain 1:   2100        -8247.685             0.019            0.013
Chain 1:   2200        -8039.902             0.017            0.013
Chain 1:   2300        -8189.593             0.016            0.013
Chain 1:   2400        -8067.500             0.017            0.015
Chain 1:   2500        -8131.618             0.017            0.015
Chain 1:   2600        -8154.531             0.016            0.015
Chain 1:   2700        -8073.235             0.016            0.015
Chain 1:   2800        -8046.321             0.011            0.012
Chain 1:   2900        -8101.743             0.011            0.010
Chain 1:   3000        -7985.440             0.012            0.015
Chain 1:   3100        -8123.830             0.012            0.015
Chain 1:   3200        -8003.436             0.011            0.015
Chain 1:   3300        -8025.323             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003204 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392125.080             1.000            1.000
Chain 1:    200     -1586191.363             2.645            4.291
Chain 1:    300      -891580.228             2.023            1.000
Chain 1:    400      -457576.119             1.755            1.000
Chain 1:    500      -357871.975             1.459            0.948
Chain 1:    600      -232767.437             1.306            0.948
Chain 1:    700      -118991.807             1.256            0.948
Chain 1:    800       -86188.272             1.146            0.948
Chain 1:    900       -66533.672             1.052            0.779
Chain 1:   1000       -51331.451             0.976            0.779
Chain 1:   1100       -38807.755             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37983.757             0.482            0.381
Chain 1:   1300       -25946.707             0.450            0.381
Chain 1:   1400       -25665.555             0.356            0.323
Chain 1:   1500       -22253.875             0.344            0.323
Chain 1:   1600       -21470.117             0.294            0.296
Chain 1:   1700       -20345.132             0.204            0.295
Chain 1:   1800       -20289.458             0.166            0.153
Chain 1:   1900       -20615.335             0.138            0.055
Chain 1:   2000       -19127.346             0.116            0.055
Chain 1:   2100       -19365.841             0.085            0.037
Chain 1:   2200       -19591.919             0.084            0.037
Chain 1:   2300       -19209.483             0.040            0.020
Chain 1:   2400       -18981.654             0.040            0.020
Chain 1:   2500       -18783.567             0.025            0.016
Chain 1:   2600       -18414.201             0.024            0.016
Chain 1:   2700       -18371.265             0.019            0.012
Chain 1:   2800       -18088.163             0.020            0.016
Chain 1:   2900       -18369.309             0.020            0.015
Chain 1:   3000       -18355.562             0.012            0.012
Chain 1:   3100       -18440.502             0.011            0.012
Chain 1:   3200       -18131.400             0.012            0.015
Chain 1:   3300       -18335.937             0.011            0.012
Chain 1:   3400       -17811.197             0.013            0.015
Chain 1:   3500       -18422.568             0.015            0.016
Chain 1:   3600       -17729.906             0.017            0.016
Chain 1:   3700       -18116.208             0.019            0.017
Chain 1:   3800       -17076.924             0.023            0.021
Chain 1:   3900       -17073.064             0.022            0.021
Chain 1:   4000       -17190.393             0.022            0.021
Chain 1:   4100       -17104.188             0.022            0.021
Chain 1:   4200       -16920.651             0.022            0.021
Chain 1:   4300       -17058.912             0.021            0.021
Chain 1:   4400       -17015.924             0.019            0.011
Chain 1:   4500       -16918.468             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001245 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49340.325             1.000            1.000
Chain 1:    200       -24298.982             1.015            1.031
Chain 1:    300       -19607.977             0.757            1.000
Chain 1:    400       -19899.263             0.571            1.000
Chain 1:    500       -14771.464             0.526            0.347
Chain 1:    600       -26811.648             0.513            0.449
Chain 1:    700       -13371.093             0.584            0.449
Chain 1:    800       -24514.549             0.568            0.455
Chain 1:    900       -11007.494             0.641            0.455
Chain 1:   1000       -13308.269             0.594            0.455
Chain 1:   1100       -14040.327             0.499            0.449
Chain 1:   1200       -16825.015             0.413            0.347
Chain 1:   1300       -12418.931             0.424            0.355
Chain 1:   1400       -10828.595             0.438            0.355
Chain 1:   1500       -10641.875             0.405            0.355
Chain 1:   1600       -11792.732             0.369            0.173
Chain 1:   1700       -10586.043             0.280            0.166
Chain 1:   1800       -12158.419             0.248            0.147
Chain 1:   1900       -12401.842             0.127            0.129
Chain 1:   2000       -11032.610             0.122            0.124
Chain 1:   2100       -11301.606             0.119            0.124
Chain 1:   2200       -11683.744             0.106            0.114
Chain 1:   2300       -10335.726             0.084            0.114
Chain 1:   2400       -11717.289             0.081            0.114
Chain 1:   2500       -10617.219             0.089            0.114
Chain 1:   2600       -13012.391             0.098            0.118
Chain 1:   2700       -13661.741             0.091            0.118
Chain 1:   2800       -16617.191             0.096            0.118
Chain 1:   2900       -14557.882             0.108            0.124
Chain 1:   3000       -16972.476             0.110            0.130
Chain 1:   3100       -12750.360             0.141            0.141
Chain 1:   3200       -10435.589             0.160            0.142
Chain 1:   3300        -9797.670             0.153            0.142
Chain 1:   3400       -10655.996             0.150            0.142
Chain 1:   3500       -10438.818             0.141            0.142
Chain 1:   3600       -18501.275             0.166            0.142
Chain 1:   3700       -10267.012             0.242            0.178
Chain 1:   3800        -9180.492             0.236            0.142
Chain 1:   3900        -9303.724             0.223            0.142
Chain 1:   4000        -9430.508             0.210            0.118
Chain 1:   4100        -9470.621             0.178            0.081
Chain 1:   4200       -10925.262             0.169            0.081
Chain 1:   4300       -13705.157             0.182            0.118
Chain 1:   4400        -9736.312             0.215            0.133
Chain 1:   4500       -11246.258             0.226            0.134
Chain 1:   4600       -10177.074             0.193            0.133
Chain 1:   4700       -11842.618             0.127            0.133
Chain 1:   4800        -9060.171             0.146            0.134
Chain 1:   4900        -9495.890             0.149            0.134
Chain 1:   5000       -15451.632             0.187            0.141
Chain 1:   5100        -9358.847             0.251            0.203
Chain 1:   5200        -9584.459             0.240            0.203
Chain 1:   5300       -14628.818             0.255            0.307
Chain 1:   5400        -8846.853             0.279            0.307
Chain 1:   5500       -14917.191             0.306            0.345
Chain 1:   5600       -13587.456             0.306            0.345
Chain 1:   5700       -12764.963             0.298            0.345
Chain 1:   5800       -13479.273             0.273            0.345
Chain 1:   5900       -11324.206             0.287            0.345
Chain 1:   6000        -8912.693             0.276            0.271
Chain 1:   6100        -9822.457             0.220            0.190
Chain 1:   6200       -10365.862             0.223            0.190
Chain 1:   6300        -9498.923             0.197            0.098
Chain 1:   6400       -12781.810             0.158            0.098
Chain 1:   6500       -12812.484             0.117            0.093
Chain 1:   6600        -8916.847             0.151            0.093
Chain 1:   6700        -9518.501             0.151            0.093
Chain 1:   6800        -9320.539             0.148            0.093
Chain 1:   6900        -9517.915             0.131            0.091
Chain 1:   7000        -8778.888             0.112            0.084
Chain 1:   7100        -8684.132             0.104            0.063
Chain 1:   7200       -10858.032             0.119            0.084
Chain 1:   7300       -11966.535             0.119            0.084
Chain 1:   7400        -9615.719             0.118            0.084
Chain 1:   7500       -11148.988             0.131            0.093
Chain 1:   7600        -8654.914             0.116            0.093
Chain 1:   7700        -8864.849             0.112            0.093
Chain 1:   7800        -9460.619             0.117            0.093
Chain 1:   7900        -8995.896             0.120            0.093
Chain 1:   8000        -8526.051             0.117            0.093
Chain 1:   8100        -9479.361             0.126            0.101
Chain 1:   8200        -8936.850             0.112            0.093
Chain 1:   8300        -8859.576             0.103            0.063
Chain 1:   8400       -10338.356             0.093            0.063
Chain 1:   8500        -9511.503             0.088            0.063
Chain 1:   8600        -8937.156             0.066            0.063
Chain 1:   8700        -9295.154             0.067            0.063
Chain 1:   8800        -8718.603             0.068            0.064
Chain 1:   8900       -14132.523             0.101            0.066
Chain 1:   9000        -9418.813             0.145            0.087
Chain 1:   9100        -8625.799             0.144            0.087
Chain 1:   9200        -8871.349             0.141            0.087
Chain 1:   9300        -8960.434             0.141            0.087
Chain 1:   9400        -9217.794             0.130            0.066
Chain 1:   9500        -9424.335             0.123            0.064
Chain 1:   9600        -9312.728             0.118            0.039
Chain 1:   9700        -8561.758             0.123            0.066
Chain 1:   9800       -10026.388             0.131            0.088
Chain 1:   9900       -11966.677             0.109            0.088
Chain 1:   10000        -8568.409             0.098            0.088
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47166.505             1.000            1.000
Chain 1:    200       -16093.626             1.465            1.931
Chain 1:    300        -8974.766             1.241            1.000
Chain 1:    400        -8825.062             0.935            1.000
Chain 1:    500        -9073.969             0.754            0.793
Chain 1:    600        -8880.709             0.632            0.793
Chain 1:    700        -8165.150             0.554            0.088
Chain 1:    800        -8318.259             0.487            0.088
Chain 1:    900        -8044.030             0.437            0.034
Chain 1:   1000        -7871.469             0.395            0.034
Chain 1:   1100        -7776.916             0.296            0.027
Chain 1:   1200        -7576.578             0.106            0.026
Chain 1:   1300        -7603.948             0.027            0.022
Chain 1:   1400        -8119.820             0.032            0.026
Chain 1:   1500        -7571.440             0.036            0.026
Chain 1:   1600        -7864.814             0.038            0.034
Chain 1:   1700        -7575.901             0.033            0.034
Chain 1:   1800        -7632.387             0.032            0.034
Chain 1:   1900        -7590.627             0.029            0.026
Chain 1:   2000        -7767.421             0.029            0.026
Chain 1:   2100        -7622.023             0.030            0.026
Chain 1:   2200        -7812.322             0.029            0.024
Chain 1:   2300        -7677.421             0.031            0.024
Chain 1:   2400        -7586.258             0.026            0.023
Chain 1:   2500        -7709.196             0.020            0.019
Chain 1:   2600        -7584.205             0.018            0.018
Chain 1:   2700        -7483.924             0.015            0.016
Chain 1:   2800        -7690.261             0.017            0.018
Chain 1:   2900        -7485.464             0.020            0.019
Chain 1:   3000        -7597.260             0.019            0.018
Chain 1:   3100        -7584.547             0.017            0.016
Chain 1:   3200        -7788.753             0.017            0.016
Chain 1:   3300        -7491.102             0.019            0.016
Chain 1:   3400        -7736.198             0.021            0.026
Chain 1:   3500        -7484.801             0.023            0.027
Chain 1:   3600        -7551.601             0.022            0.027
Chain 1:   3700        -7502.092             0.022            0.027
Chain 1:   3800        -7475.451             0.019            0.026
Chain 1:   3900        -7453.681             0.017            0.015
Chain 1:   4000        -7449.320             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003201 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85687.695             1.000            1.000
Chain 1:    200       -14105.095             3.037            5.075
Chain 1:    300       -10396.529             2.144            1.000
Chain 1:    400       -11522.683             1.632            1.000
Chain 1:    500        -9304.334             1.354            0.357
Chain 1:    600        -9761.256             1.136            0.357
Chain 1:    700        -9020.933             0.985            0.238
Chain 1:    800        -9310.550             0.866            0.238
Chain 1:    900        -9117.770             0.772            0.098
Chain 1:   1000        -9182.596             0.696            0.098
Chain 1:   1100        -8943.116             0.598            0.082   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8761.233             0.093            0.047
Chain 1:   1300        -9066.150             0.061            0.034
Chain 1:   1400        -9023.756             0.051            0.031
Chain 1:   1500        -8888.501             0.029            0.027
Chain 1:   1600        -8998.393             0.025            0.021
Chain 1:   1700        -9063.917             0.018            0.021
Chain 1:   1800        -8624.231             0.020            0.021
Chain 1:   1900        -8729.744             0.019            0.015
Chain 1:   2000        -8710.996             0.019            0.015
Chain 1:   2100        -8837.254             0.017            0.014
Chain 1:   2200        -8629.915             0.018            0.014
Chain 1:   2300        -8722.824             0.015            0.012
Chain 1:   2400        -8789.560             0.016            0.012
Chain 1:   2500        -8738.445             0.015            0.012
Chain 1:   2600        -8750.088             0.014            0.011
Chain 1:   2700        -8659.176             0.014            0.011
Chain 1:   2800        -8608.455             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002531 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8367669.252             1.000            1.000
Chain 1:    200     -1578406.142             2.651            4.301
Chain 1:    300      -890971.059             2.024            1.000
Chain 1:    400      -458231.685             1.754            1.000
Chain 1:    500      -359393.495             1.458            0.944
Chain 1:    600      -234489.296             1.304            0.944
Chain 1:    700      -120326.347             1.253            0.944
Chain 1:    800       -87459.176             1.144            0.944
Chain 1:    900       -67713.400             1.049            0.772
Chain 1:   1000       -52445.253             0.973            0.772
Chain 1:   1100       -39852.404             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39023.288             0.477            0.376
Chain 1:   1300       -26894.550             0.445            0.376
Chain 1:   1400       -26607.844             0.351            0.316
Chain 1:   1500       -23173.085             0.339            0.316
Chain 1:   1600       -22383.809             0.289            0.292
Chain 1:   1700       -21246.581             0.199            0.291
Chain 1:   1800       -21188.502             0.162            0.148
Chain 1:   1900       -21515.143             0.135            0.054
Chain 1:   2000       -20019.713             0.113            0.054
Chain 1:   2100       -20258.390             0.082            0.035
Chain 1:   2200       -20486.226             0.081            0.035
Chain 1:   2300       -20102.104             0.038            0.019
Chain 1:   2400       -19873.887             0.038            0.019
Chain 1:   2500       -19676.363             0.024            0.015
Chain 1:   2600       -19305.653             0.023            0.015
Chain 1:   2700       -19262.316             0.018            0.012
Chain 1:   2800       -18979.179             0.019            0.015
Chain 1:   2900       -19260.749             0.019            0.015
Chain 1:   3000       -19246.802             0.012            0.012
Chain 1:   3100       -19331.899             0.011            0.011
Chain 1:   3200       -19022.166             0.011            0.015
Chain 1:   3300       -19227.203             0.010            0.011
Chain 1:   3400       -18701.554             0.012            0.015
Chain 1:   3500       -19314.489             0.014            0.015
Chain 1:   3600       -18619.811             0.016            0.015
Chain 1:   3700       -19007.696             0.018            0.016
Chain 1:   3800       -17965.437             0.022            0.020
Chain 1:   3900       -17961.596             0.021            0.020
Chain 1:   4000       -18078.828             0.021            0.020
Chain 1:   4100       -17992.548             0.021            0.020
Chain 1:   4200       -17808.331             0.021            0.020
Chain 1:   4300       -17947.004             0.021            0.020
Chain 1:   4400       -17903.458             0.018            0.010
Chain 1:   4500       -17805.978             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001169 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12443.718             1.000            1.000
Chain 1:    200        -9369.201             0.664            1.000
Chain 1:    300        -8142.888             0.493            0.328
Chain 1:    400        -8359.471             0.376            0.328
Chain 1:    500        -8184.910             0.305            0.151
Chain 1:    600        -8098.140             0.256            0.151
Chain 1:    700        -8009.880             0.221            0.026
Chain 1:    800        -8015.639             0.194            0.026
Chain 1:    900        -7925.632             0.173            0.021
Chain 1:   1000        -8116.437             0.158            0.024
Chain 1:   1100        -8151.062             0.059            0.021
Chain 1:   1200        -8044.558             0.027            0.013
Chain 1:   1300        -7986.591             0.013            0.011
Chain 1:   1400        -8003.802             0.011            0.011
Chain 1:   1500        -8090.630             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61854.908             1.000            1.000
Chain 1:    200       -18041.242             1.714            2.429
Chain 1:    300        -8919.832             1.484            1.023
Chain 1:    400        -9573.322             1.130            1.023
Chain 1:    500        -7889.539             0.947            1.000
Chain 1:    600        -9023.567             0.810            1.000
Chain 1:    700        -8384.726             0.705            0.213
Chain 1:    800        -8605.387             0.620            0.213
Chain 1:    900        -7900.964             0.561            0.126
Chain 1:   1000        -7750.071             0.507            0.126
Chain 1:   1100        -7757.298             0.407            0.089
Chain 1:   1200        -7661.504             0.165            0.076
Chain 1:   1300        -7777.212             0.065            0.068
Chain 1:   1400        -7643.479             0.060            0.026
Chain 1:   1500        -7583.576             0.039            0.019
Chain 1:   1600        -7738.110             0.028            0.019
Chain 1:   1700        -7528.481             0.024            0.019
Chain 1:   1800        -7620.073             0.022            0.017
Chain 1:   1900        -7603.030             0.014            0.015
Chain 1:   2000        -7661.811             0.012            0.013
Chain 1:   2100        -7604.821             0.013            0.013
Chain 1:   2200        -7711.344             0.013            0.014
Chain 1:   2300        -7564.939             0.014            0.014
Chain 1:   2400        -7616.960             0.013            0.012
Chain 1:   2500        -7621.879             0.012            0.012
Chain 1:   2600        -7532.486             0.011            0.012
Chain 1:   2700        -7554.759             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002824 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85717.965             1.000            1.000
Chain 1:    200       -13636.327             3.143            5.286
Chain 1:    300        -9993.566             2.217            1.000
Chain 1:    400       -10903.019             1.683            1.000
Chain 1:    500        -8969.812             1.390            0.365
Chain 1:    600        -8488.784             1.168            0.365
Chain 1:    700        -8722.424             1.005            0.216
Chain 1:    800        -8921.657             0.882            0.216
Chain 1:    900        -8806.658             0.785            0.083
Chain 1:   1000        -8700.129             0.708            0.083
Chain 1:   1100        -8853.466             0.610            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8462.789             0.086            0.046
Chain 1:   1300        -8687.121             0.052            0.027
Chain 1:   1400        -8698.810             0.044            0.026
Chain 1:   1500        -8551.230             0.024            0.022
Chain 1:   1600        -8664.643             0.020            0.017
Chain 1:   1700        -8745.707             0.018            0.017
Chain 1:   1800        -8329.478             0.021            0.017
Chain 1:   1900        -8427.019             0.020            0.017
Chain 1:   2000        -8400.725             0.019            0.017
Chain 1:   2100        -8524.153             0.019            0.014
Chain 1:   2200        -8341.758             0.017            0.014
Chain 1:   2300        -8421.636             0.015            0.013
Chain 1:   2400        -8491.312             0.016            0.013
Chain 1:   2500        -8437.101             0.015            0.012
Chain 1:   2600        -8437.075             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00255 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8385687.024             1.000            1.000
Chain 1:    200     -1584103.873             2.647            4.294
Chain 1:    300      -892066.447             2.023            1.000
Chain 1:    400      -458256.427             1.754            1.000
Chain 1:    500      -358816.020             1.459            0.947
Chain 1:    600      -233675.200             1.305            0.947
Chain 1:    700      -119644.057             1.255            0.947
Chain 1:    800       -86771.522             1.145            0.947
Chain 1:    900       -67069.210             1.050            0.776
Chain 1:   1000       -51832.393             0.975            0.776
Chain 1:   1100       -39268.356             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38440.677             0.480            0.379
Chain 1:   1300       -26356.730             0.448            0.379
Chain 1:   1400       -26071.656             0.354            0.320
Chain 1:   1500       -22648.465             0.342            0.320
Chain 1:   1600       -21861.516             0.292            0.294
Chain 1:   1700       -20730.740             0.202            0.294
Chain 1:   1800       -20673.766             0.164            0.151
Chain 1:   1900       -20999.904             0.136            0.055
Chain 1:   2000       -19508.565             0.115            0.055
Chain 1:   2100       -19747.038             0.084            0.036
Chain 1:   2200       -19973.908             0.083            0.036
Chain 1:   2300       -19590.781             0.039            0.020
Chain 1:   2400       -19362.860             0.039            0.020
Chain 1:   2500       -19164.985             0.025            0.016
Chain 1:   2600       -18795.079             0.023            0.016
Chain 1:   2700       -18751.996             0.018            0.012
Chain 1:   2800       -18468.899             0.019            0.015
Chain 1:   2900       -18750.219             0.019            0.015
Chain 1:   3000       -18736.395             0.012            0.012
Chain 1:   3100       -18821.375             0.011            0.012
Chain 1:   3200       -18512.026             0.012            0.015
Chain 1:   3300       -18716.767             0.011            0.012
Chain 1:   3400       -18191.672             0.012            0.015
Chain 1:   3500       -18803.624             0.015            0.015
Chain 1:   3600       -18110.289             0.017            0.015
Chain 1:   3700       -18497.131             0.018            0.017
Chain 1:   3800       -17456.793             0.023            0.021
Chain 1:   3900       -17452.971             0.021            0.021
Chain 1:   4000       -17570.249             0.022            0.021
Chain 1:   4100       -17484.018             0.022            0.021
Chain 1:   4200       -17300.264             0.021            0.021
Chain 1:   4300       -17438.638             0.021            0.021
Chain 1:   4400       -17395.460             0.018            0.011
Chain 1:   4500       -17298.039             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001256 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12687.882             1.000            1.000
Chain 1:    200        -9573.784             0.663            1.000
Chain 1:    300        -8187.872             0.498            0.325
Chain 1:    400        -8450.125             0.381            0.325
Chain 1:    500        -8306.594             0.309            0.169
Chain 1:    600        -8155.304             0.260            0.169
Chain 1:    700        -8053.476             0.225            0.031
Chain 1:    800        -8056.386             0.197            0.031
Chain 1:    900        -7984.959             0.176            0.019
Chain 1:   1000        -8179.525             0.161            0.024
Chain 1:   1100        -8203.439             0.061            0.019
Chain 1:   1200        -8070.390             0.030            0.017
Chain 1:   1300        -8039.636             0.014            0.016
Chain 1:   1400        -8047.153             0.011            0.013
Chain 1:   1500        -8137.219             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001446 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57372.728             1.000            1.000
Chain 1:    200       -17741.172             1.617            2.234
Chain 1:    300        -8898.269             1.409            1.000
Chain 1:    400        -8236.961             1.077            1.000
Chain 1:    500        -8958.323             0.878            0.994
Chain 1:    600        -8799.973             0.734            0.994
Chain 1:    700        -8553.782             0.634            0.081
Chain 1:    800        -8134.088             0.561            0.081
Chain 1:    900        -7681.956             0.505            0.080
Chain 1:   1000        -7746.593             0.455            0.080
Chain 1:   1100        -7898.347             0.357            0.059
Chain 1:   1200        -7540.854             0.139            0.052
Chain 1:   1300        -7777.979             0.042            0.047
Chain 1:   1400        -7581.358             0.037            0.030
Chain 1:   1500        -7592.238             0.029            0.029
Chain 1:   1600        -7778.451             0.030            0.029
Chain 1:   1700        -7593.503             0.029            0.026
Chain 1:   1800        -7694.674             0.025            0.024
Chain 1:   1900        -7590.518             0.021            0.024
Chain 1:   2000        -7739.713             0.022            0.024
Chain 1:   2100        -7576.455             0.022            0.024
Chain 1:   2200        -7756.993             0.020            0.023
Chain 1:   2300        -7546.258             0.019            0.023
Chain 1:   2400        -7550.922             0.017            0.022
Chain 1:   2500        -7441.068             0.018            0.022
Chain 1:   2600        -7537.862             0.017            0.019
Chain 1:   2700        -7542.521             0.015            0.015
Chain 1:   2800        -7522.115             0.014            0.015
Chain 1:   2900        -7396.734             0.014            0.017
Chain 1:   3000        -7544.256             0.014            0.017
Chain 1:   3100        -7538.210             0.012            0.015
Chain 1:   3200        -7737.396             0.012            0.015
Chain 1:   3300        -7458.086             0.013            0.015
Chain 1:   3400        -7684.900             0.016            0.017
Chain 1:   3500        -7442.037             0.018            0.020
Chain 1:   3600        -7508.462             0.017            0.020
Chain 1:   3700        -7457.579             0.018            0.020
Chain 1:   3800        -7456.067             0.018            0.020
Chain 1:   3900        -7421.910             0.017            0.020
Chain 1:   4000        -7418.802             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003345 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87049.163             1.000            1.000
Chain 1:    200       -13819.518             3.150            5.299
Chain 1:    300       -10115.490             2.222            1.000
Chain 1:    400       -11483.303             1.696            1.000
Chain 1:    500        -9123.867             1.409            0.366
Chain 1:    600        -8510.181             1.186            0.366
Chain 1:    700        -8567.828             1.017            0.259
Chain 1:    800        -8854.677             0.894            0.259
Chain 1:    900        -9016.360             0.797            0.119
Chain 1:   1000        -8911.959             0.718            0.119
Chain 1:   1100        -8730.902             0.620            0.072   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8504.947             0.093            0.032
Chain 1:   1300        -8810.789             0.060            0.032
Chain 1:   1400        -8793.705             0.048            0.027
Chain 1:   1500        -8643.624             0.024            0.021
Chain 1:   1600        -8753.590             0.018            0.018
Chain 1:   1700        -8827.574             0.018            0.018
Chain 1:   1800        -8394.719             0.020            0.018
Chain 1:   1900        -8498.916             0.020            0.017
Chain 1:   2000        -8474.351             0.019            0.017
Chain 1:   2100        -8453.538             0.017            0.013
Chain 1:   2200        -8417.916             0.015            0.012
Chain 1:   2300        -8546.865             0.013            0.012
Chain 1:   2400        -8401.415             0.014            0.013
Chain 1:   2500        -8469.649             0.013            0.012
Chain 1:   2600        -8389.425             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003755 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8432328.872             1.000            1.000
Chain 1:    200     -1592318.892             2.648            4.296
Chain 1:    300      -893250.448             2.026            1.000
Chain 1:    400      -458942.125             1.756            1.000
Chain 1:    500      -358582.918             1.461            0.946
Chain 1:    600      -233083.109             1.307            0.946
Chain 1:    700      -119368.203             1.257            0.946
Chain 1:    800       -86619.699             1.147            0.946
Chain 1:    900       -66995.710             1.052            0.783
Chain 1:   1000       -51831.794             0.976            0.783
Chain 1:   1100       -39344.467             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38525.867             0.480            0.378
Chain 1:   1300       -26512.433             0.447            0.378
Chain 1:   1400       -26235.538             0.354            0.317
Chain 1:   1500       -22830.726             0.341            0.317
Chain 1:   1600       -22050.165             0.290            0.293
Chain 1:   1700       -20927.234             0.200            0.293
Chain 1:   1800       -20872.274             0.163            0.149
Chain 1:   1900       -21198.763             0.135            0.054
Chain 1:   2000       -19710.958             0.113            0.054
Chain 1:   2100       -19949.319             0.083            0.035
Chain 1:   2200       -20175.809             0.082            0.035
Chain 1:   2300       -19792.860             0.038            0.019
Chain 1:   2400       -19564.862             0.039            0.019
Chain 1:   2500       -19366.750             0.025            0.015
Chain 1:   2600       -18996.801             0.023            0.015
Chain 1:   2700       -18953.644             0.018            0.012
Chain 1:   2800       -18670.354             0.019            0.015
Chain 1:   2900       -18951.638             0.019            0.015
Chain 1:   3000       -18937.856             0.012            0.012
Chain 1:   3100       -19022.943             0.011            0.012
Chain 1:   3200       -18713.417             0.011            0.015
Chain 1:   3300       -18918.257             0.011            0.012
Chain 1:   3400       -18392.820             0.012            0.015
Chain 1:   3500       -19005.246             0.015            0.015
Chain 1:   3600       -18311.114             0.016            0.015
Chain 1:   3700       -18698.509             0.018            0.017
Chain 1:   3800       -17657.025             0.023            0.021
Chain 1:   3900       -17653.090             0.021            0.021
Chain 1:   4000       -17770.420             0.022            0.021
Chain 1:   4100       -17684.188             0.022            0.021
Chain 1:   4200       -17500.090             0.021            0.021
Chain 1:   4300       -17638.733             0.021            0.021
Chain 1:   4400       -17595.343             0.018            0.011
Chain 1:   4500       -17497.791             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001135 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49530.385             1.000            1.000
Chain 1:    200       -20346.898             1.217            1.434
Chain 1:    300       -14833.190             0.935            1.000
Chain 1:    400       -15146.575             0.707            1.000
Chain 1:    500       -13972.568             0.582            0.372
Chain 1:    600       -13417.867             0.492            0.372
Chain 1:    700       -15781.310             0.443            0.150
Chain 1:    800       -14096.218             0.403            0.150
Chain 1:    900       -17668.958             0.380            0.150
Chain 1:   1000       -12222.783             0.387            0.202
Chain 1:   1100       -13845.401             0.299            0.150
Chain 1:   1200       -10500.860             0.187            0.150
Chain 1:   1300       -10255.282             0.152            0.120
Chain 1:   1400       -10597.538             0.153            0.120
Chain 1:   1500       -10637.958             0.145            0.120
Chain 1:   1600       -10572.893             0.142            0.120
Chain 1:   1700       -13837.339             0.151            0.120
Chain 1:   1800       -19673.397             0.168            0.202
Chain 1:   1900       -10602.275             0.234            0.236
Chain 1:   2000       -10396.384             0.191            0.117
Chain 1:   2100        -9903.275             0.184            0.050
Chain 1:   2200        -9815.112             0.153            0.032
Chain 1:   2300        -9531.874             0.154            0.032
Chain 1:   2400        -9460.297             0.151            0.030
Chain 1:   2500       -18181.088             0.199            0.050
Chain 1:   2600       -17361.694             0.203            0.050
Chain 1:   2700       -12670.860             0.217            0.050
Chain 1:   2800        -9539.030             0.220            0.050
Chain 1:   2900       -10773.512             0.146            0.050
Chain 1:   3000        -8987.424             0.163            0.115
Chain 1:   3100       -14622.496             0.197            0.199
Chain 1:   3200        -9394.956             0.252            0.328
Chain 1:   3300        -9611.092             0.251            0.328
Chain 1:   3400        -8931.505             0.258            0.328
Chain 1:   3500        -9678.101             0.218            0.199
Chain 1:   3600        -9507.541             0.215            0.199
Chain 1:   3700        -9899.228             0.182            0.115
Chain 1:   3800        -9203.012             0.156            0.077
Chain 1:   3900        -9061.486             0.147            0.076
Chain 1:   4000        -9015.890             0.127            0.076
Chain 1:   4100        -9747.308             0.096            0.075
Chain 1:   4200        -9008.768             0.049            0.075
Chain 1:   4300        -9960.357             0.056            0.076
Chain 1:   4400        -9780.905             0.050            0.075
Chain 1:   4500        -9277.518             0.048            0.054
Chain 1:   4600        -9489.877             0.048            0.054
Chain 1:   4700        -8763.770             0.053            0.075
Chain 1:   4800       -13570.191             0.081            0.075
Chain 1:   4900       -10172.511             0.112            0.082
Chain 1:   5000        -9699.026             0.117            0.082
Chain 1:   5100        -9036.548             0.117            0.082
Chain 1:   5200        -9304.136             0.111            0.073
Chain 1:   5300        -9511.336             0.104            0.054
Chain 1:   5400       -10121.655             0.108            0.060
Chain 1:   5500        -8771.943             0.118            0.073
Chain 1:   5600       -14778.853             0.156            0.083
Chain 1:   5700        -8899.366             0.214            0.154
Chain 1:   5800        -8833.354             0.180            0.073
Chain 1:   5900        -9399.814             0.152            0.060
Chain 1:   6000        -9532.914             0.149            0.060
Chain 1:   6100        -9322.701             0.144            0.060
Chain 1:   6200        -8521.619             0.150            0.060
Chain 1:   6300       -12399.885             0.179            0.094
Chain 1:   6400       -14558.680             0.188            0.148
Chain 1:   6500        -8645.572             0.241            0.148
Chain 1:   6600        -9373.782             0.208            0.094
Chain 1:   6700        -9031.974             0.146            0.078
Chain 1:   6800        -8991.313             0.146            0.078
Chain 1:   6900        -8673.881             0.143            0.078
Chain 1:   7000       -10974.022             0.163            0.094
Chain 1:   7100       -12129.789             0.170            0.095
Chain 1:   7200        -8748.042             0.199            0.148
Chain 1:   7300        -9049.040             0.171            0.095
Chain 1:   7400       -10311.872             0.169            0.095
Chain 1:   7500       -11133.208             0.108            0.078
Chain 1:   7600        -9838.663             0.113            0.095
Chain 1:   7700        -8693.213             0.123            0.122
Chain 1:   7800        -9808.493             0.133            0.122
Chain 1:   7900        -8469.628             0.146            0.132
Chain 1:   8000        -8437.371             0.125            0.122
Chain 1:   8100        -8430.560             0.116            0.122
Chain 1:   8200       -10167.789             0.094            0.122
Chain 1:   8300        -8613.909             0.109            0.132
Chain 1:   8400        -9472.735             0.106            0.132
Chain 1:   8500        -8574.627             0.109            0.132
Chain 1:   8600        -8528.454             0.096            0.114
Chain 1:   8700        -8910.326             0.087            0.105
Chain 1:   8800        -8389.521             0.082            0.091
Chain 1:   8900       -10150.372             0.084            0.091
Chain 1:   9000       -11373.166             0.094            0.105
Chain 1:   9100        -8318.174             0.131            0.108
Chain 1:   9200        -8660.400             0.117            0.105
Chain 1:   9300        -9663.909             0.110            0.104
Chain 1:   9400       -13665.939             0.130            0.105
Chain 1:   9500        -8723.521             0.176            0.108
Chain 1:   9600        -8453.194             0.179            0.108
Chain 1:   9700        -9741.148             0.188            0.132
Chain 1:   9800        -9311.628             0.186            0.132
Chain 1:   9900       -10908.556             0.183            0.132
Chain 1:   10000        -8509.376             0.201            0.146
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00147 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55901.673             1.000            1.000
Chain 1:    200       -17477.949             1.599            2.198
Chain 1:    300        -8786.873             1.396            1.000
Chain 1:    400        -8449.431             1.057            1.000
Chain 1:    500        -9018.239             0.858            0.989
Chain 1:    600        -8633.093             0.723            0.989
Chain 1:    700        -8358.293             0.624            0.063
Chain 1:    800        -7519.206             0.560            0.112
Chain 1:    900        -8095.889             0.506            0.071
Chain 1:   1000        -7896.198             0.458            0.071
Chain 1:   1100        -7886.691             0.358            0.063
Chain 1:   1200        -7527.562             0.143            0.048
Chain 1:   1300        -7742.968             0.047            0.045
Chain 1:   1400        -7657.502             0.044            0.045
Chain 1:   1500        -7518.697             0.039            0.033
Chain 1:   1600        -7878.402             0.039            0.033
Chain 1:   1700        -7690.420             0.038            0.028
Chain 1:   1800        -7621.976             0.028            0.025
Chain 1:   1900        -7751.701             0.023            0.024
Chain 1:   2000        -7605.994             0.022            0.019
Chain 1:   2100        -7548.705             0.023            0.019
Chain 1:   2200        -7711.530             0.020            0.019
Chain 1:   2300        -7534.442             0.020            0.019
Chain 1:   2400        -7586.077             0.019            0.019
Chain 1:   2500        -7598.259             0.018            0.019
Chain 1:   2600        -7498.442             0.014            0.017
Chain 1:   2700        -7525.380             0.012            0.013
Chain 1:   2800        -7475.024             0.012            0.013
Chain 1:   2900        -7385.804             0.012            0.012
Chain 1:   3000        -7517.605             0.011            0.012
Chain 1:   3100        -7508.895             0.011            0.012
Chain 1:   3200        -7709.063             0.011            0.012
Chain 1:   3300        -7435.847             0.013            0.012
Chain 1:   3400        -7652.735             0.015            0.013
Chain 1:   3500        -7415.716             0.018            0.018
Chain 1:   3600        -7482.012             0.017            0.018
Chain 1:   3700        -7430.603             0.018            0.018
Chain 1:   3800        -7430.170             0.017            0.018
Chain 1:   3900        -7397.198             0.016            0.018
Chain 1:   4000        -7391.820             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002618 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86170.790             1.000            1.000
Chain 1:    200       -13727.086             3.139            5.277
Chain 1:    300       -10091.083             2.213            1.000
Chain 1:    400       -11165.712             1.683            1.000
Chain 1:    500        -8939.192             1.397            0.360
Chain 1:    600        -8532.379             1.172            0.360
Chain 1:    700        -8964.193             1.011            0.249
Chain 1:    800        -8847.313             0.887            0.249
Chain 1:    900        -8924.538             0.789            0.096
Chain 1:   1000        -8835.740             0.711            0.096
Chain 1:   1100        -8922.203             0.612            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8724.001             0.087            0.048
Chain 1:   1300        -8786.403             0.051            0.023
Chain 1:   1400        -8781.870             0.042            0.013
Chain 1:   1500        -8651.963             0.018            0.013
Chain 1:   1600        -8759.980             0.015            0.012
Chain 1:   1700        -8843.126             0.011            0.010
Chain 1:   1800        -8426.563             0.014            0.010
Chain 1:   1900        -8524.095             0.015            0.011
Chain 1:   2000        -8497.882             0.014            0.011
Chain 1:   2100        -8621.431             0.015            0.012
Chain 1:   2200        -8438.329             0.014            0.012
Chain 1:   2300        -8518.728             0.015            0.012
Chain 1:   2400        -8588.387             0.015            0.012
Chain 1:   2500        -8534.224             0.015            0.011
Chain 1:   2600        -8534.262             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003114 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8407984.250             1.000            1.000
Chain 1:    200     -1584473.449             2.653            4.306
Chain 1:    300      -891413.572             2.028            1.000
Chain 1:    400      -458378.801             1.757            1.000
Chain 1:    500      -358754.466             1.461            0.945
Chain 1:    600      -233567.145             1.307            0.945
Chain 1:    700      -119583.888             1.257            0.945
Chain 1:    800       -86801.067             1.147            0.945
Chain 1:    900       -67096.906             1.052            0.777
Chain 1:   1000       -51873.324             0.976            0.777
Chain 1:   1100       -39335.630             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38506.979             0.479            0.378
Chain 1:   1300       -26443.244             0.447            0.378
Chain 1:   1400       -26160.119             0.354            0.319
Chain 1:   1500       -22743.688             0.341            0.319
Chain 1:   1600       -21959.470             0.291            0.294
Chain 1:   1700       -20830.362             0.201            0.293
Chain 1:   1800       -20774.013             0.164            0.150
Chain 1:   1900       -21100.129             0.136            0.054
Chain 1:   2000       -19610.218             0.114            0.054
Chain 1:   2100       -19848.408             0.083            0.036
Chain 1:   2200       -20075.310             0.082            0.036
Chain 1:   2300       -19692.148             0.039            0.019
Chain 1:   2400       -19464.214             0.039            0.019
Chain 1:   2500       -19266.512             0.025            0.015
Chain 1:   2600       -18896.411             0.023            0.015
Chain 1:   2700       -18853.277             0.018            0.012
Chain 1:   2800       -18570.290             0.019            0.015
Chain 1:   2900       -18851.546             0.019            0.015
Chain 1:   3000       -18837.605             0.012            0.012
Chain 1:   3100       -18922.666             0.011            0.012
Chain 1:   3200       -18613.265             0.012            0.015
Chain 1:   3300       -18818.044             0.011            0.012
Chain 1:   3400       -18292.982             0.012            0.015
Chain 1:   3500       -18904.950             0.015            0.015
Chain 1:   3600       -18211.457             0.016            0.015
Chain 1:   3700       -18598.418             0.018            0.017
Chain 1:   3800       -17558.008             0.023            0.021
Chain 1:   3900       -17554.187             0.021            0.021
Chain 1:   4000       -17671.432             0.022            0.021
Chain 1:   4100       -17585.258             0.022            0.021
Chain 1:   4200       -17401.439             0.021            0.021
Chain 1:   4300       -17539.832             0.021            0.021
Chain 1:   4400       -17496.603             0.018            0.011
Chain 1:   4500       -17399.178             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001269 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12453.052             1.000            1.000
Chain 1:    200        -9463.986             0.658            1.000
Chain 1:    300        -7890.167             0.505            0.316
Chain 1:    400        -8149.632             0.387            0.316
Chain 1:    500        -8024.731             0.313            0.199
Chain 1:    600        -7882.142             0.263            0.199
Chain 1:    700        -7989.969             0.228            0.032
Chain 1:    800        -7815.151             0.202            0.032
Chain 1:    900        -7985.529             0.182            0.022
Chain 1:   1000        -7790.033             0.166            0.025
Chain 1:   1100        -7891.671             0.068            0.022
Chain 1:   1200        -7803.828             0.037            0.021
Chain 1:   1300        -7727.794             0.018            0.018
Chain 1:   1400        -7766.940             0.015            0.016
Chain 1:   1500        -7860.787             0.015            0.013
Chain 1:   1600        -7778.086             0.014            0.013
Chain 1:   1700        -7736.446             0.014            0.012
Chain 1:   1800        -7707.662             0.012            0.011
Chain 1:   1900        -7734.518             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001569 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57267.214             1.000            1.000
Chain 1:    200       -17685.287             1.619            2.238
Chain 1:    300        -8810.365             1.415            1.007
Chain 1:    400        -8238.502             1.079            1.007
Chain 1:    500        -8446.443             0.868            1.000
Chain 1:    600        -8492.695             0.724            1.000
Chain 1:    700        -7834.322             0.633            0.084
Chain 1:    800        -8195.494             0.559            0.084
Chain 1:    900        -7837.062             0.502            0.069
Chain 1:   1000        -7649.525             0.454            0.069
Chain 1:   1100        -7705.522             0.355            0.046
Chain 1:   1200        -7645.305             0.132            0.044
Chain 1:   1300        -7784.871             0.033            0.025
Chain 1:   1400        -7923.529             0.028            0.025
Chain 1:   1500        -7551.179             0.030            0.025
Chain 1:   1600        -7730.524             0.032            0.025
Chain 1:   1700        -7693.314             0.024            0.023
Chain 1:   1800        -7645.842             0.020            0.018
Chain 1:   1900        -7577.491             0.017            0.017
Chain 1:   2000        -7625.514             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00262 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86115.500             1.000            1.000
Chain 1:    200       -13657.309             3.153            5.305
Chain 1:    300        -9909.030             2.228            1.000
Chain 1:    400       -11515.397             1.706            1.000
Chain 1:    500        -8612.363             1.432            0.378
Chain 1:    600        -8654.174             1.194            0.378
Chain 1:    700        -8286.424             1.030            0.337
Chain 1:    800        -8506.707             0.904            0.337
Chain 1:    900        -8639.529             0.806            0.139
Chain 1:   1000        -8832.386             0.727            0.139
Chain 1:   1100        -8590.478             0.630            0.044   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8236.613             0.104            0.043
Chain 1:   1300        -8544.859             0.070            0.036
Chain 1:   1400        -8487.792             0.056            0.028
Chain 1:   1500        -8393.700             0.024            0.026
Chain 1:   1600        -8506.424             0.025            0.026
Chain 1:   1700        -8560.170             0.021            0.022
Chain 1:   1800        -8115.045             0.024            0.022
Chain 1:   1900        -8220.655             0.023            0.022
Chain 1:   2000        -8203.917             0.021            0.013
Chain 1:   2100        -8342.845             0.020            0.013
Chain 1:   2200        -8114.835             0.019            0.013
Chain 1:   2300        -8268.696             0.017            0.013
Chain 1:   2400        -8113.502             0.018            0.017
Chain 1:   2500        -8191.236             0.018            0.017
Chain 1:   2600        -8111.916             0.018            0.017
Chain 1:   2700        -8137.192             0.017            0.017
Chain 1:   2800        -8093.225             0.013            0.013
Chain 1:   2900        -8196.671             0.012            0.013
Chain 1:   3000        -8134.257             0.013            0.013
Chain 1:   3100        -8082.604             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002524 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8357963.104             1.000            1.000
Chain 1:    200     -1575800.690             2.652            4.304
Chain 1:    300      -890461.630             2.025            1.000
Chain 1:    400      -458251.830             1.754            1.000
Chain 1:    500      -359547.696             1.458            0.943
Chain 1:    600      -234498.036             1.304            0.943
Chain 1:    700      -120130.470             1.254            0.943
Chain 1:    800       -87185.188             1.144            0.943
Chain 1:    900       -67394.706             1.050            0.770
Chain 1:   1000       -52085.690             0.974            0.770
Chain 1:   1100       -39457.334             0.906            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38625.739             0.478            0.378
Chain 1:   1300       -26462.995             0.447            0.378
Chain 1:   1400       -26173.773             0.354            0.320
Chain 1:   1500       -22729.984             0.341            0.320
Chain 1:   1600       -21938.347             0.292            0.294
Chain 1:   1700       -20796.985             0.202            0.294
Chain 1:   1800       -20738.038             0.165            0.152
Chain 1:   1900       -21064.811             0.137            0.055
Chain 1:   2000       -19566.958             0.115            0.055
Chain 1:   2100       -19805.812             0.084            0.036
Chain 1:   2200       -20034.042             0.083            0.036
Chain 1:   2300       -19649.511             0.039            0.020
Chain 1:   2400       -19421.207             0.039            0.020
Chain 1:   2500       -19223.791             0.025            0.016
Chain 1:   2600       -18852.843             0.023            0.016
Chain 1:   2700       -18809.396             0.018            0.012
Chain 1:   2800       -18526.244             0.019            0.015
Chain 1:   2900       -18807.970             0.019            0.015
Chain 1:   3000       -18793.907             0.012            0.012
Chain 1:   3100       -18879.067             0.011            0.012
Chain 1:   3200       -18569.202             0.012            0.015
Chain 1:   3300       -18774.340             0.011            0.012
Chain 1:   3400       -18248.528             0.012            0.015
Chain 1:   3500       -18861.755             0.015            0.015
Chain 1:   3600       -18166.679             0.017            0.015
Chain 1:   3700       -18554.938             0.018            0.017
Chain 1:   3800       -17512.066             0.023            0.021
Chain 1:   3900       -17508.216             0.021            0.021
Chain 1:   4000       -17625.433             0.022            0.021
Chain 1:   4100       -17539.145             0.022            0.021
Chain 1:   4200       -17354.764             0.021            0.021
Chain 1:   4300       -17493.533             0.021            0.021
Chain 1:   4400       -17449.906             0.018            0.011
Chain 1:   4500       -17352.394             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00125 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49500.959             1.000            1.000
Chain 1:    200       -17505.749             1.414            1.828
Chain 1:    300       -19753.744             0.980            1.000
Chain 1:    400       -22783.912             0.769            1.000
Chain 1:    500       -19392.432             0.650            0.175
Chain 1:    600       -13884.403             0.608            0.397
Chain 1:    700       -14222.695             0.524            0.175
Chain 1:    800       -12255.103             0.479            0.175
Chain 1:    900       -12897.665             0.431            0.161
Chain 1:   1000       -21997.597             0.429            0.175
Chain 1:   1100       -29605.812             0.355            0.175
Chain 1:   1200       -13059.740             0.299            0.175
Chain 1:   1300       -12000.555             0.296            0.175
Chain 1:   1400       -11283.712             0.290            0.175
Chain 1:   1500       -10891.094             0.276            0.161
Chain 1:   1600       -12178.590             0.247            0.106
Chain 1:   1700       -11591.182             0.249            0.106
Chain 1:   1800       -11042.211             0.238            0.088
Chain 1:   1900       -11633.440             0.238            0.088
Chain 1:   2000       -10295.035             0.210            0.088
Chain 1:   2100       -10166.725             0.185            0.064
Chain 1:   2200       -10206.011             0.059            0.051
Chain 1:   2300       -18624.938             0.096            0.051
Chain 1:   2400       -24676.983             0.114            0.051
Chain 1:   2500       -17640.051             0.150            0.106
Chain 1:   2600       -12212.647             0.184            0.130
Chain 1:   2700       -11987.010             0.181            0.130
Chain 1:   2800       -18468.467             0.211            0.245
Chain 1:   2900        -9598.903             0.298            0.351
Chain 1:   3000        -9503.694             0.286            0.351
Chain 1:   3100        -9594.881             0.286            0.351
Chain 1:   3200       -10295.717             0.292            0.351
Chain 1:   3300       -20154.720             0.296            0.351
Chain 1:   3400       -10930.287             0.356            0.399
Chain 1:   3500       -10078.416             0.324            0.351
Chain 1:   3600       -18500.693             0.325            0.351
Chain 1:   3700        -9527.875             0.418            0.455
Chain 1:   3800        -8997.432             0.389            0.455
Chain 1:   3900       -11965.722             0.321            0.248
Chain 1:   4000        -9244.702             0.349            0.294
Chain 1:   4100       -10124.171             0.357            0.294
Chain 1:   4200       -10696.948             0.356            0.294
Chain 1:   4300        -9190.350             0.323            0.248
Chain 1:   4400        -9887.504             0.246            0.164
Chain 1:   4500        -9126.046             0.246            0.164
Chain 1:   4600       -10149.111             0.210            0.101
Chain 1:   4700       -10443.210             0.119            0.087
Chain 1:   4800       -13858.391             0.138            0.101
Chain 1:   4900        -9240.773             0.163            0.101
Chain 1:   5000       -10027.661             0.141            0.087
Chain 1:   5100        -9585.301             0.137            0.083
Chain 1:   5200       -13750.453             0.162            0.101
Chain 1:   5300       -11459.011             0.166            0.101
Chain 1:   5400       -12051.217             0.164            0.101
Chain 1:   5500        -8984.308             0.189            0.200
Chain 1:   5600        -9401.227             0.184            0.200
Chain 1:   5700        -9405.544             0.181            0.200
Chain 1:   5800        -9923.594             0.161            0.078
Chain 1:   5900       -13922.029             0.140            0.078
Chain 1:   6000        -9202.924             0.184            0.200
Chain 1:   6100        -9702.587             0.184            0.200
Chain 1:   6200        -9245.821             0.159            0.052
Chain 1:   6300       -10456.195             0.150            0.052
Chain 1:   6400        -9327.205             0.158            0.116
Chain 1:   6500        -9287.380             0.124            0.052
Chain 1:   6600        -9521.292             0.122            0.052
Chain 1:   6700        -9248.585             0.125            0.052
Chain 1:   6800        -9400.691             0.121            0.051
Chain 1:   6900       -11800.940             0.113            0.051
Chain 1:   7000        -8885.050             0.094            0.051
Chain 1:   7100        -8671.812             0.092            0.049
Chain 1:   7200        -8577.632             0.088            0.029
Chain 1:   7300       -11664.511             0.103            0.029
Chain 1:   7400       -10045.120             0.107            0.029
Chain 1:   7500       -12618.207             0.127            0.161
Chain 1:   7600        -8742.312             0.169            0.203
Chain 1:   7700        -8889.587             0.167            0.203
Chain 1:   7800        -9178.426             0.169            0.203
Chain 1:   7900        -9266.918             0.149            0.161
Chain 1:   8000        -9478.692             0.119            0.031
Chain 1:   8100       -10778.178             0.128            0.121
Chain 1:   8200        -9527.656             0.140            0.131
Chain 1:   8300        -8575.935             0.125            0.121
Chain 1:   8400        -9271.071             0.116            0.111
Chain 1:   8500       -11158.372             0.113            0.111
Chain 1:   8600        -8883.519             0.094            0.111
Chain 1:   8700        -9295.784             0.097            0.111
Chain 1:   8800        -9127.488             0.096            0.111
Chain 1:   8900       -12793.498             0.123            0.121
Chain 1:   9000       -10000.212             0.149            0.131
Chain 1:   9100        -9216.279             0.146            0.131
Chain 1:   9200        -9175.978             0.133            0.111
Chain 1:   9300        -9659.517             0.127            0.085
Chain 1:   9400       -11373.024             0.134            0.151
Chain 1:   9500       -11083.059             0.120            0.085
Chain 1:   9600        -9958.384             0.106            0.085
Chain 1:   9700       -11376.717             0.114            0.113
Chain 1:   9800       -11343.239             0.112            0.113
Chain 1:   9900       -10189.473             0.095            0.113
Chain 1:   10000        -9359.442             0.076            0.089
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001699 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58739.378             1.000            1.000
Chain 1:    200       -18390.720             1.597            2.194
Chain 1:    300        -8964.342             1.415            1.052
Chain 1:    400        -8065.297             1.089            1.052
Chain 1:    500        -9223.008             0.897            1.000
Chain 1:    600        -9898.176             0.758            1.000
Chain 1:    700        -8252.823             0.679            0.199
Chain 1:    800        -8311.868             0.595            0.199
Chain 1:    900        -7881.844             0.535            0.126
Chain 1:   1000        -7818.947             0.482            0.126
Chain 1:   1100        -7910.991             0.383            0.111
Chain 1:   1200        -7758.183             0.166            0.068
Chain 1:   1300        -7793.897             0.061            0.055
Chain 1:   1400        -7873.381             0.051            0.020
Chain 1:   1500        -7502.360             0.043            0.020
Chain 1:   1600        -7724.384             0.039            0.020
Chain 1:   1700        -7601.773             0.021            0.016
Chain 1:   1800        -7549.682             0.021            0.016
Chain 1:   1900        -7564.548             0.016            0.012
Chain 1:   2000        -7616.048             0.016            0.012
Chain 1:   2100        -7510.107             0.016            0.014
Chain 1:   2200        -7749.431             0.017            0.014
Chain 1:   2300        -7501.017             0.020            0.016
Chain 1:   2400        -7555.761             0.020            0.016
Chain 1:   2500        -7618.200             0.015            0.014
Chain 1:   2600        -7507.406             0.014            0.014
Chain 1:   2700        -7495.226             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003038 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86793.524             1.000            1.000
Chain 1:    200       -14117.486             3.074            5.148
Chain 1:    300       -10346.804             2.171            1.000
Chain 1:    400       -11987.242             1.662            1.000
Chain 1:    500        -9010.877             1.396            0.364
Chain 1:    600        -8921.900             1.165            0.364
Chain 1:    700        -8752.583             1.001            0.330
Chain 1:    800        -9009.259             0.880            0.330
Chain 1:    900        -9111.854             0.783            0.137
Chain 1:   1000        -8908.708             0.707            0.137
Chain 1:   1100        -9030.488             0.608            0.028   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8680.757             0.098            0.028
Chain 1:   1300        -8972.404             0.065            0.028
Chain 1:   1400        -8743.308             0.053            0.026
Chain 1:   1500        -8823.747             0.021            0.023
Chain 1:   1600        -8928.787             0.022            0.023
Chain 1:   1700        -8979.344             0.020            0.023
Chain 1:   1800        -8524.572             0.023            0.023
Chain 1:   1900        -8634.847             0.023            0.023
Chain 1:   2000        -8639.058             0.021            0.013
Chain 1:   2100        -8753.844             0.021            0.013
Chain 1:   2200        -8530.505             0.019            0.013
Chain 1:   2300        -8669.973             0.017            0.013
Chain 1:   2400        -8536.929             0.016            0.013
Chain 1:   2500        -8608.935             0.016            0.013
Chain 1:   2600        -8519.747             0.016            0.013
Chain 1:   2700        -8552.104             0.016            0.013
Chain 1:   2800        -8503.655             0.011            0.013
Chain 1:   2900        -8615.862             0.011            0.013
Chain 1:   3000        -8546.967             0.012            0.013
Chain 1:   3100        -8495.639             0.011            0.010
Chain 1:   3200        -8468.636             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003118 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411176.148             1.000            1.000
Chain 1:    200     -1587371.164             2.649            4.299
Chain 1:    300      -890798.778             2.027            1.000
Chain 1:    400      -458084.741             1.756            1.000
Chain 1:    500      -358291.119             1.461            0.945
Chain 1:    600      -233362.835             1.307            0.945
Chain 1:    700      -119748.415             1.255            0.945
Chain 1:    800       -86980.815             1.146            0.945
Chain 1:    900       -67360.467             1.051            0.782
Chain 1:   1000       -52192.593             0.975            0.782
Chain 1:   1100       -39692.251             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38876.724             0.478            0.377
Chain 1:   1300       -26847.063             0.445            0.377
Chain 1:   1400       -26570.415             0.352            0.315
Chain 1:   1500       -23160.473             0.338            0.315
Chain 1:   1600       -22378.529             0.288            0.291
Chain 1:   1700       -21253.376             0.199            0.291
Chain 1:   1800       -21198.173             0.161            0.147
Chain 1:   1900       -21525.069             0.134            0.053
Chain 1:   2000       -20035.086             0.112            0.053
Chain 1:   2100       -20273.752             0.082            0.035
Chain 1:   2200       -20500.575             0.081            0.035
Chain 1:   2300       -20117.208             0.038            0.019
Chain 1:   2400       -19889.020             0.038            0.019
Chain 1:   2500       -19690.869             0.024            0.015
Chain 1:   2600       -19320.605             0.023            0.015
Chain 1:   2700       -19277.311             0.018            0.012
Chain 1:   2800       -18993.814             0.019            0.015
Chain 1:   2900       -19275.332             0.019            0.015
Chain 1:   3000       -19261.553             0.012            0.012
Chain 1:   3100       -19346.677             0.011            0.011
Chain 1:   3200       -19036.916             0.011            0.015
Chain 1:   3300       -19241.922             0.010            0.011
Chain 1:   3400       -18716.021             0.012            0.015
Chain 1:   3500       -19329.160             0.014            0.015
Chain 1:   3600       -18634.095             0.016            0.015
Chain 1:   3700       -19022.215             0.018            0.016
Chain 1:   3800       -17979.251             0.022            0.020
Chain 1:   3900       -17975.265             0.021            0.020
Chain 1:   4000       -18092.619             0.021            0.020
Chain 1:   4100       -18006.290             0.021            0.020
Chain 1:   4200       -17821.867             0.021            0.020
Chain 1:   4300       -17960.766             0.021            0.020
Chain 1:   4400       -17917.118             0.018            0.010
Chain 1:   4500       -17819.501             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48972.305             1.000            1.000
Chain 1:    200       -19154.900             1.278            1.557
Chain 1:    300       -12376.877             1.035            1.000
Chain 1:    400       -25108.683             0.903            1.000
Chain 1:    500       -13701.095             0.889            0.833
Chain 1:    600       -18807.353             0.786            0.833
Chain 1:    700       -13202.416             0.734            0.548
Chain 1:    800       -14255.475             0.652            0.548
Chain 1:    900       -17305.334             0.599            0.507
Chain 1:   1000       -11121.620             0.595            0.548
Chain 1:   1100       -14945.395             0.520            0.507   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12998.198             0.380            0.425
Chain 1:   1300       -12270.068             0.331            0.272
Chain 1:   1400       -11460.693             0.287            0.256
Chain 1:   1500       -27165.094             0.262            0.256
Chain 1:   1600       -10765.533             0.387            0.256
Chain 1:   1700       -10684.672             0.345            0.176
Chain 1:   1800        -9738.929             0.347            0.176
Chain 1:   1900       -28639.594             0.396            0.256
Chain 1:   2000       -10212.423             0.521            0.256   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2100        -9368.309             0.504            0.150   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200        -9532.348             0.491            0.097
Chain 1:   2300       -11492.789             0.502            0.171   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400       -18273.357             0.532            0.371   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500        -9122.990             0.574            0.371   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600       -10485.257             0.435            0.171
Chain 1:   2700       -11716.222             0.445            0.171
Chain 1:   2800        -9653.466             0.456            0.214
Chain 1:   2900        -9378.483             0.393            0.171
Chain 1:   3000       -17774.242             0.260            0.171
Chain 1:   3100        -9792.000             0.333            0.214
Chain 1:   3200        -9250.494             0.337            0.214
Chain 1:   3300        -9451.014             0.322            0.214
Chain 1:   3400        -9594.231             0.286            0.130
Chain 1:   3500        -9077.032             0.192            0.105
Chain 1:   3600       -10129.398             0.189            0.104
Chain 1:   3700        -8788.518             0.194            0.104
Chain 1:   3800       -15313.331             0.215            0.104
Chain 1:   3900       -12927.950             0.231            0.153
Chain 1:   4000       -11085.985             0.200            0.153
Chain 1:   4100        -9052.492             0.141            0.153
Chain 1:   4200        -9285.852             0.138            0.153
Chain 1:   4300        -9607.769             0.139            0.153
Chain 1:   4400       -10469.663             0.146            0.153
Chain 1:   4500        -8594.128             0.162            0.166
Chain 1:   4600        -8551.912             0.152            0.166
Chain 1:   4700        -8655.830             0.138            0.166
Chain 1:   4800        -8425.844             0.098            0.082
Chain 1:   4900       -11546.119             0.106            0.082
Chain 1:   5000        -9385.645             0.113            0.082
Chain 1:   5100        -8478.384             0.101            0.082
Chain 1:   5200        -8677.994             0.101            0.082
Chain 1:   5300       -12861.197             0.130            0.107
Chain 1:   5400        -9131.865             0.163            0.218
Chain 1:   5500        -8385.997             0.150            0.107
Chain 1:   5600        -8328.633             0.150            0.107
Chain 1:   5700       -13368.990             0.186            0.230
Chain 1:   5800        -8860.738             0.235            0.270
Chain 1:   5900       -11470.406             0.230            0.230
Chain 1:   6000       -10490.242             0.217            0.228
Chain 1:   6100        -8965.831             0.223            0.228
Chain 1:   6200        -8847.705             0.222            0.228
Chain 1:   6300        -8605.224             0.192            0.170
Chain 1:   6400       -10593.663             0.170            0.170
Chain 1:   6500        -9115.533             0.178            0.170
Chain 1:   6600        -8339.308             0.186            0.170
Chain 1:   6700        -8454.188             0.150            0.162
Chain 1:   6800        -8544.316             0.100            0.093
Chain 1:   6900        -9113.781             0.083            0.093
Chain 1:   7000        -8536.364             0.081            0.068
Chain 1:   7100        -9113.229             0.070            0.063
Chain 1:   7200       -10066.684             0.078            0.068
Chain 1:   7300        -8455.046             0.095            0.093
Chain 1:   7400        -8711.313             0.079            0.068
Chain 1:   7500        -8546.673             0.064            0.063
Chain 1:   7600        -8292.937             0.058            0.062
Chain 1:   7700        -9275.044             0.067            0.063
Chain 1:   7800       -11279.092             0.084            0.068
Chain 1:   7900        -8144.758             0.116            0.095
Chain 1:   8000        -8230.436             0.111            0.095
Chain 1:   8100        -8174.059             0.105            0.095
Chain 1:   8200       -10021.554             0.114            0.106
Chain 1:   8300       -10070.095             0.095            0.031
Chain 1:   8400        -8666.080             0.109            0.106
Chain 1:   8500        -9730.903             0.118            0.109
Chain 1:   8600        -8778.092             0.125            0.109
Chain 1:   8700        -8207.193             0.122            0.109
Chain 1:   8800        -8964.242             0.113            0.109
Chain 1:   8900        -8829.819             0.076            0.084
Chain 1:   9000        -9375.137             0.080            0.084
Chain 1:   9100        -8502.058             0.090            0.103
Chain 1:   9200        -8285.665             0.074            0.084
Chain 1:   9300        -9726.619             0.088            0.103
Chain 1:   9400        -8990.367             0.080            0.084
Chain 1:   9500       -11608.627             0.092            0.084
Chain 1:   9600        -8187.853             0.123            0.084
Chain 1:   9700        -8099.426             0.117            0.084
Chain 1:   9800       -11562.330             0.139            0.103
Chain 1:   9900       -10908.750             0.143            0.103
Chain 1:   10000        -7992.230             0.174            0.148
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57261.966             1.000            1.000
Chain 1:    200       -17443.439             1.641            2.283
Chain 1:    300        -8722.588             1.428            1.000
Chain 1:    400        -8148.228             1.088            1.000
Chain 1:    500        -8641.201             0.882            1.000
Chain 1:    600        -9144.072             0.744            1.000
Chain 1:    700        -8434.197             0.650            0.084
Chain 1:    800        -8088.302             0.574            0.084
Chain 1:    900        -7937.352             0.512            0.070
Chain 1:   1000        -7782.265             0.463            0.070
Chain 1:   1100        -7752.351             0.363            0.057
Chain 1:   1200        -7605.257             0.137            0.055
Chain 1:   1300        -7716.395             0.039            0.043
Chain 1:   1400        -7789.306             0.032            0.020
Chain 1:   1500        -7610.978             0.029            0.020
Chain 1:   1600        -7762.021             0.026            0.019
Chain 1:   1700        -7508.555             0.021            0.019
Chain 1:   1800        -7566.617             0.017            0.019
Chain 1:   1900        -7577.702             0.015            0.019
Chain 1:   2000        -7607.860             0.014            0.014
Chain 1:   2100        -7600.250             0.013            0.014
Chain 1:   2200        -7684.957             0.013            0.011
Chain 1:   2300        -7598.335             0.012            0.011
Chain 1:   2400        -7644.068             0.012            0.011
Chain 1:   2500        -7548.466             0.011            0.011
Chain 1:   2600        -7514.774             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002499 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86044.934             1.000            1.000
Chain 1:    200       -13454.475             3.198            5.395
Chain 1:    300        -9793.650             2.256            1.000
Chain 1:    400       -10920.738             1.718            1.000
Chain 1:    500        -8544.764             1.430            0.374
Chain 1:    600        -8224.385             1.198            0.374
Chain 1:    700        -8277.343             1.028            0.278
Chain 1:    800        -8620.934             0.904            0.278
Chain 1:    900        -8540.819             0.805            0.103
Chain 1:   1000        -8548.848             0.725            0.103
Chain 1:   1100        -8458.699             0.626            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8230.410             0.089            0.039
Chain 1:   1300        -8485.758             0.055            0.030
Chain 1:   1400        -8489.832             0.044            0.028
Chain 1:   1500        -8336.757             0.018            0.018
Chain 1:   1600        -8450.097             0.016            0.013
Chain 1:   1700        -8528.688             0.016            0.013
Chain 1:   1800        -8104.138             0.017            0.013
Chain 1:   1900        -8205.800             0.018            0.013
Chain 1:   2000        -8180.356             0.018            0.013
Chain 1:   2100        -8306.243             0.018            0.015
Chain 1:   2200        -8108.202             0.018            0.015
Chain 1:   2300        -8200.708             0.016            0.013
Chain 1:   2400        -8269.349             0.017            0.013
Chain 1:   2500        -8215.563             0.016            0.012
Chain 1:   2600        -8217.119             0.014            0.011
Chain 1:   2700        -8133.786             0.014            0.011
Chain 1:   2800        -8093.381             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002527 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8409851.163             1.000            1.000
Chain 1:    200     -1590251.171             2.644            4.288
Chain 1:    300      -892013.696             2.024            1.000
Chain 1:    400      -457953.723             1.755            1.000
Chain 1:    500      -357967.272             1.460            0.948
Chain 1:    600      -232678.487             1.306            0.948
Chain 1:    700      -118993.090             1.256            0.948
Chain 1:    800       -86238.712             1.146            0.948
Chain 1:    900       -66620.187             1.052            0.783
Chain 1:   1000       -51457.979             0.976            0.783
Chain 1:   1100       -38967.237             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38148.668             0.481            0.380
Chain 1:   1300       -26136.697             0.449            0.380
Chain 1:   1400       -25858.646             0.355            0.321
Chain 1:   1500       -22454.422             0.343            0.321
Chain 1:   1600       -21673.396             0.292            0.295
Chain 1:   1700       -20551.121             0.202            0.294
Chain 1:   1800       -20496.244             0.165            0.152
Chain 1:   1900       -20822.515             0.137            0.055
Chain 1:   2000       -19335.357             0.115            0.055
Chain 1:   2100       -19573.687             0.084            0.036
Chain 1:   2200       -19799.914             0.083            0.036
Chain 1:   2300       -19417.263             0.039            0.020
Chain 1:   2400       -19189.381             0.039            0.020
Chain 1:   2500       -18991.202             0.025            0.016
Chain 1:   2600       -18621.502             0.024            0.016
Chain 1:   2700       -18578.442             0.018            0.012
Chain 1:   2800       -18295.199             0.020            0.015
Chain 1:   2900       -18576.432             0.020            0.015
Chain 1:   3000       -18562.657             0.012            0.012
Chain 1:   3100       -18647.682             0.011            0.012
Chain 1:   3200       -18338.307             0.012            0.015
Chain 1:   3300       -18543.052             0.011            0.012
Chain 1:   3400       -18017.836             0.013            0.015
Chain 1:   3500       -18629.873             0.015            0.015
Chain 1:   3600       -17936.306             0.017            0.015
Chain 1:   3700       -18323.279             0.019            0.017
Chain 1:   3800       -17282.585             0.023            0.021
Chain 1:   3900       -17278.688             0.022            0.021
Chain 1:   4000       -17396.018             0.022            0.021
Chain 1:   4100       -17309.782             0.022            0.021
Chain 1:   4200       -17125.920             0.022            0.021
Chain 1:   4300       -17264.407             0.021            0.021
Chain 1:   4400       -17221.154             0.019            0.011
Chain 1:   4500       -17123.665             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001287 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49246.968             1.000            1.000
Chain 1:    200       -20359.426             1.209            1.419
Chain 1:    300       -23479.833             0.851            1.000
Chain 1:    400       -14694.260             0.787            1.000
Chain 1:    500       -18461.401             0.671            0.598
Chain 1:    600       -12270.354             0.643            0.598
Chain 1:    700       -22295.018             0.615            0.505
Chain 1:    800       -16199.250             0.586            0.505
Chain 1:    900       -15574.888             0.525            0.450
Chain 1:   1000       -13178.856             0.491            0.450
Chain 1:   1100       -13575.701             0.394            0.376
Chain 1:   1200       -13330.888             0.253            0.204
Chain 1:   1300       -11550.471             0.256            0.204
Chain 1:   1400       -11456.713             0.197            0.182
Chain 1:   1500       -11906.357             0.180            0.154
Chain 1:   1600       -12750.849             0.136            0.066
Chain 1:   1700       -10096.837             0.117            0.066
Chain 1:   1800       -15850.606             0.116            0.066
Chain 1:   1900       -10940.432             0.157            0.154
Chain 1:   2000       -18476.309             0.180            0.154
Chain 1:   2100       -10090.779             0.260            0.263
Chain 1:   2200        -9839.798             0.261            0.263
Chain 1:   2300        -9667.961             0.247            0.263
Chain 1:   2400        -9447.421             0.248            0.263
Chain 1:   2500       -10412.829             0.254            0.263
Chain 1:   2600        -9489.361             0.257            0.263
Chain 1:   2700       -15206.756             0.268            0.363
Chain 1:   2800       -17034.955             0.243            0.107
Chain 1:   2900       -10012.474             0.268            0.107
Chain 1:   3000       -17196.751             0.269            0.107
Chain 1:   3100       -10422.473             0.251            0.107
Chain 1:   3200        -9091.483             0.263            0.146
Chain 1:   3300        -9411.158             0.265            0.146
Chain 1:   3400        -9717.919             0.265            0.146
Chain 1:   3500        -9994.315             0.259            0.146
Chain 1:   3600       -15590.703             0.285            0.359
Chain 1:   3700       -10722.700             0.293            0.359
Chain 1:   3800        -9099.194             0.300            0.359
Chain 1:   3900        -9099.358             0.230            0.178
Chain 1:   4000       -10688.984             0.203            0.149
Chain 1:   4100        -9361.880             0.152            0.146
Chain 1:   4200       -12200.692             0.161            0.149
Chain 1:   4300       -10217.736             0.177            0.178
Chain 1:   4400       -13618.873             0.199            0.194
Chain 1:   4500       -12420.811             0.205            0.194
Chain 1:   4600        -9194.611             0.205            0.194
Chain 1:   4700       -14519.884             0.196            0.194
Chain 1:   4800        -9110.050             0.237            0.233
Chain 1:   4900       -10161.818             0.248            0.233
Chain 1:   5000       -16191.842             0.270            0.250
Chain 1:   5100        -9326.932             0.330            0.351
Chain 1:   5200        -9439.375             0.308            0.351
Chain 1:   5300       -13713.468             0.319            0.351
Chain 1:   5400        -8710.355             0.352            0.367
Chain 1:   5500       -11241.027             0.365            0.367
Chain 1:   5600        -8678.585             0.359            0.367
Chain 1:   5700       -12996.618             0.356            0.332
Chain 1:   5800        -9199.503             0.338            0.332
Chain 1:   5900        -9386.478             0.329            0.332
Chain 1:   6000        -9250.699             0.293            0.312
Chain 1:   6100        -9110.105             0.221            0.295
Chain 1:   6200        -8823.273             0.223            0.295
Chain 1:   6300       -14834.618             0.233            0.295
Chain 1:   6400        -8608.852             0.248            0.295
Chain 1:   6500        -9273.690             0.232            0.295
Chain 1:   6600       -12374.052             0.228            0.251
Chain 1:   6700       -10857.107             0.209            0.140
Chain 1:   6800        -9370.960             0.183            0.140
Chain 1:   6900       -13039.919             0.209            0.159
Chain 1:   7000       -15420.100             0.223            0.159
Chain 1:   7100        -8358.266             0.306            0.251
Chain 1:   7200        -8677.854             0.307            0.251
Chain 1:   7300        -8340.240             0.270            0.159
Chain 1:   7400        -9717.485             0.212            0.154
Chain 1:   7500       -10972.394             0.216            0.154
Chain 1:   7600        -8981.975             0.213            0.154
Chain 1:   7700       -13885.627             0.235            0.159
Chain 1:   7800        -8956.768             0.274            0.222
Chain 1:   7900        -8394.665             0.252            0.154
Chain 1:   8000       -12540.872             0.270            0.222
Chain 1:   8100        -8768.995             0.229            0.222
Chain 1:   8200       -11664.205             0.250            0.248
Chain 1:   8300       -10997.230             0.252            0.248
Chain 1:   8400        -8353.805             0.269            0.316
Chain 1:   8500        -8414.928             0.259            0.316
Chain 1:   8600       -11542.198             0.263            0.316
Chain 1:   8700        -8392.231             0.266            0.316
Chain 1:   8800        -8466.882             0.212            0.271
Chain 1:   8900        -8960.994             0.210            0.271
Chain 1:   9000        -9303.562             0.181            0.248
Chain 1:   9100        -8346.093             0.149            0.115
Chain 1:   9200        -8744.079             0.129            0.061
Chain 1:   9300        -8604.322             0.125            0.055
Chain 1:   9400        -8367.510             0.096            0.046
Chain 1:   9500        -9325.094             0.105            0.055
Chain 1:   9600       -12711.394             0.105            0.055
Chain 1:   9700        -8339.603             0.120            0.055
Chain 1:   9800        -9069.662             0.127            0.080
Chain 1:   9900        -8630.627             0.127            0.080
Chain 1:   10000       -10206.430             0.138            0.103
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56910.736             1.000            1.000
Chain 1:    200       -17772.139             1.601            2.202
Chain 1:    300        -8827.817             1.405            1.013
Chain 1:    400        -8201.953             1.073            1.013
Chain 1:    500        -8550.048             0.866            1.000
Chain 1:    600        -8670.350             0.724            1.000
Chain 1:    700        -7950.496             0.634            0.091
Chain 1:    800        -8347.933             0.561            0.091
Chain 1:    900        -8043.576             0.502            0.076
Chain 1:   1000        -7763.057             0.456            0.076
Chain 1:   1100        -7860.268             0.357            0.048
Chain 1:   1200        -7636.626             0.140            0.041
Chain 1:   1300        -7706.943             0.039            0.038
Chain 1:   1400        -7932.426             0.035            0.036
Chain 1:   1500        -7608.761             0.035            0.036
Chain 1:   1600        -7784.202             0.036            0.036
Chain 1:   1700        -7595.834             0.029            0.029
Chain 1:   1800        -7648.799             0.025            0.028
Chain 1:   1900        -7649.293             0.021            0.025
Chain 1:   2000        -7834.282             0.020            0.024
Chain 1:   2100        -7712.957             0.020            0.024
Chain 1:   2200        -8020.616             0.021            0.024
Chain 1:   2300        -7636.846             0.025            0.025
Chain 1:   2400        -7702.058             0.023            0.024
Chain 1:   2500        -7673.504             0.019            0.023
Chain 1:   2600        -7571.056             0.019            0.016
Chain 1:   2700        -7582.057             0.016            0.014
Chain 1:   2800        -7676.935             0.017            0.014
Chain 1:   2900        -7437.592             0.020            0.016
Chain 1:   3000        -7580.335             0.019            0.016
Chain 1:   3100        -7578.836             0.018            0.014
Chain 1:   3200        -7782.045             0.017            0.014
Chain 1:   3300        -7505.207             0.015            0.014
Chain 1:   3400        -7727.990             0.017            0.019
Chain 1:   3500        -7488.040             0.020            0.026
Chain 1:   3600        -7554.588             0.020            0.026
Chain 1:   3700        -7503.779             0.020            0.026
Chain 1:   3800        -7501.003             0.019            0.026
Chain 1:   3900        -7468.960             0.016            0.019
Chain 1:   4000        -7464.336             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002549 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86365.906             1.000            1.000
Chain 1:    200       -13811.826             3.127            5.253
Chain 1:    300       -10115.255             2.206            1.000
Chain 1:    400       -11204.983             1.679            1.000
Chain 1:    500        -8987.397             1.392            0.365
Chain 1:    600        -9477.059             1.169            0.365
Chain 1:    700        -8413.070             1.020            0.247
Chain 1:    800        -9283.499             0.904            0.247
Chain 1:    900        -8969.313             0.808            0.126
Chain 1:   1000        -8996.905             0.727            0.126
Chain 1:   1100        -8734.078             0.630            0.097   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8468.268             0.108            0.094
Chain 1:   1300        -8727.173             0.075            0.052
Chain 1:   1400        -8724.406             0.065            0.035
Chain 1:   1500        -8626.264             0.041            0.031
Chain 1:   1600        -8732.172             0.037            0.030
Chain 1:   1700        -8800.033             0.025            0.030
Chain 1:   1800        -8364.621             0.021            0.030
Chain 1:   1900        -8469.081             0.019            0.012
Chain 1:   2000        -8444.858             0.019            0.012
Chain 1:   2100        -8587.581             0.018            0.012
Chain 1:   2200        -8375.697             0.017            0.012
Chain 1:   2300        -8534.862             0.016            0.012
Chain 1:   2400        -8371.232             0.018            0.017
Chain 1:   2500        -8442.682             0.018            0.017
Chain 1:   2600        -8354.946             0.017            0.017
Chain 1:   2700        -8389.113             0.017            0.017
Chain 1:   2800        -8349.025             0.012            0.012
Chain 1:   2900        -8442.467             0.012            0.011
Chain 1:   3000        -8275.575             0.014            0.017
Chain 1:   3100        -8431.696             0.014            0.019
Chain 1:   3200        -8303.633             0.013            0.015
Chain 1:   3300        -8311.309             0.011            0.011
Chain 1:   3400        -8471.525             0.011            0.011
Chain 1:   3500        -8480.047             0.011            0.011
Chain 1:   3600        -8260.126             0.012            0.015
Chain 1:   3700        -8406.190             0.013            0.017
Chain 1:   3800        -8266.632             0.015            0.017
Chain 1:   3900        -8201.140             0.014            0.017
Chain 1:   4000        -8276.931             0.013            0.017
Chain 1:   4100        -8274.544             0.011            0.015
Chain 1:   4200        -8256.517             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002555 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416793.220             1.000            1.000
Chain 1:    200     -1586824.897             2.652            4.304
Chain 1:    300      -890453.199             2.029            1.000
Chain 1:    400      -457853.948             1.758            1.000
Chain 1:    500      -358050.200             1.462            0.945
Chain 1:    600      -233034.095             1.308            0.945
Chain 1:    700      -119375.401             1.257            0.945
Chain 1:    800       -86645.281             1.147            0.945
Chain 1:    900       -67018.595             1.052            0.782
Chain 1:   1000       -51854.096             0.976            0.782
Chain 1:   1100       -39361.524             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38542.942             0.480            0.378
Chain 1:   1300       -26523.891             0.447            0.378
Chain 1:   1400       -26246.295             0.353            0.317
Chain 1:   1500       -22840.566             0.340            0.317
Chain 1:   1600       -22059.751             0.290            0.293
Chain 1:   1700       -20935.977             0.200            0.292
Chain 1:   1800       -20880.990             0.163            0.149
Chain 1:   1900       -21207.533             0.135            0.054
Chain 1:   2000       -19719.252             0.113            0.054
Chain 1:   2100       -19957.619             0.083            0.035
Chain 1:   2200       -20184.211             0.082            0.035
Chain 1:   2300       -19801.176             0.038            0.019
Chain 1:   2400       -19573.148             0.039            0.019
Chain 1:   2500       -19375.077             0.025            0.015
Chain 1:   2600       -19004.986             0.023            0.015
Chain 1:   2700       -18961.800             0.018            0.012
Chain 1:   2800       -18678.503             0.019            0.015
Chain 1:   2900       -18959.845             0.019            0.015
Chain 1:   3000       -18945.995             0.012            0.012
Chain 1:   3100       -19031.109             0.011            0.012
Chain 1:   3200       -18721.516             0.011            0.015
Chain 1:   3300       -18926.412             0.011            0.012
Chain 1:   3400       -18400.901             0.012            0.015
Chain 1:   3500       -19013.462             0.015            0.015
Chain 1:   3600       -18319.138             0.016            0.015
Chain 1:   3700       -18706.694             0.018            0.017
Chain 1:   3800       -17664.925             0.023            0.021
Chain 1:   3900       -17660.998             0.021            0.021
Chain 1:   4000       -17778.314             0.022            0.021
Chain 1:   4100       -17692.063             0.022            0.021
Chain 1:   4200       -17507.917             0.021            0.021
Chain 1:   4300       -17646.591             0.021            0.021
Chain 1:   4400       -17603.138             0.018            0.011
Chain 1:   4500       -17505.598             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001854 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50477.776             1.000            1.000
Chain 1:    200       -21995.264             1.147            1.295
Chain 1:    300       -22231.036             0.769            1.000
Chain 1:    400       -19203.248             0.616            1.000
Chain 1:    500       -15023.197             0.548            0.278
Chain 1:    600       -15713.614             0.464            0.278
Chain 1:    700       -23076.836             0.443            0.278
Chain 1:    800       -15956.037             0.444            0.319
Chain 1:    900       -14542.441             0.405            0.278
Chain 1:   1000       -11635.949             0.390            0.278
Chain 1:   1100       -11448.350             0.291            0.250
Chain 1:   1200       -20883.641             0.207            0.250
Chain 1:   1300       -12796.882             0.269            0.278
Chain 1:   1400       -12565.696             0.255            0.278
Chain 1:   1500       -11057.799             0.241            0.250
Chain 1:   1600       -13173.568             0.253            0.250
Chain 1:   1700       -12654.742             0.225            0.161
Chain 1:   1800       -12686.764             0.181            0.136
Chain 1:   1900       -12441.165             0.173            0.136
Chain 1:   2000       -21804.816             0.191            0.136
Chain 1:   2100       -18413.008             0.208            0.161
Chain 1:   2200       -12695.165             0.207            0.161
Chain 1:   2300       -10477.671             0.165            0.161
Chain 1:   2400       -10799.488             0.167            0.161
Chain 1:   2500       -10987.063             0.155            0.161
Chain 1:   2600       -11861.297             0.146            0.074
Chain 1:   2700       -10268.519             0.157            0.155
Chain 1:   2800       -11479.667             0.168            0.155
Chain 1:   2900        -9875.052             0.182            0.162
Chain 1:   3000       -12089.471             0.157            0.162
Chain 1:   3100        -9508.872             0.166            0.162
Chain 1:   3200        -9864.887             0.125            0.155
Chain 1:   3300       -18381.917             0.150            0.155
Chain 1:   3400        -9930.855             0.232            0.162
Chain 1:   3500        -9970.916             0.231            0.162
Chain 1:   3600       -10047.390             0.224            0.162
Chain 1:   3700       -11545.646             0.221            0.162
Chain 1:   3800       -13009.562             0.222            0.162
Chain 1:   3900       -12535.673             0.210            0.130
Chain 1:   4000        -9595.249             0.222            0.130
Chain 1:   4100        -9788.831             0.197            0.113
Chain 1:   4200        -9752.598             0.194            0.113
Chain 1:   4300       -15486.222             0.184            0.113
Chain 1:   4400       -11812.670             0.130            0.113
Chain 1:   4500        -9735.863             0.151            0.130
Chain 1:   4600        -9122.553             0.157            0.130
Chain 1:   4700       -10318.972             0.156            0.116
Chain 1:   4800        -9676.946             0.151            0.116
Chain 1:   4900        -9584.673             0.148            0.116
Chain 1:   5000       -17826.967             0.164            0.116
Chain 1:   5100        -9440.286             0.251            0.213
Chain 1:   5200       -19294.067             0.302            0.311
Chain 1:   5300        -9786.227             0.362            0.311
Chain 1:   5400       -11922.938             0.348            0.213
Chain 1:   5500        -9850.311             0.348            0.210
Chain 1:   5600        -9921.300             0.342            0.210
Chain 1:   5700       -12999.778             0.354            0.237
Chain 1:   5800        -9493.509             0.385            0.369
Chain 1:   5900       -14057.141             0.416            0.369
Chain 1:   6000       -11887.414             0.388            0.325
Chain 1:   6100        -9001.366             0.331            0.321
Chain 1:   6200        -8838.940             0.282            0.237
Chain 1:   6300       -16187.481             0.230            0.237
Chain 1:   6400        -9097.299             0.290            0.321
Chain 1:   6500       -11900.472             0.293            0.321
Chain 1:   6600       -11116.784             0.299            0.321
Chain 1:   6700       -11767.042             0.281            0.321
Chain 1:   6800        -9464.364             0.268            0.243
Chain 1:   6900        -8990.867             0.241            0.236
Chain 1:   7000        -9040.226             0.224            0.236
Chain 1:   7100        -9495.621             0.196            0.070
Chain 1:   7200       -11152.903             0.209            0.149
Chain 1:   7300        -9026.798             0.187            0.149
Chain 1:   7400        -8882.694             0.111            0.070
Chain 1:   7500        -8801.369             0.088            0.055
Chain 1:   7600       -11271.338             0.103            0.055
Chain 1:   7700        -9617.248             0.115            0.149
Chain 1:   7800       -10929.865             0.103            0.120
Chain 1:   7900        -9661.053             0.111            0.131
Chain 1:   8000        -9128.562             0.116            0.131
Chain 1:   8100        -9664.981             0.117            0.131
Chain 1:   8200       -10499.426             0.110            0.120
Chain 1:   8300        -8811.586             0.105            0.120
Chain 1:   8400        -8973.232             0.105            0.120
Chain 1:   8500       -10490.654             0.119            0.131
Chain 1:   8600       -13072.908             0.117            0.131
Chain 1:   8700        -9322.163             0.140            0.131
Chain 1:   8800       -10504.591             0.139            0.131
Chain 1:   8900        -9360.434             0.138            0.122
Chain 1:   9000        -9104.761             0.135            0.122
Chain 1:   9100        -8739.113             0.134            0.122
Chain 1:   9200       -11453.416             0.150            0.145
Chain 1:   9300        -9742.724             0.148            0.145
Chain 1:   9400       -11066.494             0.158            0.145
Chain 1:   9500       -11910.645             0.151            0.122
Chain 1:   9600        -9864.319             0.152            0.122
Chain 1:   9700        -8740.538             0.124            0.122
Chain 1:   9800       -10113.991             0.127            0.129
Chain 1:   9900        -8921.174             0.128            0.134
Chain 1:   10000        -9065.495             0.127            0.134
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61465.979             1.000            1.000
Chain 1:    200       -18851.944             1.630            2.260
Chain 1:    300        -9386.523             1.423            1.008
Chain 1:    400        -8501.289             1.093            1.008
Chain 1:    500        -8055.380             0.886            1.000
Chain 1:    600        -9024.196             0.756            1.000
Chain 1:    700        -7613.602             0.674            0.185
Chain 1:    800        -8588.401             0.604            0.185
Chain 1:    900        -8707.811             0.539            0.114
Chain 1:   1000        -7701.785             0.498            0.131
Chain 1:   1100        -8001.588             0.402            0.114
Chain 1:   1200        -7855.322             0.177            0.107
Chain 1:   1300        -7666.497             0.079            0.104
Chain 1:   1400        -8286.277             0.076            0.075
Chain 1:   1500        -7684.532             0.078            0.078
Chain 1:   1600        -7894.659             0.070            0.075
Chain 1:   1700        -7849.562             0.052            0.037
Chain 1:   1800        -7597.946             0.044            0.033
Chain 1:   1900        -7598.554             0.043            0.033
Chain 1:   2000        -7589.263             0.030            0.027
Chain 1:   2100        -7771.685             0.029            0.025
Chain 1:   2200        -7945.544             0.029            0.025
Chain 1:   2300        -7647.133             0.030            0.027
Chain 1:   2400        -7624.441             0.023            0.023
Chain 1:   2500        -7605.223             0.016            0.022
Chain 1:   2600        -7560.245             0.014            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003208 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86511.148             1.000            1.000
Chain 1:    200       -14592.103             2.964            4.929
Chain 1:    300       -10740.147             2.096            1.000
Chain 1:    400       -13034.429             1.616            1.000
Chain 1:    500       -10441.738             1.342            0.359
Chain 1:    600        -9415.608             1.137            0.359
Chain 1:    700        -9045.459             0.980            0.248
Chain 1:    800        -9199.454             0.860            0.248
Chain 1:    900        -9362.480             0.766            0.176
Chain 1:   1000        -8981.631             0.694            0.176
Chain 1:   1100        -9419.419             0.598            0.109   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8809.853             0.113            0.069
Chain 1:   1300        -9349.373             0.082            0.058
Chain 1:   1400        -9107.936             0.067            0.046
Chain 1:   1500        -9121.761             0.043            0.042
Chain 1:   1600        -9175.678             0.032            0.041
Chain 1:   1700        -9243.529             0.029            0.027
Chain 1:   1800        -8768.804             0.033            0.042
Chain 1:   1900        -8873.495             0.032            0.042
Chain 1:   2000        -8885.690             0.028            0.027
Chain 1:   2100        -9022.227             0.025            0.015
Chain 1:   2200        -8745.218             0.021            0.015
Chain 1:   2300        -8834.042             0.017            0.012
Chain 1:   2400        -8926.565             0.015            0.010
Chain 1:   2500        -8831.258             0.016            0.011
Chain 1:   2600        -8883.034             0.016            0.011
Chain 1:   2700        -8785.571             0.016            0.011
Chain 1:   2800        -8740.576             0.011            0.011
Chain 1:   2900        -8842.581             0.011            0.011
Chain 1:   3000        -8763.384             0.012            0.011
Chain 1:   3100        -8721.188             0.011            0.010
Chain 1:   3200        -8682.471             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002548 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403398.178             1.000            1.000
Chain 1:    200     -1581584.283             2.657            4.313
Chain 1:    300      -890490.863             2.030            1.000
Chain 1:    400      -458486.997             1.758            1.000
Chain 1:    500      -359303.037             1.462            0.942
Chain 1:    600      -234376.970             1.307            0.942
Chain 1:    700      -120496.781             1.255            0.942
Chain 1:    800       -87742.953             1.145            0.942
Chain 1:    900       -68056.057             1.050            0.776
Chain 1:   1000       -52850.980             0.974            0.776
Chain 1:   1100       -40313.909             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39498.120             0.475            0.373
Chain 1:   1300       -27408.314             0.442            0.373
Chain 1:   1400       -27129.668             0.349            0.311
Chain 1:   1500       -23705.282             0.336            0.311
Chain 1:   1600       -22920.865             0.286            0.289
Chain 1:   1700       -21787.025             0.196            0.288
Chain 1:   1800       -21730.648             0.159            0.144
Chain 1:   1900       -22058.050             0.132            0.052
Chain 1:   2000       -20563.426             0.110            0.052
Chain 1:   2100       -20801.926             0.080            0.034
Chain 1:   2200       -21030.039             0.079            0.034
Chain 1:   2300       -20645.526             0.037            0.019
Chain 1:   2400       -20417.095             0.037            0.019
Chain 1:   2500       -20219.483             0.024            0.015
Chain 1:   2600       -19847.940             0.022            0.015
Chain 1:   2700       -19804.447             0.017            0.011
Chain 1:   2800       -19520.874             0.018            0.015
Chain 1:   2900       -19802.815             0.018            0.014
Chain 1:   3000       -19788.762             0.011            0.011
Chain 1:   3100       -19873.981             0.011            0.011
Chain 1:   3200       -19563.710             0.011            0.014
Chain 1:   3300       -19769.220             0.010            0.011
Chain 1:   3400       -19242.568             0.012            0.014
Chain 1:   3500       -19856.895             0.014            0.015
Chain 1:   3600       -19160.391             0.016            0.015
Chain 1:   3700       -19549.578             0.017            0.016
Chain 1:   3800       -18504.442             0.022            0.020
Chain 1:   3900       -18500.522             0.020            0.020
Chain 1:   4000       -18617.773             0.021            0.020
Chain 1:   4100       -18531.312             0.021            0.020
Chain 1:   4200       -18346.514             0.020            0.020
Chain 1:   4300       -18485.602             0.020            0.020
Chain 1:   4400       -18441.514             0.017            0.010
Chain 1:   4500       -18343.965             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49133.342             1.000            1.000
Chain 1:    200       -18024.689             1.363            1.726
Chain 1:    300       -21202.412             0.959            1.000
Chain 1:    400       -21484.073             0.722            1.000
Chain 1:    500       -25443.031             0.609            0.156
Chain 1:    600       -15824.190             0.609            0.608
Chain 1:    700       -11531.228             0.575            0.372
Chain 1:    800       -22528.081             0.564            0.488
Chain 1:    900       -11193.825             0.614            0.488
Chain 1:   1000       -11962.598             0.559            0.488
Chain 1:   1100       -21880.544             0.504            0.453   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10224.550             0.446            0.453
Chain 1:   1300       -10821.599             0.436            0.453
Chain 1:   1400       -11049.577             0.437            0.453
Chain 1:   1500       -10505.281             0.427            0.453
Chain 1:   1600       -22562.238             0.419            0.453
Chain 1:   1700       -20147.094             0.394            0.453
Chain 1:   1800       -12178.114             0.411            0.453
Chain 1:   1900       -10389.600             0.327            0.172
Chain 1:   2000       -13240.887             0.342            0.215
Chain 1:   2100       -10198.053             0.326            0.215
Chain 1:   2200       -16899.537             0.252            0.215
Chain 1:   2300       -10874.559             0.302            0.298
Chain 1:   2400        -9559.557             0.313            0.298
Chain 1:   2500        -9565.964             0.308            0.298
Chain 1:   2600       -10075.699             0.260            0.215
Chain 1:   2700       -10853.289             0.255            0.215
Chain 1:   2800       -19414.316             0.234            0.215
Chain 1:   2900        -9826.846             0.314            0.298
Chain 1:   3000        -9230.675             0.299            0.298
Chain 1:   3100        -9597.760             0.273            0.138
Chain 1:   3200       -13791.076             0.264            0.138
Chain 1:   3300       -15628.936             0.220            0.118
Chain 1:   3400        -9987.437             0.263            0.118
Chain 1:   3500        -9248.035             0.271            0.118
Chain 1:   3600       -15008.978             0.304            0.304
Chain 1:   3700       -13467.078             0.308            0.304
Chain 1:   3800       -15821.783             0.279            0.149
Chain 1:   3900        -9182.261             0.254            0.149
Chain 1:   4000        -9717.788             0.253            0.149
Chain 1:   4100        -9503.657             0.251            0.149
Chain 1:   4200        -8957.936             0.227            0.118
Chain 1:   4300       -10165.530             0.227            0.119
Chain 1:   4400        -8592.642             0.189            0.119
Chain 1:   4500       -10052.118             0.196            0.145
Chain 1:   4600       -12501.745             0.177            0.145
Chain 1:   4700        -9785.209             0.193            0.149
Chain 1:   4800        -8856.114             0.189            0.145
Chain 1:   4900       -12080.321             0.143            0.145
Chain 1:   5000       -12989.080             0.145            0.145
Chain 1:   5100        -8888.324             0.188            0.183
Chain 1:   5200        -9073.411             0.184            0.183
Chain 1:   5300       -12112.181             0.198            0.196
Chain 1:   5400        -9186.780             0.211            0.251
Chain 1:   5500        -9098.866             0.198            0.251
Chain 1:   5600        -8603.265             0.184            0.251
Chain 1:   5700        -9138.906             0.162            0.105
Chain 1:   5800        -8912.336             0.154            0.070
Chain 1:   5900       -12847.073             0.158            0.070
Chain 1:   6000        -9702.833             0.183            0.251
Chain 1:   6100        -9485.654             0.139            0.059
Chain 1:   6200        -9562.909             0.138            0.059
Chain 1:   6300       -13827.831             0.144            0.059
Chain 1:   6400       -13083.870             0.118            0.058
Chain 1:   6500       -12510.888             0.121            0.058
Chain 1:   6600        -8486.826             0.163            0.059
Chain 1:   6700       -11974.768             0.186            0.291
Chain 1:   6800       -12439.818             0.188            0.291
Chain 1:   6900       -10811.536             0.172            0.151
Chain 1:   7000        -8791.019             0.163            0.151
Chain 1:   7100        -8752.433             0.161            0.151
Chain 1:   7200       -11013.241             0.180            0.205
Chain 1:   7300        -8544.452             0.178            0.205
Chain 1:   7400        -8186.729             0.177            0.205
Chain 1:   7500        -9120.923             0.183            0.205
Chain 1:   7600        -8667.053             0.141            0.151
Chain 1:   7700        -8985.378             0.115            0.102
Chain 1:   7800        -9000.584             0.111            0.102
Chain 1:   7900        -8606.890             0.101            0.052
Chain 1:   8000       -12946.161             0.112            0.052
Chain 1:   8100        -8380.000             0.166            0.102
Chain 1:   8200        -8392.351             0.145            0.052
Chain 1:   8300        -8320.339             0.117            0.046
Chain 1:   8400        -9447.397             0.125            0.052
Chain 1:   8500        -8299.710             0.128            0.052
Chain 1:   8600       -11486.795             0.151            0.119
Chain 1:   8700        -8249.607             0.187            0.138
Chain 1:   8800        -9259.352             0.197            0.138
Chain 1:   8900       -12479.861             0.218            0.258
Chain 1:   9000        -9744.688             0.213            0.258
Chain 1:   9100       -11352.660             0.173            0.142
Chain 1:   9200       -10539.713             0.180            0.142
Chain 1:   9300        -8304.678             0.206            0.258
Chain 1:   9400       -11665.714             0.223            0.269
Chain 1:   9500        -8220.729             0.251            0.277
Chain 1:   9600        -9306.859             0.235            0.269
Chain 1:   9700       -10301.231             0.206            0.258
Chain 1:   9800        -8822.139             0.211            0.258
Chain 1:   9900        -9123.766             0.189            0.168
Chain 1:   10000        -8312.208             0.171            0.142
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001483 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62129.910             1.000            1.000
Chain 1:    200       -17877.377             1.738            2.475
Chain 1:    300        -8903.148             1.494            1.008
Chain 1:    400        -9573.817             1.138            1.008
Chain 1:    500        -8601.047             0.933            1.000
Chain 1:    600        -8781.790             0.781            1.000
Chain 1:    700        -8195.851             0.680            0.113
Chain 1:    800        -8253.936             0.596            0.113
Chain 1:    900        -7995.204             0.533            0.071
Chain 1:   1000        -7956.952             0.480            0.071
Chain 1:   1100        -7762.975             0.383            0.070
Chain 1:   1200        -7668.312             0.136            0.032
Chain 1:   1300        -7815.976             0.038            0.025
Chain 1:   1400        -7721.773             0.032            0.021
Chain 1:   1500        -7630.744             0.022            0.019
Chain 1:   1600        -7791.728             0.022            0.019
Chain 1:   1700        -7573.458             0.017            0.019
Chain 1:   1800        -7633.781             0.017            0.019
Chain 1:   1900        -7647.575             0.014            0.012
Chain 1:   2000        -7708.136             0.015            0.012
Chain 1:   2100        -7669.672             0.013            0.012
Chain 1:   2200        -7754.049             0.013            0.012
Chain 1:   2300        -7665.118             0.012            0.012
Chain 1:   2400        -7704.147             0.011            0.011
Chain 1:   2500        -7672.349             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00291 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85718.352             1.000            1.000
Chain 1:    200       -13529.297             3.168            5.336
Chain 1:    300        -9949.343             2.232            1.000
Chain 1:    400       -10789.861             1.693            1.000
Chain 1:    500        -8883.618             1.398            0.360
Chain 1:    600        -8457.393             1.173            0.360
Chain 1:    700        -8606.295             1.008            0.215
Chain 1:    800        -9218.798             0.890            0.215
Chain 1:    900        -8674.296             0.798            0.078
Chain 1:   1000        -8565.511             0.720            0.078
Chain 1:   1100        -8686.489             0.621            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8500.838             0.090            0.063
Chain 1:   1300        -8668.033             0.056            0.050
Chain 1:   1400        -8673.471             0.048            0.022
Chain 1:   1500        -8535.552             0.028            0.019
Chain 1:   1600        -8646.551             0.024            0.017
Chain 1:   1700        -8732.771             0.024            0.016
Chain 1:   1800        -8332.466             0.022            0.016
Chain 1:   1900        -8431.404             0.017            0.014
Chain 1:   2000        -8402.766             0.016            0.014
Chain 1:   2100        -8522.601             0.016            0.014
Chain 1:   2200        -8313.579             0.016            0.014
Chain 1:   2300        -8463.313             0.016            0.014
Chain 1:   2400        -8344.015             0.017            0.014
Chain 1:   2500        -8407.030             0.016            0.014
Chain 1:   2600        -8428.630             0.015            0.014
Chain 1:   2700        -8347.600             0.015            0.014
Chain 1:   2800        -8321.478             0.011            0.012
Chain 1:   2900        -8376.850             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003082 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395993.780             1.000            1.000
Chain 1:    200     -1578715.769             2.659            4.318
Chain 1:    300      -889158.497             2.031            1.000
Chain 1:    400      -457146.228             1.760            1.000
Chain 1:    500      -357879.864             1.463            0.945
Chain 1:    600      -232977.432             1.309            0.945
Chain 1:    700      -119243.764             1.258            0.945
Chain 1:    800       -86492.917             1.148            0.945
Chain 1:    900       -66828.786             1.053            0.776
Chain 1:   1000       -51620.944             0.977            0.776
Chain 1:   1100       -39096.147             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38269.978             0.480            0.379
Chain 1:   1300       -26220.907             0.448            0.379
Chain 1:   1400       -25938.571             0.355            0.320
Chain 1:   1500       -22525.871             0.342            0.320
Chain 1:   1600       -21742.603             0.292            0.295
Chain 1:   1700       -20615.451             0.202            0.294
Chain 1:   1800       -20559.562             0.165            0.152
Chain 1:   1900       -20885.474             0.137            0.055
Chain 1:   2000       -19397.076             0.115            0.055
Chain 1:   2100       -19635.100             0.084            0.036
Chain 1:   2200       -19861.687             0.083            0.036
Chain 1:   2300       -19478.913             0.039            0.020
Chain 1:   2400       -19251.106             0.039            0.020
Chain 1:   2500       -19053.362             0.025            0.016
Chain 1:   2600       -18683.503             0.023            0.016
Chain 1:   2700       -18640.524             0.018            0.012
Chain 1:   2800       -18357.610             0.020            0.015
Chain 1:   2900       -18638.772             0.019            0.015
Chain 1:   3000       -18624.838             0.012            0.012
Chain 1:   3100       -18709.830             0.011            0.012
Chain 1:   3200       -18400.629             0.012            0.015
Chain 1:   3300       -18605.294             0.011            0.012
Chain 1:   3400       -18080.517             0.013            0.015
Chain 1:   3500       -18691.987             0.015            0.015
Chain 1:   3600       -17999.214             0.017            0.015
Chain 1:   3700       -18385.636             0.018            0.017
Chain 1:   3800       -17346.240             0.023            0.021
Chain 1:   3900       -17342.479             0.021            0.021
Chain 1:   4000       -17459.721             0.022            0.021
Chain 1:   4100       -17373.548             0.022            0.021
Chain 1:   4200       -17190.027             0.021            0.021
Chain 1:   4300       -17328.225             0.021            0.021
Chain 1:   4400       -17285.184             0.019            0.011
Chain 1:   4500       -17187.817             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001245 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49789.326             1.000            1.000
Chain 1:    200       -24268.072             1.026            1.052
Chain 1:    300       -19457.275             0.766            1.000
Chain 1:    400       -17908.056             0.596            1.000
Chain 1:    500       -12832.202             0.556            0.396
Chain 1:    600       -13267.449             0.469            0.396
Chain 1:    700       -15494.713             0.423            0.247
Chain 1:    800       -31252.876             0.433            0.396
Chain 1:    900       -13831.288             0.525            0.396
Chain 1:   1000       -13538.154             0.474            0.396
Chain 1:   1100       -14245.956             0.379            0.247
Chain 1:   1200       -17195.567             0.291            0.172
Chain 1:   1300       -15937.840             0.274            0.144
Chain 1:   1400       -13696.620             0.282            0.164
Chain 1:   1500       -12587.514             0.251            0.144
Chain 1:   1600       -11908.802             0.254            0.144
Chain 1:   1700       -17387.211             0.271            0.164
Chain 1:   1800       -11439.985             0.273            0.164
Chain 1:   1900       -10362.594             0.157            0.104
Chain 1:   2000       -10632.781             0.157            0.104
Chain 1:   2100       -19729.009             0.198            0.164
Chain 1:   2200       -11464.520             0.253            0.164
Chain 1:   2300       -12749.322             0.256            0.164
Chain 1:   2400       -14334.762             0.250            0.111
Chain 1:   2500       -10338.314             0.280            0.315
Chain 1:   2600       -10528.605             0.276            0.315
Chain 1:   2700       -10617.646             0.246            0.111
Chain 1:   2800       -15120.003             0.223            0.111
Chain 1:   2900       -10220.660             0.261            0.298
Chain 1:   3000       -10956.619             0.265            0.298
Chain 1:   3100        -9598.786             0.233            0.141
Chain 1:   3200       -11232.705             0.176            0.141
Chain 1:   3300       -14492.492             0.188            0.145
Chain 1:   3400        -9713.207             0.226            0.225
Chain 1:   3500       -10307.672             0.193            0.145
Chain 1:   3600       -15954.119             0.227            0.225
Chain 1:   3700        -9650.309             0.291            0.298
Chain 1:   3800        -9323.104             0.265            0.225
Chain 1:   3900        -9635.481             0.220            0.145
Chain 1:   4000       -10918.363             0.225            0.145
Chain 1:   4100        -9469.225             0.227            0.153
Chain 1:   4200       -14157.070             0.245            0.225
Chain 1:   4300       -16389.330             0.236            0.153
Chain 1:   4400        -9748.020             0.255            0.153
Chain 1:   4500       -10517.684             0.257            0.153
Chain 1:   4600        -9686.458             0.230            0.136
Chain 1:   4700        -9371.206             0.168            0.117
Chain 1:   4800        -9254.611             0.166            0.117
Chain 1:   4900        -9521.463             0.165            0.117
Chain 1:   5000       -10278.406             0.161            0.086
Chain 1:   5100        -9226.707             0.157            0.086
Chain 1:   5200       -14255.474             0.159            0.086
Chain 1:   5300       -14344.957             0.146            0.074
Chain 1:   5400        -9150.127             0.135            0.074
Chain 1:   5500        -9663.227             0.133            0.074
Chain 1:   5600        -9222.735             0.129            0.053
Chain 1:   5700        -9314.827             0.127            0.053
Chain 1:   5800        -9861.173             0.131            0.055
Chain 1:   5900        -9183.419             0.135            0.074
Chain 1:   6000        -9533.346             0.132            0.055
Chain 1:   6100        -9188.415             0.124            0.053
Chain 1:   6200       -14068.878             0.124            0.053
Chain 1:   6300        -9505.716             0.171            0.055
Chain 1:   6400        -9918.720             0.118            0.053
Chain 1:   6500        -9245.889             0.120            0.055
Chain 1:   6600        -9227.092             0.116            0.055
Chain 1:   6700       -14439.833             0.151            0.073
Chain 1:   6800       -15753.698             0.154            0.074
Chain 1:   6900        -9218.293             0.217            0.083
Chain 1:   7000        -9386.456             0.215            0.083
Chain 1:   7100       -10979.051             0.226            0.145
Chain 1:   7200        -9221.632             0.210            0.145
Chain 1:   7300       -11482.709             0.182            0.145
Chain 1:   7400       -10297.906             0.189            0.145
Chain 1:   7500        -9548.610             0.190            0.145
Chain 1:   7600        -8969.856             0.196            0.145
Chain 1:   7700       -10272.179             0.173            0.127
Chain 1:   7800       -14570.088             0.194            0.145
Chain 1:   7900        -9196.158             0.181            0.145
Chain 1:   8000        -8875.057             0.183            0.145
Chain 1:   8100        -9735.988             0.178            0.127
Chain 1:   8200        -9234.410             0.164            0.115
Chain 1:   8300        -9100.356             0.146            0.088
Chain 1:   8400        -8846.465             0.137            0.078
Chain 1:   8500       -12115.142             0.156            0.088
Chain 1:   8600       -12925.522             0.156            0.088
Chain 1:   8700        -9178.995             0.184            0.088
Chain 1:   8800       -12192.261             0.179            0.088
Chain 1:   8900        -9128.847             0.155            0.088
Chain 1:   9000       -11427.452             0.171            0.201
Chain 1:   9100        -8694.377             0.194            0.247
Chain 1:   9200        -9703.499             0.199            0.247
Chain 1:   9300        -8656.662             0.209            0.247
Chain 1:   9400       -12370.059             0.236            0.270
Chain 1:   9500       -13222.355             0.216            0.247
Chain 1:   9600        -8850.746             0.259            0.300
Chain 1:   9700        -8713.868             0.220            0.247
Chain 1:   9800        -9325.648             0.202            0.201
Chain 1:   9900        -9247.182             0.169            0.121
Chain 1:   10000        -9543.954             0.152            0.104
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57927.970             1.000            1.000
Chain 1:    200       -18348.913             1.579            2.157
Chain 1:    300        -9229.027             1.382            1.000
Chain 1:    400        -8385.941             1.061            1.000
Chain 1:    500        -8535.458             0.853            0.988
Chain 1:    600        -9139.661             0.722            0.988
Chain 1:    700        -8467.394             0.630            0.101
Chain 1:    800        -8588.616             0.553            0.101
Chain 1:    900        -8204.268             0.497            0.079
Chain 1:   1000        -8141.751             0.448            0.079
Chain 1:   1100        -8083.418             0.348            0.066
Chain 1:   1200        -7907.664             0.135            0.047
Chain 1:   1300        -7911.886             0.036            0.022
Chain 1:   1400        -8012.158             0.027            0.018
Chain 1:   1500        -7739.973             0.029            0.022
Chain 1:   1600        -7971.377             0.025            0.022
Chain 1:   1700        -7886.338             0.019            0.014
Chain 1:   1800        -7708.369             0.020            0.022
Chain 1:   1900        -7758.585             0.015            0.013
Chain 1:   2000        -7903.600             0.017            0.018
Chain 1:   2100        -7740.818             0.018            0.021
Chain 1:   2200        -7960.659             0.018            0.021
Chain 1:   2300        -7866.357             0.020            0.021
Chain 1:   2400        -7772.082             0.020            0.021
Chain 1:   2500        -7762.917             0.016            0.018
Chain 1:   2600        -7688.323             0.014            0.012
Chain 1:   2700        -7681.342             0.013            0.012
Chain 1:   2800        -7716.578             0.011            0.012
Chain 1:   2900        -7528.791             0.013            0.012
Chain 1:   3000        -7693.778             0.014            0.012
Chain 1:   3100        -7681.384             0.012            0.012
Chain 1:   3200        -7898.814             0.012            0.012
Chain 1:   3300        -7564.272             0.015            0.012
Chain 1:   3400        -7794.701             0.017            0.021
Chain 1:   3500        -7604.561             0.019            0.025
Chain 1:   3600        -7670.368             0.019            0.025
Chain 1:   3700        -7605.454             0.020            0.025
Chain 1:   3800        -7595.441             0.019            0.025
Chain 1:   3900        -7569.299             0.017            0.021
Chain 1:   4000        -7555.553             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003085 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86941.601             1.000            1.000
Chain 1:    200       -14420.066             3.015            5.029
Chain 1:    300       -10629.082             2.129            1.000
Chain 1:    400       -12585.705             1.635            1.000
Chain 1:    500        -9074.108             1.386            0.387
Chain 1:    600        -9074.191             1.155            0.387
Chain 1:    700        -8872.568             0.993            0.357
Chain 1:    800        -9467.919             0.877            0.357
Chain 1:    900        -9359.276             0.781            0.155
Chain 1:   1000        -9006.016             0.706            0.155
Chain 1:   1100        -9432.656             0.611            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9034.273             0.112            0.045
Chain 1:   1300        -9248.375             0.079            0.044
Chain 1:   1400        -9168.167             0.064            0.039
Chain 1:   1500        -9082.206             0.027            0.023
Chain 1:   1600        -9169.727             0.028            0.023
Chain 1:   1700        -9234.664             0.026            0.023
Chain 1:   1800        -8795.805             0.025            0.023
Chain 1:   1900        -8893.537             0.025            0.023
Chain 1:   2000        -8909.037             0.021            0.011
Chain 1:   2100        -9001.488             0.017            0.010
Chain 1:   2200        -8784.336             0.016            0.010
Chain 1:   2300        -8972.475             0.015            0.010
Chain 1:   2400        -8792.969             0.017            0.011
Chain 1:   2500        -8866.782             0.016            0.011
Chain 1:   2600        -8777.018             0.016            0.011
Chain 1:   2700        -8809.946             0.016            0.011
Chain 1:   2800        -8760.997             0.012            0.010
Chain 1:   2900        -8875.457             0.012            0.010
Chain 1:   3000        -8792.003             0.013            0.010
Chain 1:   3100        -8753.120             0.012            0.010
Chain 1:   3200        -8725.516             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002572 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372462.726             1.000            1.000
Chain 1:    200     -1579355.583             2.651            4.301
Chain 1:    300      -890933.760             2.025            1.000
Chain 1:    400      -458602.206             1.754            1.000
Chain 1:    500      -359613.446             1.458            0.943
Chain 1:    600      -234615.008             1.304            0.943
Chain 1:    700      -120534.962             1.253            0.943
Chain 1:    800       -87700.572             1.143            0.943
Chain 1:    900       -67976.823             1.048            0.773
Chain 1:   1000       -52725.185             0.972            0.773
Chain 1:   1100       -40148.069             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39326.315             0.476            0.374
Chain 1:   1300       -27204.591             0.443            0.374
Chain 1:   1400       -26921.305             0.350            0.313
Chain 1:   1500       -23488.138             0.337            0.313
Chain 1:   1600       -22700.459             0.287            0.290
Chain 1:   1700       -21563.297             0.198            0.289
Chain 1:   1800       -21505.645             0.161            0.146
Chain 1:   1900       -21832.672             0.133            0.053
Chain 1:   2000       -20336.879             0.112            0.053
Chain 1:   2100       -20575.557             0.081            0.035
Chain 1:   2200       -20803.621             0.080            0.035
Chain 1:   2300       -20419.173             0.038            0.019
Chain 1:   2400       -20190.847             0.038            0.019
Chain 1:   2500       -19993.356             0.024            0.015
Chain 1:   2600       -19622.237             0.023            0.015
Chain 1:   2700       -19578.816             0.017            0.012
Chain 1:   2800       -19295.519             0.019            0.015
Chain 1:   2900       -19577.266             0.019            0.014
Chain 1:   3000       -19563.254             0.011            0.012
Chain 1:   3100       -19648.418             0.011            0.011
Chain 1:   3200       -19338.441             0.011            0.014
Chain 1:   3300       -19543.702             0.010            0.011
Chain 1:   3400       -19017.637             0.012            0.014
Chain 1:   3500       -19631.180             0.014            0.015
Chain 1:   3600       -18935.691             0.016            0.015
Chain 1:   3700       -19324.180             0.018            0.016
Chain 1:   3800       -18280.666             0.022            0.020
Chain 1:   3900       -18276.795             0.020            0.020
Chain 1:   4000       -18394.020             0.021            0.020
Chain 1:   4100       -18307.680             0.021            0.020
Chain 1:   4200       -18123.199             0.020            0.020
Chain 1:   4300       -18262.044             0.020            0.020
Chain 1:   4400       -18218.282             0.018            0.010
Chain 1:   4500       -18120.766             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00132 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48388.535             1.000            1.000
Chain 1:    200       -24099.825             1.004            1.008
Chain 1:    300       -12127.427             0.998            1.000
Chain 1:    400       -14678.256             0.792            1.000
Chain 1:    500       -20117.885             0.688            0.987
Chain 1:    600       -23384.258             0.596            0.987
Chain 1:    700       -13175.825             0.622            0.775
Chain 1:    800       -11285.175             0.565            0.775
Chain 1:    900       -18142.180             0.544            0.378
Chain 1:   1000       -13834.885             0.521            0.378
Chain 1:   1100       -14932.093             0.428            0.311
Chain 1:   1200       -16865.045             0.339            0.270
Chain 1:   1300       -11860.541             0.283            0.270
Chain 1:   1400       -22584.084             0.313            0.311
Chain 1:   1500       -13146.507             0.357            0.378
Chain 1:   1600       -10658.998             0.367            0.378
Chain 1:   1700       -12924.374             0.307            0.311
Chain 1:   1800        -9896.991             0.321            0.311
Chain 1:   1900       -14173.234             0.313            0.306
Chain 1:   2000       -22208.253             0.318            0.306
Chain 1:   2100        -9243.070             0.451            0.362
Chain 1:   2200       -17427.601             0.487            0.422
Chain 1:   2300       -10940.330             0.504            0.470   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400        -8782.990             0.481            0.362
Chain 1:   2500       -13324.922             0.443            0.341
Chain 1:   2600       -12343.095             0.428            0.341
Chain 1:   2700       -11828.377             0.414            0.341
Chain 1:   2800       -10947.498             0.392            0.341
Chain 1:   2900       -16270.908             0.394            0.341
Chain 1:   3000       -12249.708             0.391            0.328
Chain 1:   3100       -12019.931             0.253            0.327
Chain 1:   3200        -8545.499             0.246            0.327
Chain 1:   3300        -8793.080             0.190            0.246
Chain 1:   3400        -8870.226             0.166            0.080
Chain 1:   3500        -8830.766             0.133            0.080
Chain 1:   3600        -9468.587             0.131            0.067
Chain 1:   3700        -9681.611             0.129            0.067
Chain 1:   3800       -11922.253             0.140            0.067
Chain 1:   3900        -8785.524             0.143            0.067
Chain 1:   4000        -9183.493             0.114            0.043
Chain 1:   4100        -9314.529             0.114            0.043
Chain 1:   4200        -9260.613             0.074            0.028
Chain 1:   4300       -14024.932             0.105            0.043
Chain 1:   4400       -12917.946             0.113            0.067
Chain 1:   4500        -8464.053             0.165            0.086
Chain 1:   4600       -11269.197             0.183            0.188
Chain 1:   4700        -8692.870             0.211            0.249
Chain 1:   4800        -8225.819             0.197            0.249
Chain 1:   4900        -8861.605             0.169            0.086
Chain 1:   5000        -9845.516             0.175            0.100
Chain 1:   5100        -8193.947             0.193            0.202
Chain 1:   5200        -8295.118             0.194            0.202
Chain 1:   5300        -9010.028             0.168            0.100
Chain 1:   5400        -8614.753             0.164            0.100
Chain 1:   5500       -12061.618             0.140            0.100
Chain 1:   5600       -14247.962             0.130            0.100
Chain 1:   5700        -8150.757             0.175            0.100
Chain 1:   5800        -8109.416             0.170            0.100
Chain 1:   5900        -8382.155             0.166            0.100
Chain 1:   6000        -9992.801             0.173            0.153
Chain 1:   6100        -9517.229             0.157            0.079
Chain 1:   6200        -8525.178             0.168            0.116
Chain 1:   6300        -9072.087             0.166            0.116
Chain 1:   6400        -8686.502             0.166            0.116
Chain 1:   6500       -10124.472             0.151            0.116
Chain 1:   6600       -10278.031             0.137            0.060
Chain 1:   6700        -8989.993             0.077            0.060
Chain 1:   6800        -8279.123             0.085            0.086
Chain 1:   6900        -8489.572             0.084            0.086
Chain 1:   7000       -11549.370             0.095            0.086
Chain 1:   7100       -11563.540             0.090            0.086
Chain 1:   7200       -10072.234             0.093            0.086
Chain 1:   7300       -12198.467             0.104            0.142
Chain 1:   7400        -8377.737             0.146            0.143
Chain 1:   7500        -8276.626             0.133            0.143
Chain 1:   7600        -8405.580             0.133            0.143
Chain 1:   7700        -7854.370             0.125            0.086
Chain 1:   7800       -13266.516             0.158            0.148
Chain 1:   7900        -7884.426             0.223            0.174
Chain 1:   8000        -8909.053             0.208            0.148
Chain 1:   8100        -7976.433             0.220            0.148
Chain 1:   8200        -7863.023             0.207            0.117
Chain 1:   8300        -7935.432             0.190            0.115
Chain 1:   8400        -8358.406             0.149            0.070
Chain 1:   8500       -10176.412             0.166            0.115
Chain 1:   8600        -8033.292             0.191            0.117
Chain 1:   8700       -10486.227             0.208            0.179
Chain 1:   8800        -7820.733             0.201            0.179
Chain 1:   8900        -8660.141             0.142            0.117
Chain 1:   9000        -7920.995             0.140            0.117
Chain 1:   9100        -7776.375             0.130            0.097
Chain 1:   9200        -7916.200             0.131            0.097
Chain 1:   9300       -10012.677             0.151            0.179
Chain 1:   9400        -7852.115             0.173            0.209
Chain 1:   9500       -10307.991             0.179            0.234
Chain 1:   9600        -8057.251             0.180            0.234
Chain 1:   9700       -11241.811             0.185            0.238
Chain 1:   9800        -8490.583             0.184            0.238
Chain 1:   9900        -9042.603             0.180            0.238
Chain 1:   10000        -8136.668             0.182            0.238
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57350.219             1.000            1.000
Chain 1:    200       -17163.792             1.671            2.341
Chain 1:    300        -8400.670             1.461            1.043
Chain 1:    400        -7844.874             1.114            1.043
Chain 1:    500        -8314.177             0.902            1.000
Chain 1:    600        -7898.059             0.761            1.000
Chain 1:    700        -7733.703             0.655            0.071
Chain 1:    800        -7960.962             0.577            0.071
Chain 1:    900        -7802.643             0.515            0.056
Chain 1:   1000        -7646.920             0.465            0.056
Chain 1:   1100        -7548.913             0.367            0.053
Chain 1:   1200        -7475.195             0.134            0.029
Chain 1:   1300        -7534.230             0.030            0.021
Chain 1:   1400        -7759.977             0.026            0.021
Chain 1:   1500        -7491.067             0.024            0.021
Chain 1:   1600        -7405.723             0.020            0.020
Chain 1:   1700        -7389.558             0.018            0.020
Chain 1:   1800        -7468.430             0.016            0.013
Chain 1:   1900        -7485.339             0.014            0.012
Chain 1:   2000        -7496.848             0.012            0.011
Chain 1:   2100        -7450.771             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003385 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85729.967             1.000            1.000
Chain 1:    200       -12967.485             3.306            5.611
Chain 1:    300        -9435.260             2.329            1.000
Chain 1:    400       -10272.914             1.767            1.000
Chain 1:    500        -8326.163             1.460            0.374
Chain 1:    600        -8300.073             1.217            0.374
Chain 1:    700        -8232.763             1.045            0.234
Chain 1:    800        -8316.472             0.915            0.234
Chain 1:    900        -8313.130             0.814            0.082
Chain 1:   1000        -8045.475             0.736            0.082
Chain 1:   1100        -8306.660             0.639            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7993.130             0.082            0.033
Chain 1:   1300        -8193.552             0.047            0.031
Chain 1:   1400        -8187.102             0.038            0.024
Chain 1:   1500        -8086.471             0.016            0.012
Chain 1:   1600        -8179.997             0.017            0.012
Chain 1:   1700        -8284.131             0.018            0.013
Chain 1:   1800        -7892.573             0.022            0.024
Chain 1:   1900        -7993.723             0.023            0.024
Chain 1:   2000        -7963.628             0.020            0.013
Chain 1:   2100        -8102.659             0.018            0.013
Chain 1:   2200        -7883.983             0.017            0.013
Chain 1:   2300        -8026.258             0.017            0.013
Chain 1:   2400        -7911.399             0.018            0.015
Chain 1:   2500        -7970.232             0.017            0.015
Chain 1:   2600        -7984.469             0.016            0.015
Chain 1:   2700        -7906.748             0.016            0.015
Chain 1:   2800        -7888.214             0.011            0.013
Chain 1:   2900        -7899.567             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002585 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410081.052             1.000            1.000
Chain 1:    200     -1587200.582             2.649            4.299
Chain 1:    300      -891616.081             2.026            1.000
Chain 1:    400      -457176.252             1.757            1.000
Chain 1:    500      -357276.842             1.462            0.950
Chain 1:    600      -232162.825             1.308            0.950
Chain 1:    700      -118539.644             1.258            0.950
Chain 1:    800       -85759.776             1.149            0.950
Chain 1:    900       -66132.180             1.054            0.780
Chain 1:   1000       -50946.635             0.978            0.780
Chain 1:   1100       -38442.377             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37616.277             0.483            0.382
Chain 1:   1300       -25605.850             0.452            0.382
Chain 1:   1400       -25324.867             0.358            0.325
Chain 1:   1500       -21920.588             0.346            0.325
Chain 1:   1600       -21138.438             0.296            0.298
Chain 1:   1700       -20017.107             0.205            0.297
Chain 1:   1800       -19961.950             0.167            0.155
Chain 1:   1900       -20287.427             0.139            0.056
Chain 1:   2000       -18802.105             0.117            0.056
Chain 1:   2100       -19040.373             0.086            0.037
Chain 1:   2200       -19265.927             0.085            0.037
Chain 1:   2300       -18884.094             0.040            0.020
Chain 1:   2400       -18656.456             0.040            0.020
Chain 1:   2500       -18458.259             0.026            0.016
Chain 1:   2600       -18089.412             0.024            0.016
Chain 1:   2700       -18046.631             0.019            0.013
Chain 1:   2800       -17763.692             0.020            0.016
Chain 1:   2900       -18044.583             0.020            0.016
Chain 1:   3000       -18030.908             0.012            0.013
Chain 1:   3100       -18115.773             0.011            0.012
Chain 1:   3200       -17806.968             0.012            0.016
Chain 1:   3300       -18011.268             0.011            0.012
Chain 1:   3400       -17487.019             0.013            0.016
Chain 1:   3500       -18097.609             0.015            0.016
Chain 1:   3600       -17405.980             0.017            0.016
Chain 1:   3700       -17791.496             0.019            0.017
Chain 1:   3800       -16753.785             0.024            0.022
Chain 1:   3900       -16749.958             0.022            0.022
Chain 1:   4000       -16867.299             0.023            0.022
Chain 1:   4100       -16781.171             0.023            0.022
Chain 1:   4200       -16597.976             0.022            0.022
Chain 1:   4300       -16736.005             0.022            0.022
Chain 1:   4400       -16693.302             0.019            0.011
Chain 1:   4500       -16595.886             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001249 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12703.685             1.000            1.000
Chain 1:    200        -9367.866             0.678            1.000
Chain 1:    300        -8272.700             0.496            0.356
Chain 1:    400        -8235.872             0.373            0.356
Chain 1:    500        -8299.165             0.300            0.132
Chain 1:    600        -8187.219             0.252            0.132
Chain 1:    700        -8066.645             0.218            0.015
Chain 1:    800        -8068.309             0.191            0.015
Chain 1:    900        -7998.124             0.171            0.014
Chain 1:   1000        -8115.580             0.155            0.014
Chain 1:   1100        -8408.811             0.059            0.014
Chain 1:   1200        -8106.334             0.027            0.014
Chain 1:   1300        -8023.778             0.015            0.014
Chain 1:   1400        -8041.552             0.014            0.014
Chain 1:   1500        -8155.027             0.015            0.014
Chain 1:   1600        -8082.315             0.015            0.014
Chain 1:   1700        -8040.847             0.014            0.010
Chain 1:   1800        -8015.730             0.014            0.010
Chain 1:   1900        -8038.004             0.013            0.010
Chain 1:   2000        -7976.246             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001392 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62412.119             1.000            1.000
Chain 1:    200       -18328.807             1.703            2.405
Chain 1:    300        -9059.179             1.476            1.023
Chain 1:    400        -9467.731             1.118            1.023
Chain 1:    500        -8316.150             0.922            1.000
Chain 1:    600        -8497.634             0.772            1.000
Chain 1:    700        -7968.650             0.671            0.138
Chain 1:    800        -8157.967             0.590            0.138
Chain 1:    900        -8131.818             0.525            0.066
Chain 1:   1000        -7864.532             0.476            0.066
Chain 1:   1100        -7852.727             0.376            0.043
Chain 1:   1200        -7637.233             0.138            0.034
Chain 1:   1300        -7862.629             0.039            0.029
Chain 1:   1400        -7603.616             0.038            0.029
Chain 1:   1500        -7536.985             0.025            0.028
Chain 1:   1600        -7710.543             0.025            0.028
Chain 1:   1700        -7732.597             0.019            0.023
Chain 1:   1800        -7666.879             0.017            0.023
Chain 1:   1900        -7573.792             0.018            0.023
Chain 1:   2000        -7676.355             0.016            0.013
Chain 1:   2100        -7577.630             0.017            0.013
Chain 1:   2200        -7820.126             0.018            0.013
Chain 1:   2300        -7525.467             0.019            0.013
Chain 1:   2400        -7525.771             0.015            0.013
Chain 1:   2500        -7543.334             0.015            0.013
Chain 1:   2600        -7515.909             0.013            0.012
Chain 1:   2700        -7503.161             0.013            0.012
Chain 1:   2800        -7507.351             0.012            0.012
Chain 1:   2900        -7370.147             0.012            0.013
Chain 1:   3000        -7510.057             0.013            0.013
Chain 1:   3100        -7514.174             0.012            0.004   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002604 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85535.333             1.000            1.000
Chain 1:    200       -13916.073             3.073            5.147
Chain 1:    300       -10193.039             2.171            1.000
Chain 1:    400       -11559.537             1.657            1.000
Chain 1:    500        -9192.276             1.378            0.365
Chain 1:    600        -9059.399             1.150            0.365
Chain 1:    700        -8523.991             0.995            0.258
Chain 1:    800        -8749.743             0.874            0.258
Chain 1:    900        -8975.135             0.780            0.118
Chain 1:   1000        -8832.248             0.703            0.118
Chain 1:   1100        -8947.786             0.604            0.063   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8519.355             0.095            0.050
Chain 1:   1300        -8827.973             0.062            0.035
Chain 1:   1400        -8803.451             0.050            0.026
Chain 1:   1500        -8685.637             0.026            0.025
Chain 1:   1600        -8792.058             0.026            0.025
Chain 1:   1700        -8850.088             0.020            0.016
Chain 1:   1800        -8407.591             0.023            0.016
Chain 1:   1900        -8514.478             0.021            0.014
Chain 1:   2000        -8500.676             0.020            0.013
Chain 1:   2100        -8618.512             0.020            0.014
Chain 1:   2200        -8412.285             0.017            0.014
Chain 1:   2300        -8507.470             0.015            0.013
Chain 1:   2400        -8574.588             0.016            0.013
Chain 1:   2500        -8522.895             0.015            0.012
Chain 1:   2600        -8536.703             0.014            0.011
Chain 1:   2700        -8444.254             0.014            0.011
Chain 1:   2800        -8391.855             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003579 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378292.695             1.000            1.000
Chain 1:    200     -1579654.208             2.652            4.304
Chain 1:    300      -890482.827             2.026            1.000
Chain 1:    400      -458006.024             1.756            1.000
Chain 1:    500      -359004.343             1.460            0.944
Chain 1:    600      -234010.046             1.305            0.944
Chain 1:    700      -119972.825             1.255            0.944
Chain 1:    800       -87154.153             1.145            0.944
Chain 1:    900       -67433.252             1.050            0.774
Chain 1:   1000       -52189.878             0.974            0.774
Chain 1:   1100       -39621.244             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38795.697             0.478            0.377
Chain 1:   1300       -26687.525             0.446            0.377
Chain 1:   1400       -26403.887             0.352            0.317
Chain 1:   1500       -22974.659             0.340            0.317
Chain 1:   1600       -22187.469             0.290            0.292
Chain 1:   1700       -21052.479             0.200            0.292
Chain 1:   1800       -20995.194             0.163            0.149
Chain 1:   1900       -21321.876             0.135            0.054
Chain 1:   2000       -19827.694             0.114            0.054
Chain 1:   2100       -20066.224             0.083            0.035
Chain 1:   2200       -20293.934             0.082            0.035
Chain 1:   2300       -19909.925             0.039            0.019
Chain 1:   2400       -19681.728             0.039            0.019
Chain 1:   2500       -19484.189             0.025            0.015
Chain 1:   2600       -19113.424             0.023            0.015
Chain 1:   2700       -19070.112             0.018            0.012
Chain 1:   2800       -18786.916             0.019            0.015
Chain 1:   2900       -19068.501             0.019            0.015
Chain 1:   3000       -19054.530             0.012            0.012
Chain 1:   3100       -19139.644             0.011            0.012
Chain 1:   3200       -18829.887             0.011            0.015
Chain 1:   3300       -19034.956             0.011            0.012
Chain 1:   3400       -18509.243             0.012            0.015
Chain 1:   3500       -19122.248             0.014            0.015
Chain 1:   3600       -18427.459             0.016            0.015
Chain 1:   3700       -18815.405             0.018            0.016
Chain 1:   3800       -17772.988             0.022            0.021
Chain 1:   3900       -17769.139             0.021            0.021
Chain 1:   4000       -17886.366             0.022            0.021
Chain 1:   4100       -17800.083             0.022            0.021
Chain 1:   4200       -17615.845             0.021            0.021
Chain 1:   4300       -17754.523             0.021            0.021
Chain 1:   4400       -17710.942             0.018            0.010
Chain 1:   4500       -17613.475             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12606.924             1.000            1.000
Chain 1:    200        -9316.038             0.677            1.000
Chain 1:    300        -8169.342             0.498            0.353
Chain 1:    400        -8307.802             0.378            0.353
Chain 1:    500        -7914.374             0.312            0.140
Chain 1:    600        -8044.822             0.263            0.140
Chain 1:    700        -8040.386             0.225            0.050
Chain 1:    800        -8022.433             0.197            0.050
Chain 1:    900        -8074.845             0.176            0.017
Chain 1:   1000        -8023.890             0.159            0.017
Chain 1:   1100        -8097.425             0.060            0.016
Chain 1:   1200        -7999.138             0.026            0.012
Chain 1:   1300        -7914.780             0.013            0.011
Chain 1:   1400        -7944.171             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58872.561             1.000            1.000
Chain 1:    200       -17970.482             1.638            2.276
Chain 1:    300        -8957.543             1.427            1.006
Chain 1:    400        -9534.884             1.086            1.006
Chain 1:    500        -7852.517             0.911            1.000
Chain 1:    600        -8049.037             0.764            1.000
Chain 1:    700        -7965.609             0.656            0.214
Chain 1:    800        -8354.429             0.580            0.214
Chain 1:    900        -7942.291             0.521            0.061
Chain 1:   1000        -7830.127             0.470            0.061
Chain 1:   1100        -7714.979             0.372            0.052
Chain 1:   1200        -7673.109             0.145            0.047
Chain 1:   1300        -7743.158             0.045            0.024
Chain 1:   1400        -7666.772             0.040            0.015
Chain 1:   1500        -7590.140             0.020            0.014
Chain 1:   1600        -7671.385             0.018            0.011
Chain 1:   1700        -7539.112             0.019            0.014
Chain 1:   1800        -7481.598             0.015            0.011
Chain 1:   1900        -7578.205             0.011            0.011
Chain 1:   2000        -7597.893             0.010            0.010
Chain 1:   2100        -7533.980             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002512 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86823.160             1.000            1.000
Chain 1:    200       -13759.696             3.155            5.310
Chain 1:    300       -10053.378             2.226            1.000
Chain 1:    400       -11475.286             1.701            1.000
Chain 1:    500        -8943.327             1.417            0.369
Chain 1:    600        -8727.692             1.185            0.369
Chain 1:    700        -8770.947             1.016            0.283
Chain 1:    800        -8368.810             0.895            0.283
Chain 1:    900        -8398.160             0.796            0.124
Chain 1:   1000        -8609.417             0.719            0.124
Chain 1:   1100        -8821.245             0.622            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8397.451             0.096            0.048
Chain 1:   1300        -8728.398             0.063            0.038
Chain 1:   1400        -8663.804             0.051            0.025
Chain 1:   1500        -8566.150             0.024            0.025
Chain 1:   1600        -8667.183             0.022            0.024
Chain 1:   1700        -8732.167             0.023            0.024
Chain 1:   1800        -8294.972             0.023            0.024
Chain 1:   1900        -8399.630             0.024            0.024
Chain 1:   2000        -8376.841             0.022            0.012
Chain 1:   2100        -8516.047             0.021            0.012
Chain 1:   2200        -8305.937             0.019            0.012
Chain 1:   2300        -8464.553             0.017            0.012
Chain 1:   2400        -8301.403             0.018            0.016
Chain 1:   2500        -8374.039             0.018            0.016
Chain 1:   2600        -8285.368             0.017            0.016
Chain 1:   2700        -8319.327             0.017            0.016
Chain 1:   2800        -8278.782             0.012            0.012
Chain 1:   2900        -8373.153             0.012            0.011
Chain 1:   3000        -8209.542             0.014            0.016
Chain 1:   3100        -8361.810             0.014            0.018
Chain 1:   3200        -8233.138             0.013            0.016
Chain 1:   3300        -8243.976             0.011            0.011
Chain 1:   3400        -8411.264             0.011            0.011
Chain 1:   3500        -8421.054             0.011            0.011
Chain 1:   3600        -8189.452             0.012            0.016
Chain 1:   3700        -8336.665             0.014            0.018
Chain 1:   3800        -8195.431             0.015            0.018
Chain 1:   3900        -8129.485             0.015            0.018
Chain 1:   4000        -8208.762             0.014            0.017
Chain 1:   4100        -8201.017             0.012            0.016
Chain 1:   4200        -8185.938             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003495 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8444505.097             1.000            1.000
Chain 1:    200     -1592464.390             2.651            4.303
Chain 1:    300      -891880.538             2.029            1.000
Chain 1:    400      -458143.741             1.759            1.000
Chain 1:    500      -357565.279             1.463            0.947
Chain 1:    600      -232364.029             1.309            0.947
Chain 1:    700      -118981.627             1.258            0.947
Chain 1:    800       -86327.327             1.148            0.947
Chain 1:    900       -66766.819             1.053            0.786
Chain 1:   1000       -51656.718             0.977            0.786
Chain 1:   1100       -39217.911             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38408.948             0.481            0.378
Chain 1:   1300       -26439.676             0.447            0.378
Chain 1:   1400       -26168.867             0.354            0.317
Chain 1:   1500       -22775.460             0.341            0.317
Chain 1:   1600       -21998.794             0.290            0.293
Chain 1:   1700       -20880.718             0.200            0.293
Chain 1:   1800       -20827.328             0.163            0.149
Chain 1:   1900       -21153.872             0.135            0.054
Chain 1:   2000       -19668.830             0.113            0.054
Chain 1:   2100       -19906.934             0.083            0.035
Chain 1:   2200       -20132.998             0.082            0.035
Chain 1:   2300       -19750.488             0.038            0.019
Chain 1:   2400       -19522.504             0.039            0.019
Chain 1:   2500       -19324.264             0.025            0.015
Chain 1:   2600       -18954.192             0.023            0.015
Chain 1:   2700       -18911.218             0.018            0.012
Chain 1:   2800       -18627.701             0.019            0.015
Chain 1:   2900       -18909.089             0.019            0.015
Chain 1:   3000       -18895.319             0.012            0.012
Chain 1:   3100       -18980.312             0.011            0.012
Chain 1:   3200       -18670.781             0.011            0.015
Chain 1:   3300       -18875.737             0.011            0.012
Chain 1:   3400       -18350.096             0.012            0.015
Chain 1:   3500       -18962.621             0.015            0.015
Chain 1:   3600       -18268.491             0.016            0.015
Chain 1:   3700       -18655.759             0.018            0.017
Chain 1:   3800       -17614.102             0.023            0.021
Chain 1:   3900       -17610.185             0.021            0.021
Chain 1:   4000       -17727.545             0.022            0.021
Chain 1:   4100       -17641.146             0.022            0.021
Chain 1:   4200       -17457.170             0.021            0.021
Chain 1:   4300       -17595.753             0.021            0.021
Chain 1:   4400       -17552.307             0.018            0.011
Chain 1:   4500       -17454.787             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11988.953             1.000            1.000
Chain 1:    200        -8981.702             0.667            1.000
Chain 1:    300        -7723.104             0.499            0.335
Chain 1:    400        -7902.816             0.380            0.335
Chain 1:    500        -7764.941             0.308            0.163
Chain 1:    600        -7670.639             0.258            0.163
Chain 1:    700        -7593.192             0.223            0.023
Chain 1:    800        -7603.805             0.195            0.023
Chain 1:    900        -7512.375             0.175            0.018
Chain 1:   1000        -7694.263             0.160            0.023
Chain 1:   1100        -7732.631             0.060            0.018
Chain 1:   1200        -7630.935             0.028            0.013
Chain 1:   1300        -7567.830             0.013            0.012
Chain 1:   1400        -7588.921             0.011            0.012
Chain 1:   1500        -7674.653             0.010            0.011
Chain 1:   1600        -7644.725             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49055.166             1.000            1.000
Chain 1:    200       -15589.861             1.573            2.147
Chain 1:    300        -8542.229             1.324            1.000
Chain 1:    400        -8072.906             1.007            1.000
Chain 1:    500        -8058.661             0.806            0.825
Chain 1:    600        -8812.393             0.686            0.825
Chain 1:    700        -7723.636             0.608            0.141
Chain 1:    800        -7964.509             0.536            0.141
Chain 1:    900        -7900.091             0.477            0.086
Chain 1:   1000        -7801.057             0.431            0.086
Chain 1:   1100        -7762.182             0.331            0.058
Chain 1:   1200        -7801.330             0.117            0.030
Chain 1:   1300        -7765.760             0.035            0.013
Chain 1:   1400        -7877.217             0.031            0.013
Chain 1:   1500        -7618.466             0.034            0.014
Chain 1:   1600        -7526.721             0.027            0.013
Chain 1:   1700        -7515.739             0.013            0.012
Chain 1:   1800        -7544.650             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002861 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85627.430             1.000            1.000
Chain 1:    200       -13105.957             3.267            5.533
Chain 1:    300        -9506.599             2.304            1.000
Chain 1:    400       -10168.559             1.744            1.000
Chain 1:    500        -8455.985             1.436            0.379
Chain 1:    600        -8357.532             1.199            0.379
Chain 1:    700        -8374.267             1.028            0.203
Chain 1:    800        -8470.785             0.901            0.203
Chain 1:    900        -8420.199             0.801            0.065
Chain 1:   1000        -8057.630             0.726            0.065
Chain 1:   1100        -8417.744             0.630            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8031.869             0.081            0.045
Chain 1:   1300        -8375.999             0.048            0.043
Chain 1:   1400        -8233.844             0.043            0.041
Chain 1:   1500        -8106.589             0.024            0.017
Chain 1:   1600        -8217.522             0.024            0.017
Chain 1:   1700        -8306.645             0.025            0.017
Chain 1:   1800        -7908.485             0.029            0.041
Chain 1:   1900        -8008.515             0.030            0.041
Chain 1:   2000        -7979.313             0.026            0.017
Chain 1:   2100        -8099.739             0.023            0.016
Chain 1:   2200        -7876.896             0.021            0.016
Chain 1:   2300        -8037.898             0.019            0.016
Chain 1:   2400        -7919.791             0.018            0.015
Chain 1:   2500        -7983.552             0.018            0.015
Chain 1:   2600        -8004.776             0.017            0.015
Chain 1:   2700        -7924.224             0.017            0.015
Chain 1:   2800        -7898.793             0.012            0.012
Chain 1:   2900        -7953.765             0.011            0.010
Chain 1:   3000        -7838.659             0.012            0.015
Chain 1:   3100        -7975.745             0.013            0.015
Chain 1:   3200        -7856.101             0.011            0.015
Chain 1:   3300        -7877.127             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003367 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8386422.342             1.000            1.000
Chain 1:    200     -1583795.644             2.648            4.295
Chain 1:    300      -892081.571             2.024            1.000
Chain 1:    400      -457851.325             1.755            1.000
Chain 1:    500      -358272.917             1.459            0.948
Chain 1:    600      -233059.581             1.306            0.948
Chain 1:    700      -119109.418             1.256            0.948
Chain 1:    800       -86214.109             1.147            0.948
Chain 1:    900       -66514.298             1.052            0.775
Chain 1:   1000       -51264.854             0.977            0.775
Chain 1:   1100       -38700.051             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37870.493             0.482            0.382
Chain 1:   1300       -25798.683             0.451            0.382
Chain 1:   1400       -25512.623             0.357            0.325
Chain 1:   1500       -22092.448             0.345            0.325
Chain 1:   1600       -21305.902             0.295            0.297
Chain 1:   1700       -20177.452             0.205            0.296
Chain 1:   1800       -20120.792             0.167            0.155
Chain 1:   1900       -20446.550             0.139            0.056
Chain 1:   2000       -18956.990             0.117            0.056
Chain 1:   2100       -19195.415             0.086            0.037
Chain 1:   2200       -19421.745             0.085            0.037
Chain 1:   2300       -19039.185             0.040            0.020
Chain 1:   2400       -18811.408             0.040            0.020
Chain 1:   2500       -18613.355             0.026            0.016
Chain 1:   2600       -18243.888             0.024            0.016
Chain 1:   2700       -18200.986             0.019            0.012
Chain 1:   2800       -17917.921             0.020            0.016
Chain 1:   2900       -18199.136             0.020            0.015
Chain 1:   3000       -18185.311             0.012            0.012
Chain 1:   3100       -18270.219             0.011            0.012
Chain 1:   3200       -17961.122             0.012            0.015
Chain 1:   3300       -18165.705             0.011            0.012
Chain 1:   3400       -17640.952             0.013            0.015
Chain 1:   3500       -18252.271             0.015            0.016
Chain 1:   3600       -17559.799             0.017            0.016
Chain 1:   3700       -17945.987             0.019            0.017
Chain 1:   3800       -16906.865             0.023            0.022
Chain 1:   3900       -16903.068             0.022            0.022
Chain 1:   4000       -17020.375             0.023            0.022
Chain 1:   4100       -16934.129             0.023            0.022
Chain 1:   4200       -16750.694             0.022            0.022
Chain 1:   4300       -16888.875             0.022            0.022
Chain 1:   4400       -16845.938             0.019            0.011
Chain 1:   4500       -16748.522             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001359 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.59 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49237.783             1.000            1.000
Chain 1:    200       -19933.283             1.235            1.470
Chain 1:    300       -16792.627             0.886            1.000
Chain 1:    400       -12782.570             0.743            1.000
Chain 1:    500       -13344.882             0.603            0.314
Chain 1:    600       -15654.242             0.527            0.314
Chain 1:    700       -17062.154             0.463            0.187
Chain 1:    800       -13515.136             0.438            0.262
Chain 1:    900       -10908.490             0.416            0.239
Chain 1:   1000       -12079.279             0.384            0.239
Chain 1:   1100       -22136.303             0.330            0.239
Chain 1:   1200       -11505.579             0.275            0.239
Chain 1:   1300       -12170.755             0.262            0.239
Chain 1:   1400       -10334.374             0.248            0.178
Chain 1:   1500       -10284.828             0.244            0.178
Chain 1:   1600       -10167.927             0.231            0.178
Chain 1:   1700       -16326.783             0.260            0.239
Chain 1:   1800       -11246.444             0.279            0.239
Chain 1:   1900       -11773.298             0.260            0.178
Chain 1:   2000        -9856.634             0.270            0.194
Chain 1:   2100       -11988.214             0.242            0.178
Chain 1:   2200       -17923.418             0.183            0.178
Chain 1:   2300       -10357.153             0.250            0.194
Chain 1:   2400        -9249.972             0.244            0.194
Chain 1:   2500        -9746.621             0.249            0.194
Chain 1:   2600       -19624.930             0.298            0.331
Chain 1:   2700        -9703.700             0.363            0.331
Chain 1:   2800       -18967.307             0.366            0.331
Chain 1:   2900       -15728.124             0.382            0.331
Chain 1:   3000        -8838.922             0.441            0.488
Chain 1:   3100        -8894.031             0.424            0.488
Chain 1:   3200       -15369.106             0.433            0.488
Chain 1:   3300       -10881.411             0.401            0.421
Chain 1:   3400        -9199.165             0.407            0.421
Chain 1:   3500       -10621.276             0.416            0.421
Chain 1:   3600       -14159.938             0.390            0.412
Chain 1:   3700       -13573.862             0.292            0.250
Chain 1:   3800       -16105.230             0.259            0.206
Chain 1:   3900       -10083.677             0.298            0.250
Chain 1:   4000        -9527.534             0.226            0.183
Chain 1:   4100        -8990.983             0.232            0.183
Chain 1:   4200       -10646.031             0.205            0.157
Chain 1:   4300       -10042.897             0.170            0.155
Chain 1:   4400        -8930.591             0.164            0.134
Chain 1:   4500       -10323.220             0.164            0.135
Chain 1:   4600       -14709.041             0.169            0.135
Chain 1:   4700        -9705.156             0.216            0.155
Chain 1:   4800       -10247.344             0.206            0.135
Chain 1:   4900       -10646.036             0.150            0.125
Chain 1:   5000       -10293.549             0.147            0.125
Chain 1:   5100        -8605.355             0.161            0.135
Chain 1:   5200       -10776.474             0.166            0.135
Chain 1:   5300       -15619.963             0.191            0.196
Chain 1:   5400       -16368.057             0.183            0.196
Chain 1:   5500       -10287.461             0.228            0.201
Chain 1:   5600        -8664.988             0.217            0.196
Chain 1:   5700       -11947.844             0.193            0.196
Chain 1:   5800       -10855.457             0.198            0.196
Chain 1:   5900       -13461.405             0.213            0.196
Chain 1:   6000        -9092.887             0.258            0.201
Chain 1:   6100        -9897.787             0.247            0.201
Chain 1:   6200        -8941.007             0.237            0.194
Chain 1:   6300        -8771.095             0.208            0.187
Chain 1:   6400       -10076.782             0.217            0.187
Chain 1:   6500        -8977.790             0.170            0.130
Chain 1:   6600        -8528.540             0.156            0.122
Chain 1:   6700        -8412.293             0.130            0.107
Chain 1:   6800       -11961.593             0.150            0.122
Chain 1:   6900        -8776.657             0.167            0.122
Chain 1:   7000        -8618.976             0.120            0.107
Chain 1:   7100        -8260.498             0.117            0.107
Chain 1:   7200       -12245.368             0.138            0.122
Chain 1:   7300        -8217.241             0.186            0.130
Chain 1:   7400        -8810.165             0.179            0.122
Chain 1:   7500        -8827.455             0.167            0.067
Chain 1:   7600        -8394.976             0.167            0.067
Chain 1:   7700        -8288.973             0.167            0.067
Chain 1:   7800       -12983.102             0.174            0.067
Chain 1:   7900        -8429.487             0.191            0.067
Chain 1:   8000       -11316.353             0.215            0.255
Chain 1:   8100        -8780.040             0.239            0.289
Chain 1:   8200        -9627.091             0.216            0.255
Chain 1:   8300        -8400.821             0.181            0.146
Chain 1:   8400       -11708.806             0.203            0.255
Chain 1:   8500        -8446.543             0.241            0.283
Chain 1:   8600        -8475.579             0.236            0.283
Chain 1:   8700        -8229.416             0.238            0.283
Chain 1:   8800       -12674.896             0.237            0.283
Chain 1:   8900       -10524.427             0.204            0.255
Chain 1:   9000        -8774.525             0.198            0.204
Chain 1:   9100        -8527.546             0.172            0.199
Chain 1:   9200        -8436.425             0.164            0.199
Chain 1:   9300        -8601.866             0.152            0.199
Chain 1:   9400        -8758.044             0.125            0.030
Chain 1:   9500        -8419.122             0.090            0.030
Chain 1:   9600        -8457.239             0.091            0.030
Chain 1:   9700        -9655.896             0.100            0.040
Chain 1:   9800        -8957.874             0.073            0.040
Chain 1:   9900       -10846.045             0.070            0.040
Chain 1:   10000        -8480.185             0.078            0.040
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61901.088             1.000            1.000
Chain 1:    200       -17815.422             1.737            2.475
Chain 1:    300        -8844.198             1.496            1.014
Chain 1:    400        -8135.973             1.144            1.014
Chain 1:    500        -8697.467             0.928            1.000
Chain 1:    600        -8696.652             0.773            1.000
Chain 1:    700        -7508.062             0.686            0.158
Chain 1:    800        -7651.569             0.602            0.158
Chain 1:    900        -7967.609             0.540            0.087
Chain 1:   1000        -7606.339             0.490            0.087
Chain 1:   1100        -7570.298             0.391            0.065
Chain 1:   1200        -7753.706             0.146            0.047
Chain 1:   1300        -7590.124             0.047            0.040
Chain 1:   1400        -7896.217             0.042            0.039
Chain 1:   1500        -7543.346             0.040            0.039
Chain 1:   1600        -7685.582             0.042            0.039
Chain 1:   1700        -7461.047             0.029            0.030
Chain 1:   1800        -7579.052             0.029            0.030
Chain 1:   1900        -7585.752             0.025            0.024
Chain 1:   2000        -7610.374             0.020            0.022
Chain 1:   2100        -7538.692             0.021            0.022
Chain 1:   2200        -7647.821             0.020            0.019
Chain 1:   2300        -7536.092             0.019            0.016
Chain 1:   2400        -7598.246             0.016            0.015
Chain 1:   2500        -7522.810             0.013            0.014
Chain 1:   2600        -7469.709             0.011            0.010
Chain 1:   2700        -7498.161             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86628.635             1.000            1.000
Chain 1:    200       -13545.644             3.198            5.395
Chain 1:    300        -9919.933             2.254            1.000
Chain 1:    400       -10768.494             1.710            1.000
Chain 1:    500        -8893.904             1.410            0.365
Chain 1:    600        -8528.374             1.182            0.365
Chain 1:    700        -8559.509             1.014            0.211
Chain 1:    800        -9202.781             0.896            0.211
Chain 1:    900        -8694.333             0.803            0.079
Chain 1:   1000        -8556.278             0.724            0.079
Chain 1:   1100        -8706.101             0.626            0.070   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8332.884             0.091            0.058
Chain 1:   1300        -8630.325             0.058            0.045
Chain 1:   1400        -8641.375             0.050            0.043
Chain 1:   1500        -8484.635             0.031            0.034
Chain 1:   1600        -8603.637             0.028            0.018
Chain 1:   1700        -8688.028             0.028            0.018
Chain 1:   1800        -8277.139             0.026            0.018
Chain 1:   1900        -8372.918             0.022            0.017
Chain 1:   2000        -8345.834             0.020            0.017
Chain 1:   2100        -8467.742             0.020            0.014
Chain 1:   2200        -8292.492             0.018            0.014
Chain 1:   2300        -8369.466             0.015            0.014
Chain 1:   2400        -8437.680             0.016            0.014
Chain 1:   2500        -8383.284             0.015            0.011
Chain 1:   2600        -8381.887             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002501 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8442733.177             1.000            1.000
Chain 1:    200     -1591035.525             2.653            4.306
Chain 1:    300      -891517.461             2.030            1.000
Chain 1:    400      -457647.725             1.760            1.000
Chain 1:    500      -357306.950             1.464            0.948
Chain 1:    600      -232120.748             1.310            0.948
Chain 1:    700      -118755.220             1.259            0.948
Chain 1:    800       -86078.796             1.149            0.948
Chain 1:    900       -66517.458             1.054            0.785
Chain 1:   1000       -51396.900             0.978            0.785
Chain 1:   1100       -38954.412             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38139.151             0.482            0.380
Chain 1:   1300       -26180.345             0.449            0.380
Chain 1:   1400       -25906.978             0.355            0.319
Chain 1:   1500       -22516.329             0.342            0.319
Chain 1:   1600       -21739.263             0.292            0.294
Chain 1:   1700       -20623.482             0.202            0.294
Chain 1:   1800       -20570.056             0.164            0.151
Chain 1:   1900       -20896.083             0.136            0.054
Chain 1:   2000       -19412.792             0.114            0.054
Chain 1:   2100       -19650.916             0.084            0.036
Chain 1:   2200       -19876.415             0.083            0.036
Chain 1:   2300       -19494.467             0.039            0.020
Chain 1:   2400       -19266.713             0.039            0.020
Chain 1:   2500       -19068.308             0.025            0.016
Chain 1:   2600       -18699.016             0.023            0.016
Chain 1:   2700       -18656.154             0.018            0.012
Chain 1:   2800       -18372.894             0.019            0.015
Chain 1:   2900       -18653.925             0.019            0.015
Chain 1:   3000       -18640.242             0.012            0.012
Chain 1:   3100       -18725.202             0.011            0.012
Chain 1:   3200       -18416.046             0.012            0.015
Chain 1:   3300       -18620.625             0.011            0.012
Chain 1:   3400       -18095.688             0.012            0.015
Chain 1:   3500       -18707.223             0.015            0.015
Chain 1:   3600       -18014.282             0.017            0.015
Chain 1:   3700       -18400.722             0.018            0.017
Chain 1:   3800       -17360.960             0.023            0.021
Chain 1:   3900       -17357.044             0.021            0.021
Chain 1:   4000       -17474.406             0.022            0.021
Chain 1:   4100       -17388.188             0.022            0.021
Chain 1:   4200       -17204.541             0.021            0.021
Chain 1:   4300       -17342.904             0.021            0.021
Chain 1:   4400       -17299.817             0.019            0.011
Chain 1:   4500       -17202.316             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001287 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48615.707             1.000            1.000
Chain 1:    200       -17905.568             1.358            1.715
Chain 1:    300       -19836.161             0.937            1.000
Chain 1:    400       -12184.513             0.860            1.000
Chain 1:    500       -18416.795             0.756            0.628
Chain 1:    600       -11388.588             0.733            0.628
Chain 1:    700       -13268.105             0.648            0.617
Chain 1:    800       -12151.163             0.579            0.617
Chain 1:    900       -13516.055             0.526            0.338
Chain 1:   1000       -12519.431             0.481            0.338
Chain 1:   1100        -9882.169             0.408            0.267
Chain 1:   1200       -12562.438             0.258            0.213
Chain 1:   1300       -13344.547             0.254            0.213
Chain 1:   1400       -20454.715             0.226            0.213
Chain 1:   1500       -11258.164             0.273            0.213
Chain 1:   1600       -11517.358             0.214            0.142
Chain 1:   1700       -20920.813             0.245            0.213
Chain 1:   1800        -9390.416             0.358            0.267
Chain 1:   1900        -9466.583             0.349            0.267
Chain 1:   2000        -9669.754             0.343            0.267
Chain 1:   2100       -10304.319             0.323            0.213
Chain 1:   2200       -19034.794             0.347            0.348
Chain 1:   2300        -9066.890             0.451            0.449
Chain 1:   2400       -17605.764             0.465            0.459
Chain 1:   2500        -9858.177             0.462            0.459
Chain 1:   2600        -8872.626             0.471            0.459
Chain 1:   2700        -9093.749             0.428            0.459
Chain 1:   2800       -10463.040             0.319            0.131
Chain 1:   2900        -9332.304             0.330            0.131
Chain 1:   3000        -8592.213             0.336            0.131
Chain 1:   3100        -9792.996             0.343            0.131
Chain 1:   3200        -9484.376             0.300            0.123
Chain 1:   3300        -8968.456             0.196            0.121
Chain 1:   3400        -9912.860             0.157            0.111
Chain 1:   3500        -8770.345             0.091            0.111
Chain 1:   3600        -9979.734             0.092            0.121
Chain 1:   3700        -9314.631             0.097            0.121
Chain 1:   3800        -8760.384             0.090            0.095
Chain 1:   3900        -8693.454             0.079            0.086
Chain 1:   4000        -8527.655             0.072            0.071
Chain 1:   4100        -8629.266             0.061            0.063
Chain 1:   4200       -12625.530             0.089            0.071
Chain 1:   4300       -10023.261             0.110            0.095
Chain 1:   4400       -11504.522             0.113            0.121
Chain 1:   4500        -8601.406             0.134            0.121
Chain 1:   4600       -14863.902             0.164            0.129
Chain 1:   4700       -11437.380             0.187            0.260
Chain 1:   4800        -8389.071             0.217            0.300
Chain 1:   4900       -10943.906             0.239            0.300
Chain 1:   5000        -9794.377             0.249            0.300
Chain 1:   5100        -8588.043             0.262            0.300
Chain 1:   5200        -8924.865             0.234            0.260
Chain 1:   5300        -8922.341             0.208            0.233
Chain 1:   5400        -8844.264             0.196            0.233
Chain 1:   5500        -9359.695             0.168            0.140
Chain 1:   5600        -8881.683             0.131            0.117
Chain 1:   5700        -9051.559             0.103            0.055
Chain 1:   5800        -8696.752             0.071            0.054
Chain 1:   5900        -8356.860             0.051            0.041
Chain 1:   6000        -9152.650             0.048            0.041
Chain 1:   6100        -8371.435             0.044            0.041
Chain 1:   6200        -8136.696             0.043            0.041
Chain 1:   6300       -10641.573             0.066            0.054
Chain 1:   6400        -9380.246             0.079            0.055
Chain 1:   6500        -8481.889             0.084            0.087
Chain 1:   6600        -8433.119             0.079            0.087
Chain 1:   6700       -10795.201             0.099            0.093
Chain 1:   6800        -8171.468             0.127            0.106
Chain 1:   6900       -11455.553             0.152            0.134
Chain 1:   7000        -8085.046             0.185            0.219
Chain 1:   7100        -9290.812             0.188            0.219
Chain 1:   7200        -8127.328             0.200            0.219
Chain 1:   7300        -9642.783             0.192            0.157
Chain 1:   7400        -8217.117             0.196            0.173
Chain 1:   7500       -11643.334             0.215            0.219
Chain 1:   7600       -10696.276             0.223            0.219
Chain 1:   7700        -8143.851             0.232            0.287
Chain 1:   7800        -9026.459             0.210            0.173
Chain 1:   7900       -10084.485             0.192            0.157
Chain 1:   8000        -9275.563             0.159            0.143
Chain 1:   8100       -10043.146             0.154            0.143
Chain 1:   8200        -9773.742             0.142            0.105
Chain 1:   8300       -11109.482             0.138            0.105
Chain 1:   8400       -10651.037             0.125            0.098
Chain 1:   8500        -8126.357             0.127            0.098
Chain 1:   8600        -9959.852             0.137            0.105
Chain 1:   8700        -8187.886             0.127            0.105
Chain 1:   8800        -8101.288             0.118            0.105
Chain 1:   8900       -12075.418             0.141            0.120
Chain 1:   9000       -10303.565             0.149            0.172
Chain 1:   9100        -8192.109             0.167            0.184
Chain 1:   9200        -8089.948             0.166            0.184
Chain 1:   9300        -9587.976             0.169            0.184
Chain 1:   9400        -8326.139             0.180            0.184
Chain 1:   9500       -10077.118             0.166            0.174
Chain 1:   9600        -8747.931             0.163            0.172
Chain 1:   9700        -7863.295             0.153            0.156
Chain 1:   9800       -10279.640             0.175            0.172
Chain 1:   9900       -10133.031             0.144            0.156
Chain 1:   10000        -8025.596             0.153            0.156
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56590.117             1.000            1.000
Chain 1:    200       -17053.260             1.659            2.318
Chain 1:    300        -8554.424             1.437            1.000
Chain 1:    400        -8742.651             1.083            1.000
Chain 1:    500        -8488.223             0.873            0.994
Chain 1:    600        -8873.676             0.734            0.994
Chain 1:    700        -7669.514             0.652            0.157
Chain 1:    800        -7976.536             0.575            0.157
Chain 1:    900        -7761.051             0.514            0.043
Chain 1:   1000        -7685.799             0.464            0.043
Chain 1:   1100        -7699.569             0.364            0.038
Chain 1:   1200        -7763.421             0.133            0.030
Chain 1:   1300        -7533.871             0.037            0.030
Chain 1:   1400        -7816.065             0.038            0.030
Chain 1:   1500        -7573.582             0.039            0.032
Chain 1:   1600        -7750.642             0.036            0.030
Chain 1:   1700        -7470.483             0.024            0.030
Chain 1:   1800        -7488.863             0.021            0.028
Chain 1:   1900        -7581.269             0.019            0.023
Chain 1:   2000        -7580.884             0.018            0.023
Chain 1:   2100        -7571.195             0.018            0.023
Chain 1:   2200        -7643.498             0.018            0.023
Chain 1:   2300        -7523.901             0.017            0.016
Chain 1:   2400        -7585.758             0.014            0.012
Chain 1:   2500        -7425.368             0.013            0.012
Chain 1:   2600        -7488.807             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002887 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86338.386             1.000            1.000
Chain 1:    200       -13149.347             3.283            5.566
Chain 1:    300        -9616.687             2.311            1.000
Chain 1:    400       -10581.362             1.756            1.000
Chain 1:    500        -8508.388             1.454            0.367
Chain 1:    600        -8198.608             1.218            0.367
Chain 1:    700        -8241.215             1.044            0.244
Chain 1:    800        -8683.530             0.920            0.244
Chain 1:    900        -8491.333             0.821            0.091
Chain 1:   1000        -8175.892             0.742            0.091
Chain 1:   1100        -8530.114             0.646            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8197.819             0.094            0.042
Chain 1:   1300        -8196.080             0.057            0.041
Chain 1:   1400        -8197.934             0.048            0.039
Chain 1:   1500        -8231.863             0.024            0.038
Chain 1:   1600        -8237.419             0.020            0.023
Chain 1:   1700        -8171.573             0.021            0.023
Chain 1:   1800        -8052.807             0.017            0.015
Chain 1:   1900        -8169.223             0.016            0.014
Chain 1:   2000        -8129.339             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002536 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417620.222             1.000            1.000
Chain 1:    200     -1587259.561             2.652            4.303
Chain 1:    300      -890977.077             2.028            1.000
Chain 1:    400      -457449.702             1.758            1.000
Chain 1:    500      -357493.578             1.462            0.948
Chain 1:    600      -232389.495             1.308            0.948
Chain 1:    700      -118676.790             1.258            0.948
Chain 1:    800       -85928.819             1.149            0.948
Chain 1:    900       -66292.044             1.054            0.781
Chain 1:   1000       -51107.388             0.978            0.781
Chain 1:   1100       -38611.453             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37785.899             0.483            0.381
Chain 1:   1300       -25780.710             0.451            0.381
Chain 1:   1400       -25500.964             0.357            0.324
Chain 1:   1500       -22098.785             0.345            0.324
Chain 1:   1600       -21317.619             0.295            0.297
Chain 1:   1700       -20196.542             0.204            0.296
Chain 1:   1800       -20141.552             0.166            0.154
Chain 1:   1900       -20467.069             0.138            0.056
Chain 1:   2000       -18982.146             0.117            0.056
Chain 1:   2100       -19220.312             0.085            0.037
Chain 1:   2200       -19445.892             0.084            0.037
Chain 1:   2300       -19064.005             0.040            0.020
Chain 1:   2400       -18836.353             0.040            0.020
Chain 1:   2500       -18638.260             0.026            0.016
Chain 1:   2600       -18269.230             0.024            0.016
Chain 1:   2700       -18226.427             0.019            0.012
Chain 1:   2800       -17943.515             0.020            0.016
Chain 1:   2900       -18224.431             0.020            0.015
Chain 1:   3000       -18210.685             0.012            0.012
Chain 1:   3100       -18295.574             0.011            0.012
Chain 1:   3200       -17986.702             0.012            0.015
Chain 1:   3300       -18191.080             0.011            0.012
Chain 1:   3400       -17666.783             0.013            0.015
Chain 1:   3500       -18277.429             0.015            0.016
Chain 1:   3600       -17585.675             0.017            0.016
Chain 1:   3700       -17971.288             0.019            0.017
Chain 1:   3800       -16933.412             0.023            0.021
Chain 1:   3900       -16929.584             0.022            0.021
Chain 1:   4000       -17046.909             0.023            0.021
Chain 1:   4100       -16960.776             0.023            0.021
Chain 1:   4200       -16777.549             0.022            0.021
Chain 1:   4300       -16915.584             0.022            0.021
Chain 1:   4400       -16872.834             0.019            0.011
Chain 1:   4500       -16775.425             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001274 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48424.899             1.000            1.000
Chain 1:    200       -20620.824             1.174            1.348
Chain 1:    300       -19934.810             0.794            1.000
Chain 1:    400       -14393.688             0.692            1.000
Chain 1:    500       -17422.831             0.588            0.385
Chain 1:    600       -14989.833             0.517            0.385
Chain 1:    700       -14002.808             0.453            0.174
Chain 1:    800       -15028.082             0.405            0.174
Chain 1:    900       -21917.509             0.395            0.174
Chain 1:   1000       -10373.016             0.467            0.314
Chain 1:   1100       -11546.093             0.377            0.174
Chain 1:   1200       -10564.303             0.252            0.162
Chain 1:   1300       -19909.326             0.295            0.174
Chain 1:   1400       -10663.937             0.343            0.174
Chain 1:   1500       -10056.813             0.332            0.162
Chain 1:   1600        -9981.085             0.316            0.102
Chain 1:   1700       -12848.773             0.332            0.223
Chain 1:   1800       -10448.872             0.348            0.230
Chain 1:   1900       -22940.705             0.371            0.230
Chain 1:   2000        -9666.027             0.397            0.230
Chain 1:   2100        -9651.765             0.387            0.230
Chain 1:   2200       -10799.225             0.388            0.230
Chain 1:   2300       -10388.144             0.345            0.223
Chain 1:   2400       -19384.766             0.305            0.223
Chain 1:   2500       -19186.969             0.300            0.223
Chain 1:   2600        -9872.101             0.394            0.230
Chain 1:   2700       -10007.784             0.373            0.230
Chain 1:   2800        -9997.665             0.350            0.106
Chain 1:   2900        -9165.084             0.304            0.091
Chain 1:   3000        -9589.490             0.171            0.044
Chain 1:   3100        -8682.452             0.182            0.091
Chain 1:   3200        -9308.668             0.178            0.067
Chain 1:   3300        -9473.891             0.176            0.067
Chain 1:   3400       -15105.837             0.167            0.067
Chain 1:   3500        -9852.311             0.219            0.091
Chain 1:   3600        -9150.719             0.132            0.077
Chain 1:   3700        -8541.370             0.138            0.077
Chain 1:   3800       -10030.222             0.153            0.091
Chain 1:   3900        -9688.799             0.147            0.077
Chain 1:   4000        -8670.403             0.154            0.104
Chain 1:   4100       -10477.229             0.161            0.117
Chain 1:   4200       -10598.745             0.156            0.117
Chain 1:   4300       -11990.082             0.166            0.117
Chain 1:   4400        -8912.909             0.163            0.117
Chain 1:   4500        -8587.155             0.113            0.116
Chain 1:   4600        -8609.801             0.106            0.116
Chain 1:   4700        -9033.212             0.103            0.116
Chain 1:   4800       -12026.382             0.113            0.116
Chain 1:   4900       -14022.129             0.124            0.117
Chain 1:   5000       -13477.026             0.116            0.116
Chain 1:   5100        -8799.850             0.152            0.116
Chain 1:   5200        -9890.686             0.162            0.116
Chain 1:   5300        -9535.816             0.154            0.110
Chain 1:   5400       -12393.127             0.143            0.110
Chain 1:   5500       -10495.391             0.157            0.142
Chain 1:   5600       -11457.812             0.165            0.142
Chain 1:   5700        -8739.847             0.192            0.181
Chain 1:   5800        -8641.529             0.168            0.142
Chain 1:   5900       -13181.588             0.188            0.181
Chain 1:   6000        -8310.889             0.243            0.231
Chain 1:   6100        -8401.378             0.191            0.181
Chain 1:   6200        -9506.137             0.191            0.181
Chain 1:   6300        -9224.000             0.191            0.181
Chain 1:   6400       -10833.763             0.182            0.149
Chain 1:   6500        -8702.597             0.189            0.149
Chain 1:   6600        -8909.592             0.183            0.149
Chain 1:   6700        -9669.607             0.159            0.116
Chain 1:   6800       -12892.714             0.183            0.149
Chain 1:   6900       -10093.147             0.177            0.149
Chain 1:   7000       -11258.883             0.128            0.116
Chain 1:   7100       -10185.586             0.138            0.116
Chain 1:   7200        -8677.960             0.144            0.149
Chain 1:   7300       -11096.870             0.162            0.174
Chain 1:   7400        -8426.902             0.179            0.218
Chain 1:   7500       -10480.518             0.174            0.196
Chain 1:   7600        -8897.657             0.190            0.196
Chain 1:   7700        -8798.383             0.183            0.196
Chain 1:   7800       -10636.595             0.175            0.178
Chain 1:   7900        -9512.783             0.159            0.174
Chain 1:   8000        -8468.436             0.161            0.174
Chain 1:   8100        -8236.919             0.154            0.174
Chain 1:   8200        -8712.697             0.142            0.173
Chain 1:   8300        -8136.690             0.127            0.123
Chain 1:   8400        -8121.584             0.095            0.118
Chain 1:   8500        -8299.698             0.078            0.071
Chain 1:   8600        -8298.029             0.060            0.055
Chain 1:   8700        -8129.553             0.061            0.055
Chain 1:   8800       -10124.700             0.064            0.055
Chain 1:   8900        -8247.955             0.075            0.055
Chain 1:   9000        -9953.884             0.079            0.055
Chain 1:   9100        -8891.882             0.089            0.071
Chain 1:   9200        -9230.830             0.087            0.071
Chain 1:   9300        -9318.695             0.081            0.037
Chain 1:   9400       -11419.288             0.099            0.119
Chain 1:   9500        -8110.748             0.137            0.171
Chain 1:   9600        -8186.442             0.138            0.171
Chain 1:   9700       -11274.894             0.164            0.184
Chain 1:   9800        -8091.969             0.183            0.184
Chain 1:   9900       -10656.259             0.185            0.184
Chain 1:   10000        -8673.057             0.190            0.229
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001453 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56657.206             1.000            1.000
Chain 1:    200       -17133.501             1.653            2.307
Chain 1:    300        -8580.527             1.435            1.000
Chain 1:    400        -9037.447             1.089            1.000
Chain 1:    500        -8582.572             0.881            0.997
Chain 1:    600        -8447.853             0.737            0.997
Chain 1:    700        -7730.855             0.645            0.093
Chain 1:    800        -8116.651             0.570            0.093
Chain 1:    900        -7875.451             0.510            0.053
Chain 1:   1000        -7842.930             0.460            0.053
Chain 1:   1100        -7715.800             0.361            0.051
Chain 1:   1200        -7592.869             0.132            0.048
Chain 1:   1300        -7745.434             0.035            0.031
Chain 1:   1400        -7821.207             0.031            0.020
Chain 1:   1500        -7604.993             0.028            0.020
Chain 1:   1600        -7518.460             0.028            0.020
Chain 1:   1700        -7519.046             0.018            0.016
Chain 1:   1800        -7580.014             0.014            0.016
Chain 1:   1900        -7484.346             0.013            0.013
Chain 1:   2000        -7582.657             0.014            0.013
Chain 1:   2100        -7628.159             0.013            0.013
Chain 1:   2200        -7672.887             0.011            0.012
Chain 1:   2300        -7574.051             0.011            0.012
Chain 1:   2400        -7609.531             0.010            0.012
Chain 1:   2500        -7624.516             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003076 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86082.919             1.000            1.000
Chain 1:    200       -13181.940             3.265            5.530
Chain 1:    300        -9676.118             2.298            1.000
Chain 1:    400       -10487.958             1.743            1.000
Chain 1:    500        -8552.230             1.439            0.362
Chain 1:    600        -8260.206             1.205            0.362
Chain 1:    700        -8576.944             1.038            0.226
Chain 1:    800        -8782.464             0.912            0.226
Chain 1:    900        -8544.251             0.813            0.077
Chain 1:   1000        -8315.125             0.735            0.077
Chain 1:   1100        -8584.405             0.638            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8334.466             0.088            0.035
Chain 1:   1300        -8291.843             0.052            0.031
Chain 1:   1400        -8298.357             0.044            0.030
Chain 1:   1500        -8316.589             0.022            0.028
Chain 1:   1600        -8315.336             0.019            0.028
Chain 1:   1700        -8255.630             0.016            0.023
Chain 1:   1800        -8134.121             0.015            0.015
Chain 1:   1900        -8248.158             0.013            0.014
Chain 1:   2000        -8209.377             0.011            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003176 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397677.258             1.000            1.000
Chain 1:    200     -1582978.683             2.652            4.305
Chain 1:    300      -889770.233             2.028            1.000
Chain 1:    400      -456551.512             1.758            1.000
Chain 1:    500      -357073.442             1.462            0.949
Chain 1:    600      -232314.333             1.308            0.949
Chain 1:    700      -118734.163             1.258            0.949
Chain 1:    800       -85991.496             1.148            0.949
Chain 1:    900       -66363.489             1.054            0.779
Chain 1:   1000       -51172.546             0.978            0.779
Chain 1:   1100       -38665.020             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37838.008             0.482            0.381
Chain 1:   1300       -25820.301             0.451            0.381
Chain 1:   1400       -25538.519             0.357            0.323
Chain 1:   1500       -22133.253             0.344            0.323
Chain 1:   1600       -21351.028             0.294            0.297
Chain 1:   1700       -20228.435             0.204            0.296
Chain 1:   1800       -20173.051             0.166            0.154
Chain 1:   1900       -20498.477             0.138            0.055
Chain 1:   2000       -19013.005             0.116            0.055
Chain 1:   2100       -19251.143             0.085            0.037
Chain 1:   2200       -19476.816             0.084            0.037
Chain 1:   2300       -19094.911             0.040            0.020
Chain 1:   2400       -18867.312             0.040            0.020
Chain 1:   2500       -18669.286             0.026            0.016
Chain 1:   2600       -18300.337             0.024            0.016
Chain 1:   2700       -18257.557             0.019            0.012
Chain 1:   2800       -17974.755             0.020            0.016
Chain 1:   2900       -18255.613             0.020            0.015
Chain 1:   3000       -18241.857             0.012            0.012
Chain 1:   3100       -18326.725             0.011            0.012
Chain 1:   3200       -18017.938             0.012            0.015
Chain 1:   3300       -18222.259             0.011            0.012
Chain 1:   3400       -17698.129             0.013            0.015
Chain 1:   3500       -18308.558             0.015            0.016
Chain 1:   3600       -17617.126             0.017            0.016
Chain 1:   3700       -18002.526             0.019            0.017
Chain 1:   3800       -16965.152             0.023            0.021
Chain 1:   3900       -16961.369             0.022            0.021
Chain 1:   4000       -17078.666             0.022            0.021
Chain 1:   4100       -16992.568             0.023            0.021
Chain 1:   4200       -16809.464             0.022            0.021
Chain 1:   4300       -16947.409             0.022            0.021
Chain 1:   4400       -16904.754             0.019            0.011
Chain 1:   4500       -16807.378             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001306 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49395.915             1.000            1.000
Chain 1:    200       -20274.024             1.218            1.436
Chain 1:    300       -13287.166             0.987            1.000
Chain 1:    400       -12499.488             0.756            1.000
Chain 1:    500       -15247.863             0.641            0.526
Chain 1:    600       -17599.658             0.557            0.526
Chain 1:    700       -16234.658             0.489            0.180
Chain 1:    800       -12769.849             0.462            0.271
Chain 1:    900       -20152.297             0.451            0.271
Chain 1:   1000       -13767.997             0.452            0.366
Chain 1:   1100       -12864.279             0.359            0.271
Chain 1:   1200       -16863.439             0.240            0.237
Chain 1:   1300       -12070.604             0.227            0.237
Chain 1:   1400       -15333.788             0.242            0.237
Chain 1:   1500       -12977.962             0.242            0.237
Chain 1:   1600       -26628.975             0.280            0.271
Chain 1:   1700       -10241.808             0.431            0.366
Chain 1:   1800       -10883.936             0.410            0.366
Chain 1:   1900       -11579.589             0.379            0.237
Chain 1:   2000       -10495.026             0.343            0.213
Chain 1:   2100        -9418.918             0.348            0.213
Chain 1:   2200       -10274.790             0.332            0.182
Chain 1:   2300       -16462.281             0.330            0.182
Chain 1:   2400        -9626.803             0.380            0.182
Chain 1:   2500        -9872.893             0.364            0.114
Chain 1:   2600       -10437.149             0.318            0.103
Chain 1:   2700       -11765.220             0.170            0.103
Chain 1:   2800        -9573.070             0.187            0.113
Chain 1:   2900       -10470.395             0.189            0.113
Chain 1:   3000       -11135.867             0.185            0.113
Chain 1:   3100        -9591.162             0.190            0.113
Chain 1:   3200        -9931.057             0.185            0.113
Chain 1:   3300       -10847.847             0.156            0.086
Chain 1:   3400       -15530.641             0.115            0.086
Chain 1:   3500       -11343.406             0.149            0.113
Chain 1:   3600       -16101.379             0.173            0.161
Chain 1:   3700        -9416.021             0.233            0.229
Chain 1:   3800       -11537.729             0.229            0.184
Chain 1:   3900        -9872.269             0.237            0.184
Chain 1:   4000        -9242.855             0.238            0.184
Chain 1:   4100        -9443.162             0.224            0.184
Chain 1:   4200       -14863.882             0.257            0.296
Chain 1:   4300       -10136.470             0.295            0.302
Chain 1:   4400       -10022.625             0.266            0.296
Chain 1:   4500       -10120.551             0.230            0.184
Chain 1:   4600       -14503.113             0.231            0.184
Chain 1:   4700       -11700.619             0.184            0.184
Chain 1:   4800        -9186.307             0.193            0.240
Chain 1:   4900        -9072.819             0.177            0.240
Chain 1:   5000       -13673.022             0.204            0.274
Chain 1:   5100        -9179.103             0.251            0.302
Chain 1:   5200        -9330.902             0.216            0.274
Chain 1:   5300        -9703.441             0.173            0.240
Chain 1:   5400       -12426.581             0.194            0.240
Chain 1:   5500       -12768.047             0.195            0.240
Chain 1:   5600        -8986.806             0.207            0.240
Chain 1:   5700        -9534.792             0.189            0.219
Chain 1:   5800        -9557.798             0.162            0.057
Chain 1:   5900       -14293.857             0.194            0.219
Chain 1:   6000        -9912.904             0.204            0.219
Chain 1:   6100        -9238.491             0.163            0.073
Chain 1:   6200        -8963.321             0.164            0.073
Chain 1:   6300       -14469.661             0.198            0.219
Chain 1:   6400        -8646.491             0.244            0.331
Chain 1:   6500        -9187.206             0.247            0.331
Chain 1:   6600        -8666.380             0.211            0.073
Chain 1:   6700        -9833.279             0.217            0.119
Chain 1:   6800        -8929.124             0.227            0.119
Chain 1:   6900       -11408.767             0.216            0.119
Chain 1:   7000        -8644.195             0.203            0.119
Chain 1:   7100        -8868.040             0.199            0.119
Chain 1:   7200        -8824.608             0.196            0.119
Chain 1:   7300        -8754.037             0.159            0.101
Chain 1:   7400       -12940.700             0.124            0.101
Chain 1:   7500       -10473.648             0.141            0.119
Chain 1:   7600        -8787.640             0.155            0.192
Chain 1:   7700        -9070.444             0.146            0.192
Chain 1:   7800        -9050.178             0.136            0.192
Chain 1:   7900        -9067.893             0.114            0.031
Chain 1:   8000       -12503.827             0.110            0.031
Chain 1:   8100        -9395.890             0.140            0.192
Chain 1:   8200        -8621.378             0.149            0.192
Chain 1:   8300        -9244.725             0.155            0.192
Chain 1:   8400        -8740.157             0.128            0.090
Chain 1:   8500       -15206.912             0.147            0.090
Chain 1:   8600        -8427.482             0.209            0.090
Chain 1:   8700        -9037.301             0.212            0.090
Chain 1:   8800        -8619.722             0.217            0.090
Chain 1:   8900        -8677.731             0.217            0.090
Chain 1:   9000        -9800.846             0.201            0.090
Chain 1:   9100       -12304.721             0.189            0.090
Chain 1:   9200        -8611.506             0.222            0.115
Chain 1:   9300        -8568.222             0.216            0.115
Chain 1:   9400        -8641.745             0.211            0.115
Chain 1:   9500        -9600.202             0.179            0.100
Chain 1:   9600       -10258.747             0.105            0.067
Chain 1:   9700        -9412.182             0.107            0.090
Chain 1:   9800        -8783.695             0.109            0.090
Chain 1:   9900        -8671.524             0.110            0.090
Chain 1:   10000        -8494.029             0.101            0.072
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58822.318             1.000            1.000
Chain 1:    200       -18259.306             1.611            2.221
Chain 1:    300        -8923.501             1.423            1.046
Chain 1:    400        -8196.924             1.089            1.046
Chain 1:    500        -8687.293             0.883            1.000
Chain 1:    600        -9041.486             0.742            1.000
Chain 1:    700        -7950.123             0.656            0.137
Chain 1:    800        -8241.835             0.578            0.137
Chain 1:    900        -8105.425             0.516            0.089
Chain 1:   1000        -7930.683             0.466            0.089
Chain 1:   1100        -7676.078             0.370            0.056
Chain 1:   1200        -7740.766             0.148            0.039
Chain 1:   1300        -7767.461             0.044            0.035
Chain 1:   1400        -8086.587             0.039            0.035
Chain 1:   1500        -7645.819             0.039            0.035
Chain 1:   1600        -7804.397             0.037            0.033
Chain 1:   1700        -7760.442             0.024            0.022
Chain 1:   1800        -7772.621             0.021            0.020
Chain 1:   1900        -7649.394             0.021            0.020
Chain 1:   2000        -7736.777             0.020            0.016
Chain 1:   2100        -7637.430             0.018            0.013
Chain 1:   2200        -7899.031             0.020            0.016
Chain 1:   2300        -7600.912             0.024            0.020
Chain 1:   2400        -7589.248             0.020            0.016
Chain 1:   2500        -7599.502             0.014            0.013
Chain 1:   2600        -7581.942             0.013            0.011
Chain 1:   2700        -7501.483             0.013            0.011
Chain 1:   2800        -7567.324             0.014            0.011
Chain 1:   2900        -7429.297             0.014            0.011
Chain 1:   3000        -7588.295             0.015            0.013
Chain 1:   3100        -7576.842             0.014            0.011
Chain 1:   3200        -7789.820             0.013            0.011
Chain 1:   3300        -7499.758             0.013            0.011
Chain 1:   3400        -7744.564             0.016            0.019
Chain 1:   3500        -7488.060             0.019            0.021
Chain 1:   3600        -7555.076             0.020            0.021
Chain 1:   3700        -7505.412             0.020            0.021
Chain 1:   3800        -7506.544             0.019            0.021
Chain 1:   3900        -7464.914             0.018            0.021
Chain 1:   4000        -7457.041             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002872 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86267.629             1.000            1.000
Chain 1:    200       -13959.818             3.090            5.180
Chain 1:    300       -10281.330             2.179            1.000
Chain 1:    400       -11258.010             1.656            1.000
Chain 1:    500        -9273.973             1.368            0.358
Chain 1:    600        -8740.045             1.150            0.358
Chain 1:    700        -9169.233             0.992            0.214
Chain 1:    800        -9581.517             0.874            0.214
Chain 1:    900        -8975.985             0.784            0.087
Chain 1:   1000        -9078.981             0.707            0.087
Chain 1:   1100        -8913.902             0.609            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8678.706             0.093            0.061
Chain 1:   1300        -8953.360             0.061            0.047
Chain 1:   1400        -8919.922             0.052            0.043
Chain 1:   1500        -8812.534             0.032            0.031
Chain 1:   1600        -8920.749             0.027            0.027
Chain 1:   1700        -8994.241             0.023            0.019
Chain 1:   1800        -8563.306             0.024            0.019
Chain 1:   1900        -8667.280             0.019            0.012
Chain 1:   2000        -8642.579             0.018            0.012
Chain 1:   2100        -8779.435             0.017            0.012
Chain 1:   2200        -8572.455             0.017            0.012
Chain 1:   2300        -8671.184             0.015            0.012
Chain 1:   2400        -8733.942             0.016            0.012
Chain 1:   2500        -8674.998             0.015            0.012
Chain 1:   2600        -8680.521             0.014            0.011
Chain 1:   2700        -8595.336             0.014            0.011
Chain 1:   2800        -8551.782             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002758 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380449.465             1.000            1.000
Chain 1:    200     -1583444.166             2.646            4.293
Chain 1:    300      -892251.552             2.022            1.000
Chain 1:    400      -458906.059             1.753            1.000
Chain 1:    500      -359420.769             1.458            0.944
Chain 1:    600      -234208.839             1.304            0.944
Chain 1:    700      -120091.207             1.253            0.944
Chain 1:    800       -87201.769             1.144            0.944
Chain 1:    900       -67477.758             1.049            0.775
Chain 1:   1000       -52218.471             0.973            0.775
Chain 1:   1100       -39641.968             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38814.086             0.478            0.377
Chain 1:   1300       -26714.173             0.446            0.377
Chain 1:   1400       -26428.654             0.353            0.317
Chain 1:   1500       -23001.247             0.340            0.317
Chain 1:   1600       -22213.693             0.290            0.292
Chain 1:   1700       -21080.609             0.200            0.292
Chain 1:   1800       -21023.364             0.163            0.149
Chain 1:   1900       -21349.748             0.135            0.054
Chain 1:   2000       -19856.829             0.113            0.054
Chain 1:   2100       -20095.402             0.083            0.035
Chain 1:   2200       -20322.650             0.082            0.035
Chain 1:   2300       -19939.114             0.038            0.019
Chain 1:   2400       -19711.043             0.039            0.019
Chain 1:   2500       -19513.292             0.025            0.015
Chain 1:   2600       -19142.997             0.023            0.015
Chain 1:   2700       -19099.795             0.018            0.012
Chain 1:   2800       -18816.618             0.019            0.015
Chain 1:   2900       -19098.119             0.019            0.015
Chain 1:   3000       -19084.192             0.012            0.012
Chain 1:   3100       -19169.234             0.011            0.012
Chain 1:   3200       -18859.697             0.011            0.015
Chain 1:   3300       -19064.606             0.011            0.012
Chain 1:   3400       -18539.179             0.012            0.015
Chain 1:   3500       -19151.650             0.014            0.015
Chain 1:   3600       -18457.628             0.016            0.015
Chain 1:   3700       -18844.991             0.018            0.016
Chain 1:   3800       -17803.627             0.022            0.021
Chain 1:   3900       -17799.794             0.021            0.021
Chain 1:   4000       -17917.066             0.022            0.021
Chain 1:   4100       -17830.772             0.022            0.021
Chain 1:   4200       -17646.787             0.021            0.021
Chain 1:   4300       -17785.319             0.021            0.021
Chain 1:   4400       -17741.958             0.018            0.010
Chain 1:   4500       -17644.496             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001291 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12202.380             1.000            1.000
Chain 1:    200        -9044.089             0.675            1.000
Chain 1:    300        -8132.625             0.487            0.349
Chain 1:    400        -8118.005             0.366            0.349
Chain 1:    500        -8020.344             0.295            0.112
Chain 1:    600        -7939.038             0.248            0.112
Chain 1:    700        -7868.338             0.213            0.012
Chain 1:    800        -7874.767             0.187            0.012
Chain 1:    900        -7939.915             0.167            0.010
Chain 1:   1000        -7936.379             0.150            0.010
Chain 1:   1100        -7983.159             0.051            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001434 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61484.134             1.000            1.000
Chain 1:    200       -17469.741             1.760            2.519
Chain 1:    300        -8704.668             1.509            1.007
Chain 1:    400        -8189.648             1.147            1.007
Chain 1:    500        -8278.152             0.920            1.000
Chain 1:    600        -8006.821             0.772            1.000
Chain 1:    700        -7625.059             0.669            0.063
Chain 1:    800        -8002.884             0.591            0.063
Chain 1:    900        -7834.536             0.528            0.050
Chain 1:   1000        -7586.400             0.479            0.050
Chain 1:   1100        -7566.648             0.379            0.047
Chain 1:   1200        -7514.778             0.128            0.034
Chain 1:   1300        -7730.990             0.030            0.033
Chain 1:   1400        -7615.067             0.025            0.028
Chain 1:   1500        -7546.142             0.025            0.028
Chain 1:   1600        -7518.690             0.022            0.021
Chain 1:   1700        -7458.245             0.017            0.015
Chain 1:   1800        -7523.200             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003158 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85952.864             1.000            1.000
Chain 1:    200       -13258.607             3.241            5.483
Chain 1:    300        -9726.019             2.282            1.000
Chain 1:    400       -10572.827             1.732            1.000
Chain 1:    500        -8605.710             1.431            0.363
Chain 1:    600        -8533.487             1.194            0.363
Chain 1:    700        -8519.286             1.024            0.229
Chain 1:    800        -8581.558             0.897            0.229
Chain 1:    900        -8611.382             0.797            0.080
Chain 1:   1000        -8331.943             0.721            0.080
Chain 1:   1100        -8608.464             0.624            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8257.825             0.080            0.034
Chain 1:   1300        -8315.442             0.044            0.032
Chain 1:   1400        -8313.449             0.036            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00317 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398404.782             1.000            1.000
Chain 1:    200     -1581253.847             2.656            4.311
Chain 1:    300      -889627.975             2.030            1.000
Chain 1:    400      -457098.063             1.759            1.000
Chain 1:    500      -357549.695             1.463            0.946
Chain 1:    600      -232619.432             1.308            0.946
Chain 1:    700      -118896.071             1.258            0.946
Chain 1:    800       -86135.874             1.148            0.946
Chain 1:    900       -66485.434             1.054            0.777
Chain 1:   1000       -51281.329             0.978            0.777
Chain 1:   1100       -38768.147             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37939.450             0.481            0.380
Chain 1:   1300       -25910.251             0.450            0.380
Chain 1:   1400       -25628.313             0.356            0.323
Chain 1:   1500       -22219.963             0.344            0.323
Chain 1:   1600       -21437.296             0.294            0.296
Chain 1:   1700       -20313.101             0.204            0.296
Chain 1:   1800       -20257.473             0.166            0.153
Chain 1:   1900       -20583.010             0.138            0.055
Chain 1:   2000       -19096.600             0.116            0.055
Chain 1:   2100       -19334.676             0.085            0.037
Chain 1:   2200       -19560.635             0.084            0.037
Chain 1:   2300       -19178.447             0.040            0.020
Chain 1:   2400       -18950.788             0.040            0.020
Chain 1:   2500       -18752.874             0.025            0.016
Chain 1:   2600       -18383.617             0.024            0.016
Chain 1:   2700       -18340.800             0.019            0.012
Chain 1:   2800       -18057.995             0.020            0.016
Chain 1:   2900       -18338.882             0.020            0.015
Chain 1:   3000       -18325.125             0.012            0.012
Chain 1:   3100       -18410.029             0.011            0.012
Chain 1:   3200       -18101.119             0.012            0.015
Chain 1:   3300       -18305.525             0.011            0.012
Chain 1:   3400       -17781.230             0.013            0.015
Chain 1:   3500       -18391.951             0.015            0.016
Chain 1:   3600       -17700.107             0.017            0.016
Chain 1:   3700       -18085.800             0.019            0.017
Chain 1:   3800       -17047.860             0.023            0.021
Chain 1:   3900       -17044.084             0.022            0.021
Chain 1:   4000       -17161.354             0.022            0.021
Chain 1:   4100       -17075.247             0.022            0.021
Chain 1:   4200       -16892.031             0.022            0.021
Chain 1:   4300       -17030.044             0.022            0.021
Chain 1:   4400       -16987.275             0.019            0.011
Chain 1:   4500       -16889.906             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00133 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49502.322             1.000            1.000
Chain 1:    200       -13249.253             1.868            2.736
Chain 1:    300       -16248.636             1.307            1.000
Chain 1:    400       -27614.208             1.083            1.000
Chain 1:    500       -15623.583             1.020            0.767
Chain 1:    600       -20105.171             0.887            0.767
Chain 1:    700       -15142.266             0.807            0.412
Chain 1:    800       -21388.190             0.743            0.412
Chain 1:    900       -12497.242             0.739            0.412
Chain 1:   1000       -13566.300             0.673            0.412
Chain 1:   1100       -10792.592             0.599            0.328   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12179.130             0.337            0.292
Chain 1:   1300       -13913.137             0.331            0.292
Chain 1:   1400       -12344.201             0.302            0.257
Chain 1:   1500        -9953.503             0.250            0.240
Chain 1:   1600       -10039.255             0.228            0.240
Chain 1:   1700       -17113.230             0.237            0.240
Chain 1:   1800        -9892.839             0.280            0.240
Chain 1:   1900       -10005.563             0.210            0.127
Chain 1:   2000       -17792.647             0.246            0.240
Chain 1:   2100       -10874.046             0.284            0.240
Chain 1:   2200        -9761.697             0.284            0.240
Chain 1:   2300       -11356.252             0.286            0.240
Chain 1:   2400        -9300.730             0.295            0.240
Chain 1:   2500       -12851.609             0.299            0.276
Chain 1:   2600       -18090.087             0.327            0.290
Chain 1:   2700        -9543.490             0.375            0.290
Chain 1:   2800       -11109.755             0.316            0.276
Chain 1:   2900        -9206.975             0.336            0.276
Chain 1:   3000        -9626.057             0.296            0.221
Chain 1:   3100       -11057.766             0.246            0.207
Chain 1:   3200        -9418.954             0.252            0.207
Chain 1:   3300       -10671.033             0.249            0.207
Chain 1:   3400        -9237.265             0.243            0.174
Chain 1:   3500        -9312.722             0.216            0.155
Chain 1:   3600        -9313.440             0.187            0.141
Chain 1:   3700        -9147.894             0.099            0.129
Chain 1:   3800        -8985.850             0.087            0.117
Chain 1:   3900        -8940.873             0.067            0.044
Chain 1:   4000       -10042.124             0.074            0.110
Chain 1:   4100       -11190.246             0.071            0.103
Chain 1:   4200       -10319.274             0.062            0.084
Chain 1:   4300       -10464.355             0.052            0.018
Chain 1:   4400       -13474.336             0.058            0.018
Chain 1:   4500       -12930.673             0.062            0.042
Chain 1:   4600        -8818.936             0.108            0.084
Chain 1:   4700       -12891.535             0.138            0.103
Chain 1:   4800        -9166.954             0.177            0.110
Chain 1:   4900        -9076.411             0.177            0.110
Chain 1:   5000        -9558.308             0.172            0.103
Chain 1:   5100       -10857.903             0.173            0.120
Chain 1:   5200       -10919.897             0.165            0.120
Chain 1:   5300       -10995.678             0.165            0.120
Chain 1:   5400        -9459.501             0.159            0.120
Chain 1:   5500        -8839.526             0.161            0.120
Chain 1:   5600        -9013.846             0.117            0.070
Chain 1:   5700       -13061.108             0.116            0.070
Chain 1:   5800        -8607.552             0.127            0.070
Chain 1:   5900       -15475.439             0.171            0.120
Chain 1:   6000        -8622.138             0.245            0.162
Chain 1:   6100        -9598.853             0.243            0.162
Chain 1:   6200        -8654.260             0.254            0.162
Chain 1:   6300       -14361.774             0.293            0.310
Chain 1:   6400        -8768.312             0.340            0.397
Chain 1:   6500       -10616.077             0.351            0.397
Chain 1:   6600       -11340.644             0.355            0.397
Chain 1:   6700        -9417.162             0.344            0.397
Chain 1:   6800       -13705.299             0.324            0.313
Chain 1:   6900       -11958.826             0.294            0.204
Chain 1:   7000       -13261.230             0.225            0.174
Chain 1:   7100        -8627.393             0.268            0.204
Chain 1:   7200        -8446.318             0.259            0.204
Chain 1:   7300        -8576.031             0.221            0.174
Chain 1:   7400        -8764.494             0.159            0.146
Chain 1:   7500        -8639.638             0.143            0.098
Chain 1:   7600        -9208.331             0.143            0.098
Chain 1:   7700        -9763.242             0.129            0.062
Chain 1:   7800        -9046.136             0.105            0.062
Chain 1:   7900        -9706.706             0.097            0.062
Chain 1:   8000        -8590.670             0.101            0.062
Chain 1:   8100        -8565.791             0.047            0.057
Chain 1:   8200        -9200.765             0.052            0.062
Chain 1:   8300        -8360.856             0.060            0.068
Chain 1:   8400       -11613.729             0.086            0.069
Chain 1:   8500        -8489.794             0.122            0.079
Chain 1:   8600       -11412.748             0.141            0.100
Chain 1:   8700       -10100.108             0.148            0.130
Chain 1:   8800        -9307.060             0.149            0.130
Chain 1:   8900        -8510.605             0.152            0.130
Chain 1:   9000        -9740.693             0.151            0.126
Chain 1:   9100        -8380.253             0.167            0.130
Chain 1:   9200        -9294.101             0.170            0.130
Chain 1:   9300        -8745.949             0.166            0.130
Chain 1:   9400        -9867.935             0.150            0.126
Chain 1:   9500       -12315.487             0.133            0.126
Chain 1:   9600        -8725.649             0.148            0.126
Chain 1:   9700        -9815.599             0.146            0.114
Chain 1:   9800        -8766.094             0.150            0.120
Chain 1:   9900       -11943.587             0.167            0.126
Chain 1:   10000        -8470.715             0.195            0.162
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0014 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63617.147             1.000            1.000
Chain 1:    200       -18386.862             1.730            2.460
Chain 1:    300        -8836.055             1.514            1.081
Chain 1:    400        -8573.378             1.143            1.081
Chain 1:    500        -8279.645             0.921            1.000
Chain 1:    600        -9111.119             0.783            1.000
Chain 1:    700        -7705.731             0.697            0.182
Chain 1:    800        -7701.668             0.610            0.182
Chain 1:    900        -7653.648             0.543            0.091
Chain 1:   1000        -7694.010             0.489            0.091
Chain 1:   1100        -7575.563             0.391            0.035
Chain 1:   1200        -7653.589             0.146            0.031
Chain 1:   1300        -7768.849             0.039            0.016
Chain 1:   1400        -7817.643             0.037            0.015
Chain 1:   1500        -7494.695             0.038            0.015
Chain 1:   1600        -7678.064             0.031            0.015
Chain 1:   1700        -7483.582             0.015            0.015
Chain 1:   1800        -7532.005             0.016            0.015
Chain 1:   1900        -7527.510             0.015            0.015
Chain 1:   2000        -7617.794             0.016            0.015
Chain 1:   2100        -7515.359             0.016            0.014
Chain 1:   2200        -7650.654             0.016            0.015
Chain 1:   2300        -7503.332             0.017            0.018
Chain 1:   2400        -7563.347             0.017            0.018
Chain 1:   2500        -7561.383             0.013            0.014
Chain 1:   2600        -7462.721             0.012            0.013
Chain 1:   2700        -7506.989             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86656.569             1.000            1.000
Chain 1:    200       -13719.355             3.158            5.316
Chain 1:    300       -10050.559             2.227            1.000
Chain 1:    400       -10987.960             1.692            1.000
Chain 1:    500        -9036.536             1.397            0.365
Chain 1:    600        -8497.856             1.174            0.365
Chain 1:    700        -8701.575             1.010            0.216
Chain 1:    800        -9336.779             0.892            0.216
Chain 1:    900        -8827.423             0.799            0.085
Chain 1:   1000        -8658.082             0.721            0.085
Chain 1:   1100        -8695.897             0.622            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8477.482             0.093            0.063
Chain 1:   1300        -8681.011             0.059            0.058
Chain 1:   1400        -8700.525             0.050            0.026
Chain 1:   1500        -8589.764             0.030            0.023
Chain 1:   1600        -8697.465             0.025            0.023
Chain 1:   1700        -8779.386             0.024            0.020
Chain 1:   1800        -8353.540             0.022            0.020
Chain 1:   1900        -8455.899             0.017            0.013
Chain 1:   2000        -8430.549             0.016            0.012
Chain 1:   2100        -8556.991             0.017            0.013
Chain 1:   2200        -8357.166             0.017            0.013
Chain 1:   2300        -8450.875             0.015            0.012
Chain 1:   2400        -8519.194             0.016            0.012
Chain 1:   2500        -8465.440             0.015            0.012
Chain 1:   2600        -8467.461             0.014            0.011
Chain 1:   2700        -8383.868             0.014            0.011
Chain 1:   2800        -8342.893             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411716.363             1.000            1.000
Chain 1:    200     -1585334.463             2.653            4.306
Chain 1:    300      -890315.817             2.029            1.000
Chain 1:    400      -457166.594             1.759            1.000
Chain 1:    500      -357524.337             1.463            0.947
Chain 1:    600      -232625.474             1.308            0.947
Chain 1:    700      -119168.929             1.257            0.947
Chain 1:    800       -86443.422             1.148            0.947
Chain 1:    900       -66847.706             1.053            0.781
Chain 1:   1000       -51690.938             0.977            0.781
Chain 1:   1100       -39209.791             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38393.720             0.480            0.379
Chain 1:   1300       -26390.905             0.447            0.379
Chain 1:   1400       -26115.179             0.354            0.318
Chain 1:   1500       -22712.225             0.341            0.318
Chain 1:   1600       -21931.879             0.291            0.293
Chain 1:   1700       -20810.483             0.201            0.293
Chain 1:   1800       -20755.975             0.163            0.150
Chain 1:   1900       -21082.259             0.136            0.054
Chain 1:   2000       -19595.517             0.114            0.054
Chain 1:   2100       -19833.953             0.083            0.036
Chain 1:   2200       -20059.974             0.082            0.036
Chain 1:   2300       -19677.501             0.039            0.019
Chain 1:   2400       -19449.570             0.039            0.019
Chain 1:   2500       -19251.322             0.025            0.015
Chain 1:   2600       -18881.667             0.023            0.015
Chain 1:   2700       -18838.684             0.018            0.012
Chain 1:   2800       -18555.337             0.019            0.015
Chain 1:   2900       -18836.598             0.019            0.015
Chain 1:   3000       -18822.891             0.012            0.012
Chain 1:   3100       -18907.860             0.011            0.012
Chain 1:   3200       -18598.516             0.012            0.015
Chain 1:   3300       -18803.264             0.011            0.012
Chain 1:   3400       -18278.018             0.012            0.015
Chain 1:   3500       -18890.034             0.015            0.015
Chain 1:   3600       -18196.508             0.016            0.015
Chain 1:   3700       -18583.417             0.018            0.017
Chain 1:   3800       -17542.732             0.023            0.021
Chain 1:   3900       -17538.806             0.021            0.021
Chain 1:   4000       -17656.172             0.022            0.021
Chain 1:   4100       -17569.869             0.022            0.021
Chain 1:   4200       -17386.040             0.021            0.021
Chain 1:   4300       -17524.536             0.021            0.021
Chain 1:   4400       -17481.297             0.018            0.011
Chain 1:   4500       -17383.764             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001238 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13191.040             1.000            1.000
Chain 1:    200        -9821.810             0.672            1.000
Chain 1:    300        -8451.799             0.502            0.343
Chain 1:    400        -8166.970             0.385            0.343
Chain 1:    500        -8035.519             0.311            0.162
Chain 1:    600        -8050.399             0.260            0.162
Chain 1:    700        -7979.198             0.224            0.035
Chain 1:    800        -7947.594             0.196            0.035
Chain 1:    900        -8222.347             0.178            0.033
Chain 1:   1000        -8042.390             0.163            0.033
Chain 1:   1100        -8085.495             0.063            0.022
Chain 1:   1200        -7980.232             0.030            0.016
Chain 1:   1300        -7962.118             0.014            0.013
Chain 1:   1400        -7970.440             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58387.259             1.000            1.000
Chain 1:    200       -17925.249             1.629            2.257
Chain 1:    300        -8812.756             1.430            1.034
Chain 1:    400        -8244.125             1.090            1.034
Chain 1:    500        -8502.110             0.878            1.000
Chain 1:    600        -8778.640             0.737            1.000
Chain 1:    700        -8425.131             0.638            0.069
Chain 1:    800        -8396.302             0.558            0.069
Chain 1:    900        -7937.206             0.503            0.058
Chain 1:   1000        -7887.573             0.453            0.058
Chain 1:   1100        -7737.637             0.355            0.042
Chain 1:   1200        -7722.367             0.130            0.032
Chain 1:   1300        -7914.519             0.029            0.030
Chain 1:   1400        -7964.735             0.022            0.024
Chain 1:   1500        -7669.611             0.023            0.024
Chain 1:   1600        -7822.657             0.022            0.020
Chain 1:   1700        -7620.586             0.020            0.020
Chain 1:   1800        -7657.910             0.021            0.020
Chain 1:   1900        -7677.981             0.015            0.019
Chain 1:   2000        -7640.338             0.015            0.019
Chain 1:   2100        -7660.243             0.013            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002495 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86254.863             1.000            1.000
Chain 1:    200       -13674.171             3.154            5.308
Chain 1:    300       -10003.764             2.225            1.000
Chain 1:    400       -10964.310             1.691            1.000
Chain 1:    500        -8996.543             1.396            0.367
Chain 1:    600        -8739.750             1.168            0.367
Chain 1:    700        -8488.368             1.006            0.219
Chain 1:    800        -8662.043             0.883            0.219
Chain 1:    900        -8788.008             0.786            0.088
Chain 1:   1000        -8522.553             0.711            0.088
Chain 1:   1100        -8816.268             0.614            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8410.451             0.088            0.033
Chain 1:   1300        -8692.777             0.054            0.032
Chain 1:   1400        -8695.815             0.046            0.031
Chain 1:   1500        -8538.544             0.026            0.030
Chain 1:   1600        -8651.966             0.024            0.030
Chain 1:   1700        -8728.853             0.022            0.020
Chain 1:   1800        -8300.798             0.025            0.031
Chain 1:   1900        -8403.827             0.025            0.031
Chain 1:   2000        -8378.945             0.022            0.018
Chain 1:   2100        -8506.537             0.020            0.015
Chain 1:   2200        -8304.742             0.018            0.015
Chain 1:   2300        -8399.679             0.016            0.013
Chain 1:   2400        -8467.047             0.017            0.013
Chain 1:   2500        -8413.164             0.015            0.012
Chain 1:   2600        -8415.985             0.014            0.011
Chain 1:   2700        -8332.000             0.014            0.011
Chain 1:   2800        -8290.141             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003229 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402999.641             1.000            1.000
Chain 1:    200     -1583826.447             2.653            4.306
Chain 1:    300      -890076.306             2.028            1.000
Chain 1:    400      -457076.275             1.758            1.000
Chain 1:    500      -357585.429             1.462            0.947
Chain 1:    600      -232897.519             1.308            0.947
Chain 1:    700      -119333.654             1.257            0.947
Chain 1:    800       -86546.368             1.147            0.947
Chain 1:    900       -66924.235             1.052            0.779
Chain 1:   1000       -51738.581             0.976            0.779
Chain 1:   1100       -39225.410             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38406.778             0.480            0.379
Chain 1:   1300       -26375.024             0.447            0.379
Chain 1:   1400       -26096.290             0.354            0.319
Chain 1:   1500       -22685.198             0.341            0.319
Chain 1:   1600       -21902.241             0.291            0.294
Chain 1:   1700       -20777.419             0.201            0.293
Chain 1:   1800       -20722.048             0.164            0.150
Chain 1:   1900       -21048.392             0.136            0.054
Chain 1:   2000       -19559.633             0.114            0.054
Chain 1:   2100       -19798.267             0.083            0.036
Chain 1:   2200       -20024.542             0.082            0.036
Chain 1:   2300       -19641.821             0.039            0.019
Chain 1:   2400       -19413.831             0.039            0.019
Chain 1:   2500       -19215.633             0.025            0.016
Chain 1:   2600       -18845.919             0.023            0.016
Chain 1:   2700       -18802.881             0.018            0.012
Chain 1:   2800       -18519.500             0.019            0.015
Chain 1:   2900       -18800.880             0.019            0.015
Chain 1:   3000       -18787.142             0.012            0.012
Chain 1:   3100       -18872.113             0.011            0.012
Chain 1:   3200       -18562.742             0.012            0.015
Chain 1:   3300       -18767.509             0.011            0.012
Chain 1:   3400       -18242.204             0.012            0.015
Chain 1:   3500       -18854.351             0.015            0.015
Chain 1:   3600       -18160.697             0.016            0.015
Chain 1:   3700       -18547.726             0.018            0.017
Chain 1:   3800       -17506.826             0.023            0.021
Chain 1:   3900       -17502.908             0.021            0.021
Chain 1:   4000       -17620.265             0.022            0.021
Chain 1:   4100       -17533.942             0.022            0.021
Chain 1:   4200       -17350.063             0.021            0.021
Chain 1:   4300       -17488.599             0.021            0.021
Chain 1:   4400       -17445.340             0.018            0.011
Chain 1:   4500       -17347.793             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49334.292             1.000            1.000
Chain 1:    200       -16945.215             1.456            1.911
Chain 1:    300       -17518.650             0.981            1.000
Chain 1:    400       -13160.668             0.819            1.000
Chain 1:    500       -16553.242             0.696            0.331
Chain 1:    600       -16101.239             0.585            0.331
Chain 1:    700       -15834.643             0.504            0.205
Chain 1:    800       -15174.014             0.446            0.205
Chain 1:    900       -15006.954             0.398            0.044
Chain 1:   1000       -13612.028             0.368            0.102
Chain 1:   1100       -11110.758             0.291            0.102
Chain 1:   1200       -12755.118             0.112            0.102
Chain 1:   1300       -10481.554             0.131            0.129
Chain 1:   1400       -16257.232             0.133            0.129
Chain 1:   1500       -10250.414             0.171            0.129
Chain 1:   1600       -11712.921             0.181            0.129
Chain 1:   1700       -12246.914             0.184            0.129
Chain 1:   1800       -26084.576             0.232            0.217
Chain 1:   1900       -10000.076             0.392            0.225
Chain 1:   2000       -10115.923             0.383            0.225
Chain 1:   2100       -10264.617             0.362            0.217
Chain 1:   2200       -14239.780             0.377            0.279
Chain 1:   2300       -12652.333             0.368            0.279
Chain 1:   2400       -10271.260             0.356            0.232
Chain 1:   2500       -10624.041             0.300            0.125
Chain 1:   2600       -10258.611             0.291            0.125
Chain 1:   2700       -13726.682             0.312            0.232
Chain 1:   2800        -9667.224             0.301            0.232
Chain 1:   2900       -10489.817             0.148            0.125
Chain 1:   3000       -13084.652             0.167            0.198
Chain 1:   3100        -9109.569             0.209            0.232
Chain 1:   3200        -9839.272             0.189            0.198
Chain 1:   3300        -9932.959             0.177            0.198
Chain 1:   3400       -16646.975             0.194            0.198
Chain 1:   3500       -11573.090             0.235            0.253
Chain 1:   3600       -10502.369             0.241            0.253
Chain 1:   3700       -11127.991             0.222            0.198
Chain 1:   3800        -9266.298             0.200            0.198
Chain 1:   3900        -9170.244             0.193            0.198
Chain 1:   4000        -9936.348             0.181            0.102
Chain 1:   4100       -10508.244             0.143            0.077
Chain 1:   4200       -15732.091             0.168            0.102
Chain 1:   4300       -14164.991             0.179            0.111
Chain 1:   4400        -9017.298             0.195            0.111
Chain 1:   4500        -9195.386             0.153            0.102
Chain 1:   4600       -14939.231             0.182            0.111
Chain 1:   4700       -10125.321             0.224            0.201
Chain 1:   4800        -8845.068             0.218            0.145
Chain 1:   4900        -9251.243             0.221            0.145
Chain 1:   5000        -8989.309             0.217            0.145
Chain 1:   5100       -13343.486             0.244            0.326
Chain 1:   5200       -10153.572             0.242            0.314
Chain 1:   5300       -10749.583             0.236            0.314
Chain 1:   5400       -14703.889             0.206            0.269
Chain 1:   5500       -10851.342             0.240            0.314
Chain 1:   5600        -8670.407             0.226            0.269
Chain 1:   5700       -12619.733             0.210            0.269
Chain 1:   5800        -8597.037             0.243            0.313
Chain 1:   5900       -11813.287             0.265            0.313
Chain 1:   6000        -8973.312             0.294            0.314
Chain 1:   6100        -9054.696             0.262            0.313
Chain 1:   6200        -8603.962             0.236            0.272
Chain 1:   6300        -9526.649             0.240            0.272
Chain 1:   6400        -8600.430             0.224            0.272
Chain 1:   6500        -8912.386             0.192            0.252
Chain 1:   6600       -11047.075             0.186            0.193
Chain 1:   6700       -10087.681             0.165            0.108
Chain 1:   6800        -8637.841             0.135            0.108
Chain 1:   6900       -10364.425             0.124            0.108
Chain 1:   7000        -8567.179             0.113            0.108
Chain 1:   7100       -15572.243             0.157            0.167
Chain 1:   7200        -9499.808             0.216            0.168
Chain 1:   7300        -9456.185             0.207            0.168
Chain 1:   7400       -12750.060             0.222            0.193
Chain 1:   7500        -8483.511             0.269            0.210
Chain 1:   7600        -9311.373             0.258            0.210
Chain 1:   7700       -10905.449             0.263            0.210
Chain 1:   7800        -9087.311             0.267            0.210
Chain 1:   7900        -8612.342             0.256            0.210
Chain 1:   8000        -9484.835             0.244            0.200
Chain 1:   8100        -8653.112             0.208            0.146
Chain 1:   8200       -11113.693             0.167            0.146
Chain 1:   8300        -8463.329             0.197            0.200
Chain 1:   8400       -12399.250             0.203            0.200
Chain 1:   8500        -8713.525             0.195            0.200
Chain 1:   8600        -9212.724             0.192            0.200
Chain 1:   8700        -9728.117             0.183            0.200
Chain 1:   8800        -9380.841             0.166            0.096
Chain 1:   8900       -11216.979             0.177            0.164
Chain 1:   9000       -11554.096             0.171            0.164
Chain 1:   9100        -9104.147             0.188            0.221
Chain 1:   9200        -8416.975             0.174            0.164
Chain 1:   9300       -11091.841             0.167            0.164
Chain 1:   9400        -9308.853             0.154            0.164
Chain 1:   9500        -8460.797             0.122            0.100
Chain 1:   9600        -8600.597             0.118            0.100
Chain 1:   9700       -10118.484             0.128            0.150
Chain 1:   9800        -9117.199             0.135            0.150
Chain 1:   9900        -9848.171             0.126            0.110
Chain 1:   10000       -10425.048             0.129            0.110
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004038 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 40.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61920.924             1.000            1.000
Chain 1:    200       -18111.430             1.709            2.419
Chain 1:    300        -9016.006             1.476            1.009
Chain 1:    400        -9739.714             1.125            1.009
Chain 1:    500        -8114.212             0.940            1.000
Chain 1:    600        -8508.400             0.791            1.000
Chain 1:    700        -8186.700             0.684            0.200
Chain 1:    800        -8356.167             0.601            0.200
Chain 1:    900        -8167.744             0.537            0.074
Chain 1:   1000        -7820.673             0.488            0.074
Chain 1:   1100        -7866.706             0.388            0.046
Chain 1:   1200        -7801.771             0.147            0.044
Chain 1:   1300        -7831.078             0.047            0.039
Chain 1:   1400        -7719.184             0.041            0.023
Chain 1:   1500        -7625.538             0.022            0.020
Chain 1:   1600        -7833.666             0.020            0.020
Chain 1:   1700        -7576.947             0.019            0.020
Chain 1:   1800        -7711.798             0.019            0.017
Chain 1:   1900        -7794.442             0.018            0.014
Chain 1:   2000        -7721.020             0.014            0.012
Chain 1:   2100        -7667.263             0.014            0.012
Chain 1:   2200        -7811.679             0.015            0.014
Chain 1:   2300        -7635.255             0.017            0.017
Chain 1:   2400        -7704.038             0.017            0.017
Chain 1:   2500        -7717.317             0.016            0.017
Chain 1:   2600        -7608.445             0.015            0.014
Chain 1:   2700        -7661.772             0.012            0.011
Chain 1:   2800        -7711.997             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003372 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86695.928             1.000            1.000
Chain 1:    200       -13776.559             3.147            5.293
Chain 1:    300       -10127.959             2.218            1.000
Chain 1:    400       -11071.482             1.685            1.000
Chain 1:    500        -9108.083             1.391            0.360
Chain 1:    600        -9003.628             1.161            0.360
Chain 1:    700        -8689.129             1.000            0.216
Chain 1:    800        -9131.277             0.881            0.216
Chain 1:    900        -8924.164             0.786            0.085
Chain 1:   1000        -8800.154             0.709            0.085
Chain 1:   1100        -8930.546             0.610            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8479.012             0.086            0.048
Chain 1:   1300        -8686.519             0.053            0.036
Chain 1:   1400        -8808.951             0.045            0.024
Chain 1:   1500        -8697.360             0.025            0.023
Chain 1:   1600        -8809.237             0.025            0.023
Chain 1:   1700        -8883.770             0.023            0.015
Chain 1:   1800        -8468.947             0.023            0.015
Chain 1:   1900        -8565.392             0.021            0.014
Chain 1:   2000        -8538.917             0.020            0.014
Chain 1:   2100        -8662.472             0.020            0.014
Chain 1:   2200        -8480.048             0.017            0.014
Chain 1:   2300        -8559.870             0.016            0.013
Chain 1:   2400        -8629.554             0.015            0.013
Chain 1:   2500        -8575.366             0.014            0.011
Chain 1:   2600        -8575.319             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415613.928             1.000            1.000
Chain 1:    200     -1583787.301             2.657            4.314
Chain 1:    300      -890691.394             2.031            1.000
Chain 1:    400      -458183.585             1.759            1.000
Chain 1:    500      -358635.009             1.463            0.944
Chain 1:    600      -233546.133             1.308            0.944
Chain 1:    700      -119618.252             1.257            0.944
Chain 1:    800       -86840.953             1.147            0.944
Chain 1:    900       -67149.896             1.052            0.778
Chain 1:   1000       -51923.364             0.977            0.778
Chain 1:   1100       -39382.669             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38554.527             0.479            0.377
Chain 1:   1300       -26484.505             0.447            0.377
Chain 1:   1400       -26201.282             0.354            0.318
Chain 1:   1500       -22783.458             0.341            0.318
Chain 1:   1600       -21998.972             0.291            0.293
Chain 1:   1700       -20869.260             0.201            0.293
Chain 1:   1800       -20812.673             0.164            0.150
Chain 1:   1900       -21138.888             0.136            0.054
Chain 1:   2000       -19648.668             0.114            0.054
Chain 1:   2100       -19886.771             0.083            0.036
Chain 1:   2200       -20113.795             0.082            0.036
Chain 1:   2300       -19730.540             0.039            0.019
Chain 1:   2400       -19502.633             0.039            0.019
Chain 1:   2500       -19304.977             0.025            0.015
Chain 1:   2600       -18934.766             0.023            0.015
Chain 1:   2700       -18891.640             0.018            0.012
Chain 1:   2800       -18608.700             0.019            0.015
Chain 1:   2900       -18889.965             0.019            0.015
Chain 1:   3000       -18875.995             0.012            0.012
Chain 1:   3100       -18961.052             0.011            0.012
Chain 1:   3200       -18651.614             0.012            0.015
Chain 1:   3300       -18856.440             0.011            0.012
Chain 1:   3400       -18331.336             0.012            0.015
Chain 1:   3500       -18943.317             0.015            0.015
Chain 1:   3600       -18249.853             0.016            0.015
Chain 1:   3700       -18636.840             0.018            0.017
Chain 1:   3800       -17596.368             0.023            0.021
Chain 1:   3900       -17592.571             0.021            0.021
Chain 1:   4000       -17709.809             0.022            0.021
Chain 1:   4100       -17623.622             0.022            0.021
Chain 1:   4200       -17439.820             0.021            0.021
Chain 1:   4300       -17578.186             0.021            0.021
Chain 1:   4400       -17534.970             0.018            0.011
Chain 1:   4500       -17437.560             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48637.477             1.000            1.000
Chain 1:    200       -18338.926             1.326            1.652
Chain 1:    300       -17537.522             0.899            1.000
Chain 1:    400       -15123.837             0.714            1.000
Chain 1:    500       -18405.310             0.607            0.178
Chain 1:    600       -11310.412             0.611            0.627
Chain 1:    700       -12339.166             0.535            0.178
Chain 1:    800       -16566.453             0.500            0.255
Chain 1:    900       -22123.284             0.473            0.251
Chain 1:   1000       -12904.626             0.497            0.255
Chain 1:   1100       -19724.521             0.431            0.255
Chain 1:   1200       -19128.436             0.269            0.251
Chain 1:   1300       -10940.120             0.339            0.255
Chain 1:   1400       -10295.231             0.330            0.255
Chain 1:   1500       -10768.287             0.316            0.255
Chain 1:   1600       -12201.421             0.265            0.251
Chain 1:   1700        -9235.999             0.289            0.255
Chain 1:   1800       -13001.113             0.293            0.290
Chain 1:   1900       -10090.383             0.296            0.290
Chain 1:   2000       -10290.007             0.227            0.288
Chain 1:   2100       -10300.759             0.192            0.117
Chain 1:   2200        -9481.302             0.198            0.117
Chain 1:   2300       -15014.971             0.160            0.117
Chain 1:   2400       -13713.273             0.163            0.117
Chain 1:   2500       -10281.934             0.192            0.288
Chain 1:   2600        -9548.344             0.188            0.288
Chain 1:   2700        -9256.326             0.159            0.095
Chain 1:   2800       -10277.013             0.140            0.095
Chain 1:   2900       -10070.059             0.113            0.086
Chain 1:   3000        -8604.194             0.128            0.095
Chain 1:   3100       -11239.051             0.152            0.099
Chain 1:   3200       -10755.201             0.148            0.099
Chain 1:   3300       -10315.153             0.115            0.095
Chain 1:   3400        -8832.935             0.122            0.099
Chain 1:   3500        -8679.780             0.091            0.077
Chain 1:   3600        -9492.194             0.091            0.086
Chain 1:   3700        -9150.311             0.092            0.086
Chain 1:   3800       -10030.087             0.091            0.086
Chain 1:   3900        -8970.018             0.101            0.088
Chain 1:   4000       -11521.239             0.106            0.088
Chain 1:   4100        -9065.066             0.109            0.088
Chain 1:   4200        -9468.070             0.109            0.088
Chain 1:   4300       -10214.582             0.112            0.088
Chain 1:   4400        -8666.344             0.113            0.088
Chain 1:   4500       -10724.635             0.131            0.118
Chain 1:   4600        -8260.446             0.152            0.179
Chain 1:   4700        -9926.305             0.165            0.179
Chain 1:   4800       -10914.839             0.165            0.179
Chain 1:   4900        -9230.216             0.172            0.183
Chain 1:   5000        -9036.879             0.152            0.179
Chain 1:   5100        -8445.388             0.132            0.168
Chain 1:   5200        -8551.276             0.129            0.168
Chain 1:   5300       -11480.380             0.147            0.179
Chain 1:   5400        -8842.072             0.159            0.183
Chain 1:   5500        -9339.099             0.145            0.168
Chain 1:   5600        -8542.651             0.124            0.093
Chain 1:   5700        -8790.484             0.111            0.091
Chain 1:   5800        -9787.824             0.112            0.093
Chain 1:   5900       -14748.095             0.127            0.093
Chain 1:   6000        -8744.943             0.194            0.102
Chain 1:   6100       -10162.838             0.200            0.140
Chain 1:   6200        -9362.363             0.208            0.140
Chain 1:   6300       -10751.787             0.195            0.129
Chain 1:   6400       -11282.007             0.170            0.102
Chain 1:   6500        -8714.526             0.194            0.129
Chain 1:   6600        -8160.424             0.192            0.129
Chain 1:   6700       -12720.977             0.225            0.140
Chain 1:   6800        -8976.262             0.256            0.295
Chain 1:   6900       -11497.631             0.245            0.219
Chain 1:   7000        -9210.058             0.201            0.219
Chain 1:   7100        -8128.030             0.200            0.219
Chain 1:   7200        -8372.210             0.194            0.219
Chain 1:   7300        -9486.922             0.193            0.219
Chain 1:   7400       -10630.136             0.199            0.219
Chain 1:   7500        -8830.866             0.190            0.204
Chain 1:   7600        -8549.028             0.187            0.204
Chain 1:   7700        -8654.944             0.152            0.133
Chain 1:   7800        -8696.230             0.111            0.117
Chain 1:   7900        -8107.846             0.096            0.108
Chain 1:   8000        -8660.093             0.078            0.073
Chain 1:   8100        -8045.245             0.072            0.073
Chain 1:   8200        -9354.406             0.083            0.076
Chain 1:   8300        -9434.059             0.072            0.073
Chain 1:   8400        -8111.683             0.078            0.073
Chain 1:   8500        -8653.526             0.064            0.064
Chain 1:   8600        -8441.285             0.063            0.064
Chain 1:   8700        -8613.561             0.064            0.064
Chain 1:   8800        -8099.453             0.070            0.064
Chain 1:   8900        -8362.308             0.065            0.063
Chain 1:   9000        -8506.271             0.061            0.063
Chain 1:   9100       -10817.688             0.074            0.063
Chain 1:   9200        -9039.889             0.080            0.063
Chain 1:   9300        -9508.138             0.084            0.063
Chain 1:   9400        -8168.816             0.084            0.063
Chain 1:   9500        -8371.755             0.080            0.049
Chain 1:   9600        -8128.189             0.081            0.049
Chain 1:   9700        -9426.071             0.093            0.063
Chain 1:   9800        -8424.486             0.098            0.119
Chain 1:   9900        -9024.786             0.102            0.119
Chain 1:   10000        -8279.603             0.109            0.119
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001698 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57940.470             1.000            1.000
Chain 1:    200       -17325.044             1.672            2.344
Chain 1:    300        -8520.811             1.459            1.033
Chain 1:    400        -8143.521             1.106            1.033
Chain 1:    500        -8262.905             0.888            1.000
Chain 1:    600        -8653.033             0.747            1.000
Chain 1:    700        -7971.590             0.653            0.085
Chain 1:    800        -8052.201             0.572            0.085
Chain 1:    900        -7954.860             0.510            0.046
Chain 1:   1000        -7732.914             0.462            0.046
Chain 1:   1100        -7634.015             0.363            0.045
Chain 1:   1200        -7571.071             0.130            0.029
Chain 1:   1300        -7736.873             0.028            0.021
Chain 1:   1400        -7815.307             0.025            0.014
Chain 1:   1500        -7600.185             0.026            0.021
Chain 1:   1600        -7523.417             0.023            0.013
Chain 1:   1700        -7512.794             0.014            0.012
Chain 1:   1800        -7566.828             0.014            0.012
Chain 1:   1900        -7584.006             0.013            0.010
Chain 1:   2000        -7607.029             0.011            0.010
Chain 1:   2100        -7588.124             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003229 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86258.151             1.000            1.000
Chain 1:    200       -13127.182             3.285            5.571
Chain 1:    300        -9638.011             2.311            1.000
Chain 1:    400       -10526.790             1.754            1.000
Chain 1:    500        -8480.494             1.452            0.362
Chain 1:    600        -8246.617             1.215            0.362
Chain 1:    700        -8557.599             1.046            0.241
Chain 1:    800        -8603.434             0.916            0.241
Chain 1:    900        -8530.205             0.815            0.084
Chain 1:   1000        -8275.698             0.737            0.084
Chain 1:   1100        -8511.097             0.640            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8249.784             0.086            0.032
Chain 1:   1300        -8413.658             0.051            0.031
Chain 1:   1400        -8335.002             0.044            0.028
Chain 1:   1500        -8290.026             0.020            0.028
Chain 1:   1600        -8290.063             0.017            0.019
Chain 1:   1700        -8232.292             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.007099 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 70.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424329.354             1.000            1.000
Chain 1:    200     -1588826.691             2.651            4.302
Chain 1:    300      -890592.914             2.029            1.000
Chain 1:    400      -457408.230             1.758            1.000
Chain 1:    500      -357234.896             1.463            0.947
Chain 1:    600      -232151.084             1.309            0.947
Chain 1:    700      -118575.960             1.259            0.947
Chain 1:    800       -85849.539             1.149            0.947
Chain 1:    900       -66228.474             1.054            0.784
Chain 1:   1000       -51049.752             0.979            0.784
Chain 1:   1100       -38564.567             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37738.103             0.483            0.381
Chain 1:   1300       -25746.354             0.451            0.381
Chain 1:   1400       -25466.739             0.357            0.324
Chain 1:   1500       -22068.483             0.345            0.324
Chain 1:   1600       -21288.454             0.295            0.297
Chain 1:   1700       -20168.962             0.204            0.296
Chain 1:   1800       -20114.329             0.166            0.154
Chain 1:   1900       -20439.665             0.138            0.056
Chain 1:   2000       -18956.110             0.117            0.056
Chain 1:   2100       -19194.100             0.085            0.037
Chain 1:   2200       -19419.472             0.084            0.037
Chain 1:   2300       -19037.837             0.040            0.020
Chain 1:   2400       -18810.265             0.040            0.020
Chain 1:   2500       -18612.202             0.026            0.016
Chain 1:   2600       -18243.342             0.024            0.016
Chain 1:   2700       -18200.620             0.019            0.012
Chain 1:   2800       -17917.771             0.020            0.016
Chain 1:   2900       -18198.604             0.020            0.015
Chain 1:   3000       -18184.831             0.012            0.012
Chain 1:   3100       -18269.703             0.011            0.012
Chain 1:   3200       -17960.991             0.012            0.015
Chain 1:   3300       -18165.261             0.011            0.012
Chain 1:   3400       -17641.209             0.013            0.015
Chain 1:   3500       -18251.501             0.015            0.016
Chain 1:   3600       -17560.216             0.017            0.016
Chain 1:   3700       -17945.457             0.019            0.017
Chain 1:   3800       -16908.338             0.023            0.021
Chain 1:   3900       -16904.550             0.022            0.021
Chain 1:   4000       -17021.856             0.023            0.021
Chain 1:   4100       -16935.756             0.023            0.021
Chain 1:   4200       -16752.708             0.022            0.021
Chain 1:   4300       -16890.613             0.022            0.021
Chain 1:   4400       -16847.997             0.019            0.011
Chain 1:   4500       -16750.616             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005875 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 58.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50298.678             1.000            1.000
Chain 1:    200       -15751.723             1.597            2.193
Chain 1:    300       -48127.466             1.289            1.000
Chain 1:    400       -15271.914             1.504            2.151
Chain 1:    500       -14269.216             1.218            1.000
Chain 1:    600       -15701.853             1.030            1.000
Chain 1:    700       -16708.478             0.891            0.673
Chain 1:    800       -15833.661             0.787            0.673
Chain 1:    900       -13100.678             0.723            0.209
Chain 1:   1000       -15407.483             0.665            0.209
Chain 1:   1100       -12207.231             0.591            0.209   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -11359.017             0.380            0.150
Chain 1:   1300       -13574.150             0.329            0.150
Chain 1:   1400       -11519.004             0.131            0.150
Chain 1:   1500       -12320.353             0.131            0.150
Chain 1:   1600       -16014.604             0.145            0.163
Chain 1:   1700       -15494.239             0.142            0.163
Chain 1:   1800       -11444.540             0.172            0.178
Chain 1:   1900       -12413.696             0.159            0.163
Chain 1:   2000       -15159.816             0.162            0.178
Chain 1:   2100       -11351.916             0.169            0.178
Chain 1:   2200       -10642.572             0.169            0.178
Chain 1:   2300       -10936.225             0.155            0.178
Chain 1:   2400       -10736.108             0.139            0.078
Chain 1:   2500       -10588.033             0.134            0.078
Chain 1:   2600       -10385.901             0.113            0.067
Chain 1:   2700        -9903.507             0.114            0.067
Chain 1:   2800       -14372.098             0.110            0.067
Chain 1:   2900       -10452.616             0.140            0.067
Chain 1:   3000       -10035.811             0.126            0.049
Chain 1:   3100       -10610.583             0.098            0.049
Chain 1:   3200       -10540.807             0.092            0.042
Chain 1:   3300       -19823.966             0.136            0.049
Chain 1:   3400        -9726.389             0.238            0.054
Chain 1:   3500       -19861.400             0.287            0.311
Chain 1:   3600        -9948.813             0.385            0.375
Chain 1:   3700        -9891.846             0.381            0.375
Chain 1:   3800        -9385.941             0.355            0.375
Chain 1:   3900       -10439.182             0.328            0.101
Chain 1:   4000       -12158.082             0.338            0.141
Chain 1:   4100       -11700.848             0.336            0.141
Chain 1:   4200       -14002.775             0.352            0.164
Chain 1:   4300       -12165.922             0.320            0.151
Chain 1:   4400        -9671.462             0.242            0.151
Chain 1:   4500       -10746.582             0.201            0.141
Chain 1:   4600        -9764.414             0.111            0.101
Chain 1:   4700       -10007.452             0.113            0.101
Chain 1:   4800        -9201.481             0.117            0.101
Chain 1:   4900       -11348.052             0.126            0.141
Chain 1:   5000       -19211.038             0.152            0.151
Chain 1:   5100       -10195.383             0.237            0.164
Chain 1:   5200       -10590.911             0.224            0.151
Chain 1:   5300       -17072.499             0.247            0.189
Chain 1:   5400        -9193.431             0.307            0.189
Chain 1:   5500       -12302.208             0.322            0.253
Chain 1:   5600        -8982.542             0.349            0.370
Chain 1:   5700        -9253.399             0.350            0.370
Chain 1:   5800       -10070.353             0.349            0.370
Chain 1:   5900       -11075.941             0.339            0.370
Chain 1:   6000       -10404.554             0.305            0.253
Chain 1:   6100        -9818.710             0.222            0.091
Chain 1:   6200        -9882.386             0.219            0.091
Chain 1:   6300       -12539.884             0.202            0.091
Chain 1:   6400       -14611.306             0.131            0.091
Chain 1:   6500        -9022.730             0.167            0.091
Chain 1:   6600        -9381.493             0.134            0.081
Chain 1:   6700       -13223.174             0.160            0.091
Chain 1:   6800        -8793.893             0.203            0.142
Chain 1:   6900       -10186.974             0.207            0.142
Chain 1:   7000        -9516.750             0.208            0.142
Chain 1:   7100        -9930.429             0.206            0.142
Chain 1:   7200        -9017.844             0.216            0.142
Chain 1:   7300        -9279.050             0.197            0.137
Chain 1:   7400       -10998.032             0.199            0.137
Chain 1:   7500        -8934.888             0.160            0.137
Chain 1:   7600        -9212.737             0.159            0.137
Chain 1:   7700        -9721.722             0.135            0.101
Chain 1:   7800       -10810.847             0.095            0.101
Chain 1:   7900        -9042.945             0.101            0.101
Chain 1:   8000       -11008.935             0.112            0.101
Chain 1:   8100       -11149.432             0.109            0.101
Chain 1:   8200       -11547.265             0.102            0.101
Chain 1:   8300        -8918.958             0.129            0.156
Chain 1:   8400        -8918.399             0.113            0.101
Chain 1:   8500        -8835.787             0.091            0.052
Chain 1:   8600       -14360.466             0.126            0.101
Chain 1:   8700        -8813.300             0.184            0.179
Chain 1:   8800        -9972.539             0.186            0.179
Chain 1:   8900        -9857.734             0.167            0.116
Chain 1:   9000        -9370.832             0.155            0.052
Chain 1:   9100       -10104.934             0.161            0.073
Chain 1:   9200        -9570.270             0.163            0.073
Chain 1:   9300        -9136.380             0.138            0.056
Chain 1:   9400        -9521.939             0.142            0.056
Chain 1:   9500        -8523.217             0.153            0.073
Chain 1:   9600       -10765.361             0.135            0.073
Chain 1:   9700        -8699.704             0.096            0.073
Chain 1:   9800       -11309.554             0.107            0.073
Chain 1:   9900       -11354.865             0.107            0.073
Chain 1:   10000       -11228.878             0.103            0.073
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001441 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47456.974             1.000            1.000
Chain 1:    200       -16708.229             1.420            1.840
Chain 1:    300        -9358.022             1.209            1.000
Chain 1:    400        -8392.712             0.935            1.000
Chain 1:    500        -8590.598             0.753            0.785
Chain 1:    600        -9288.834             0.640            0.785
Chain 1:    700        -9549.505             0.552            0.115
Chain 1:    800        -8454.916             0.499            0.129
Chain 1:    900        -8401.631             0.445            0.115
Chain 1:   1000        -7769.294             0.408            0.115
Chain 1:   1100        -7924.939             0.310            0.081
Chain 1:   1200        -8125.981             0.129            0.075
Chain 1:   1300        -8243.318             0.052            0.027
Chain 1:   1400        -8490.711             0.043            0.027
Chain 1:   1500        -7710.180             0.051            0.029
Chain 1:   1600        -7905.676             0.046            0.027
Chain 1:   1700        -7731.266             0.045            0.025
Chain 1:   1800        -7702.865             0.033            0.025
Chain 1:   1900        -7607.616             0.033            0.025
Chain 1:   2000        -7745.639             0.027            0.023
Chain 1:   2100        -7645.252             0.026            0.023
Chain 1:   2200        -8018.398             0.029            0.023
Chain 1:   2300        -7623.766             0.032            0.025
Chain 1:   2400        -7628.448             0.029            0.023
Chain 1:   2500        -7665.454             0.020            0.018
Chain 1:   2600        -7636.137             0.018            0.013
Chain 1:   2700        -7450.026             0.018            0.013
Chain 1:   2800        -7581.926             0.019            0.017
Chain 1:   2900        -7411.949             0.020            0.018
Chain 1:   3000        -7571.324             0.021            0.021
Chain 1:   3100        -7571.265             0.019            0.021
Chain 1:   3200        -7785.532             0.017            0.021
Chain 1:   3300        -7491.311             0.016            0.021
Chain 1:   3400        -7822.425             0.020            0.023
Chain 1:   3500        -7456.938             0.025            0.025
Chain 1:   3600        -7519.041             0.025            0.025
Chain 1:   3700        -7478.295             0.023            0.023
Chain 1:   3800        -7490.703             0.022            0.023
Chain 1:   3900        -7453.105             0.020            0.021
Chain 1:   4000        -7427.443             0.018            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003516 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87958.313             1.000            1.000
Chain 1:    200       -14676.423             2.997            4.993
Chain 1:    300       -10688.996             2.122            1.000
Chain 1:    400       -13565.455             1.645            1.000
Chain 1:    500        -8896.412             1.421            0.525
Chain 1:    600        -9634.930             1.197            0.525
Chain 1:    700        -8991.921             1.036            0.373
Chain 1:    800        -8768.517             0.910            0.373
Chain 1:    900        -8686.350             0.810            0.212
Chain 1:   1000        -9702.421             0.739            0.212
Chain 1:   1100        -9416.646             0.642            0.105   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8651.106             0.152            0.088
Chain 1:   1300        -9077.975             0.119            0.077
Chain 1:   1400        -9131.831             0.098            0.072
Chain 1:   1500        -8971.139             0.048            0.047
Chain 1:   1600        -9078.177             0.041            0.030
Chain 1:   1700        -9111.892             0.034            0.025
Chain 1:   1800        -8639.898             0.037            0.030
Chain 1:   1900        -8738.220             0.038            0.030
Chain 1:   2000        -8744.679             0.027            0.018
Chain 1:   2100        -8929.961             0.026            0.018
Chain 1:   2200        -8574.907             0.022            0.018
Chain 1:   2300        -8639.867             0.018            0.012
Chain 1:   2400        -8742.651             0.018            0.012
Chain 1:   2500        -8635.327             0.018            0.012
Chain 1:   2600        -8704.438             0.017            0.012
Chain 1:   2700        -8609.673             0.018            0.012
Chain 1:   2800        -8583.980             0.013            0.011
Chain 1:   2900        -8663.809             0.013            0.011
Chain 1:   3000        -8617.796             0.013            0.011
Chain 1:   3100        -8563.112             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003383 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412139.060             1.000            1.000
Chain 1:    200     -1586739.357             2.651            4.302
Chain 1:    300      -891539.644             2.027            1.000
Chain 1:    400      -458337.180             1.757            1.000
Chain 1:    500      -358797.664             1.461            0.945
Chain 1:    600      -233990.397             1.306            0.945
Chain 1:    700      -120346.775             1.255            0.944
Chain 1:    800       -87597.599             1.144            0.944
Chain 1:    900       -67981.173             1.049            0.780
Chain 1:   1000       -52824.327             0.973            0.780
Chain 1:   1100       -40321.936             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39517.903             0.476            0.374
Chain 1:   1300       -27464.447             0.442            0.374
Chain 1:   1400       -27191.739             0.348            0.310
Chain 1:   1500       -23774.151             0.335            0.310
Chain 1:   1600       -22991.738             0.285            0.289
Chain 1:   1700       -21862.368             0.196            0.287
Chain 1:   1800       -21807.001             0.159            0.144
Chain 1:   1900       -22134.838             0.131            0.052
Chain 1:   2000       -20640.958             0.110            0.052
Chain 1:   2100       -20879.997             0.080            0.034
Chain 1:   2200       -21107.698             0.079            0.034
Chain 1:   2300       -20723.293             0.037            0.019
Chain 1:   2400       -20494.695             0.037            0.019
Chain 1:   2500       -20296.602             0.024            0.015
Chain 1:   2600       -19925.223             0.022            0.015
Chain 1:   2700       -19881.687             0.017            0.011
Chain 1:   2800       -19597.721             0.018            0.014
Chain 1:   2900       -19879.748             0.018            0.014
Chain 1:   3000       -19865.934             0.011            0.011
Chain 1:   3100       -19951.171             0.010            0.011
Chain 1:   3200       -19640.717             0.011            0.014
Chain 1:   3300       -19846.304             0.010            0.011
Chain 1:   3400       -19319.184             0.012            0.014
Chain 1:   3500       -19934.125             0.014            0.014
Chain 1:   3600       -19236.743             0.016            0.014
Chain 1:   3700       -19626.550             0.017            0.016
Chain 1:   3800       -18579.963             0.022            0.020
Chain 1:   3900       -18575.874             0.020            0.020
Chain 1:   4000       -18693.243             0.021            0.020
Chain 1:   4100       -18606.676             0.021            0.020
Chain 1:   4200       -18421.516             0.020            0.020
Chain 1:   4300       -18560.942             0.020            0.020
Chain 1:   4400       -18516.628             0.017            0.010
Chain 1:   4500       -18418.900             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001743 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12884.665             1.000            1.000
Chain 1:    200        -9787.319             0.658            1.000
Chain 1:    300        -8555.351             0.487            0.316
Chain 1:    400        -8649.659             0.368            0.316
Chain 1:    500        -8560.529             0.296            0.144
Chain 1:    600        -8446.231             0.249            0.144
Chain 1:    700        -8368.508             0.215            0.014
Chain 1:    800        -8356.162             0.188            0.014
Chain 1:    900        -8414.402             0.168            0.011
Chain 1:   1000        -8391.781             0.152            0.011
Chain 1:   1100        -8491.034             0.053            0.011
Chain 1:   1200        -8407.306             0.022            0.010
Chain 1:   1300        -8326.703             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001416 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58549.761             1.000            1.000
Chain 1:    200       -18183.561             1.610            2.220
Chain 1:    300        -8887.831             1.422            1.046
Chain 1:    400        -8109.445             1.090            1.046
Chain 1:    500        -8420.062             0.880            1.000
Chain 1:    600        -7961.185             0.743            1.000
Chain 1:    700        -7767.278             0.640            0.096
Chain 1:    800        -7790.778             0.561            0.096
Chain 1:    900        -7995.857             0.501            0.058
Chain 1:   1000        -7859.207             0.453            0.058
Chain 1:   1100        -7748.840             0.354            0.037
Chain 1:   1200        -7736.392             0.132            0.026
Chain 1:   1300        -7665.079             0.029            0.025
Chain 1:   1400        -7919.062             0.022            0.025
Chain 1:   1500        -7577.050             0.023            0.025
Chain 1:   1600        -7744.363             0.019            0.022
Chain 1:   1700        -7597.875             0.019            0.019
Chain 1:   1800        -7582.883             0.019            0.019
Chain 1:   1900        -7580.609             0.016            0.017
Chain 1:   2000        -7645.392             0.015            0.014
Chain 1:   2100        -7556.073             0.015            0.012
Chain 1:   2200        -7776.670             0.018            0.019
Chain 1:   2300        -7586.832             0.019            0.022
Chain 1:   2400        -7710.306             0.018            0.019
Chain 1:   2500        -7589.065             0.015            0.016
Chain 1:   2600        -7521.062             0.014            0.016
Chain 1:   2700        -7508.613             0.012            0.012
Chain 1:   2800        -7522.459             0.012            0.012
Chain 1:   2900        -7384.502             0.014            0.016
Chain 1:   3000        -7525.094             0.015            0.016
Chain 1:   3100        -7529.760             0.014            0.016
Chain 1:   3200        -7742.253             0.013            0.016
Chain 1:   3300        -7445.873             0.015            0.016
Chain 1:   3400        -7697.715             0.017            0.019
Chain 1:   3500        -7438.254             0.019            0.019
Chain 1:   3600        -7502.137             0.018            0.019
Chain 1:   3700        -7455.136             0.019            0.019
Chain 1:   3800        -7441.904             0.019            0.019
Chain 1:   3900        -7410.984             0.017            0.019
Chain 1:   4000        -7404.447             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005065 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 50.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87148.029             1.000            1.000
Chain 1:    200       -13987.672             3.115            5.230
Chain 1:    300       -10347.615             2.194            1.000
Chain 1:    400       -11319.580             1.667            1.000
Chain 1:    500        -9200.366             1.380            0.352
Chain 1:    600        -8795.724             1.157            0.352
Chain 1:    700        -8963.528             0.995            0.230
Chain 1:    800        -9533.473             0.878            0.230
Chain 1:    900        -9112.199             0.785            0.086
Chain 1:   1000        -9007.437             0.708            0.086
Chain 1:   1100        -9197.492             0.610            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8706.517             0.093            0.056
Chain 1:   1300        -9044.901             0.061            0.046
Chain 1:   1400        -9053.899             0.053            0.046
Chain 1:   1500        -8909.319             0.031            0.037
Chain 1:   1600        -9025.642             0.028            0.021
Chain 1:   1700        -9104.372             0.027            0.021
Chain 1:   1800        -8690.769             0.026            0.021
Chain 1:   1900        -8786.597             0.022            0.016
Chain 1:   2000        -8760.428             0.021            0.016
Chain 1:   2100        -8883.384             0.021            0.014
Chain 1:   2200        -8702.960             0.017            0.014
Chain 1:   2300        -8781.653             0.014            0.013
Chain 1:   2400        -8851.422             0.015            0.013
Chain 1:   2500        -8797.066             0.014            0.011
Chain 1:   2600        -8796.702             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005043 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 50.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418102.804             1.000            1.000
Chain 1:    200     -1588124.082             2.650            4.301
Chain 1:    300      -891252.504             2.028            1.000
Chain 1:    400      -458541.004             1.757            1.000
Chain 1:    500      -358557.909             1.461            0.944
Chain 1:    600      -233375.639             1.307            0.944
Chain 1:    700      -119615.149             1.256            0.944
Chain 1:    800       -86860.275             1.146            0.944
Chain 1:    900       -67209.916             1.051            0.782
Chain 1:   1000       -52018.591             0.975            0.782
Chain 1:   1100       -39513.960             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38689.887             0.479            0.377
Chain 1:   1300       -26665.583             0.446            0.377
Chain 1:   1400       -26385.534             0.353            0.316
Chain 1:   1500       -22979.080             0.340            0.316
Chain 1:   1600       -22197.389             0.290            0.292
Chain 1:   1700       -21073.443             0.200            0.292
Chain 1:   1800       -21018.120             0.162            0.148
Chain 1:   1900       -21344.248             0.135            0.053
Chain 1:   2000       -19856.825             0.113            0.053
Chain 1:   2100       -20095.004             0.082            0.035
Chain 1:   2200       -20321.389             0.081            0.035
Chain 1:   2300       -19938.648             0.038            0.019
Chain 1:   2400       -19710.766             0.038            0.019
Chain 1:   2500       -19512.799             0.025            0.015
Chain 1:   2600       -19143.005             0.023            0.015
Chain 1:   2700       -19099.938             0.018            0.012
Chain 1:   2800       -18816.862             0.019            0.015
Chain 1:   2900       -19098.060             0.019            0.015
Chain 1:   3000       -19084.182             0.012            0.012
Chain 1:   3100       -19169.221             0.011            0.012
Chain 1:   3200       -18859.909             0.011            0.015
Chain 1:   3300       -19064.610             0.011            0.012
Chain 1:   3400       -18539.587             0.012            0.015
Chain 1:   3500       -19151.393             0.014            0.015
Chain 1:   3600       -18458.098             0.016            0.015
Chain 1:   3700       -18844.897             0.018            0.016
Chain 1:   3800       -17804.687             0.022            0.021
Chain 1:   3900       -17800.825             0.021            0.021
Chain 1:   4000       -17918.129             0.022            0.021
Chain 1:   4100       -17831.930             0.022            0.021
Chain 1:   4200       -17648.146             0.021            0.021
Chain 1:   4300       -17786.553             0.021            0.021
Chain 1:   4400       -17743.388             0.018            0.010
Chain 1:   4500       -17645.907             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001291 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12703.227             1.000            1.000
Chain 1:    200        -9495.088             0.669            1.000
Chain 1:    300        -8334.653             0.492            0.338
Chain 1:    400        -8516.717             0.375            0.338
Chain 1:    500        -8513.608             0.300            0.139
Chain 1:    600        -8298.639             0.254            0.139
Chain 1:    700        -8208.831             0.219            0.026
Chain 1:    800        -8236.597             0.192            0.026
Chain 1:    900        -8356.841             0.173            0.021
Chain 1:   1000        -8246.141             0.157            0.021
Chain 1:   1100        -8289.907             0.057            0.014
Chain 1:   1200        -8229.497             0.024            0.013
Chain 1:   1300        -8170.058             0.011            0.011
Chain 1:   1400        -8206.166             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003052 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62235.597             1.000            1.000
Chain 1:    200       -18300.648             1.700            2.401
Chain 1:    300        -9102.806             1.470            1.010
Chain 1:    400        -9587.637             1.115            1.010
Chain 1:    500        -8700.084             0.913            1.000
Chain 1:    600        -8693.470             0.761            1.000
Chain 1:    700        -8927.907             0.656            0.102
Chain 1:    800        -8367.643             0.582            0.102
Chain 1:    900        -8029.164             0.522            0.067
Chain 1:   1000        -8056.327             0.470            0.067
Chain 1:   1100        -7832.990             0.373            0.051
Chain 1:   1200        -7728.018             0.134            0.042
Chain 1:   1300        -7863.797             0.035            0.029
Chain 1:   1400        -7952.570             0.031            0.026
Chain 1:   1500        -7644.588             0.025            0.026
Chain 1:   1600        -7867.958             0.028            0.028
Chain 1:   1700        -7676.105             0.028            0.028
Chain 1:   1800        -7700.687             0.021            0.025
Chain 1:   1900        -7673.070             0.017            0.017
Chain 1:   2000        -7752.874             0.018            0.017
Chain 1:   2100        -7671.600             0.016            0.014
Chain 1:   2200        -7786.123             0.016            0.015
Chain 1:   2300        -7595.078             0.017            0.015
Chain 1:   2400        -7659.449             0.017            0.015
Chain 1:   2500        -7645.870             0.013            0.011
Chain 1:   2600        -7637.509             0.010            0.010
Chain 1:   2700        -7548.001             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003479 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85581.572             1.000            1.000
Chain 1:    200       -13842.522             3.091            5.183
Chain 1:    300       -10206.231             2.180            1.000
Chain 1:    400       -11083.744             1.654            1.000
Chain 1:    500        -9187.191             1.365            0.356
Chain 1:    600        -8953.409             1.142            0.356
Chain 1:    700        -9146.104             0.982            0.206
Chain 1:    800        -9546.808             0.864            0.206
Chain 1:    900        -9007.207             0.775            0.079
Chain 1:   1000        -8646.888             0.702            0.079
Chain 1:   1100        -9029.905             0.606            0.060   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8650.593             0.092            0.044
Chain 1:   1300        -8903.066             0.059            0.042
Chain 1:   1400        -8911.666             0.051            0.042
Chain 1:   1500        -8763.175             0.032            0.042
Chain 1:   1600        -8877.250             0.031            0.042
Chain 1:   1700        -8957.423             0.030            0.042
Chain 1:   1800        -8540.361             0.030            0.042
Chain 1:   1900        -8638.268             0.026            0.028
Chain 1:   2000        -8612.145             0.022            0.017
Chain 1:   2100        -8735.786             0.019            0.014
Chain 1:   2200        -8551.786             0.017            0.014
Chain 1:   2300        -8632.897             0.015            0.013
Chain 1:   2400        -8702.539             0.016            0.013
Chain 1:   2500        -8648.428             0.014            0.011
Chain 1:   2600        -8648.546             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399044.124             1.000            1.000
Chain 1:    200     -1583261.152             2.652            4.305
Chain 1:    300      -891370.587             2.027            1.000
Chain 1:    400      -458445.338             1.756            1.000
Chain 1:    500      -358731.859             1.461            0.944
Chain 1:    600      -233804.011             1.306            0.944
Chain 1:    700      -119791.327             1.256            0.944
Chain 1:    800       -86951.527             1.146            0.944
Chain 1:    900       -67252.558             1.051            0.776
Chain 1:   1000       -52020.881             0.975            0.776
Chain 1:   1100       -39469.107             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38642.828             0.479            0.378
Chain 1:   1300       -26567.146             0.447            0.378
Chain 1:   1400       -26284.170             0.353            0.318
Chain 1:   1500       -22863.146             0.340            0.318
Chain 1:   1600       -22077.278             0.291            0.293
Chain 1:   1700       -20947.048             0.201            0.293
Chain 1:   1800       -20890.444             0.163            0.150
Chain 1:   1900       -21216.583             0.135            0.054
Chain 1:   2000       -19725.743             0.114            0.054
Chain 1:   2100       -19964.132             0.083            0.036
Chain 1:   2200       -20190.961             0.082            0.036
Chain 1:   2300       -19807.890             0.039            0.019
Chain 1:   2400       -19579.935             0.039            0.019
Chain 1:   2500       -19382.134             0.025            0.015
Chain 1:   2600       -19012.065             0.023            0.015
Chain 1:   2700       -18969.015             0.018            0.012
Chain 1:   2800       -18685.894             0.019            0.015
Chain 1:   2900       -18967.245             0.019            0.015
Chain 1:   3000       -18953.424             0.012            0.012
Chain 1:   3100       -19038.378             0.011            0.012
Chain 1:   3200       -18729.002             0.011            0.015
Chain 1:   3300       -18933.792             0.011            0.012
Chain 1:   3400       -18408.639             0.012            0.015
Chain 1:   3500       -19020.634             0.014            0.015
Chain 1:   3600       -18327.255             0.016            0.015
Chain 1:   3700       -18714.090             0.018            0.017
Chain 1:   3800       -17673.670             0.023            0.021
Chain 1:   3900       -17669.853             0.021            0.021
Chain 1:   4000       -17787.141             0.022            0.021
Chain 1:   4100       -17700.854             0.022            0.021
Chain 1:   4200       -17517.125             0.021            0.021
Chain 1:   4300       -17655.481             0.021            0.021
Chain 1:   4400       -17612.269             0.018            0.010
Chain 1:   4500       -17514.857             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48519.467             1.000            1.000
Chain 1:    200       -20789.350             1.167            1.334
Chain 1:    300       -14252.284             0.931            1.000
Chain 1:    400       -16404.771             0.731            1.000
Chain 1:    500       -16962.116             0.591            0.459
Chain 1:    600       -11273.611             0.577            0.505
Chain 1:    700       -14946.979             0.530            0.459
Chain 1:    800       -22402.233             0.505            0.459
Chain 1:    900       -12808.598             0.532            0.459
Chain 1:   1000       -14545.446             0.491            0.459
Chain 1:   1100       -22193.352             0.425            0.345
Chain 1:   1200       -11841.232             0.379            0.345
Chain 1:   1300       -11413.358             0.337            0.333
Chain 1:   1400       -11391.173             0.324            0.333
Chain 1:   1500       -12008.575             0.326            0.333
Chain 1:   1600       -10090.852             0.295            0.246
Chain 1:   1700        -9495.910             0.276            0.190
Chain 1:   1800       -15707.870             0.283            0.190
Chain 1:   1900       -10588.386             0.256            0.190
Chain 1:   2000        -9415.056             0.257            0.190
Chain 1:   2100        -9899.505             0.227            0.125
Chain 1:   2200       -11440.481             0.153            0.125
Chain 1:   2300        -9276.278             0.173            0.135
Chain 1:   2400        -8874.429             0.177            0.135
Chain 1:   2500       -12485.460             0.201            0.190
Chain 1:   2600       -10045.914             0.206            0.233
Chain 1:   2700        -9045.955             0.211            0.233
Chain 1:   2800       -15253.185             0.212            0.233
Chain 1:   2900        -9116.423             0.231            0.233
Chain 1:   3000        -8935.853             0.221            0.233
Chain 1:   3100        -8671.575             0.219            0.233
Chain 1:   3200        -8481.573             0.207            0.233
Chain 1:   3300        -9164.874             0.192            0.111
Chain 1:   3400       -17790.813             0.236            0.243
Chain 1:   3500       -10416.895             0.277            0.243
Chain 1:   3600        -9198.606             0.266            0.132
Chain 1:   3700       -11695.076             0.277            0.213
Chain 1:   3800        -8510.156             0.273            0.213
Chain 1:   3900        -9663.672             0.218            0.132
Chain 1:   4000        -9142.352             0.222            0.132
Chain 1:   4100        -8482.883             0.226            0.132
Chain 1:   4200        -9780.692             0.237            0.133
Chain 1:   4300        -9618.599             0.232            0.133
Chain 1:   4400        -8838.135             0.192            0.132
Chain 1:   4500        -8953.323             0.123            0.119
Chain 1:   4600       -14423.262             0.147            0.119
Chain 1:   4700       -13319.730             0.134            0.088
Chain 1:   4800        -8313.320             0.157            0.088
Chain 1:   4900        -8960.740             0.152            0.083
Chain 1:   5000        -9477.543             0.152            0.083
Chain 1:   5100        -8416.259             0.157            0.088
Chain 1:   5200        -8709.330             0.147            0.083
Chain 1:   5300        -9201.051             0.151            0.083
Chain 1:   5400       -12796.426             0.170            0.083
Chain 1:   5500        -8100.123             0.227            0.126
Chain 1:   5600        -8319.065             0.191            0.083
Chain 1:   5700        -8293.006             0.183            0.072
Chain 1:   5800        -8650.813             0.127            0.055
Chain 1:   5900        -8060.497             0.127            0.055
Chain 1:   6000       -10964.155             0.148            0.073
Chain 1:   6100       -10839.452             0.137            0.053
Chain 1:   6200       -12731.876             0.148            0.073
Chain 1:   6300        -9132.819             0.182            0.149
Chain 1:   6400       -11767.606             0.177            0.149
Chain 1:   6500        -9727.845             0.140            0.149
Chain 1:   6600        -8468.531             0.152            0.149
Chain 1:   6700        -8468.321             0.152            0.149
Chain 1:   6800        -9502.015             0.158            0.149
Chain 1:   6900        -8610.478             0.161            0.149
Chain 1:   7000        -8145.255             0.141            0.149
Chain 1:   7100       -10504.004             0.162            0.149
Chain 1:   7200        -8589.348             0.169            0.210
Chain 1:   7300        -8755.943             0.132            0.149
Chain 1:   7400       -13984.086             0.147            0.149
Chain 1:   7500       -10992.655             0.153            0.149
Chain 1:   7600        -9118.683             0.159            0.206
Chain 1:   7700        -8246.145             0.169            0.206
Chain 1:   7800        -8067.207             0.161            0.206
Chain 1:   7900        -8231.394             0.152            0.206
Chain 1:   8000        -7971.702             0.150            0.206
Chain 1:   8100        -7979.027             0.127            0.106
Chain 1:   8200        -8282.515             0.109            0.037
Chain 1:   8300       -10324.215             0.127            0.106
Chain 1:   8400        -8974.139             0.104            0.106
Chain 1:   8500        -8023.952             0.089            0.106
Chain 1:   8600        -8429.256             0.073            0.048
Chain 1:   8700        -8129.299             0.066            0.037
Chain 1:   8800        -9993.270             0.083            0.048
Chain 1:   8900        -8635.387             0.097            0.118
Chain 1:   9000       -10931.883             0.114            0.150
Chain 1:   9100        -9413.980             0.130            0.157
Chain 1:   9200        -8646.145             0.136            0.157
Chain 1:   9300        -8658.574             0.116            0.150
Chain 1:   9400       -10726.029             0.120            0.157
Chain 1:   9500       -10534.899             0.110            0.157
Chain 1:   9600        -8122.949             0.135            0.161
Chain 1:   9700        -7992.813             0.133            0.161
Chain 1:   9800        -8492.706             0.120            0.157
Chain 1:   9900        -8829.576             0.108            0.089
Chain 1:   10000        -7939.503             0.098            0.089
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56516.998             1.000            1.000
Chain 1:    200       -17064.184             1.656            2.312
Chain 1:    300        -8507.974             1.439            1.006
Chain 1:    400        -8296.875             1.086            1.006
Chain 1:    500        -8143.164             0.872            1.000
Chain 1:    600        -8310.625             0.730            1.000
Chain 1:    700        -7693.621             0.637            0.080
Chain 1:    800        -7953.078             0.562            0.080
Chain 1:    900        -7996.395             0.500            0.033
Chain 1:   1000        -7582.583             0.455            0.055
Chain 1:   1100        -7529.534             0.356            0.033
Chain 1:   1200        -7531.313             0.125            0.025
Chain 1:   1300        -7711.105             0.027            0.023
Chain 1:   1400        -7771.362             0.025            0.020
Chain 1:   1500        -7513.347             0.027            0.023
Chain 1:   1600        -7507.442             0.025            0.023
Chain 1:   1700        -7419.142             0.018            0.012
Chain 1:   1800        -7473.555             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003735 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86416.417             1.000            1.000
Chain 1:    200       -13161.129             3.283            5.566
Chain 1:    300        -9578.160             2.313            1.000
Chain 1:    400       -10575.616             1.759            1.000
Chain 1:    500        -8517.173             1.455            0.374
Chain 1:    600        -8409.997             1.215            0.374
Chain 1:    700        -8216.181             1.045            0.242
Chain 1:    800        -8379.379             0.916            0.242
Chain 1:    900        -8423.743             0.815            0.094
Chain 1:   1000        -8181.609             0.737            0.094
Chain 1:   1100        -8401.229             0.639            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8082.235             0.087            0.030
Chain 1:   1300        -8296.499             0.052            0.026
Chain 1:   1400        -8279.795             0.043            0.026
Chain 1:   1500        -8181.274             0.020            0.024
Chain 1:   1600        -8282.626             0.020            0.024
Chain 1:   1700        -8370.451             0.018            0.019
Chain 1:   1800        -7974.403             0.021            0.026
Chain 1:   1900        -8075.874             0.022            0.026
Chain 1:   2000        -8046.394             0.019            0.013
Chain 1:   2100        -8168.782             0.018            0.013
Chain 1:   2200        -7949.911             0.017            0.013
Chain 1:   2300        -8104.560             0.016            0.013
Chain 1:   2400        -8118.327             0.016            0.013
Chain 1:   2500        -8087.855             0.016            0.013
Chain 1:   2600        -8090.512             0.014            0.013
Chain 1:   2700        -7996.744             0.015            0.013
Chain 1:   2800        -7967.829             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003713 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395349.285             1.000            1.000
Chain 1:    200     -1583814.029             2.650            4.301
Chain 1:    300      -890974.760             2.026            1.000
Chain 1:    400      -457852.414             1.756            1.000
Chain 1:    500      -358251.395             1.460            0.946
Chain 1:    600      -233011.386             1.307            0.946
Chain 1:    700      -119022.461             1.257            0.946
Chain 1:    800       -86198.588             1.147            0.946
Chain 1:    900       -66503.891             1.053            0.778
Chain 1:   1000       -51270.626             0.977            0.778
Chain 1:   1100       -38726.013             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37895.792             0.482            0.381
Chain 1:   1300       -25835.226             0.451            0.381
Chain 1:   1400       -25551.020             0.357            0.324
Chain 1:   1500       -22134.571             0.345            0.324
Chain 1:   1600       -21349.559             0.295            0.297
Chain 1:   1700       -20221.564             0.204            0.296
Chain 1:   1800       -20165.079             0.167            0.154
Chain 1:   1900       -20490.887             0.139            0.056
Chain 1:   2000       -19001.838             0.117            0.056
Chain 1:   2100       -19240.163             0.086            0.037
Chain 1:   2200       -19466.615             0.085            0.037
Chain 1:   2300       -19083.877             0.040            0.020
Chain 1:   2400       -18856.076             0.040            0.020
Chain 1:   2500       -18658.243             0.026            0.016
Chain 1:   2600       -18288.691             0.024            0.016
Chain 1:   2700       -18245.691             0.019            0.012
Chain 1:   2800       -17962.809             0.020            0.016
Chain 1:   2900       -18243.894             0.020            0.015
Chain 1:   3000       -18230.044             0.012            0.012
Chain 1:   3100       -18315.023             0.011            0.012
Chain 1:   3200       -18005.908             0.012            0.015
Chain 1:   3300       -18210.456             0.011            0.012
Chain 1:   3400       -17685.841             0.013            0.015
Chain 1:   3500       -18297.113             0.015            0.016
Chain 1:   3600       -17604.543             0.017            0.016
Chain 1:   3700       -17990.831             0.019            0.017
Chain 1:   3800       -16951.782             0.023            0.021
Chain 1:   3900       -16947.970             0.022            0.021
Chain 1:   4000       -17065.237             0.023            0.021
Chain 1:   4100       -16979.115             0.023            0.021
Chain 1:   4200       -16795.588             0.022            0.021
Chain 1:   4300       -16933.795             0.022            0.021
Chain 1:   4400       -16890.845             0.019            0.011
Chain 1:   4500       -16793.424             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001215 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48412.767             1.000            1.000
Chain 1:    200       -20222.068             1.197            1.394
Chain 1:    300       -19757.398             0.806            1.000
Chain 1:    400       -14414.646             0.697            1.000
Chain 1:    500       -18839.171             0.605            0.371
Chain 1:    600       -16099.631             0.532            0.371
Chain 1:    700       -14983.161             0.467            0.235
Chain 1:    800       -17827.431             0.428            0.235
Chain 1:    900       -12277.586             0.431            0.235
Chain 1:   1000       -10123.171             0.409            0.235
Chain 1:   1100       -13736.335             0.336            0.235
Chain 1:   1200       -10624.581             0.225            0.235
Chain 1:   1300       -11348.402             0.229            0.235
Chain 1:   1400       -25524.436             0.248            0.235
Chain 1:   1500       -10540.087             0.367            0.263
Chain 1:   1600        -9819.304             0.357            0.263
Chain 1:   1700        -9570.529             0.352            0.263
Chain 1:   1800       -11561.627             0.353            0.263
Chain 1:   1900        -9338.211             0.332            0.238
Chain 1:   2000       -11962.499             0.333            0.238
Chain 1:   2100       -11003.366             0.315            0.219
Chain 1:   2200       -12338.316             0.297            0.172
Chain 1:   2300       -11534.627             0.297            0.172
Chain 1:   2400       -13182.632             0.254            0.125
Chain 1:   2500        -8790.939             0.162            0.125
Chain 1:   2600        -8807.819             0.155            0.125
Chain 1:   2700       -10759.748             0.170            0.172
Chain 1:   2800       -11508.452             0.160            0.125
Chain 1:   2900        -9665.138             0.155            0.125
Chain 1:   3000       -13094.920             0.159            0.125
Chain 1:   3100       -14388.081             0.159            0.125
Chain 1:   3200        -8564.020             0.217            0.181
Chain 1:   3300       -14991.562             0.252            0.191
Chain 1:   3400       -16841.012             0.251            0.191
Chain 1:   3500        -9034.609             0.287            0.191
Chain 1:   3600       -14989.188             0.327            0.262
Chain 1:   3700        -9055.344             0.374            0.397
Chain 1:   3800        -9020.590             0.368            0.397
Chain 1:   3900        -8980.766             0.350            0.397
Chain 1:   4000        -8833.497             0.325            0.397
Chain 1:   4100        -8759.338             0.317            0.397
Chain 1:   4200        -8866.895             0.250            0.110
Chain 1:   4300        -8923.066             0.208            0.017
Chain 1:   4400        -8673.330             0.200            0.017
Chain 1:   4500        -8632.061             0.114            0.012
Chain 1:   4600        -8274.482             0.078            0.012
Chain 1:   4700        -8321.853             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -55049.429             1.000            1.000
Chain 1:    200       -16885.728             1.630            2.260
Chain 1:    300        -8522.271             1.414            1.000
Chain 1:    400        -8777.344             1.068            1.000
Chain 1:    500        -8514.325             0.860            0.981
Chain 1:    600        -8435.369             0.718            0.981
Chain 1:    700        -7733.252             0.629            0.091
Chain 1:    800        -7982.596             0.554            0.091
Chain 1:    900        -7741.048             0.496            0.031
Chain 1:   1000        -7761.013             0.447            0.031
Chain 1:   1100        -7577.722             0.349            0.031
Chain 1:   1200        -7565.142             0.123            0.031
Chain 1:   1300        -7618.861             0.026            0.029
Chain 1:   1400        -7775.061             0.025            0.024
Chain 1:   1500        -7552.568             0.025            0.024
Chain 1:   1600        -7533.431             0.024            0.024
Chain 1:   1700        -7488.599             0.016            0.020
Chain 1:   1800        -7549.812             0.013            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003597 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85148.724             1.000            1.000
Chain 1:    200       -13151.247             3.237            5.475
Chain 1:    300        -9575.312             2.283            1.000
Chain 1:    400       -10521.868             1.734            1.000
Chain 1:    500        -8514.165             1.435            0.373
Chain 1:    600        -8094.270             1.204            0.373
Chain 1:    700        -8213.776             1.034            0.236
Chain 1:    800        -8860.584             0.914            0.236
Chain 1:    900        -8385.244             0.819            0.090
Chain 1:   1000        -8221.535             0.739            0.090
Chain 1:   1100        -8472.962             0.642            0.073   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7991.976             0.101            0.060
Chain 1:   1300        -8315.136             0.067            0.057
Chain 1:   1400        -8298.363             0.058            0.052
Chain 1:   1500        -8180.508             0.036            0.039
Chain 1:   1600        -8288.322             0.032            0.030
Chain 1:   1700        -8366.923             0.032            0.030
Chain 1:   1800        -7966.553             0.029            0.030
Chain 1:   1900        -8068.110             0.025            0.020
Chain 1:   2000        -8038.914             0.023            0.014
Chain 1:   2100        -8159.395             0.022            0.014
Chain 1:   2200        -7936.011             0.019            0.014
Chain 1:   2300        -8097.336             0.017            0.014
Chain 1:   2400        -7979.126             0.018            0.015
Chain 1:   2500        -8043.296             0.017            0.015
Chain 1:   2600        -8064.175             0.016            0.015
Chain 1:   2700        -7983.768             0.016            0.015
Chain 1:   2800        -7958.532             0.012            0.013
Chain 1:   2900        -8013.209             0.011            0.010
Chain 1:   3000        -7898.507             0.012            0.015
Chain 1:   3100        -8035.396             0.013            0.015
Chain 1:   3200        -7915.893             0.011            0.015
Chain 1:   3300        -7936.809             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006408 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 64.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8379663.153             1.000            1.000
Chain 1:    200     -1580777.035             2.650            4.301
Chain 1:    300      -890968.677             2.025            1.000
Chain 1:    400      -457930.890             1.755            1.000
Chain 1:    500      -358760.387             1.459            0.946
Chain 1:    600      -233590.425             1.306            0.946
Chain 1:    700      -119351.614             1.256            0.946
Chain 1:    800       -86451.029             1.146            0.946
Chain 1:    900       -66696.491             1.052            0.774
Chain 1:   1000       -51414.645             0.976            0.774
Chain 1:   1100       -38823.869             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37985.724             0.481            0.381
Chain 1:   1300       -25878.862             0.450            0.381
Chain 1:   1400       -25589.571             0.357            0.324
Chain 1:   1500       -22161.183             0.345            0.324
Chain 1:   1600       -21372.351             0.295            0.297
Chain 1:   1700       -20238.973             0.205            0.296
Chain 1:   1800       -20181.132             0.167            0.155
Chain 1:   1900       -20506.951             0.139            0.056
Chain 1:   2000       -19014.920             0.117            0.056
Chain 1:   2100       -19253.369             0.086            0.037
Chain 1:   2200       -19480.332             0.085            0.037
Chain 1:   2300       -19097.161             0.040            0.020
Chain 1:   2400       -18869.291             0.040            0.020
Chain 1:   2500       -18671.638             0.026            0.016
Chain 1:   2600       -18301.911             0.024            0.016
Chain 1:   2700       -18258.809             0.019            0.012
Chain 1:   2800       -17976.013             0.020            0.016
Chain 1:   2900       -18257.145             0.020            0.015
Chain 1:   3000       -18243.276             0.012            0.012
Chain 1:   3100       -18328.267             0.011            0.012
Chain 1:   3200       -18019.109             0.012            0.015
Chain 1:   3300       -18223.660             0.011            0.012
Chain 1:   3400       -17699.018             0.013            0.015
Chain 1:   3500       -18310.443             0.015            0.016
Chain 1:   3600       -17617.689             0.017            0.016
Chain 1:   3700       -18004.147             0.019            0.017
Chain 1:   3800       -16964.889             0.023            0.021
Chain 1:   3900       -16961.102             0.022            0.021
Chain 1:   4000       -17078.335             0.023            0.021
Chain 1:   4100       -16992.242             0.023            0.021
Chain 1:   4200       -16808.640             0.022            0.021
Chain 1:   4300       -16946.880             0.022            0.021
Chain 1:   4400       -16903.884             0.019            0.011
Chain 1:   4500       -16806.488             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001942 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49091.618             1.000            1.000
Chain 1:    200       -16215.345             1.514            2.027
Chain 1:    300       -25009.888             1.126            1.000
Chain 1:    400       -15932.735             0.987            1.000
Chain 1:    500       -17083.458             0.803            0.570
Chain 1:    600       -18691.425             0.684            0.570
Chain 1:    700       -13141.172             0.646            0.422
Chain 1:    800       -13275.871             0.567            0.422
Chain 1:    900       -13406.304             0.505            0.352
Chain 1:   1000       -12774.512             0.459            0.352
Chain 1:   1100       -11436.906             0.371            0.117
Chain 1:   1200       -16349.433             0.198            0.117
Chain 1:   1300       -12524.589             0.194            0.117
Chain 1:   1400       -11412.113             0.147            0.097
Chain 1:   1500       -10821.667             0.145            0.097
Chain 1:   1600       -19544.800             0.181            0.117
Chain 1:   1700       -12613.281             0.194            0.117
Chain 1:   1800       -16206.263             0.215            0.222
Chain 1:   1900        -9725.863             0.281            0.300
Chain 1:   2000       -15444.430             0.313            0.305
Chain 1:   2100       -10268.067             0.352            0.370
Chain 1:   2200       -12122.843             0.337            0.370
Chain 1:   2300       -11719.170             0.310            0.370
Chain 1:   2400        -9682.546             0.321            0.370
Chain 1:   2500       -10459.181             0.323            0.370
Chain 1:   2600       -10461.605             0.278            0.222
Chain 1:   2700        -9795.637             0.230            0.210
Chain 1:   2800       -10123.458             0.211            0.153
Chain 1:   2900        -9612.148             0.150            0.074
Chain 1:   3000        -9125.909             0.118            0.068
Chain 1:   3100       -12996.555             0.098            0.068
Chain 1:   3200       -10104.764             0.111            0.068
Chain 1:   3300        -9360.265             0.116            0.074
Chain 1:   3400        -9565.331             0.097            0.068
Chain 1:   3500        -9940.218             0.093            0.053
Chain 1:   3600        -8875.612             0.105            0.068
Chain 1:   3700       -11017.496             0.118            0.080
Chain 1:   3800       -15466.026             0.143            0.120
Chain 1:   3900        -9352.563             0.203            0.194
Chain 1:   4000        -9527.818             0.200            0.194
Chain 1:   4100        -9358.156             0.172            0.120
Chain 1:   4200       -10353.239             0.153            0.096
Chain 1:   4300        -9659.311             0.152            0.096
Chain 1:   4400       -10155.063             0.155            0.096
Chain 1:   4500        -9391.532             0.159            0.096
Chain 1:   4600        -8794.350             0.154            0.081
Chain 1:   4700        -8665.650             0.136            0.072
Chain 1:   4800       -11716.214             0.133            0.072
Chain 1:   4900        -9064.139             0.097            0.072
Chain 1:   5000        -9465.230             0.099            0.072
Chain 1:   5100        -8836.055             0.105            0.072
Chain 1:   5200        -8851.702             0.095            0.071
Chain 1:   5300       -10260.307             0.102            0.071
Chain 1:   5400        -8834.274             0.113            0.081
Chain 1:   5500       -12366.926             0.134            0.137
Chain 1:   5600        -8504.610             0.172            0.161
Chain 1:   5700       -13571.935             0.208            0.260
Chain 1:   5800        -8622.246             0.239            0.286
Chain 1:   5900       -14661.272             0.251            0.286
Chain 1:   6000        -9724.725             0.298            0.373
Chain 1:   6100       -10101.473             0.294            0.373
Chain 1:   6200        -8468.086             0.314            0.373
Chain 1:   6300        -8747.974             0.303            0.373
Chain 1:   6400       -10574.987             0.304            0.373
Chain 1:   6500        -8447.520             0.301            0.373
Chain 1:   6600       -11857.421             0.284            0.288
Chain 1:   6700        -8545.902             0.286            0.288
Chain 1:   6800       -12858.728             0.262            0.288
Chain 1:   6900        -9633.450             0.254            0.288
Chain 1:   7000       -13178.087             0.230            0.269
Chain 1:   7100       -10882.011             0.247            0.269
Chain 1:   7200        -8840.935             0.251            0.269
Chain 1:   7300       -10677.699             0.265            0.269
Chain 1:   7400        -8972.909             0.267            0.269
Chain 1:   7500        -8695.196             0.245            0.269
Chain 1:   7600        -9829.553             0.228            0.231
Chain 1:   7700        -8789.450             0.201            0.211
Chain 1:   7800        -8731.505             0.168            0.190
Chain 1:   7900        -8518.582             0.137            0.172
Chain 1:   8000       -10833.123             0.131            0.172
Chain 1:   8100        -9095.559             0.129            0.172
Chain 1:   8200        -8658.511             0.111            0.118
Chain 1:   8300       -13003.248             0.128            0.118
Chain 1:   8400        -8559.739             0.161            0.118
Chain 1:   8500        -8523.849             0.158            0.118
Chain 1:   8600        -9700.760             0.158            0.121
Chain 1:   8700        -8641.780             0.159            0.123
Chain 1:   8800        -8349.691             0.162            0.123
Chain 1:   8900       -10226.098             0.177            0.183
Chain 1:   9000       -10576.457             0.159            0.123
Chain 1:   9100        -9096.840             0.157            0.123
Chain 1:   9200        -9457.252             0.155            0.123
Chain 1:   9300        -8299.820             0.136            0.123
Chain 1:   9400        -9906.419             0.100            0.123
Chain 1:   9500        -8819.314             0.112            0.123
Chain 1:   9600        -9446.059             0.107            0.123
Chain 1:   9700        -8642.867             0.104            0.123
Chain 1:   9800       -10117.466             0.115            0.139
Chain 1:   9900        -9358.792             0.104            0.123
Chain 1:   10000        -8935.760             0.106            0.123
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001466 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62115.965             1.000            1.000
Chain 1:    200       -17931.434             1.732            2.464
Chain 1:    300        -8943.137             1.490            1.005
Chain 1:    400        -8330.034             1.136            1.005
Chain 1:    500        -8494.893             0.912            1.000
Chain 1:    600        -8897.771             0.768            1.000
Chain 1:    700        -7922.575             0.676            0.123
Chain 1:    800        -8127.423             0.594            0.123
Chain 1:    900        -7956.662             0.531            0.074
Chain 1:   1000        -7885.017             0.479            0.074
Chain 1:   1100        -7874.057             0.379            0.045
Chain 1:   1200        -7675.246             0.135            0.026
Chain 1:   1300        -7809.358             0.036            0.025
Chain 1:   1400        -7922.473             0.030            0.021
Chain 1:   1500        -7672.035             0.032            0.025
Chain 1:   1600        -7871.124             0.030            0.025
Chain 1:   1700        -7584.004             0.021            0.025
Chain 1:   1800        -7713.107             0.020            0.021
Chain 1:   1900        -7633.849             0.019            0.017
Chain 1:   2000        -7690.901             0.019            0.017
Chain 1:   2100        -7673.972             0.019            0.017
Chain 1:   2200        -7782.482             0.018            0.017
Chain 1:   2300        -7659.087             0.018            0.016
Chain 1:   2400        -7723.328             0.017            0.016
Chain 1:   2500        -7645.896             0.015            0.014
Chain 1:   2600        -7590.118             0.013            0.010
Chain 1:   2700        -7636.098             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005367 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 53.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86215.138             1.000            1.000
Chain 1:    200       -13576.767             3.175            5.350
Chain 1:    300        -9995.300             2.236            1.000
Chain 1:    400       -10874.041             1.697            1.000
Chain 1:    500        -8949.912             1.401            0.358
Chain 1:    600        -8545.927             1.175            0.358
Chain 1:    700        -8537.358             1.008            0.215
Chain 1:    800        -9276.343             0.892            0.215
Chain 1:    900        -8814.022             0.798            0.081
Chain 1:   1000        -8580.526             0.721            0.081
Chain 1:   1100        -8742.908             0.623            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8505.539             0.091            0.052
Chain 1:   1300        -8710.074             0.057            0.047
Chain 1:   1400        -8706.062             0.049            0.028
Chain 1:   1500        -8603.529             0.029            0.027
Chain 1:   1600        -8705.467             0.025            0.023
Chain 1:   1700        -8793.273             0.026            0.023
Chain 1:   1800        -8395.185             0.023            0.023
Chain 1:   1900        -8496.156             0.019            0.019
Chain 1:   2000        -8466.974             0.017            0.012
Chain 1:   2100        -8588.063             0.016            0.012
Chain 1:   2200        -8366.164             0.016            0.012
Chain 1:   2300        -8525.013             0.016            0.012
Chain 1:   2400        -8536.998             0.016            0.012
Chain 1:   2500        -8508.927             0.015            0.012
Chain 1:   2600        -8511.483             0.014            0.012
Chain 1:   2700        -8417.650             0.014            0.012
Chain 1:   2800        -8387.996             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003389 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8394683.390             1.000            1.000
Chain 1:    200     -1585072.613             2.648            4.296
Chain 1:    300      -891714.861             2.025            1.000
Chain 1:    400      -458251.648             1.755            1.000
Chain 1:    500      -358571.491             1.460            0.946
Chain 1:    600      -233399.353             1.306            0.946
Chain 1:    700      -119444.936             1.255            0.946
Chain 1:    800       -86623.513             1.146            0.946
Chain 1:    900       -66930.059             1.051            0.778
Chain 1:   1000       -51698.792             0.976            0.778
Chain 1:   1100       -39153.966             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38325.437             0.480            0.379
Chain 1:   1300       -26265.150             0.448            0.379
Chain 1:   1400       -25981.584             0.355            0.320
Chain 1:   1500       -22565.053             0.342            0.320
Chain 1:   1600       -21780.124             0.292            0.295
Chain 1:   1700       -20652.111             0.202            0.294
Chain 1:   1800       -20595.802             0.165            0.151
Chain 1:   1900       -20921.567             0.137            0.055
Chain 1:   2000       -19432.578             0.115            0.055
Chain 1:   2100       -19670.918             0.084            0.036
Chain 1:   2200       -19897.298             0.083            0.036
Chain 1:   2300       -19514.660             0.039            0.020
Chain 1:   2400       -19286.849             0.039            0.020
Chain 1:   2500       -19089.008             0.025            0.016
Chain 1:   2600       -18719.441             0.023            0.016
Chain 1:   2700       -18676.469             0.018            0.012
Chain 1:   2800       -18393.513             0.019            0.015
Chain 1:   2900       -18674.670             0.019            0.015
Chain 1:   3000       -18660.819             0.012            0.012
Chain 1:   3100       -18745.775             0.011            0.012
Chain 1:   3200       -18436.660             0.012            0.015
Chain 1:   3300       -18641.234             0.011            0.012
Chain 1:   3400       -18116.579             0.012            0.015
Chain 1:   3500       -18727.858             0.015            0.015
Chain 1:   3600       -18035.317             0.017            0.015
Chain 1:   3700       -18421.556             0.018            0.017
Chain 1:   3800       -17382.522             0.023            0.021
Chain 1:   3900       -17378.714             0.021            0.021
Chain 1:   4000       -17495.994             0.022            0.021
Chain 1:   4100       -17409.823             0.022            0.021
Chain 1:   4200       -17226.343             0.021            0.021
Chain 1:   4300       -17364.522             0.021            0.021
Chain 1:   4400       -17321.565             0.018            0.011
Chain 1:   4500       -17224.164             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001743 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11811.467             1.000            1.000
Chain 1:    200        -8766.864             0.674            1.000
Chain 1:    300        -7814.559             0.490            0.347
Chain 1:    400        -7909.965             0.370            0.347
Chain 1:    500        -7790.406             0.299            0.122
Chain 1:    600        -7650.814             0.252            0.122
Chain 1:    700        -7601.324             0.217            0.018
Chain 1:    800        -7608.463             0.190            0.018
Chain 1:    900        -7542.902             0.170            0.015
Chain 1:   1000        -7639.500             0.154            0.015
Chain 1:   1100        -7693.095             0.055            0.013
Chain 1:   1200        -7611.316             0.021            0.012
Chain 1:   1300        -7575.688             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001409 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56417.151             1.000            1.000
Chain 1:    200       -16777.570             1.681            2.363
Chain 1:    300        -8444.939             1.450            1.000
Chain 1:    400        -8545.829             1.090            1.000
Chain 1:    500        -8049.936             0.885            0.987
Chain 1:    600        -8519.707             0.746            0.987
Chain 1:    700        -7880.297             0.651            0.081
Chain 1:    800        -8030.574             0.572            0.081
Chain 1:    900        -7775.515             0.512            0.062
Chain 1:   1000        -7713.661             0.462            0.062
Chain 1:   1100        -7682.951             0.362            0.055
Chain 1:   1200        -7611.443             0.127            0.033
Chain 1:   1300        -7577.716             0.029            0.019
Chain 1:   1400        -7745.677             0.030            0.022
Chain 1:   1500        -7573.051             0.026            0.022
Chain 1:   1600        -7470.079             0.022            0.019
Chain 1:   1700        -7455.509             0.014            0.014
Chain 1:   1800        -7489.623             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003611 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85749.051             1.000            1.000
Chain 1:    200       -12865.895             3.332            5.665
Chain 1:    300        -9398.307             2.345            1.000
Chain 1:    400       -10049.508             1.775            1.000
Chain 1:    500        -8264.309             1.463            0.369
Chain 1:    600        -8461.083             1.223            0.369
Chain 1:    700        -8241.465             1.052            0.216
Chain 1:    800        -8488.969             0.924            0.216
Chain 1:    900        -8286.301             0.824            0.065
Chain 1:   1000        -8071.996             0.744            0.065
Chain 1:   1100        -8328.910             0.648            0.031   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8114.898             0.084            0.029
Chain 1:   1300        -8039.303             0.048            0.027
Chain 1:   1400        -8052.853             0.041            0.027
Chain 1:   1500        -8071.752             0.020            0.026
Chain 1:   1600        -8068.939             0.018            0.026
Chain 1:   1700        -8017.895             0.016            0.024
Chain 1:   1800        -7895.764             0.014            0.015
Chain 1:   1900        -8006.436             0.013            0.014
Chain 1:   2000        -7971.231             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00322 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8411705.078             1.000            1.000
Chain 1:    200     -1588321.683             2.648            4.296
Chain 1:    300      -891400.148             2.026            1.000
Chain 1:    400      -457343.007             1.757            1.000
Chain 1:    500      -357170.974             1.461            0.949
Chain 1:    600      -232035.206             1.308            0.949
Chain 1:    700      -118380.437             1.258            0.949
Chain 1:    800       -85628.314             1.149            0.949
Chain 1:    900       -66000.268             1.054            0.782
Chain 1:   1000       -50811.187             0.979            0.782
Chain 1:   1100       -38313.771             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37484.996             0.484            0.382
Chain 1:   1300       -25484.472             0.453            0.382
Chain 1:   1400       -25203.464             0.359            0.326
Chain 1:   1500       -21802.685             0.346            0.326
Chain 1:   1600       -21021.657             0.296            0.299
Chain 1:   1700       -19901.500             0.206            0.297
Chain 1:   1800       -19846.512             0.168            0.156
Chain 1:   1900       -20171.654             0.140            0.056
Chain 1:   2000       -18687.967             0.118            0.056
Chain 1:   2100       -18925.983             0.086            0.037
Chain 1:   2200       -19151.271             0.085            0.037
Chain 1:   2300       -18769.764             0.040            0.020
Chain 1:   2400       -18542.253             0.040            0.020
Chain 1:   2500       -18344.179             0.026            0.016
Chain 1:   2600       -17975.499             0.024            0.016
Chain 1:   2700       -17932.821             0.019            0.013
Chain 1:   2800       -17650.049             0.020            0.016
Chain 1:   2900       -17930.799             0.020            0.016
Chain 1:   3000       -17917.088             0.012            0.013
Chain 1:   3100       -18001.916             0.012            0.012
Chain 1:   3200       -17693.297             0.012            0.016
Chain 1:   3300       -17897.494             0.011            0.012
Chain 1:   3400       -17373.613             0.013            0.016
Chain 1:   3500       -17983.629             0.015            0.016
Chain 1:   3600       -17292.740             0.017            0.016
Chain 1:   3700       -17677.677             0.019            0.017
Chain 1:   3800       -16641.162             0.024            0.022
Chain 1:   3900       -16637.393             0.022            0.022
Chain 1:   4000       -16754.699             0.023            0.022
Chain 1:   4100       -16668.617             0.023            0.022
Chain 1:   4200       -16485.717             0.022            0.022
Chain 1:   4300       -16623.523             0.022            0.022
Chain 1:   4400       -16581.011             0.019            0.011
Chain 1:   4500       -16483.665             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001523 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49072.775             1.000            1.000
Chain 1:    200       -13412.072             1.829            2.659
Chain 1:    300       -20130.908             1.331            1.000
Chain 1:    400       -20482.895             1.002            1.000
Chain 1:    500       -18393.970             0.825            0.334
Chain 1:    600       -15802.077             0.715            0.334
Chain 1:    700       -15883.862             0.613            0.164
Chain 1:    800       -14772.318             0.546            0.164
Chain 1:    900       -12763.769             0.503            0.157
Chain 1:   1000       -23041.048             0.497            0.164
Chain 1:   1100       -30424.074             0.421            0.164
Chain 1:   1200       -12734.875             0.294            0.164
Chain 1:   1300       -11663.714             0.270            0.157
Chain 1:   1400       -11672.008             0.269            0.157
Chain 1:   1500       -10328.483             0.270            0.157
Chain 1:   1600       -10052.597             0.257            0.130
Chain 1:   1700       -11394.826             0.268            0.130
Chain 1:   1800       -11022.156             0.264            0.130
Chain 1:   1900       -17083.416             0.283            0.130
Chain 1:   2000       -11599.614             0.286            0.130
Chain 1:   2100        -9495.887             0.284            0.130
Chain 1:   2200        -9951.821             0.150            0.118
Chain 1:   2300       -10626.244             0.147            0.118
Chain 1:   2400        -9160.742             0.163            0.130
Chain 1:   2500        -9732.192             0.156            0.118
Chain 1:   2600       -16446.358             0.194            0.160
Chain 1:   2700        -9313.189             0.259            0.222
Chain 1:   2800       -10671.358             0.268            0.222
Chain 1:   2900        -9485.712             0.245            0.160
Chain 1:   3000       -10009.992             0.203            0.127
Chain 1:   3100        -8785.453             0.195            0.127
Chain 1:   3200        -9506.088             0.198            0.127
Chain 1:   3300        -9756.999             0.194            0.127
Chain 1:   3400       -10127.575             0.182            0.125
Chain 1:   3500       -14094.328             0.204            0.127
Chain 1:   3600       -11045.156             0.191            0.127
Chain 1:   3700        -9226.895             0.134            0.127
Chain 1:   3800       -10790.313             0.135            0.139
Chain 1:   3900       -16329.653             0.157            0.145
Chain 1:   4000        -9824.371             0.218            0.197
Chain 1:   4100        -9562.207             0.207            0.197
Chain 1:   4200        -9396.545             0.201            0.197
Chain 1:   4300       -12251.617             0.222            0.233
Chain 1:   4400       -10238.668             0.238            0.233
Chain 1:   4500        -9319.168             0.219            0.197
Chain 1:   4600       -11255.778             0.209            0.197
Chain 1:   4700       -10870.182             0.193            0.172
Chain 1:   4800        -8735.786             0.203            0.197
Chain 1:   4900       -10141.868             0.183            0.172
Chain 1:   5000       -13494.879             0.141            0.172
Chain 1:   5100        -8681.926             0.194            0.197
Chain 1:   5200       -11874.908             0.219            0.233
Chain 1:   5300       -10087.530             0.213            0.197
Chain 1:   5400        -8620.871             0.211            0.177
Chain 1:   5500        -9270.652             0.208            0.177
Chain 1:   5600       -14256.927             0.226            0.244
Chain 1:   5700       -15335.942             0.229            0.244
Chain 1:   5800       -16617.204             0.212            0.177
Chain 1:   5900       -10606.188             0.255            0.248
Chain 1:   6000        -9002.810             0.248            0.178
Chain 1:   6100        -9023.432             0.193            0.177
Chain 1:   6200        -8549.579             0.172            0.170
Chain 1:   6300        -9242.960             0.161            0.077
Chain 1:   6400       -13289.113             0.175            0.077
Chain 1:   6500        -9554.085             0.207            0.178
Chain 1:   6600        -8578.950             0.183            0.114
Chain 1:   6700        -8672.178             0.177            0.114
Chain 1:   6800        -8777.596             0.171            0.114
Chain 1:   6900       -13534.585             0.149            0.114
Chain 1:   7000        -9608.680             0.172            0.114
Chain 1:   7100        -9936.232             0.176            0.114
Chain 1:   7200        -8404.959             0.188            0.182
Chain 1:   7300        -9032.571             0.188            0.182
Chain 1:   7400        -8363.519             0.165            0.114
Chain 1:   7500        -8412.681             0.127            0.080
Chain 1:   7600       -10719.277             0.137            0.080
Chain 1:   7700        -9550.564             0.148            0.122
Chain 1:   7800       -14084.957             0.179            0.182
Chain 1:   7900        -8430.460             0.211            0.182
Chain 1:   8000        -8606.248             0.172            0.122
Chain 1:   8100        -8507.846             0.170            0.122
Chain 1:   8200        -8983.675             0.157            0.080
Chain 1:   8300        -8418.000             0.157            0.080
Chain 1:   8400        -8364.076             0.149            0.067
Chain 1:   8500       -10611.615             0.170            0.122
Chain 1:   8600        -8725.550             0.170            0.122
Chain 1:   8700        -8403.946             0.162            0.067
Chain 1:   8800        -8515.347             0.131            0.053
Chain 1:   8900       -10608.949             0.084            0.053
Chain 1:   9000       -10107.432             0.086            0.053
Chain 1:   9100        -8893.261             0.099            0.067
Chain 1:   9200        -8333.455             0.100            0.067
Chain 1:   9300       -10011.666             0.110            0.137
Chain 1:   9400       -10305.951             0.113            0.137
Chain 1:   9500       -11142.868             0.099            0.075
Chain 1:   9600        -8483.086             0.109            0.075
Chain 1:   9700        -8539.557             0.106            0.075
Chain 1:   9800       -12277.807             0.135            0.137
Chain 1:   9900        -9112.113             0.150            0.137
Chain 1:   10000        -9837.355             0.152            0.137
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58388.160             1.000            1.000
Chain 1:    200       -17728.011             1.647            2.294
Chain 1:    300        -8675.487             1.446            1.043
Chain 1:    400        -8170.241             1.100            1.043
Chain 1:    500        -8470.459             0.887            1.000
Chain 1:    600        -8665.174             0.743            1.000
Chain 1:    700        -8619.090             0.637            0.062
Chain 1:    800        -8193.771             0.564            0.062
Chain 1:    900        -7651.272             0.509            0.062
Chain 1:   1000        -7782.214             0.460            0.062
Chain 1:   1100        -7658.341             0.362            0.052
Chain 1:   1200        -7733.061             0.133            0.035
Chain 1:   1300        -7733.255             0.029            0.022
Chain 1:   1400        -7798.120             0.024            0.017
Chain 1:   1500        -7532.861             0.024            0.017
Chain 1:   1600        -7710.721             0.024            0.017
Chain 1:   1700        -7440.524             0.027            0.023
Chain 1:   1800        -7590.717             0.024            0.020
Chain 1:   1900        -7537.038             0.017            0.017
Chain 1:   2000        -7624.643             0.017            0.016
Chain 1:   2100        -7533.816             0.016            0.012
Chain 1:   2200        -7657.780             0.017            0.016
Chain 1:   2300        -7515.875             0.019            0.019
Chain 1:   2400        -7619.977             0.019            0.019
Chain 1:   2500        -7582.842             0.016            0.016
Chain 1:   2600        -7487.231             0.015            0.014
Chain 1:   2700        -7480.347             0.012            0.013
Chain 1:   2800        -7464.836             0.010            0.012
Chain 1:   2900        -7345.950             0.011            0.013
Chain 1:   3000        -7485.918             0.012            0.014
Chain 1:   3100        -7477.306             0.011            0.014
Chain 1:   3200        -7674.381             0.011            0.014
Chain 1:   3300        -7406.888             0.013            0.014
Chain 1:   3400        -7615.326             0.015            0.016
Chain 1:   3500        -7387.834             0.017            0.019
Chain 1:   3600        -7452.178             0.017            0.019
Chain 1:   3700        -7401.373             0.017            0.019
Chain 1:   3800        -7404.765             0.017            0.019
Chain 1:   3900        -7370.370             0.016            0.019
Chain 1:   4000        -7365.259             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00271 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86521.232             1.000            1.000
Chain 1:    200       -13640.949             3.171            5.343
Chain 1:    300       -10015.576             2.235            1.000
Chain 1:    400       -10836.418             1.695            1.000
Chain 1:    500        -8979.183             1.397            0.362
Chain 1:    600        -8498.118             1.174            0.362
Chain 1:    700        -8502.760             1.006            0.207
Chain 1:    800        -9330.621             0.892            0.207
Chain 1:    900        -8805.269             0.799            0.089
Chain 1:   1000        -8626.459             0.721            0.089
Chain 1:   1100        -8897.341             0.624            0.076   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8628.803             0.093            0.060
Chain 1:   1300        -8725.791             0.058            0.057
Chain 1:   1400        -8748.386             0.051            0.031
Chain 1:   1500        -8576.606             0.032            0.030
Chain 1:   1600        -8696.064             0.028            0.021
Chain 1:   1700        -8780.258             0.029            0.021
Chain 1:   1800        -8370.116             0.025            0.021
Chain 1:   1900        -8465.883             0.020            0.020
Chain 1:   2000        -8438.705             0.018            0.014
Chain 1:   2100        -8560.249             0.017            0.014
Chain 1:   2200        -8407.138             0.015            0.014
Chain 1:   2300        -8464.001             0.015            0.014
Chain 1:   2400        -8530.757             0.015            0.014
Chain 1:   2500        -8476.460             0.014            0.011
Chain 1:   2600        -8474.721             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003714 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392881.448             1.000            1.000
Chain 1:    200     -1583906.877             2.649            4.299
Chain 1:    300      -890803.885             2.026            1.000
Chain 1:    400      -457802.494             1.756            1.000
Chain 1:    500      -358264.550             1.460            0.946
Chain 1:    600      -233224.321             1.306            0.946
Chain 1:    700      -119392.448             1.256            0.946
Chain 1:    800       -86598.417             1.146            0.946
Chain 1:    900       -66932.858             1.051            0.778
Chain 1:   1000       -51723.214             0.976            0.778
Chain 1:   1100       -39194.731             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38370.321             0.480            0.379
Chain 1:   1300       -26322.632             0.448            0.379
Chain 1:   1400       -26041.029             0.354            0.320
Chain 1:   1500       -22627.571             0.342            0.320
Chain 1:   1600       -21843.719             0.292            0.294
Chain 1:   1700       -20717.103             0.202            0.294
Chain 1:   1800       -20661.156             0.164            0.151
Chain 1:   1900       -20987.216             0.136            0.054
Chain 1:   2000       -19498.371             0.115            0.054
Chain 1:   2100       -19736.740             0.084            0.036
Chain 1:   2200       -19963.193             0.083            0.036
Chain 1:   2300       -19580.395             0.039            0.020
Chain 1:   2400       -19352.539             0.039            0.020
Chain 1:   2500       -19154.585             0.025            0.016
Chain 1:   2600       -18784.898             0.023            0.016
Chain 1:   2700       -18741.852             0.018            0.012
Chain 1:   2800       -18458.807             0.019            0.015
Chain 1:   2900       -18739.997             0.019            0.015
Chain 1:   3000       -18726.175             0.012            0.012
Chain 1:   3100       -18811.169             0.011            0.012
Chain 1:   3200       -18501.906             0.012            0.015
Chain 1:   3300       -18706.567             0.011            0.012
Chain 1:   3400       -18181.649             0.012            0.015
Chain 1:   3500       -18793.313             0.015            0.015
Chain 1:   3600       -18100.239             0.017            0.015
Chain 1:   3700       -18486.901             0.018            0.017
Chain 1:   3800       -17446.998             0.023            0.021
Chain 1:   3900       -17443.145             0.021            0.021
Chain 1:   4000       -17560.442             0.022            0.021
Chain 1:   4100       -17474.264             0.022            0.021
Chain 1:   4200       -17290.560             0.021            0.021
Chain 1:   4300       -17428.904             0.021            0.021
Chain 1:   4400       -17385.804             0.018            0.011
Chain 1:   4500       -17288.356             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001471 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49741.377             1.000            1.000
Chain 1:    200       -16793.721             1.481            1.962
Chain 1:    300       -19214.077             1.029            1.000
Chain 1:    400       -19597.356             0.777            1.000
Chain 1:    500       -12585.765             0.733            0.557
Chain 1:    600       -17150.583             0.655            0.557
Chain 1:    700       -15238.980             0.579            0.266
Chain 1:    800       -15616.976             0.510            0.266
Chain 1:    900       -11199.567             0.497            0.266
Chain 1:   1000       -11125.806             0.448            0.266
Chain 1:   1100       -10937.304             0.350            0.126
Chain 1:   1200       -13723.054             0.174            0.126
Chain 1:   1300       -12152.027             0.174            0.129
Chain 1:   1400       -16150.943             0.197            0.203
Chain 1:   1500       -10838.546             0.190            0.203
Chain 1:   1600       -25997.583             0.222            0.203
Chain 1:   1700       -12261.515             0.322            0.248
Chain 1:   1800       -10553.907             0.335            0.248
Chain 1:   1900       -11256.488             0.302            0.203
Chain 1:   2000       -12187.917             0.309            0.203
Chain 1:   2100       -11117.243             0.317            0.203
Chain 1:   2200       -19736.678             0.340            0.248
Chain 1:   2300        -9519.698             0.435            0.437
Chain 1:   2400        -9392.252             0.411            0.437
Chain 1:   2500        -9590.515             0.364            0.162
Chain 1:   2600       -11138.887             0.320            0.139
Chain 1:   2700        -9674.148             0.223            0.139
Chain 1:   2800       -10615.752             0.216            0.096
Chain 1:   2900       -11190.027             0.215            0.096
Chain 1:   3000        -9445.201             0.226            0.139
Chain 1:   3100        -9958.024             0.221            0.139
Chain 1:   3200       -15458.173             0.213            0.139
Chain 1:   3300        -9911.255             0.162            0.139
Chain 1:   3400       -10380.294             0.165            0.139
Chain 1:   3500       -14010.201             0.189            0.151
Chain 1:   3600       -10823.549             0.204            0.185
Chain 1:   3700       -11633.411             0.196            0.185
Chain 1:   3800       -13682.675             0.202            0.185
Chain 1:   3900       -14647.732             0.204            0.185
Chain 1:   4000       -13738.760             0.192            0.150
Chain 1:   4100        -9198.218             0.236            0.259
Chain 1:   4200       -11637.756             0.221            0.210
Chain 1:   4300       -13011.525             0.176            0.150
Chain 1:   4400        -8970.829             0.216            0.210
Chain 1:   4500        -9912.145             0.200            0.150
Chain 1:   4600       -13091.092             0.195            0.150
Chain 1:   4700       -12288.012             0.194            0.150
Chain 1:   4800       -10173.981             0.200            0.208
Chain 1:   4900        -8659.249             0.211            0.208
Chain 1:   5000        -9750.535             0.216            0.208
Chain 1:   5100        -8835.382             0.177            0.175
Chain 1:   5200       -11182.241             0.177            0.175
Chain 1:   5300        -9216.354             0.187            0.208
Chain 1:   5400       -12275.294             0.167            0.208
Chain 1:   5500       -10100.409             0.179            0.210
Chain 1:   5600        -9165.825             0.165            0.208
Chain 1:   5700        -8578.203             0.166            0.208
Chain 1:   5800        -9139.429             0.151            0.175
Chain 1:   5900       -12077.152             0.158            0.210
Chain 1:   6000        -9764.689             0.170            0.213
Chain 1:   6100        -8899.481             0.170            0.213
Chain 1:   6200        -9593.435             0.156            0.213
Chain 1:   6300        -9472.323             0.136            0.102
Chain 1:   6400        -8456.705             0.123            0.102
Chain 1:   6500        -9482.005             0.112            0.102
Chain 1:   6600        -8775.448             0.110            0.097
Chain 1:   6700        -9234.509             0.108            0.097
Chain 1:   6800        -8756.583             0.108            0.097
Chain 1:   6900        -9505.836             0.091            0.081
Chain 1:   7000       -14782.157             0.103            0.081
Chain 1:   7100        -8517.833             0.167            0.081
Chain 1:   7200        -8956.060             0.165            0.081
Chain 1:   7300       -12015.008             0.189            0.108
Chain 1:   7400        -8407.996             0.220            0.108
Chain 1:   7500       -11689.317             0.237            0.255
Chain 1:   7600       -12820.906             0.238            0.255
Chain 1:   7700        -8657.660             0.281            0.281
Chain 1:   7800        -8582.626             0.276            0.281
Chain 1:   7900        -8668.260             0.269            0.281
Chain 1:   8000       -11454.792             0.258            0.255
Chain 1:   8100       -12103.716             0.190            0.243
Chain 1:   8200        -8481.756             0.228            0.255
Chain 1:   8300        -8627.985             0.204            0.243
Chain 1:   8400       -10778.141             0.181            0.199
Chain 1:   8500        -8434.986             0.181            0.199
Chain 1:   8600        -8454.528             0.172            0.199
Chain 1:   8700        -8924.371             0.129            0.054
Chain 1:   8800       -12204.963             0.155            0.199
Chain 1:   8900       -11640.256             0.159            0.199
Chain 1:   9000       -11156.969             0.139            0.054
Chain 1:   9100        -9501.540             0.151            0.174
Chain 1:   9200        -8736.029             0.117            0.088
Chain 1:   9300        -8599.078             0.117            0.088
Chain 1:   9400        -8629.200             0.097            0.053
Chain 1:   9500        -9418.847             0.078            0.053
Chain 1:   9600       -11019.868             0.092            0.084
Chain 1:   9700        -8402.738             0.118            0.088
Chain 1:   9800        -9027.398             0.098            0.084
Chain 1:   9900        -9397.289             0.097            0.084
Chain 1:   10000        -8195.799             0.108            0.088
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00323 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57112.925             1.000            1.000
Chain 1:    200       -17852.221             1.600            2.199
Chain 1:    300        -8829.540             1.407            1.022
Chain 1:    400        -7980.290             1.082            1.022
Chain 1:    500        -8719.293             0.882            1.000
Chain 1:    600        -8503.512             0.740            1.000
Chain 1:    700        -7966.465             0.644            0.106
Chain 1:    800        -8135.131             0.566            0.106
Chain 1:    900        -8016.884             0.505            0.085
Chain 1:   1000        -7830.122             0.456            0.085
Chain 1:   1100        -7881.018             0.357            0.067
Chain 1:   1200        -7792.202             0.138            0.025
Chain 1:   1300        -7897.725             0.037            0.024
Chain 1:   1400        -7929.284             0.027            0.021
Chain 1:   1500        -7594.788             0.023            0.021
Chain 1:   1600        -7781.543             0.023            0.021
Chain 1:   1700        -7505.006             0.020            0.021
Chain 1:   1800        -7634.548             0.020            0.017
Chain 1:   1900        -7588.850             0.019            0.017
Chain 1:   2000        -7686.167             0.018            0.013
Chain 1:   2100        -7600.306             0.018            0.013
Chain 1:   2200        -7755.090             0.019            0.017
Chain 1:   2300        -7594.137             0.020            0.020
Chain 1:   2400        -7587.337             0.019            0.020
Chain 1:   2500        -7652.623             0.016            0.017
Chain 1:   2600        -7554.942             0.015            0.013
Chain 1:   2700        -7580.389             0.011            0.013
Chain 1:   2800        -7636.776             0.010            0.011
Chain 1:   2900        -7416.495             0.013            0.013
Chain 1:   3000        -7558.405             0.013            0.013
Chain 1:   3100        -7560.512             0.012            0.013
Chain 1:   3200        -7768.694             0.013            0.013
Chain 1:   3300        -7487.296             0.015            0.013
Chain 1:   3400        -7717.811             0.018            0.019
Chain 1:   3500        -7470.833             0.020            0.027
Chain 1:   3600        -7537.047             0.020            0.027
Chain 1:   3700        -7486.642             0.020            0.027
Chain 1:   3800        -7484.010             0.019            0.027
Chain 1:   3900        -7449.987             0.017            0.019
Chain 1:   4000        -7444.889             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003241 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87702.783             1.000            1.000
Chain 1:    200       -13862.910             3.163            5.326
Chain 1:    300       -10149.200             2.231            1.000
Chain 1:    400       -11514.996             1.703            1.000
Chain 1:    500        -9028.927             1.417            0.366
Chain 1:    600        -8844.574             1.185            0.366
Chain 1:    700        -9266.836             1.022            0.275
Chain 1:    800        -8444.513             0.906            0.275
Chain 1:    900        -8535.396             0.807            0.119
Chain 1:   1000        -8704.282             0.728            0.119
Chain 1:   1100        -8971.593             0.631            0.097   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8511.524             0.104            0.054
Chain 1:   1300        -8842.224             0.071            0.046
Chain 1:   1400        -8778.473             0.060            0.037
Chain 1:   1500        -8675.283             0.033            0.030
Chain 1:   1600        -8777.237             0.033            0.030
Chain 1:   1700        -8842.355             0.029            0.019
Chain 1:   1800        -8407.005             0.024            0.019
Chain 1:   1900        -8511.385             0.024            0.019
Chain 1:   2000        -8487.084             0.023            0.012
Chain 1:   2100        -8633.696             0.021            0.012
Chain 1:   2200        -8419.066             0.018            0.012
Chain 1:   2300        -8575.057             0.017            0.012
Chain 1:   2400        -8414.304             0.018            0.017
Chain 1:   2500        -8485.261             0.017            0.017
Chain 1:   2600        -8397.541             0.017            0.017
Chain 1:   2700        -8431.548             0.017            0.017
Chain 1:   2800        -8391.710             0.012            0.012
Chain 1:   2900        -8484.874             0.012            0.011
Chain 1:   3000        -8316.863             0.014            0.017
Chain 1:   3100        -8474.222             0.014            0.018
Chain 1:   3200        -8346.367             0.013            0.015
Chain 1:   3300        -8354.052             0.011            0.011
Chain 1:   3400        -8512.630             0.011            0.011
Chain 1:   3500        -8518.446             0.010            0.011
Chain 1:   3600        -8302.978             0.012            0.015
Chain 1:   3700        -8448.485             0.013            0.017
Chain 1:   3800        -8309.564             0.015            0.017
Chain 1:   3900        -8244.217             0.014            0.017
Chain 1:   4000        -8319.389             0.013            0.017
Chain 1:   4100        -8309.889             0.011            0.015
Chain 1:   4200        -8299.856             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003594 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8467646.593             1.000            1.000
Chain 1:    200     -1595688.239             2.653            4.307
Chain 1:    300      -892783.756             2.031            1.000
Chain 1:    400      -458438.477             1.760            1.000
Chain 1:    500      -357650.060             1.465            0.947
Chain 1:    600      -232212.972             1.311            0.947
Chain 1:    700      -118930.433             1.259            0.947
Chain 1:    800       -86288.242             1.149            0.947
Chain 1:    900       -66754.107             1.054            0.787
Chain 1:   1000       -51669.669             0.978            0.787
Chain 1:   1100       -39255.834             0.909            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38448.436             0.481            0.378
Chain 1:   1300       -26508.523             0.447            0.378
Chain 1:   1400       -26239.419             0.354            0.316
Chain 1:   1500       -22853.945             0.340            0.316
Chain 1:   1600       -22079.269             0.290            0.293
Chain 1:   1700       -20965.123             0.200            0.292
Chain 1:   1800       -20912.332             0.162            0.148
Chain 1:   1900       -21238.894             0.134            0.053
Chain 1:   2000       -19755.747             0.113            0.053
Chain 1:   2100       -19993.750             0.082            0.035
Chain 1:   2200       -20219.543             0.081            0.035
Chain 1:   2300       -19837.263             0.038            0.019
Chain 1:   2400       -19609.360             0.038            0.019
Chain 1:   2500       -19410.916             0.025            0.015
Chain 1:   2600       -19041.114             0.023            0.015
Chain 1:   2700       -18998.133             0.018            0.012
Chain 1:   2800       -18714.648             0.019            0.015
Chain 1:   2900       -18995.911             0.019            0.015
Chain 1:   3000       -18982.168             0.012            0.012
Chain 1:   3100       -19067.190             0.011            0.012
Chain 1:   3200       -18757.700             0.011            0.015
Chain 1:   3300       -18962.574             0.011            0.012
Chain 1:   3400       -18437.009             0.012            0.015
Chain 1:   3500       -19049.389             0.014            0.015
Chain 1:   3600       -18355.391             0.016            0.015
Chain 1:   3700       -18742.608             0.018            0.016
Chain 1:   3800       -17701.109             0.023            0.021
Chain 1:   3900       -17697.151             0.021            0.021
Chain 1:   4000       -17814.546             0.022            0.021
Chain 1:   4100       -17728.219             0.022            0.021
Chain 1:   4200       -17544.198             0.021            0.021
Chain 1:   4300       -17682.817             0.021            0.021
Chain 1:   4400       -17639.424             0.018            0.010
Chain 1:   4500       -17541.870             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001495 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12932.383             1.000            1.000
Chain 1:    200        -9787.186             0.661            1.000
Chain 1:    300        -8084.968             0.511            0.321
Chain 1:    400        -8372.487             0.392            0.321
Chain 1:    500        -8109.920             0.320            0.211
Chain 1:    600        -8077.881             0.267            0.211
Chain 1:    700        -7957.043             0.231            0.034
Chain 1:    800        -8134.298             0.205            0.034
Chain 1:    900        -7866.492             0.186            0.034
Chain 1:   1000        -8118.245             0.170            0.034
Chain 1:   1100        -8361.493             0.073            0.032
Chain 1:   1200        -7989.349             0.046            0.032
Chain 1:   1300        -8106.592             0.026            0.031
Chain 1:   1400        -7943.472             0.025            0.029
Chain 1:   1500        -8163.146             0.024            0.027
Chain 1:   1600        -7983.908             0.026            0.027
Chain 1:   1700        -7920.700             0.025            0.027
Chain 1:   1800        -7888.503             0.024            0.027
Chain 1:   1900        -7903.177             0.020            0.022
Chain 1:   2000        -7848.657             0.018            0.021
Chain 1:   2100        -7854.629             0.015            0.014
Chain 1:   2200        -7832.490             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001698 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58697.294             1.000            1.000
Chain 1:    200       -18292.353             1.604            2.209
Chain 1:    300        -8953.916             1.417            1.043
Chain 1:    400        -8029.156             1.092            1.043
Chain 1:    500        -8827.527             0.891            1.000
Chain 1:    600        -8502.041             0.749            1.000
Chain 1:    700        -7786.098             0.655            0.115
Chain 1:    800        -8344.866             0.582            0.115
Chain 1:    900        -8013.870             0.522            0.092
Chain 1:   1000        -7975.046             0.470            0.092
Chain 1:   1100        -7525.922             0.376            0.090
Chain 1:   1200        -7816.663             0.159            0.067
Chain 1:   1300        -7711.855             0.056            0.060
Chain 1:   1400        -7889.659             0.047            0.041
Chain 1:   1500        -7516.454             0.043            0.041
Chain 1:   1600        -7789.399             0.042            0.041
Chain 1:   1700        -7614.401             0.035            0.037
Chain 1:   1800        -7618.276             0.029            0.035
Chain 1:   1900        -7595.170             0.025            0.023
Chain 1:   2000        -7734.507             0.026            0.023
Chain 1:   2100        -7610.976             0.022            0.023
Chain 1:   2200        -7768.843             0.020            0.020
Chain 1:   2300        -7606.079             0.021            0.021
Chain 1:   2400        -7567.047             0.019            0.020
Chain 1:   2500        -7406.864             0.016            0.020
Chain 1:   2600        -7527.001             0.015            0.018
Chain 1:   2700        -7519.328             0.012            0.016
Chain 1:   2800        -7525.431             0.012            0.016
Chain 1:   2900        -7371.221             0.014            0.018
Chain 1:   3000        -7519.754             0.014            0.020
Chain 1:   3100        -7513.980             0.013            0.020
Chain 1:   3200        -7765.540             0.014            0.020
Chain 1:   3300        -7436.166             0.016            0.020
Chain 1:   3400        -7705.043             0.019            0.021
Chain 1:   3500        -7433.722             0.021            0.021
Chain 1:   3600        -7487.933             0.020            0.021
Chain 1:   3700        -7447.188             0.020            0.021
Chain 1:   3800        -7415.134             0.021            0.021
Chain 1:   3900        -7401.018             0.019            0.020
Chain 1:   4000        -7396.042             0.017            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003979 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86832.080             1.000            1.000
Chain 1:    200       -14054.472             3.089            5.178
Chain 1:    300       -10202.368             2.185            1.000
Chain 1:    400       -12455.010             1.684            1.000
Chain 1:    500        -8571.654             1.438            0.453
Chain 1:    600        -8476.723             1.200            0.453
Chain 1:    700        -8824.002             1.034            0.378
Chain 1:    800        -9127.693             0.909            0.378
Chain 1:    900        -9006.542             0.810            0.181
Chain 1:   1000        -8982.722             0.729            0.181
Chain 1:   1100        -8883.766             0.630            0.039   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8369.037             0.118            0.039
Chain 1:   1300        -8876.910             0.086            0.039
Chain 1:   1400        -8584.964             0.072            0.034
Chain 1:   1500        -8656.664             0.027            0.033
Chain 1:   1600        -8727.129             0.027            0.033
Chain 1:   1700        -8782.323             0.024            0.013
Chain 1:   1800        -8311.809             0.026            0.013
Chain 1:   1900        -8426.027             0.026            0.014
Chain 1:   2000        -8444.422             0.026            0.014
Chain 1:   2100        -8550.343             0.026            0.014
Chain 1:   2200        -8303.245             0.023            0.014
Chain 1:   2300        -8455.744             0.019            0.014
Chain 1:   2400        -8336.706             0.017            0.014
Chain 1:   2500        -8395.212             0.017            0.014
Chain 1:   2600        -8300.946             0.017            0.014
Chain 1:   2700        -8336.786             0.017            0.014
Chain 1:   2800        -8299.777             0.012            0.012
Chain 1:   2900        -8404.093             0.012            0.012
Chain 1:   3000        -8312.362             0.012            0.012
Chain 1:   3100        -8278.963             0.012            0.011
Chain 1:   3200        -8247.187             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003504 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8399446.300             1.000            1.000
Chain 1:    200     -1585337.316             2.649            4.298
Chain 1:    300      -890684.197             2.026            1.000
Chain 1:    400      -458063.572             1.756            1.000
Chain 1:    500      -358379.882             1.460            0.944
Chain 1:    600      -233439.629             1.306            0.944
Chain 1:    700      -119772.209             1.255            0.944
Chain 1:    800       -86977.777             1.145            0.944
Chain 1:    900       -67349.256             1.050            0.780
Chain 1:   1000       -52179.339             0.974            0.780
Chain 1:   1100       -39668.019             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38857.028             0.478            0.377
Chain 1:   1300       -26810.925             0.445            0.377
Chain 1:   1400       -26534.855             0.352            0.315
Chain 1:   1500       -23119.426             0.339            0.315
Chain 1:   1600       -22336.541             0.289            0.291
Chain 1:   1700       -21208.960             0.199            0.291
Chain 1:   1800       -21153.570             0.162            0.148
Chain 1:   1900       -21480.765             0.134            0.053
Chain 1:   2000       -19988.919             0.112            0.053
Chain 1:   2100       -20227.799             0.082            0.035
Chain 1:   2200       -20454.918             0.081            0.035
Chain 1:   2300       -20071.210             0.038            0.019
Chain 1:   2400       -19842.892             0.038            0.019
Chain 1:   2500       -19644.745             0.024            0.015
Chain 1:   2600       -19274.087             0.023            0.015
Chain 1:   2700       -19230.763             0.018            0.012
Chain 1:   2800       -18947.034             0.019            0.015
Chain 1:   2900       -19228.771             0.019            0.015
Chain 1:   3000       -19215.009             0.012            0.012
Chain 1:   3100       -19300.150             0.011            0.012
Chain 1:   3200       -18990.169             0.011            0.015
Chain 1:   3300       -19195.385             0.010            0.012
Chain 1:   3400       -18669.051             0.012            0.015
Chain 1:   3500       -19282.806             0.014            0.015
Chain 1:   3600       -18587.019             0.016            0.015
Chain 1:   3700       -18975.641             0.018            0.016
Chain 1:   3800       -17931.492             0.022            0.020
Chain 1:   3900       -17927.491             0.021            0.020
Chain 1:   4000       -18044.849             0.021            0.020
Chain 1:   4100       -17958.399             0.021            0.020
Chain 1:   4200       -17773.789             0.021            0.020
Chain 1:   4300       -17912.833             0.021            0.020
Chain 1:   4400       -17868.980             0.018            0.010
Chain 1:   4500       -17771.338             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12533.623             1.000            1.000
Chain 1:    200        -9335.083             0.671            1.000
Chain 1:    300        -8021.874             0.502            0.343
Chain 1:    400        -8102.862             0.379            0.343
Chain 1:    500        -8070.428             0.304            0.164
Chain 1:    600        -7950.552             0.256            0.164
Chain 1:    700        -8018.872             0.221            0.015
Chain 1:    800        -7859.086             0.196            0.020
Chain 1:    900        -7772.497             0.175            0.015
Chain 1:   1000        -7965.430             0.160            0.020
Chain 1:   1100        -7986.497             0.060            0.015
Chain 1:   1200        -7874.572             0.027            0.014
Chain 1:   1300        -7832.006             0.012            0.011
Chain 1:   1400        -7854.461             0.011            0.011
Chain 1:   1500        -7944.117             0.012            0.011
Chain 1:   1600        -7885.855             0.011            0.011
Chain 1:   1700        -7831.836             0.011            0.011
Chain 1:   1800        -7809.239             0.009            0.007   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001446 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62034.949             1.000            1.000
Chain 1:    200       -17870.818             1.736            2.471
Chain 1:    300        -8862.153             1.496            1.017
Chain 1:    400        -8298.250             1.139            1.017
Chain 1:    500        -8525.210             0.916            1.000
Chain 1:    600        -8157.251             0.771            1.000
Chain 1:    700        -7801.394             0.668            0.068
Chain 1:    800        -8154.409             0.590            0.068
Chain 1:    900        -7934.184             0.527            0.046
Chain 1:   1000        -7604.795             0.479            0.046
Chain 1:   1100        -7633.466             0.379            0.045
Chain 1:   1200        -7665.281             0.132            0.043
Chain 1:   1300        -7752.244             0.032            0.043
Chain 1:   1400        -7815.293             0.026            0.028
Chain 1:   1500        -7550.364             0.027            0.035
Chain 1:   1600        -7629.373             0.023            0.028
Chain 1:   1700        -7536.964             0.020            0.012
Chain 1:   1800        -7556.191             0.016            0.011
Chain 1:   1900        -7580.518             0.013            0.010
Chain 1:   2000        -7565.597             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003246 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85870.437             1.000            1.000
Chain 1:    200       -13495.619             3.181            5.363
Chain 1:    300        -9858.262             2.244            1.000
Chain 1:    400       -10642.706             1.701            1.000
Chain 1:    500        -8809.756             1.403            0.369
Chain 1:    600        -8343.178             1.178            0.369
Chain 1:    700        -8204.651             1.012            0.208
Chain 1:    800        -8916.192             0.896            0.208
Chain 1:    900        -8693.136             0.799            0.080
Chain 1:   1000        -8516.795             0.721            0.080
Chain 1:   1100        -8641.732             0.623            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8198.215             0.092            0.056
Chain 1:   1300        -8418.217             0.058            0.054
Chain 1:   1400        -8576.419             0.052            0.026
Chain 1:   1500        -8408.382             0.033            0.026
Chain 1:   1600        -8522.675             0.029            0.021
Chain 1:   1700        -8598.011             0.028            0.021
Chain 1:   1800        -8180.462             0.025            0.021
Chain 1:   1900        -8278.397             0.024            0.020
Chain 1:   2000        -8252.239             0.022            0.018
Chain 1:   2100        -8376.440             0.022            0.018
Chain 1:   2200        -8189.063             0.019            0.018
Chain 1:   2300        -8272.941             0.017            0.015
Chain 1:   2400        -8342.391             0.016            0.013
Chain 1:   2500        -8288.359             0.015            0.012
Chain 1:   2600        -8288.752             0.014            0.010
Chain 1:   2700        -8205.901             0.014            0.010
Chain 1:   2800        -8167.350             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003747 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8388325.418             1.000            1.000
Chain 1:    200     -1580634.647             2.653            4.307
Chain 1:    300      -890297.842             2.027            1.000
Chain 1:    400      -457478.430             1.757            1.000
Chain 1:    500      -358340.811             1.461            0.946
Chain 1:    600      -233325.895             1.307            0.946
Chain 1:    700      -119417.641             1.256            0.946
Chain 1:    800       -86597.472             1.147            0.946
Chain 1:    900       -66900.679             1.052            0.775
Chain 1:   1000       -51671.853             0.976            0.775
Chain 1:   1100       -39119.711             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38293.607             0.480            0.379
Chain 1:   1300       -26217.443             0.448            0.379
Chain 1:   1400       -25933.950             0.355            0.321
Chain 1:   1500       -22512.826             0.342            0.321
Chain 1:   1600       -21727.021             0.292            0.295
Chain 1:   1700       -20596.676             0.203            0.294
Chain 1:   1800       -20540.003             0.165            0.152
Chain 1:   1900       -20866.218             0.137            0.055
Chain 1:   2000       -19375.195             0.115            0.055
Chain 1:   2100       -19613.638             0.084            0.036
Chain 1:   2200       -19840.522             0.083            0.036
Chain 1:   2300       -19457.358             0.039            0.020
Chain 1:   2400       -19229.374             0.039            0.020
Chain 1:   2500       -19031.560             0.025            0.016
Chain 1:   2600       -18661.569             0.024            0.016
Chain 1:   2700       -18618.463             0.018            0.012
Chain 1:   2800       -18335.376             0.020            0.015
Chain 1:   2900       -18616.707             0.020            0.015
Chain 1:   3000       -18602.828             0.012            0.012
Chain 1:   3100       -18687.846             0.011            0.012
Chain 1:   3200       -18378.469             0.012            0.015
Chain 1:   3300       -18583.233             0.011            0.012
Chain 1:   3400       -18058.125             0.013            0.015
Chain 1:   3500       -18670.139             0.015            0.015
Chain 1:   3600       -17976.642             0.017            0.015
Chain 1:   3700       -18363.625             0.019            0.017
Chain 1:   3800       -17323.111             0.023            0.021
Chain 1:   3900       -17319.277             0.021            0.021
Chain 1:   4000       -17436.552             0.022            0.021
Chain 1:   4100       -17350.333             0.022            0.021
Chain 1:   4200       -17166.517             0.022            0.021
Chain 1:   4300       -17304.941             0.021            0.021
Chain 1:   4400       -17261.715             0.019            0.011
Chain 1:   4500       -17164.263             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001704 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13273.355             1.000            1.000
Chain 1:    200       -10051.473             0.660            1.000
Chain 1:    300        -8709.911             0.492            0.321
Chain 1:    400        -8924.675             0.375            0.321
Chain 1:    500        -8809.992             0.302            0.154
Chain 1:    600        -8648.912             0.255            0.154
Chain 1:    700        -8545.233             0.220            0.024
Chain 1:    800        -8507.592             0.193            0.024
Chain 1:    900        -8516.643             0.172            0.019
Chain 1:   1000        -8668.151             0.157            0.019
Chain 1:   1100        -8646.281             0.057            0.017
Chain 1:   1200        -8575.134             0.026            0.013
Chain 1:   1300        -8488.431             0.011            0.012
Chain 1:   1400        -8500.942             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62557.668             1.000            1.000
Chain 1:    200       -18894.808             1.655            2.311
Chain 1:    300        -9362.830             1.443            1.018
Chain 1:    400       -10208.127             1.103            1.018
Chain 1:    500        -8497.046             0.923            1.000
Chain 1:    600        -9062.837             0.779            1.000
Chain 1:    700        -9116.902             0.669            0.201
Chain 1:    800        -8320.039             0.597            0.201
Chain 1:    900        -8122.048             0.534            0.096
Chain 1:   1000        -7921.513             0.483            0.096
Chain 1:   1100        -7701.907             0.386            0.083
Chain 1:   1200        -7889.331             0.157            0.062
Chain 1:   1300        -7834.225             0.056            0.029
Chain 1:   1400        -7820.476             0.048            0.025
Chain 1:   1500        -7623.519             0.030            0.025
Chain 1:   1600        -7745.466             0.025            0.024
Chain 1:   1700        -7675.540             0.026            0.024
Chain 1:   1800        -7770.401             0.017            0.024
Chain 1:   1900        -7659.772             0.016            0.016
Chain 1:   2000        -7768.297             0.015            0.014
Chain 1:   2100        -7578.412             0.015            0.014
Chain 1:   2200        -7885.005             0.016            0.014
Chain 1:   2300        -7749.436             0.017            0.016
Chain 1:   2400        -7577.772             0.020            0.017
Chain 1:   2500        -7662.207             0.018            0.016
Chain 1:   2600        -7588.356             0.017            0.014
Chain 1:   2700        -7455.878             0.018            0.017
Chain 1:   2800        -7625.243             0.019            0.018
Chain 1:   2900        -7413.084             0.021            0.022
Chain 1:   3000        -7570.836             0.021            0.022
Chain 1:   3100        -7550.424             0.019            0.021
Chain 1:   3200        -7805.024             0.019            0.021
Chain 1:   3300        -7462.447             0.021            0.022
Chain 1:   3400        -7579.661             0.021            0.021
Chain 1:   3500        -7488.383             0.021            0.021
Chain 1:   3600        -7520.829             0.020            0.021
Chain 1:   3700        -7468.436             0.019            0.021
Chain 1:   3800        -7430.746             0.017            0.015
Chain 1:   3900        -7434.079             0.015            0.012
Chain 1:   4000        -7426.970             0.013            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003213 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86982.366             1.000            1.000
Chain 1:    200       -14436.394             3.013            5.025
Chain 1:    300       -10693.843             2.125            1.000
Chain 1:    400       -12330.374             1.627            1.000
Chain 1:    500        -9271.622             1.368            0.350
Chain 1:    600        -9294.110             1.140            0.350
Chain 1:    700        -9114.162             0.980            0.330
Chain 1:    800        -9342.108             0.861            0.330
Chain 1:    900        -9485.542             0.767            0.133
Chain 1:   1000        -9126.063             0.694            0.133
Chain 1:   1100        -9465.339             0.597            0.039   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9012.215             0.100            0.039
Chain 1:   1300        -9322.218             0.068            0.036
Chain 1:   1400        -9158.584             0.057            0.033
Chain 1:   1500        -9159.744             0.024            0.024
Chain 1:   1600        -9269.117             0.025            0.024
Chain 1:   1700        -9322.907             0.023            0.024
Chain 1:   1800        -8872.842             0.026            0.033
Chain 1:   1900        -8981.459             0.026            0.033
Chain 1:   2000        -8974.509             0.022            0.018
Chain 1:   2100        -9151.403             0.020            0.018
Chain 1:   2200        -8875.839             0.018            0.018
Chain 1:   2300        -9063.024             0.017            0.018
Chain 1:   2400        -8876.975             0.017            0.019
Chain 1:   2500        -8954.348             0.018            0.019
Chain 1:   2600        -8868.475             0.018            0.019
Chain 1:   2700        -8899.801             0.018            0.019
Chain 1:   2800        -8851.810             0.013            0.012
Chain 1:   2900        -8960.800             0.013            0.012
Chain 1:   3000        -8910.149             0.014            0.012
Chain 1:   3100        -8842.894             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003779 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408023.458             1.000            1.000
Chain 1:    200     -1583636.191             2.655            4.309
Chain 1:    300      -891085.257             2.029            1.000
Chain 1:    400      -458102.615             1.758            1.000
Chain 1:    500      -358532.501             1.462            0.945
Chain 1:    600      -233784.384             1.307            0.945
Chain 1:    700      -120133.546             1.256            0.945
Chain 1:    800       -87364.289             1.146            0.945
Chain 1:    900       -67732.667             1.050            0.777
Chain 1:   1000       -52550.158             0.974            0.777
Chain 1:   1100       -40036.590             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39220.673             0.477            0.375
Chain 1:   1300       -27172.209             0.443            0.375
Chain 1:   1400       -26894.375             0.350            0.313
Chain 1:   1500       -23479.685             0.337            0.313
Chain 1:   1600       -22696.678             0.287            0.290
Chain 1:   1700       -21569.089             0.197            0.289
Chain 1:   1800       -21513.464             0.160            0.145
Chain 1:   1900       -21840.245             0.133            0.052
Chain 1:   2000       -20349.600             0.111            0.052
Chain 1:   2100       -20588.082             0.081            0.034
Chain 1:   2200       -20815.048             0.080            0.034
Chain 1:   2300       -20431.681             0.037            0.019
Chain 1:   2400       -20203.547             0.038            0.019
Chain 1:   2500       -20005.534             0.024            0.015
Chain 1:   2600       -19635.024             0.022            0.015
Chain 1:   2700       -19591.866             0.017            0.012
Chain 1:   2800       -19308.404             0.019            0.015
Chain 1:   2900       -19589.962             0.019            0.014
Chain 1:   3000       -19576.172             0.011            0.012
Chain 1:   3100       -19661.200             0.011            0.011
Chain 1:   3200       -19351.447             0.011            0.014
Chain 1:   3300       -19556.550             0.010            0.011
Chain 1:   3400       -19030.642             0.012            0.014
Chain 1:   3500       -19643.683             0.014            0.015
Chain 1:   3600       -18948.918             0.016            0.015
Chain 1:   3700       -19336.750             0.018            0.016
Chain 1:   3800       -18294.135             0.022            0.020
Chain 1:   3900       -18290.234             0.020            0.020
Chain 1:   4000       -18407.555             0.021            0.020
Chain 1:   4100       -18321.133             0.021            0.020
Chain 1:   4200       -18136.946             0.020            0.020
Chain 1:   4300       -18275.672             0.020            0.020
Chain 1:   4400       -18232.070             0.018            0.010
Chain 1:   4500       -18134.544             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001892 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48504.762             1.000            1.000
Chain 1:    200       -17434.642             1.391            1.782
Chain 1:    300       -17567.739             0.930            1.000
Chain 1:    400       -12674.476             0.794            1.000
Chain 1:    500       -14003.411             0.654            0.386
Chain 1:    600       -12054.739             0.572            0.386
Chain 1:    700       -14755.757             0.516            0.183
Chain 1:    800       -11698.449             0.485            0.261
Chain 1:    900       -17372.795             0.467            0.261
Chain 1:   1000       -11498.627             0.471            0.327
Chain 1:   1100       -30352.918             0.434            0.327
Chain 1:   1200       -12607.178             0.396            0.327
Chain 1:   1300       -12069.822             0.400            0.327
Chain 1:   1400       -14990.395             0.381            0.261
Chain 1:   1500       -10507.684             0.414            0.327
Chain 1:   1600       -13427.390             0.419            0.327
Chain 1:   1700       -11780.842             0.415            0.327
Chain 1:   1800       -20286.313             0.431            0.419
Chain 1:   1900        -9501.128             0.512            0.427   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2000       -10400.913             0.469            0.419
Chain 1:   2100       -10079.316             0.410            0.217
Chain 1:   2200        -9529.524             0.275            0.195
Chain 1:   2300       -16808.226             0.314            0.217
Chain 1:   2400        -8734.485             0.387            0.419
Chain 1:   2500       -12013.114             0.372            0.273
Chain 1:   2600        -9760.814             0.373            0.273
Chain 1:   2700        -9754.984             0.359            0.273
Chain 1:   2800        -8880.453             0.327            0.231
Chain 1:   2900        -9158.058             0.217            0.098
Chain 1:   3000       -14969.018             0.247            0.231
Chain 1:   3100        -9734.273             0.297            0.273
Chain 1:   3200       -15386.109             0.328            0.367
Chain 1:   3300        -8828.815             0.359            0.367
Chain 1:   3400        -9053.181             0.269            0.273
Chain 1:   3500       -15466.979             0.284            0.367
Chain 1:   3600       -17248.916             0.271            0.367
Chain 1:   3700        -9267.967             0.357            0.388
Chain 1:   3800        -9547.805             0.350            0.388
Chain 1:   3900       -13459.615             0.376            0.388
Chain 1:   4000        -8969.645             0.387            0.415
Chain 1:   4100       -11340.815             0.354            0.367
Chain 1:   4200       -12716.310             0.328            0.291
Chain 1:   4300        -9936.191             0.282            0.280
Chain 1:   4400       -14966.344             0.313            0.291
Chain 1:   4500        -9117.567             0.336            0.291
Chain 1:   4600       -13079.159             0.356            0.303
Chain 1:   4700       -13173.926             0.271            0.291
Chain 1:   4800       -11767.018             0.280            0.291
Chain 1:   4900        -8368.119             0.291            0.303
Chain 1:   5000       -10681.933             0.263            0.280
Chain 1:   5100        -8580.892             0.266            0.280
Chain 1:   5200       -14455.958             0.296            0.303
Chain 1:   5300        -8304.663             0.342            0.336
Chain 1:   5400       -11828.552             0.338            0.303
Chain 1:   5500        -8532.259             0.313            0.303
Chain 1:   5600        -9929.204             0.297            0.298
Chain 1:   5700        -8549.184             0.312            0.298
Chain 1:   5800       -10376.755             0.318            0.298
Chain 1:   5900        -8551.526             0.298            0.245
Chain 1:   6000       -12844.859             0.310            0.298
Chain 1:   6100        -8374.094             0.339            0.334
Chain 1:   6200        -9643.563             0.312            0.298
Chain 1:   6300       -13507.782             0.266            0.286
Chain 1:   6400        -8050.385             0.304            0.286
Chain 1:   6500        -8715.033             0.273            0.213
Chain 1:   6600        -9746.827             0.270            0.213
Chain 1:   6700       -12614.881             0.276            0.227
Chain 1:   6800        -8994.598             0.299            0.286
Chain 1:   6900        -9242.846             0.280            0.286
Chain 1:   7000        -8205.703             0.259            0.227
Chain 1:   7100        -8226.492             0.206            0.132
Chain 1:   7200        -8686.304             0.198            0.126
Chain 1:   7300        -9125.623             0.175            0.106
Chain 1:   7400        -8141.004             0.119            0.106
Chain 1:   7500        -8021.362             0.113            0.106
Chain 1:   7600       -10209.961             0.124            0.121
Chain 1:   7700        -8030.147             0.128            0.121
Chain 1:   7800        -9167.850             0.100            0.121
Chain 1:   7900        -8673.664             0.103            0.121
Chain 1:   8000        -9437.233             0.099            0.081
Chain 1:   8100        -8476.525             0.110            0.113
Chain 1:   8200        -8567.366             0.106            0.113
Chain 1:   8300        -8055.891             0.107            0.113
Chain 1:   8400       -11801.837             0.127            0.113
Chain 1:   8500       -10548.601             0.137            0.119
Chain 1:   8600        -8278.289             0.143            0.119
Chain 1:   8700        -9071.935             0.125            0.113
Chain 1:   8800        -8385.747             0.121            0.087
Chain 1:   8900        -8386.488             0.115            0.087
Chain 1:   9000       -10405.618             0.126            0.113
Chain 1:   9100        -8367.600             0.139            0.119
Chain 1:   9200       -12492.358             0.171            0.194
Chain 1:   9300        -8308.138             0.215            0.244
Chain 1:   9400        -8041.232             0.187            0.194
Chain 1:   9500       -10756.762             0.200            0.244
Chain 1:   9600        -9917.777             0.181            0.194
Chain 1:   9700        -8244.835             0.193            0.203
Chain 1:   9800        -8791.975             0.191            0.203
Chain 1:   9900        -9816.520             0.201            0.203
Chain 1:   10000        -8860.350             0.193            0.203
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00168 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61295.868             1.000            1.000
Chain 1:    200       -17606.988             1.741            2.481
Chain 1:    300        -8683.204             1.503            1.028
Chain 1:    400        -9084.816             1.138            1.028
Chain 1:    500        -7647.838             0.948            1.000
Chain 1:    600        -7955.078             0.797            1.000
Chain 1:    700        -7634.525             0.689            0.188
Chain 1:    800        -8068.436             0.609            0.188
Chain 1:    900        -7720.901             0.547            0.054
Chain 1:   1000        -7633.345             0.493            0.054
Chain 1:   1100        -7439.959             0.396            0.045
Chain 1:   1200        -7561.677             0.149            0.044
Chain 1:   1300        -7570.706             0.047            0.042
Chain 1:   1400        -7565.553             0.042            0.039
Chain 1:   1500        -7459.959             0.025            0.026
Chain 1:   1600        -7411.548             0.022            0.016
Chain 1:   1700        -7426.740             0.018            0.014
Chain 1:   1800        -7491.892             0.013            0.011
Chain 1:   1900        -7458.238             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86091.125             1.000            1.000
Chain 1:    200       -13303.702             3.236            5.471
Chain 1:    300        -9674.927             2.282            1.000
Chain 1:    400       -10510.030             1.731            1.000
Chain 1:    500        -8611.633             1.429            0.375
Chain 1:    600        -8181.299             1.200            0.375
Chain 1:    700        -8455.097             1.033            0.220
Chain 1:    800        -8779.198             0.909            0.220
Chain 1:    900        -8493.230             0.811            0.079
Chain 1:   1000        -8336.795             0.732            0.079
Chain 1:   1100        -8541.384             0.634            0.053   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8039.294             0.094            0.053
Chain 1:   1300        -8385.598             0.060            0.041
Chain 1:   1400        -8383.127             0.052            0.037
Chain 1:   1500        -8257.607             0.032            0.034
Chain 1:   1600        -8363.217             0.028            0.032
Chain 1:   1700        -8449.071             0.026            0.024
Chain 1:   1800        -8040.197             0.027            0.024
Chain 1:   1900        -8136.326             0.025            0.019
Chain 1:   2000        -8108.779             0.023            0.015
Chain 1:   2100        -8230.127             0.022            0.015
Chain 1:   2200        -8057.769             0.018            0.015
Chain 1:   2300        -8135.746             0.015            0.013
Chain 1:   2400        -8200.850             0.016            0.013
Chain 1:   2500        -8146.423             0.015            0.012
Chain 1:   2600        -8145.001             0.014            0.010
Chain 1:   2700        -8061.850             0.014            0.010
Chain 1:   2800        -8027.467             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003237 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393651.989             1.000            1.000
Chain 1:    200     -1580359.246             2.656            4.311
Chain 1:    300      -890829.289             2.028            1.000
Chain 1:    400      -457847.539             1.758            1.000
Chain 1:    500      -358648.557             1.462            0.946
Chain 1:    600      -233434.602             1.307            0.946
Chain 1:    700      -119368.379             1.257            0.946
Chain 1:    800       -86493.278             1.147            0.946
Chain 1:    900       -66766.763             1.053            0.774
Chain 1:   1000       -51508.540             0.977            0.774
Chain 1:   1100       -38938.009             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38106.003             0.480            0.380
Chain 1:   1300       -26015.711             0.450            0.380
Chain 1:   1400       -25728.410             0.356            0.323
Chain 1:   1500       -22304.254             0.344            0.323
Chain 1:   1600       -21517.253             0.294            0.296
Chain 1:   1700       -20385.714             0.204            0.295
Chain 1:   1800       -20328.435             0.166            0.154
Chain 1:   1900       -20654.489             0.138            0.056
Chain 1:   2000       -19163.159             0.116            0.056
Chain 1:   2100       -19401.532             0.085            0.037
Chain 1:   2200       -19628.454             0.084            0.037
Chain 1:   2300       -19245.273             0.040            0.020
Chain 1:   2400       -19017.397             0.040            0.020
Chain 1:   2500       -18819.611             0.025            0.016
Chain 1:   2600       -18449.701             0.024            0.016
Chain 1:   2700       -18406.612             0.019            0.012
Chain 1:   2800       -18123.689             0.020            0.016
Chain 1:   2900       -18404.901             0.020            0.015
Chain 1:   3000       -18390.977             0.012            0.012
Chain 1:   3100       -18476.023             0.011            0.012
Chain 1:   3200       -18166.689             0.012            0.015
Chain 1:   3300       -18371.412             0.011            0.012
Chain 1:   3400       -17846.476             0.013            0.015
Chain 1:   3500       -18458.219             0.015            0.016
Chain 1:   3600       -17765.050             0.017            0.016
Chain 1:   3700       -18151.846             0.019            0.017
Chain 1:   3800       -17111.832             0.023            0.021
Chain 1:   3900       -17108.031             0.022            0.021
Chain 1:   4000       -17225.272             0.022            0.021
Chain 1:   4100       -17139.107             0.022            0.021
Chain 1:   4200       -16955.408             0.022            0.021
Chain 1:   4300       -17093.737             0.021            0.021
Chain 1:   4400       -17050.633             0.019            0.011
Chain 1:   4500       -16953.198             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48393.313             1.000            1.000
Chain 1:    200       -19286.448             1.255            1.509
Chain 1:    300       -20700.908             0.859            1.000
Chain 1:    400       -14197.697             0.759            1.000
Chain 1:    500       -12699.212             0.631            0.458
Chain 1:    600       -13581.981             0.536            0.458
Chain 1:    700       -12204.722             0.476            0.118
Chain 1:    800       -13477.119             0.428            0.118
Chain 1:    900       -11205.496             0.403            0.118
Chain 1:   1000       -10008.223             0.375            0.120
Chain 1:   1100       -11842.369             0.290            0.120
Chain 1:   1200       -10754.112             0.150            0.118
Chain 1:   1300       -11268.624             0.147            0.118
Chain 1:   1400       -15867.554             0.130            0.118
Chain 1:   1500        -9409.132             0.187            0.120
Chain 1:   1600        -9599.174             0.183            0.120
Chain 1:   1700       -11992.903             0.191            0.155
Chain 1:   1800        -9481.964             0.208            0.200
Chain 1:   1900       -10833.785             0.201            0.155
Chain 1:   2000       -10254.560             0.194            0.155
Chain 1:   2100        -9363.769             0.188            0.125
Chain 1:   2200        -9097.106             0.181            0.125
Chain 1:   2300       -11726.578             0.199            0.200
Chain 1:   2400        -8628.598             0.206            0.200
Chain 1:   2500        -9626.409             0.148            0.125
Chain 1:   2600        -9217.734             0.150            0.125
Chain 1:   2700       -14078.699             0.165            0.125
Chain 1:   2800        -8692.180             0.200            0.125
Chain 1:   2900       -10245.284             0.203            0.152
Chain 1:   3000       -16230.600             0.234            0.224
Chain 1:   3100        -9075.471             0.303            0.345
Chain 1:   3200        -8764.970             0.304            0.345
Chain 1:   3300       -10893.055             0.301            0.345
Chain 1:   3400        -8449.632             0.294            0.289
Chain 1:   3500       -13175.262             0.320            0.345
Chain 1:   3600        -9147.462             0.359            0.359
Chain 1:   3700        -8700.828             0.330            0.359
Chain 1:   3800        -8219.267             0.274            0.289
Chain 1:   3900       -11664.660             0.288            0.295
Chain 1:   4000        -8448.578             0.289            0.295
Chain 1:   4100        -8309.214             0.212            0.289
Chain 1:   4200       -12455.116             0.242            0.295
Chain 1:   4300       -11450.442             0.231            0.295
Chain 1:   4400        -9504.665             0.223            0.295
Chain 1:   4500        -8639.297             0.197            0.205
Chain 1:   4600       -11603.374             0.178            0.205
Chain 1:   4700       -14641.983             0.194            0.208
Chain 1:   4800        -8447.632             0.261            0.255
Chain 1:   4900        -8308.917             0.234            0.208
Chain 1:   5000        -8311.830             0.196            0.205
Chain 1:   5100       -16580.333             0.244            0.208
Chain 1:   5200        -9449.866             0.286            0.208
Chain 1:   5300       -10055.496             0.283            0.208
Chain 1:   5400        -8614.126             0.279            0.208
Chain 1:   5500        -8500.643             0.271            0.208
Chain 1:   5600       -12238.319             0.276            0.208
Chain 1:   5700        -8980.569             0.291            0.305
Chain 1:   5800        -8760.408             0.220            0.167
Chain 1:   5900        -8892.762             0.220            0.167
Chain 1:   6000        -8412.944             0.226            0.167
Chain 1:   6100       -10382.631             0.195            0.167
Chain 1:   6200        -9943.855             0.124            0.060
Chain 1:   6300        -8146.390             0.140            0.167
Chain 1:   6400       -12240.982             0.157            0.190
Chain 1:   6500        -9744.219             0.181            0.221
Chain 1:   6600        -7984.977             0.173            0.220
Chain 1:   6700        -7996.578             0.136            0.190
Chain 1:   6800       -12848.557             0.172            0.220
Chain 1:   6900        -8357.218             0.224            0.221
Chain 1:   7000        -7803.648             0.225            0.221
Chain 1:   7100       -11645.999             0.239            0.256
Chain 1:   7200        -8861.771             0.266            0.314
Chain 1:   7300        -8251.366             0.252            0.314
Chain 1:   7400        -9619.393             0.232            0.256
Chain 1:   7500        -8879.589             0.215            0.220
Chain 1:   7600       -10733.961             0.210            0.173
Chain 1:   7700        -7822.468             0.247            0.314
Chain 1:   7800        -8622.423             0.219            0.173
Chain 1:   7900        -8457.839             0.167            0.142
Chain 1:   8000        -7918.080             0.167            0.142
Chain 1:   8100        -9335.123             0.149            0.142
Chain 1:   8200       -10478.256             0.129            0.109
Chain 1:   8300        -8222.687             0.149            0.142
Chain 1:   8400        -7827.591             0.139            0.109
Chain 1:   8500        -7814.155             0.131            0.109
Chain 1:   8600        -8393.555             0.121            0.093
Chain 1:   8700        -8353.546             0.084            0.069
Chain 1:   8800        -9260.205             0.085            0.069
Chain 1:   8900       -12231.780             0.107            0.098
Chain 1:   9000        -8696.176             0.141            0.109
Chain 1:   9100        -8481.753             0.128            0.098
Chain 1:   9200        -8200.702             0.121            0.069
Chain 1:   9300        -9730.645             0.109            0.069
Chain 1:   9400       -11095.365             0.116            0.098
Chain 1:   9500        -9327.643             0.135            0.123
Chain 1:   9600        -8053.608             0.144            0.157
Chain 1:   9700        -9277.417             0.157            0.157
Chain 1:   9800        -8467.927             0.156            0.157
Chain 1:   9900        -9469.336             0.143            0.132
Chain 1:   10000        -7700.704             0.125            0.132
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001509 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57487.153             1.000            1.000
Chain 1:    200       -17079.993             1.683            2.366
Chain 1:    300        -8410.413             1.466            1.031
Chain 1:    400        -7841.490             1.117            1.031
Chain 1:    500        -8240.556             0.904            1.000
Chain 1:    600        -8495.046             0.758            1.000
Chain 1:    700        -7712.155             0.664            0.102
Chain 1:    800        -7889.989             0.584            0.102
Chain 1:    900        -7879.678             0.519            0.073
Chain 1:   1000        -7604.928             0.471            0.073
Chain 1:   1100        -7651.109             0.372            0.048
Chain 1:   1200        -7568.083             0.136            0.036
Chain 1:   1300        -7607.817             0.033            0.030
Chain 1:   1400        -7587.193             0.026            0.023
Chain 1:   1500        -7552.649             0.022            0.011
Chain 1:   1600        -7473.685             0.020            0.011
Chain 1:   1700        -7448.594             0.010            0.006   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86093.081             1.000            1.000
Chain 1:    200       -12917.329             3.332            5.665
Chain 1:    300        -9399.236             2.346            1.000
Chain 1:    400       -10268.431             1.781            1.000
Chain 1:    500        -8281.002             1.473            0.374
Chain 1:    600        -7988.753             1.233            0.374
Chain 1:    700        -8287.967             1.062            0.240
Chain 1:    800        -8462.380             0.932            0.240
Chain 1:    900        -8313.907             0.831            0.085
Chain 1:   1000        -7996.913             0.751            0.085
Chain 1:   1100        -8293.391             0.655            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7956.675             0.093            0.040
Chain 1:   1300        -8171.460             0.058            0.037
Chain 1:   1400        -8159.213             0.050            0.036
Chain 1:   1500        -8063.984             0.027            0.036
Chain 1:   1600        -8151.348             0.024            0.026
Chain 1:   1700        -8256.819             0.022            0.021
Chain 1:   1800        -7868.742             0.025            0.026
Chain 1:   1900        -7968.464             0.024            0.026
Chain 1:   2000        -7938.390             0.021            0.013
Chain 1:   2100        -8080.311             0.019            0.013
Chain 1:   2200        -7860.043             0.017            0.013
Chain 1:   2300        -8002.379             0.017            0.013
Chain 1:   2400        -7888.049             0.018            0.014
Chain 1:   2500        -7945.240             0.017            0.014
Chain 1:   2600        -7958.946             0.017            0.014
Chain 1:   2700        -7880.601             0.016            0.014
Chain 1:   2800        -7863.591             0.012            0.013
Chain 1:   2900        -7872.635             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8412564.278             1.000            1.000
Chain 1:    200     -1589556.354             2.646            4.292
Chain 1:    300      -891022.362             2.025            1.000
Chain 1:    400      -457159.776             1.756            1.000
Chain 1:    500      -357020.394             1.461            0.949
Chain 1:    600      -231934.179             1.308            0.949
Chain 1:    700      -118348.092             1.258            0.949
Chain 1:    800       -85627.683             1.148            0.949
Chain 1:    900       -66011.384             1.054            0.784
Chain 1:   1000       -50841.084             0.978            0.784
Chain 1:   1100       -38357.494             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37532.606             0.484            0.382
Chain 1:   1300       -25541.439             0.452            0.382
Chain 1:   1400       -25262.483             0.359            0.325
Chain 1:   1500       -21863.853             0.346            0.325
Chain 1:   1600       -21083.634             0.296            0.298
Chain 1:   1700       -19964.324             0.205            0.297
Chain 1:   1800       -19909.701             0.167            0.155
Chain 1:   1900       -20235.146             0.139            0.056
Chain 1:   2000       -18751.208             0.117            0.056
Chain 1:   2100       -18989.376             0.086            0.037
Chain 1:   2200       -19214.739             0.085            0.037
Chain 1:   2300       -18833.049             0.040            0.020
Chain 1:   2400       -18605.443             0.040            0.020
Chain 1:   2500       -18407.302             0.026            0.016
Chain 1:   2600       -18038.476             0.024            0.016
Chain 1:   2700       -17995.711             0.019            0.013
Chain 1:   2800       -17712.817             0.020            0.016
Chain 1:   2900       -17993.663             0.020            0.016
Chain 1:   3000       -17979.929             0.012            0.013
Chain 1:   3100       -18064.818             0.011            0.012
Chain 1:   3200       -17756.050             0.012            0.016
Chain 1:   3300       -17960.339             0.011            0.012
Chain 1:   3400       -17436.193             0.013            0.016
Chain 1:   3500       -18046.631             0.015            0.016
Chain 1:   3600       -17355.126             0.017            0.016
Chain 1:   3700       -17740.547             0.019            0.017
Chain 1:   3800       -16703.090             0.024            0.022
Chain 1:   3900       -16699.264             0.022            0.022
Chain 1:   4000       -16816.589             0.023            0.022
Chain 1:   4100       -16730.491             0.023            0.022
Chain 1:   4200       -16547.338             0.022            0.022
Chain 1:   4300       -16685.326             0.022            0.022
Chain 1:   4400       -16642.654             0.019            0.011
Chain 1:   4500       -16545.242             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001405 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49063.640             1.000            1.000
Chain 1:    200       -19625.353             1.250            1.500
Chain 1:    300       -22384.739             0.874            1.000
Chain 1:    400       -29484.223             0.716            1.000
Chain 1:    500       -12925.228             0.829            1.000
Chain 1:    600       -17039.634             0.731            1.000
Chain 1:    700       -14440.728             0.652            0.241
Chain 1:    800       -16006.143             0.583            0.241
Chain 1:    900       -19925.158             0.540            0.241
Chain 1:   1000       -17921.018             0.497            0.241
Chain 1:   1100       -16442.608             0.406            0.197
Chain 1:   1200       -15427.828             0.263            0.180
Chain 1:   1300       -10942.661             0.292            0.197
Chain 1:   1400       -11038.513             0.268            0.180
Chain 1:   1500       -11926.565             0.148            0.112
Chain 1:   1600        -9984.759             0.143            0.112
Chain 1:   1700       -18716.691             0.172            0.112
Chain 1:   1800       -10059.281             0.248            0.194
Chain 1:   1900       -10243.728             0.230            0.112
Chain 1:   2000        -9829.647             0.223            0.090
Chain 1:   2100       -10359.562             0.219            0.074
Chain 1:   2200       -11045.834             0.219            0.074
Chain 1:   2300        -9420.241             0.195            0.074
Chain 1:   2400        -9853.101             0.199            0.074
Chain 1:   2500        -9588.454             0.194            0.062
Chain 1:   2600       -10217.009             0.181            0.062
Chain 1:   2700       -11220.009             0.143            0.062
Chain 1:   2800       -10269.073             0.066            0.062
Chain 1:   2900        -9581.350             0.071            0.062
Chain 1:   3000        -9190.114             0.072            0.062
Chain 1:   3100       -12593.325             0.093            0.072
Chain 1:   3200        -9671.499             0.117            0.089
Chain 1:   3300        -9565.902             0.101            0.072
Chain 1:   3400       -16802.818             0.140            0.089
Chain 1:   3500       -12754.129             0.169            0.093
Chain 1:   3600        -9151.457             0.202            0.270
Chain 1:   3700        -9276.878             0.195            0.270
Chain 1:   3800        -8716.757             0.192            0.270
Chain 1:   3900        -8837.231             0.186            0.270
Chain 1:   4000       -18674.020             0.234            0.302
Chain 1:   4100        -9147.830             0.311            0.317
Chain 1:   4200        -8928.679             0.284            0.317
Chain 1:   4300        -9800.759             0.291            0.317
Chain 1:   4400        -9005.730             0.257            0.089
Chain 1:   4500        -9790.278             0.234            0.088
Chain 1:   4600       -14623.691             0.227            0.088
Chain 1:   4700       -13379.835             0.235            0.089
Chain 1:   4800        -8805.188             0.281            0.093
Chain 1:   4900        -9112.733             0.283            0.093
Chain 1:   5000       -11031.877             0.247            0.093
Chain 1:   5100        -9003.326             0.166            0.093
Chain 1:   5200        -9242.803             0.166            0.093
Chain 1:   5300       -10922.188             0.172            0.154
Chain 1:   5400       -13653.322             0.184            0.174
Chain 1:   5500       -10896.442             0.201            0.200
Chain 1:   5600       -14636.774             0.193            0.200
Chain 1:   5700        -9114.963             0.245            0.225
Chain 1:   5800       -12468.542             0.220            0.225
Chain 1:   5900        -9940.622             0.242            0.253
Chain 1:   6000        -8905.755             0.236            0.253
Chain 1:   6100        -8483.476             0.218            0.253
Chain 1:   6200        -8601.991             0.217            0.253
Chain 1:   6300        -9508.889             0.211            0.253
Chain 1:   6400       -11914.897             0.211            0.253
Chain 1:   6500        -8936.930             0.219            0.254
Chain 1:   6600        -9128.167             0.196            0.202
Chain 1:   6700       -11389.729             0.155            0.199
Chain 1:   6800       -14315.855             0.149            0.199
Chain 1:   6900       -11553.058             0.147            0.199
Chain 1:   7000       -15367.647             0.161            0.202
Chain 1:   7100        -9936.195             0.210            0.204
Chain 1:   7200       -12627.871             0.230            0.213
Chain 1:   7300        -8754.607             0.265            0.239
Chain 1:   7400        -9004.067             0.247            0.239
Chain 1:   7500        -9364.429             0.218            0.213
Chain 1:   7600        -8764.488             0.223            0.213
Chain 1:   7700        -8938.401             0.205            0.213
Chain 1:   7800       -11995.907             0.210            0.239
Chain 1:   7900        -8399.368             0.229            0.248
Chain 1:   8000        -8259.017             0.206            0.213
Chain 1:   8100        -8530.617             0.154            0.068
Chain 1:   8200        -8903.579             0.137            0.042
Chain 1:   8300        -8671.389             0.095            0.038
Chain 1:   8400       -12140.268             0.121            0.042
Chain 1:   8500       -11695.600             0.121            0.042
Chain 1:   8600        -8674.704             0.149            0.042
Chain 1:   8700        -8656.408             0.147            0.042
Chain 1:   8800        -8431.732             0.125            0.038
Chain 1:   8900        -9213.227             0.090            0.038
Chain 1:   9000       -10813.289             0.103            0.042
Chain 1:   9100        -9294.398             0.117            0.085
Chain 1:   9200        -8762.592             0.118            0.085
Chain 1:   9300        -8709.178             0.116            0.085
Chain 1:   9400        -9140.051             0.093            0.061
Chain 1:   9500        -8072.460             0.102            0.085
Chain 1:   9600        -8535.145             0.073            0.061
Chain 1:   9700       -11204.065             0.096            0.085
Chain 1:   9800       -10825.890             0.097            0.085
Chain 1:   9900       -10500.870             0.092            0.061
Chain 1:   10000       -10134.311             0.080            0.054
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61886.573             1.000            1.000
Chain 1:    200       -18005.480             1.719            2.437
Chain 1:    300        -8959.751             1.482            1.010
Chain 1:    400        -9451.964             1.125            1.010
Chain 1:    500        -8106.861             0.933            1.000
Chain 1:    600        -8496.069             0.785            1.000
Chain 1:    700        -8241.630             0.677            0.166
Chain 1:    800        -7880.995             0.598            0.166
Chain 1:    900        -8061.964             0.534            0.052
Chain 1:   1000        -7862.928             0.483            0.052
Chain 1:   1100        -7759.554             0.385            0.046
Chain 1:   1200        -7871.746             0.143            0.046
Chain 1:   1300        -7684.716             0.044            0.031
Chain 1:   1400        -7868.972             0.041            0.025
Chain 1:   1500        -7666.098             0.027            0.025
Chain 1:   1600        -7852.302             0.025            0.024
Chain 1:   1700        -7487.926             0.027            0.024
Chain 1:   1800        -7699.756             0.025            0.024
Chain 1:   1900        -7654.208             0.023            0.024
Chain 1:   2000        -7698.128             0.021            0.024
Chain 1:   2100        -7628.520             0.021            0.024
Chain 1:   2200        -7760.205             0.021            0.024
Chain 1:   2300        -7664.868             0.020            0.023
Chain 1:   2400        -7666.390             0.018            0.017
Chain 1:   2500        -7837.927             0.017            0.017
Chain 1:   2600        -7574.619             0.018            0.017
Chain 1:   2700        -7610.474             0.014            0.012
Chain 1:   2800        -7625.601             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0041 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86839.387             1.000            1.000
Chain 1:    200       -13688.299             3.172            5.344
Chain 1:    300       -10005.600             2.237            1.000
Chain 1:    400       -10926.973             1.699            1.000
Chain 1:    500        -9000.214             1.402            0.368
Chain 1:    600        -8433.559             1.180            0.368
Chain 1:    700        -8878.729             1.018            0.214
Chain 1:    800        -9031.476             0.893            0.214
Chain 1:    900        -8779.491             0.797            0.084
Chain 1:   1000        -8771.959             0.717            0.084
Chain 1:   1100        -8677.696             0.619            0.067   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8419.001             0.087            0.050
Chain 1:   1300        -8694.883             0.054            0.032
Chain 1:   1400        -8647.864             0.046            0.031
Chain 1:   1500        -8538.459             0.026            0.029
Chain 1:   1600        -8645.888             0.020            0.017
Chain 1:   1700        -8723.936             0.016            0.013
Chain 1:   1800        -8295.421             0.019            0.013
Chain 1:   1900        -8398.774             0.018            0.012
Chain 1:   2000        -8373.755             0.018            0.012
Chain 1:   2100        -8502.392             0.018            0.013
Chain 1:   2200        -8299.896             0.018            0.013
Chain 1:   2300        -8395.057             0.016            0.012
Chain 1:   2400        -8461.815             0.016            0.012
Chain 1:   2500        -8407.793             0.015            0.012
Chain 1:   2600        -8410.947             0.014            0.011
Chain 1:   2700        -8326.787             0.014            0.011
Chain 1:   2800        -8284.590             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003648 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413272.620             1.000            1.000
Chain 1:    200     -1585062.464             2.654            4.308
Chain 1:    300      -890916.919             2.029            1.000
Chain 1:    400      -457672.006             1.758            1.000
Chain 1:    500      -358144.202             1.462            0.947
Chain 1:    600      -233123.666             1.308            0.947
Chain 1:    700      -119371.687             1.257            0.947
Chain 1:    800       -86607.768             1.147            0.947
Chain 1:    900       -66958.333             1.052            0.779
Chain 1:   1000       -51762.532             0.977            0.779
Chain 1:   1100       -39245.788             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38425.357             0.480            0.378
Chain 1:   1300       -26382.567             0.448            0.378
Chain 1:   1400       -26103.100             0.354            0.319
Chain 1:   1500       -22690.867             0.341            0.319
Chain 1:   1600       -21907.933             0.291            0.294
Chain 1:   1700       -20781.437             0.201            0.293
Chain 1:   1800       -20725.679             0.164            0.150
Chain 1:   1900       -21052.071             0.136            0.054
Chain 1:   2000       -19562.745             0.114            0.054
Chain 1:   2100       -19801.185             0.084            0.036
Chain 1:   2200       -20027.854             0.083            0.036
Chain 1:   2300       -19644.786             0.039            0.019
Chain 1:   2400       -19416.798             0.039            0.019
Chain 1:   2500       -19218.834             0.025            0.016
Chain 1:   2600       -18848.827             0.023            0.016
Chain 1:   2700       -18805.689             0.018            0.012
Chain 1:   2800       -18522.509             0.019            0.015
Chain 1:   2900       -18803.821             0.019            0.015
Chain 1:   3000       -18789.972             0.012            0.012
Chain 1:   3100       -18875.026             0.011            0.012
Chain 1:   3200       -18565.530             0.012            0.015
Chain 1:   3300       -18770.377             0.011            0.012
Chain 1:   3400       -18245.067             0.012            0.015
Chain 1:   3500       -18857.300             0.015            0.015
Chain 1:   3600       -18163.453             0.016            0.015
Chain 1:   3700       -18550.696             0.018            0.017
Chain 1:   3800       -17509.593             0.023            0.021
Chain 1:   3900       -17505.696             0.021            0.021
Chain 1:   4000       -17623.002             0.022            0.021
Chain 1:   4100       -17536.762             0.022            0.021
Chain 1:   4200       -17352.791             0.021            0.021
Chain 1:   4300       -17491.325             0.021            0.021
Chain 1:   4400       -17448.013             0.018            0.011
Chain 1:   4500       -17350.501             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001276 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12923.509             1.000            1.000
Chain 1:    200        -9422.015             0.686            1.000
Chain 1:    300        -8138.275             0.510            0.372
Chain 1:    400        -8117.421             0.383            0.372
Chain 1:    500        -8010.625             0.309            0.158
Chain 1:    600        -7885.819             0.260            0.158
Chain 1:    700        -7799.718             0.225            0.016
Chain 1:    800        -7827.179             0.197            0.016
Chain 1:    900        -7896.797             0.176            0.013
Chain 1:   1000        -7855.379             0.159            0.013
Chain 1:   1100        -7821.175             0.059            0.011
Chain 1:   1200        -7828.687             0.022            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001428 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58011.235             1.000            1.000
Chain 1:    200       -17693.450             1.639            2.279
Chain 1:    300        -8673.925             1.440            1.040
Chain 1:    400        -8046.253             1.099            1.040
Chain 1:    500        -8416.182             0.888            1.000
Chain 1:    600        -8246.127             0.744            1.000
Chain 1:    700        -8448.969             0.641            0.078
Chain 1:    800        -8258.044             0.564            0.078
Chain 1:    900        -7919.328             0.506            0.044
Chain 1:   1000        -7835.856             0.456            0.044
Chain 1:   1100        -7776.041             0.357            0.043
Chain 1:   1200        -7552.985             0.132            0.030
Chain 1:   1300        -7618.680             0.029            0.024
Chain 1:   1400        -7803.301             0.023            0.024
Chain 1:   1500        -7594.751             0.022            0.024
Chain 1:   1600        -7455.742             0.022            0.024
Chain 1:   1700        -7482.892             0.020            0.023
Chain 1:   1800        -7586.974             0.019            0.019
Chain 1:   1900        -7571.245             0.015            0.014
Chain 1:   2000        -7583.020             0.014            0.014
Chain 1:   2100        -7574.745             0.013            0.014
Chain 1:   2200        -7651.269             0.011            0.010
Chain 1:   2300        -7542.350             0.012            0.014
Chain 1:   2400        -7568.229             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003069 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86553.291             1.000            1.000
Chain 1:    200       -13513.082             3.203            5.405
Chain 1:    300        -9838.178             2.260            1.000
Chain 1:    400       -10998.814             1.721            1.000
Chain 1:    500        -8818.337             1.426            0.374
Chain 1:    600        -8423.092             1.196            0.374
Chain 1:    700        -8351.484             1.027            0.247
Chain 1:    800        -8780.207             0.904            0.247
Chain 1:    900        -8531.721             0.807            0.106
Chain 1:   1000        -8356.386             0.729            0.106
Chain 1:   1100        -8655.928             0.632            0.049   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8251.071             0.096            0.049
Chain 1:   1300        -8518.988             0.062            0.047
Chain 1:   1400        -8521.824             0.052            0.035
Chain 1:   1500        -8368.346             0.029            0.031
Chain 1:   1600        -8483.461             0.025            0.029
Chain 1:   1700        -8556.294             0.025            0.029
Chain 1:   1800        -8127.056             0.026            0.029
Chain 1:   1900        -8230.699             0.024            0.021
Chain 1:   2000        -8205.785             0.022            0.018
Chain 1:   2100        -8336.619             0.021            0.016
Chain 1:   2200        -8133.185             0.018            0.016
Chain 1:   2300        -8228.343             0.016            0.014
Chain 1:   2400        -8293.978             0.017            0.014
Chain 1:   2500        -8239.395             0.016            0.013
Chain 1:   2600        -8243.162             0.014            0.012
Chain 1:   2700        -8158.698             0.015            0.012
Chain 1:   2800        -8115.912             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003063 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8445341.535             1.000            1.000
Chain 1:    200     -1591479.709             2.653            4.307
Chain 1:    300      -890485.792             2.031            1.000
Chain 1:    400      -457211.791             1.760            1.000
Chain 1:    500      -356837.811             1.465            0.948
Chain 1:    600      -231930.755             1.310            0.948
Chain 1:    700      -118696.205             1.259            0.948
Chain 1:    800       -86023.045             1.149            0.948
Chain 1:    900       -66479.984             1.054            0.787
Chain 1:   1000       -51371.045             0.978            0.787
Chain 1:   1100       -38935.142             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38125.826             0.482            0.380
Chain 1:   1300       -26169.341             0.449            0.380
Chain 1:   1400       -25898.397             0.355            0.319
Chain 1:   1500       -22507.169             0.342            0.319
Chain 1:   1600       -21730.608             0.292            0.294
Chain 1:   1700       -20614.603             0.202            0.294
Chain 1:   1800       -20561.458             0.164            0.151
Chain 1:   1900       -20887.841             0.136            0.054
Chain 1:   2000       -19403.919             0.114            0.054
Chain 1:   2100       -19642.184             0.084            0.036
Chain 1:   2200       -19867.730             0.083            0.036
Chain 1:   2300       -19485.662             0.039            0.020
Chain 1:   2400       -19257.764             0.039            0.020
Chain 1:   2500       -19059.309             0.025            0.016
Chain 1:   2600       -18689.701             0.023            0.016
Chain 1:   2700       -18646.840             0.018            0.012
Chain 1:   2800       -18363.293             0.019            0.015
Chain 1:   2900       -18644.596             0.019            0.015
Chain 1:   3000       -18630.913             0.012            0.012
Chain 1:   3100       -18715.847             0.011            0.012
Chain 1:   3200       -18406.521             0.012            0.015
Chain 1:   3300       -18611.303             0.011            0.012
Chain 1:   3400       -18085.928             0.013            0.015
Chain 1:   3500       -18698.012             0.015            0.015
Chain 1:   3600       -18004.449             0.017            0.015
Chain 1:   3700       -18391.291             0.018            0.017
Chain 1:   3800       -17350.444             0.023            0.021
Chain 1:   3900       -17346.505             0.021            0.021
Chain 1:   4000       -17463.905             0.022            0.021
Chain 1:   4100       -17377.514             0.022            0.021
Chain 1:   4200       -17193.718             0.021            0.021
Chain 1:   4300       -17332.220             0.021            0.021
Chain 1:   4400       -17288.955             0.019            0.011
Chain 1:   4500       -17191.406             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001352 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.52 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48598.713             1.000            1.000
Chain 1:    200       -22137.911             1.098            1.195
Chain 1:    300       -19639.217             0.774            1.000
Chain 1:    400       -12603.005             0.720            1.000
Chain 1:    500       -23672.700             0.670            0.558
Chain 1:    600       -14010.176             0.673            0.690
Chain 1:    700       -15971.175             0.594            0.558
Chain 1:    800       -10369.372             0.588            0.558
Chain 1:    900       -12451.018             0.541            0.540
Chain 1:   1000       -17542.540             0.516            0.540
Chain 1:   1100       -16219.717             0.424            0.468
Chain 1:   1200       -12699.898             0.332            0.290
Chain 1:   1300       -11349.873             0.331            0.290
Chain 1:   1400       -10613.923             0.282            0.277
Chain 1:   1500       -10715.487             0.237            0.167
Chain 1:   1600       -11545.274             0.175            0.123
Chain 1:   1700        -9974.284             0.178            0.158
Chain 1:   1800       -13372.919             0.150            0.158
Chain 1:   1900       -10898.563             0.156            0.158
Chain 1:   2000        -9689.088             0.139            0.125
Chain 1:   2100        -9041.850             0.138            0.125
Chain 1:   2200       -10856.864             0.127            0.125
Chain 1:   2300        -9424.708             0.130            0.152
Chain 1:   2400       -10280.640             0.132            0.152
Chain 1:   2500       -10404.132             0.132            0.152
Chain 1:   2600        -8636.092             0.145            0.158
Chain 1:   2700       -10302.616             0.146            0.162
Chain 1:   2800       -20551.196             0.170            0.162
Chain 1:   2900        -9634.891             0.261            0.162
Chain 1:   3000       -10346.356             0.255            0.162
Chain 1:   3100       -15087.057             0.280            0.167
Chain 1:   3200       -12217.285             0.286            0.205
Chain 1:   3300        -9014.868             0.307            0.235
Chain 1:   3400        -8268.201             0.307            0.235
Chain 1:   3500        -9006.662             0.314            0.235
Chain 1:   3600        -8407.245             0.301            0.235
Chain 1:   3700        -8365.288             0.285            0.235
Chain 1:   3800       -12927.861             0.271            0.235
Chain 1:   3900        -9017.706             0.201            0.235
Chain 1:   4000        -8864.839             0.196            0.235
Chain 1:   4100        -8345.687             0.170            0.090
Chain 1:   4200       -10841.001             0.170            0.090
Chain 1:   4300        -9036.270             0.154            0.090
Chain 1:   4400        -9289.339             0.148            0.082
Chain 1:   4500        -8588.937             0.148            0.082
Chain 1:   4600       -13642.051             0.178            0.200
Chain 1:   4700       -11964.847             0.192            0.200
Chain 1:   4800        -8195.581             0.202            0.200
Chain 1:   4900        -8856.625             0.166            0.140
Chain 1:   5000       -14395.488             0.203            0.200
Chain 1:   5100        -9163.193             0.254            0.230
Chain 1:   5200        -8698.972             0.236            0.200
Chain 1:   5300        -8616.911             0.217            0.140
Chain 1:   5400       -13002.824             0.248            0.337
Chain 1:   5500        -8775.756             0.288            0.370
Chain 1:   5600        -8831.112             0.252            0.337
Chain 1:   5700        -8605.793             0.240            0.337
Chain 1:   5800       -10647.516             0.214            0.192
Chain 1:   5900       -11149.987             0.211            0.192
Chain 1:   6000        -9122.638             0.194            0.192
Chain 1:   6100        -7965.183             0.152            0.145
Chain 1:   6200        -7982.352             0.147            0.145
Chain 1:   6300        -8377.929             0.151            0.145
Chain 1:   6400        -9761.511             0.131            0.142
Chain 1:   6500       -12760.119             0.106            0.142
Chain 1:   6600        -9257.332             0.144            0.145
Chain 1:   6700        -9233.226             0.141            0.145
Chain 1:   6800       -10392.231             0.133            0.142
Chain 1:   6900       -11357.171             0.137            0.142
Chain 1:   7000        -8704.891             0.145            0.142
Chain 1:   7100        -7919.026             0.141            0.112
Chain 1:   7200       -10041.969             0.162            0.142
Chain 1:   7300       -10360.962             0.160            0.142
Chain 1:   7400        -7982.085             0.176            0.211
Chain 1:   7500        -8268.865             0.156            0.112
Chain 1:   7600       -10721.766             0.141            0.112
Chain 1:   7700       -10986.218             0.143            0.112
Chain 1:   7800       -10816.948             0.133            0.099
Chain 1:   7900       -10967.475             0.126            0.099
Chain 1:   8000        -9014.743             0.117            0.099
Chain 1:   8100        -7983.629             0.120            0.129
Chain 1:   8200        -8426.591             0.104            0.053
Chain 1:   8300        -8941.987             0.107            0.058
Chain 1:   8400        -8035.183             0.089            0.058
Chain 1:   8500        -8962.405             0.095            0.103
Chain 1:   8600        -7993.247             0.085            0.103
Chain 1:   8700        -7850.505             0.084            0.103
Chain 1:   8800        -8047.992             0.085            0.103
Chain 1:   8900       -10056.891             0.104            0.113
Chain 1:   9000        -7906.996             0.109            0.113
Chain 1:   9100        -7949.191             0.097            0.103
Chain 1:   9200        -8431.908             0.097            0.103
Chain 1:   9300        -9029.180             0.098            0.103
Chain 1:   9400        -9760.346             0.094            0.075
Chain 1:   9500        -9992.101             0.086            0.066
Chain 1:   9600        -8008.894             0.099            0.066
Chain 1:   9700       -10341.242             0.120            0.075
Chain 1:   9800       -10451.110             0.118            0.075
Chain 1:   9900        -8044.357             0.128            0.075
Chain 1:   10000        -7917.895             0.103            0.066
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00152 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62548.905             1.000            1.000
Chain 1:    200       -17576.610             1.779            2.559
Chain 1:    300        -8511.061             1.541            1.065
Chain 1:    400        -8092.230             1.169            1.065
Chain 1:    500        -8195.637             0.938            1.000
Chain 1:    600        -8554.339             0.788            1.000
Chain 1:    700        -7870.444             0.688            0.087
Chain 1:    800        -7953.886             0.603            0.087
Chain 1:    900        -7747.193             0.539            0.052
Chain 1:   1000        -7673.945             0.486            0.052
Chain 1:   1100        -7633.467             0.387            0.042
Chain 1:   1200        -7530.334             0.132            0.027
Chain 1:   1300        -7692.782             0.028            0.021
Chain 1:   1400        -7734.035             0.023            0.014
Chain 1:   1500        -7583.096             0.024            0.020
Chain 1:   1600        -7491.706             0.021            0.014
Chain 1:   1700        -7463.765             0.013            0.012
Chain 1:   1800        -7493.440             0.012            0.012
Chain 1:   1900        -7560.319             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003199 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86211.509             1.000            1.000
Chain 1:    200       -12924.191             3.335            5.671
Chain 1:    300        -9417.940             2.348            1.000
Chain 1:    400       -10309.865             1.782            1.000
Chain 1:    500        -8255.802             1.476            0.372
Chain 1:    600        -8057.004             1.234            0.372
Chain 1:    700        -8307.018             1.062            0.249
Chain 1:    800        -8458.256             0.931            0.249
Chain 1:    900        -8317.268             0.830            0.087
Chain 1:   1000        -8046.649             0.750            0.087
Chain 1:   1100        -8249.729             0.653            0.034   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8042.644             0.088            0.030
Chain 1:   1300        -8205.486             0.053            0.026
Chain 1:   1400        -8131.850             0.045            0.025
Chain 1:   1500        -8080.978             0.021            0.025
Chain 1:   1600        -8078.491             0.018            0.020
Chain 1:   1700        -8021.486             0.016            0.018
Chain 1:   1800        -7900.751             0.016            0.017
Chain 1:   1900        -8012.889             0.016            0.015
Chain 1:   2000        -7975.844             0.013            0.014
Chain 1:   2100        -8120.480             0.012            0.014
Chain 1:   2200        -7901.882             0.012            0.014
Chain 1:   2300        -8032.195             0.012            0.014
Chain 1:   2400        -8048.437             0.011            0.014
Chain 1:   2500        -8014.206             0.011            0.014
Chain 1:   2600        -8007.551             0.011            0.014
Chain 1:   2700        -7919.222             0.011            0.014
Chain 1:   2800        -7905.227             0.010            0.011
Chain 1:   2900        -7904.004             0.009            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003483 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8428836.626             1.000            1.000
Chain 1:    200     -1588605.715             2.653            4.306
Chain 1:    300      -890999.529             2.030            1.000
Chain 1:    400      -457119.659             1.759            1.000
Chain 1:    500      -356954.308             1.464            0.949
Chain 1:    600      -231809.020             1.310            0.949
Chain 1:    700      -118286.407             1.260            0.949
Chain 1:    800       -85572.379             1.150            0.949
Chain 1:    900       -65966.940             1.055            0.783
Chain 1:   1000       -50802.575             0.980            0.783
Chain 1:   1100       -38328.272             0.912            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37501.555             0.484            0.382
Chain 1:   1300       -25522.012             0.452            0.382
Chain 1:   1400       -25242.793             0.359            0.325
Chain 1:   1500       -21848.080             0.346            0.325
Chain 1:   1600       -21068.759             0.296            0.298
Chain 1:   1700       -19951.077             0.205            0.297
Chain 1:   1800       -19896.686             0.167            0.155
Chain 1:   1900       -20221.963             0.139            0.056
Chain 1:   2000       -18739.364             0.117            0.056
Chain 1:   2100       -18977.226             0.086            0.037
Chain 1:   2200       -19202.484             0.085            0.037
Chain 1:   2300       -18820.988             0.040            0.020
Chain 1:   2400       -18593.505             0.040            0.020
Chain 1:   2500       -18395.332             0.026            0.016
Chain 1:   2600       -18026.631             0.024            0.016
Chain 1:   2700       -17983.922             0.019            0.013
Chain 1:   2800       -17701.136             0.020            0.016
Chain 1:   2900       -17981.858             0.020            0.016
Chain 1:   3000       -17968.128             0.012            0.013
Chain 1:   3100       -18052.992             0.011            0.012
Chain 1:   3200       -17744.315             0.012            0.016
Chain 1:   3300       -17948.529             0.011            0.012
Chain 1:   3400       -17424.556             0.013            0.016
Chain 1:   3500       -18034.699             0.015            0.016
Chain 1:   3600       -17343.594             0.017            0.016
Chain 1:   3700       -17728.728             0.019            0.017
Chain 1:   3800       -16691.851             0.024            0.022
Chain 1:   3900       -16688.052             0.022            0.022
Chain 1:   4000       -16805.374             0.023            0.022
Chain 1:   4100       -16719.326             0.023            0.022
Chain 1:   4200       -16536.297             0.022            0.022
Chain 1:   4300       -16674.188             0.022            0.022
Chain 1:   4400       -16631.618             0.019            0.011
Chain 1:   4500       -16534.249             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13003.081             1.000            1.000
Chain 1:    200        -9551.773             0.681            1.000
Chain 1:    300        -8123.345             0.512            0.361
Chain 1:    400        -8015.745             0.388            0.361
Chain 1:    500        -8102.902             0.312            0.176
Chain 1:    600        -7934.298             0.264            0.176
Chain 1:    700        -7851.892             0.228            0.021
Chain 1:    800        -7858.466             0.199            0.021
Chain 1:    900        -7781.625             0.178            0.013
Chain 1:   1000        -7962.859             0.163            0.021
Chain 1:   1100        -7989.045             0.063            0.013
Chain 1:   1200        -7886.552             0.028            0.013
Chain 1:   1300        -7820.778             0.011            0.011
Chain 1:   1400        -7844.303             0.010            0.010
Chain 1:   1500        -7931.402             0.010            0.010
Chain 1:   1600        -7894.550             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001535 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58046.399             1.000            1.000
Chain 1:    200       -17627.075             1.647            2.293
Chain 1:    300        -8675.121             1.442            1.032
Chain 1:    400        -8178.908             1.096            1.032
Chain 1:    500        -7804.937             0.887            1.000
Chain 1:    600        -8334.285             0.750            1.000
Chain 1:    700        -8075.543             0.647            0.064
Chain 1:    800        -8212.120             0.568            0.064
Chain 1:    900        -7874.151             0.510            0.061
Chain 1:   1000        -7556.753             0.463            0.061
Chain 1:   1100        -7791.390             0.366            0.048
Chain 1:   1200        -7829.782             0.137            0.043
Chain 1:   1300        -7687.014             0.036            0.042
Chain 1:   1400        -7794.025             0.031            0.032
Chain 1:   1500        -7617.741             0.029            0.030
Chain 1:   1600        -7747.758             0.024            0.023
Chain 1:   1700        -7530.238             0.024            0.023
Chain 1:   1800        -7614.598             0.023            0.023
Chain 1:   1900        -7610.228             0.019            0.019
Chain 1:   2000        -7591.783             0.015            0.017
Chain 1:   2100        -7586.739             0.012            0.014
Chain 1:   2200        -7693.508             0.013            0.014
Chain 1:   2300        -7596.940             0.012            0.014
Chain 1:   2400        -7641.293             0.012            0.013
Chain 1:   2500        -7472.228             0.012            0.013
Chain 1:   2600        -7517.537             0.010            0.011
Chain 1:   2700        -7557.781             0.008            0.006   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002698 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86577.131             1.000            1.000
Chain 1:    200       -13469.609             3.214            5.428
Chain 1:    300        -9832.317             2.266            1.000
Chain 1:    400       -10667.178             1.719            1.000
Chain 1:    500        -8795.294             1.418            0.370
Chain 1:    600        -8306.036             1.191            0.370
Chain 1:    700        -8409.653             1.023            0.213
Chain 1:    800        -8794.051             0.900            0.213
Chain 1:    900        -8650.510             0.802            0.078
Chain 1:   1000        -8360.606             0.725            0.078
Chain 1:   1100        -8585.955             0.628            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8448.677             0.087            0.044
Chain 1:   1300        -8546.512             0.051            0.035
Chain 1:   1400        -8555.643             0.043            0.026
Chain 1:   1500        -8388.792             0.024            0.020
Chain 1:   1600        -8509.196             0.020            0.017
Chain 1:   1700        -8592.184             0.019            0.017
Chain 1:   1800        -8178.737             0.020            0.017
Chain 1:   1900        -8274.623             0.020            0.016
Chain 1:   2000        -8248.050             0.016            0.014
Chain 1:   2100        -8370.827             0.015            0.014
Chain 1:   2200        -8190.872             0.016            0.014
Chain 1:   2300        -8269.627             0.016            0.014
Chain 1:   2400        -8339.330             0.016            0.014
Chain 1:   2500        -8284.775             0.015            0.012
Chain 1:   2600        -8284.270             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003622 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8441607.783             1.000            1.000
Chain 1:    200     -1587866.871             2.658            4.316
Chain 1:    300      -890554.004             2.033            1.000
Chain 1:    400      -457548.224             1.761            1.000
Chain 1:    500      -357304.051             1.465            0.946
Chain 1:    600      -232320.591             1.311            0.946
Chain 1:    700      -118849.203             1.260            0.946
Chain 1:    800       -86129.945             1.150            0.946
Chain 1:    900       -66538.253             1.055            0.783
Chain 1:   1000       -51391.814             0.979            0.783
Chain 1:   1100       -38925.030             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38107.280             0.481            0.380
Chain 1:   1300       -26121.732             0.449            0.380
Chain 1:   1400       -25846.610             0.355            0.320
Chain 1:   1500       -22448.589             0.342            0.320
Chain 1:   1600       -21669.636             0.292            0.295
Chain 1:   1700       -20550.291             0.202            0.294
Chain 1:   1800       -20496.081             0.164            0.151
Chain 1:   1900       -20822.203             0.137            0.054
Chain 1:   2000       -19337.051             0.115            0.054
Chain 1:   2100       -19575.250             0.084            0.036
Chain 1:   2200       -19801.058             0.083            0.036
Chain 1:   2300       -19418.838             0.039            0.020
Chain 1:   2400       -19190.999             0.039            0.020
Chain 1:   2500       -18992.744             0.025            0.016
Chain 1:   2600       -18623.224             0.023            0.016
Chain 1:   2700       -18580.338             0.018            0.012
Chain 1:   2800       -18297.073             0.020            0.015
Chain 1:   2900       -18578.221             0.020            0.015
Chain 1:   3000       -18564.516             0.012            0.012
Chain 1:   3100       -18649.461             0.011            0.012
Chain 1:   3200       -18340.250             0.012            0.015
Chain 1:   3300       -18544.894             0.011            0.012
Chain 1:   3400       -18019.878             0.013            0.015
Chain 1:   3500       -18631.549             0.015            0.015
Chain 1:   3600       -17938.478             0.017            0.015
Chain 1:   3700       -18325.027             0.019            0.017
Chain 1:   3800       -17285.044             0.023            0.021
Chain 1:   3900       -17281.146             0.022            0.021
Chain 1:   4000       -17398.507             0.022            0.021
Chain 1:   4100       -17312.243             0.022            0.021
Chain 1:   4200       -17128.569             0.022            0.021
Chain 1:   4300       -17266.947             0.021            0.021
Chain 1:   4400       -17223.836             0.019            0.011
Chain 1:   4500       -17126.326             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001569 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48609.787             1.000            1.000
Chain 1:    200       -16638.601             1.461            1.922
Chain 1:    300       -12834.803             1.073            1.000
Chain 1:    400       -22304.833             0.911            1.000
Chain 1:    500       -16110.708             0.805            0.425
Chain 1:    600       -11144.545             0.745            0.446
Chain 1:    700       -20557.343             0.704            0.446
Chain 1:    800       -22875.946             0.629            0.446
Chain 1:    900       -11487.879             0.669            0.446
Chain 1:   1000       -13049.753             0.614            0.446
Chain 1:   1100       -14349.805             0.523            0.425   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -14482.429             0.332            0.384
Chain 1:   1300       -10541.189             0.340            0.384
Chain 1:   1400       -15645.464             0.330            0.374
Chain 1:   1500        -9830.391             0.351            0.374
Chain 1:   1600       -10176.666             0.310            0.326
Chain 1:   1700       -10727.519             0.269            0.120
Chain 1:   1800       -12117.909             0.270            0.120
Chain 1:   1900        -9858.867             0.194            0.120
Chain 1:   2000        -9891.867             0.182            0.115
Chain 1:   2100        -9780.865             0.174            0.115
Chain 1:   2200       -11381.057             0.188            0.141
Chain 1:   2300        -9153.422             0.175            0.141
Chain 1:   2400        -9622.146             0.147            0.115
Chain 1:   2500       -21131.298             0.142            0.115
Chain 1:   2600        -9341.042             0.265            0.141
Chain 1:   2700        -9382.647             0.260            0.141
Chain 1:   2800        -9154.393             0.251            0.141
Chain 1:   2900       -10025.104             0.237            0.087
Chain 1:   3000        -9089.073             0.247            0.103
Chain 1:   3100        -9290.339             0.248            0.103
Chain 1:   3200        -9067.998             0.236            0.087
Chain 1:   3300       -12568.647             0.240            0.087
Chain 1:   3400       -12689.315             0.236            0.087
Chain 1:   3500       -14916.291             0.196            0.087
Chain 1:   3600        -9071.943             0.135            0.087
Chain 1:   3700        -8839.536             0.137            0.087
Chain 1:   3800        -8367.453             0.140            0.087
Chain 1:   3900        -9494.978             0.143            0.103
Chain 1:   4000        -8820.990             0.141            0.076
Chain 1:   4100        -9326.628             0.144            0.076
Chain 1:   4200        -9386.359             0.142            0.076
Chain 1:   4300        -9648.135             0.117            0.056
Chain 1:   4400       -14382.382             0.149            0.076
Chain 1:   4500        -8838.022             0.197            0.076
Chain 1:   4600        -8621.258             0.135            0.056
Chain 1:   4700        -9237.846             0.139            0.067
Chain 1:   4800       -10083.265             0.142            0.076
Chain 1:   4900        -9813.352             0.132            0.067
Chain 1:   5000       -14475.674             0.157            0.067
Chain 1:   5100       -15518.954             0.158            0.067
Chain 1:   5200       -14967.615             0.161            0.067
Chain 1:   5300        -8790.086             0.229            0.084
Chain 1:   5400        -9307.710             0.202            0.067
Chain 1:   5500       -12674.184             0.165            0.067
Chain 1:   5600       -12133.359             0.167            0.067
Chain 1:   5700       -12746.415             0.165            0.067
Chain 1:   5800        -8464.368             0.208            0.067
Chain 1:   5900       -10279.736             0.223            0.177
Chain 1:   6000       -10985.688             0.197            0.067
Chain 1:   6100       -11756.229             0.197            0.066
Chain 1:   6200        -8245.265             0.235            0.177
Chain 1:   6300        -8902.193             0.173            0.074
Chain 1:   6400        -9876.326             0.177            0.099
Chain 1:   6500       -11690.129             0.166            0.099
Chain 1:   6600       -13427.548             0.174            0.129
Chain 1:   6700        -8139.377             0.234            0.155
Chain 1:   6800        -8297.340             0.186            0.129
Chain 1:   6900       -11442.030             0.196            0.129
Chain 1:   7000        -8052.673             0.231            0.155
Chain 1:   7100        -9865.655             0.243            0.184
Chain 1:   7200        -8311.965             0.219            0.184
Chain 1:   7300        -8293.405             0.212            0.184
Chain 1:   7400        -8544.374             0.205            0.184
Chain 1:   7500        -7937.066             0.197            0.184
Chain 1:   7600       -10027.891             0.205            0.187
Chain 1:   7700        -8122.761             0.164            0.187
Chain 1:   7800        -9394.402             0.175            0.187
Chain 1:   7900        -8945.659             0.153            0.184
Chain 1:   8000        -8477.261             0.116            0.135
Chain 1:   8100        -8689.916             0.100            0.077
Chain 1:   8200        -8743.771             0.082            0.055
Chain 1:   8300       -12966.709             0.115            0.077
Chain 1:   8400        -8070.424             0.172            0.135
Chain 1:   8500        -9754.246             0.182            0.173
Chain 1:   8600        -9925.669             0.163            0.135
Chain 1:   8700        -8144.998             0.161            0.135
Chain 1:   8800        -8813.813             0.155            0.076
Chain 1:   8900       -11100.190             0.171            0.173
Chain 1:   9000        -7958.743             0.205            0.206
Chain 1:   9100        -8138.310             0.205            0.206
Chain 1:   9200        -9956.699             0.222            0.206
Chain 1:   9300        -8130.373             0.212            0.206
Chain 1:   9400        -8464.094             0.155            0.183
Chain 1:   9500       -12906.485             0.173            0.206
Chain 1:   9600        -9416.218             0.208            0.219
Chain 1:   9700        -9276.524             0.188            0.206
Chain 1:   9800        -7972.254             0.196            0.206
Chain 1:   9900        -9276.819             0.190            0.183
Chain 1:   10000        -7795.872             0.169            0.183
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001369 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56824.817             1.000            1.000
Chain 1:    200       -17290.024             1.643            2.287
Chain 1:    300        -8692.019             1.425            1.000
Chain 1:    400        -7944.848             1.092            1.000
Chain 1:    500        -8415.892             0.885            0.989
Chain 1:    600        -8853.932             0.746            0.989
Chain 1:    700        -7759.871             0.659            0.141
Chain 1:    800        -7708.245             0.578            0.141
Chain 1:    900        -7957.867             0.517            0.094
Chain 1:   1000        -7986.609             0.466            0.094
Chain 1:   1100        -7808.703             0.368            0.056
Chain 1:   1200        -7722.409             0.141            0.049
Chain 1:   1300        -7855.790             0.043            0.031
Chain 1:   1400        -7942.665             0.035            0.023
Chain 1:   1500        -7662.694             0.033            0.023
Chain 1:   1600        -7672.608             0.028            0.017
Chain 1:   1700        -7577.821             0.015            0.013
Chain 1:   1800        -7645.091             0.016            0.013
Chain 1:   1900        -7652.620             0.013            0.011
Chain 1:   2000        -7657.388             0.012            0.011
Chain 1:   2100        -7659.175             0.010            0.011
Chain 1:   2200        -7749.190             0.010            0.011
Chain 1:   2300        -7662.869             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003282 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.82 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86195.606             1.000            1.000
Chain 1:    200       -13309.060             3.238            5.476
Chain 1:    300        -9665.647             2.284            1.000
Chain 1:    400       -10567.381             1.735            1.000
Chain 1:    500        -8498.924             1.436            0.377
Chain 1:    600        -8113.606             1.205            0.377
Chain 1:    700        -8193.245             1.034            0.243
Chain 1:    800        -8546.353             0.910            0.243
Chain 1:    900        -8473.626             0.810            0.085
Chain 1:   1000        -8113.190             0.733            0.085
Chain 1:   1100        -8333.867             0.636            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8146.618             0.091            0.044
Chain 1:   1300        -8350.452             0.055            0.041
Chain 1:   1400        -8359.281             0.047            0.026
Chain 1:   1500        -8208.726             0.024            0.024
Chain 1:   1600        -8323.313             0.021            0.023
Chain 1:   1700        -8401.711             0.021            0.023
Chain 1:   1800        -7980.863             0.022            0.023
Chain 1:   1900        -8080.763             0.023            0.023
Chain 1:   2000        -8054.883             0.018            0.018
Chain 1:   2100        -8179.710             0.017            0.015
Chain 1:   2200        -7987.142             0.017            0.015
Chain 1:   2300        -8075.415             0.016            0.014
Chain 1:   2400        -8144.568             0.017            0.014
Chain 1:   2500        -8090.646             0.016            0.012
Chain 1:   2600        -8091.466             0.014            0.011
Chain 1:   2700        -8008.475             0.014            0.011
Chain 1:   2800        -7969.150             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003358 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8385613.955             1.000            1.000
Chain 1:    200     -1580743.458             2.652            4.305
Chain 1:    300      -889944.209             2.027            1.000
Chain 1:    400      -457364.053             1.757            1.000
Chain 1:    500      -358181.146             1.461            0.946
Chain 1:    600      -233147.187             1.307            0.946
Chain 1:    700      -119241.162             1.256            0.946
Chain 1:    800       -86421.480             1.147            0.946
Chain 1:    900       -66725.141             1.052            0.776
Chain 1:   1000       -51490.100             0.977            0.776
Chain 1:   1100       -38936.346             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38108.217             0.481            0.380
Chain 1:   1300       -26033.070             0.449            0.380
Chain 1:   1400       -25748.789             0.356            0.322
Chain 1:   1500       -22328.314             0.343            0.322
Chain 1:   1600       -21542.557             0.293            0.296
Chain 1:   1700       -20412.538             0.203            0.295
Chain 1:   1800       -20355.852             0.166            0.153
Chain 1:   1900       -20682.082             0.138            0.055
Chain 1:   2000       -19191.127             0.116            0.055
Chain 1:   2100       -19429.579             0.085            0.036
Chain 1:   2200       -19656.478             0.084            0.036
Chain 1:   2300       -19273.266             0.040            0.020
Chain 1:   2400       -19045.295             0.040            0.020
Chain 1:   2500       -18847.455             0.025            0.016
Chain 1:   2600       -18477.460             0.024            0.016
Chain 1:   2700       -18434.320             0.018            0.012
Chain 1:   2800       -18151.243             0.020            0.016
Chain 1:   2900       -18432.568             0.020            0.015
Chain 1:   3000       -18418.670             0.012            0.012
Chain 1:   3100       -18503.704             0.011            0.012
Chain 1:   3200       -18194.309             0.012            0.015
Chain 1:   3300       -18399.081             0.011            0.012
Chain 1:   3400       -17873.930             0.013            0.015
Chain 1:   3500       -18486.001             0.015            0.016
Chain 1:   3600       -17792.424             0.017            0.016
Chain 1:   3700       -18179.473             0.019            0.017
Chain 1:   3800       -17138.824             0.023            0.021
Chain 1:   3900       -17134.978             0.022            0.021
Chain 1:   4000       -17252.255             0.022            0.021
Chain 1:   4100       -17166.042             0.022            0.021
Chain 1:   4200       -16982.165             0.022            0.021
Chain 1:   4300       -17120.629             0.021            0.021
Chain 1:   4400       -17077.399             0.019            0.011
Chain 1:   4500       -16979.918             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001424 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50156.626             1.000            1.000
Chain 1:    200       -20623.576             1.216            1.432
Chain 1:    300       -20446.267             0.814            1.000
Chain 1:    400       -14256.231             0.719            1.000
Chain 1:    500       -25775.117             0.664            0.447
Chain 1:    600       -39064.774             0.610            0.447
Chain 1:    700       -22376.992             0.630            0.447
Chain 1:    800       -17038.119             0.590            0.447
Chain 1:    900       -14566.272             0.543            0.434
Chain 1:   1000       -11686.895             0.514            0.434
Chain 1:   1100       -11630.789             0.414            0.340
Chain 1:   1200       -11568.919             0.272            0.313
Chain 1:   1300       -14212.204             0.289            0.313
Chain 1:   1400       -19822.647             0.274            0.283
Chain 1:   1500       -24168.419             0.247            0.246
Chain 1:   1600       -20667.840             0.230            0.186
Chain 1:   1700       -12436.412             0.222            0.186
Chain 1:   1800       -10449.424             0.210            0.186
Chain 1:   1900       -11581.778             0.202            0.186
Chain 1:   2000       -14825.858             0.200            0.186
Chain 1:   2100       -10815.138             0.236            0.190
Chain 1:   2200       -17897.787             0.275            0.219
Chain 1:   2300       -10914.722             0.321            0.283
Chain 1:   2400       -20601.942             0.339            0.371
Chain 1:   2500       -10742.974             0.413            0.396
Chain 1:   2600       -10428.232             0.399            0.396
Chain 1:   2700       -14196.387             0.360            0.371
Chain 1:   2800       -11897.787             0.360            0.371
Chain 1:   2900       -11379.694             0.355            0.371
Chain 1:   3000       -10024.339             0.346            0.371
Chain 1:   3100       -10475.985             0.314            0.265
Chain 1:   3200       -12809.363             0.292            0.193
Chain 1:   3300       -10252.448             0.253            0.193
Chain 1:   3400       -17108.871             0.246            0.193
Chain 1:   3500       -12755.069             0.189            0.193
Chain 1:   3600       -18366.739             0.216            0.249
Chain 1:   3700        -9982.947             0.274            0.249
Chain 1:   3800       -10680.248             0.261            0.249
Chain 1:   3900       -17135.565             0.294            0.306
Chain 1:   4000        -9666.059             0.358            0.341
Chain 1:   4100       -10503.159             0.361            0.341
Chain 1:   4200        -9694.156             0.351            0.341
Chain 1:   4300       -18421.503             0.374            0.377
Chain 1:   4400        -9651.663             0.425            0.377
Chain 1:   4500       -10002.082             0.394            0.377
Chain 1:   4600        -9733.404             0.366            0.377
Chain 1:   4700        -9590.988             0.284            0.083
Chain 1:   4800        -9564.705             0.278            0.083
Chain 1:   4900       -10565.922             0.249            0.083
Chain 1:   5000       -20042.544             0.219            0.083
Chain 1:   5100        -9661.725             0.319            0.095
Chain 1:   5200        -9752.593             0.311            0.095
Chain 1:   5300        -9505.910             0.267            0.035
Chain 1:   5400       -10508.336             0.185            0.035
Chain 1:   5500       -14563.111             0.210            0.095
Chain 1:   5600       -10623.319             0.244            0.095
Chain 1:   5700        -9958.609             0.249            0.095
Chain 1:   5800        -9624.477             0.252            0.095
Chain 1:   5900       -16365.680             0.284            0.278
Chain 1:   6000       -10370.301             0.295            0.278
Chain 1:   6100       -13545.937             0.211            0.234
Chain 1:   6200       -11622.125             0.226            0.234
Chain 1:   6300       -13469.127             0.237            0.234
Chain 1:   6400       -13336.009             0.229            0.234
Chain 1:   6500       -14493.667             0.209            0.166
Chain 1:   6600        -9479.132             0.225            0.166
Chain 1:   6700       -13775.359             0.249            0.234
Chain 1:   6800       -10737.281             0.274            0.283
Chain 1:   6900       -14259.800             0.258            0.247
Chain 1:   7000       -15080.251             0.205            0.234
Chain 1:   7100        -9086.626             0.248            0.247
Chain 1:   7200       -10512.089             0.245            0.247
Chain 1:   7300       -13050.142             0.250            0.247
Chain 1:   7400       -13348.466             0.252            0.247
Chain 1:   7500       -12529.636             0.250            0.247
Chain 1:   7600       -10843.653             0.213            0.194
Chain 1:   7700       -10876.143             0.182            0.155
Chain 1:   7800        -9306.219             0.171            0.155
Chain 1:   7900        -9442.879             0.147            0.136
Chain 1:   8000        -9397.703             0.142            0.136
Chain 1:   8100        -9806.673             0.081            0.065
Chain 1:   8200        -9408.705             0.071            0.042
Chain 1:   8300        -9270.536             0.053            0.042
Chain 1:   8400        -9543.114             0.054            0.042
Chain 1:   8500        -9583.621             0.048            0.029
Chain 1:   8600        -9675.511             0.033            0.015
Chain 1:   8700       -10580.201             0.041            0.029
Chain 1:   8800        -9112.591             0.041            0.029
Chain 1:   8900       -12807.811             0.068            0.042
Chain 1:   9000       -10506.124             0.090            0.042
Chain 1:   9100        -9345.980             0.098            0.086
Chain 1:   9200       -10977.800             0.108            0.124
Chain 1:   9300        -9744.882             0.120            0.127
Chain 1:   9400        -9294.171             0.122            0.127
Chain 1:   9500        -9684.785             0.125            0.127
Chain 1:   9600        -9928.070             0.127            0.127
Chain 1:   9700        -9151.264             0.127            0.127
Chain 1:   9800       -13537.652             0.143            0.127
Chain 1:   9900       -12865.125             0.119            0.124
Chain 1:   10000       -13630.251             0.103            0.085
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001661 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47737.995             1.000            1.000
Chain 1:    200       -17025.498             1.402            1.804
Chain 1:    300        -9523.009             1.197            1.000
Chain 1:    400        -8502.376             0.928            1.000
Chain 1:    500        -8686.672             0.747            0.788
Chain 1:    600        -9958.746             0.643            0.788
Chain 1:    700        -9025.139             0.566            0.128
Chain 1:    800        -8652.260             0.501            0.128
Chain 1:    900        -8393.978             0.449            0.120
Chain 1:   1000        -8287.176             0.405            0.120
Chain 1:   1100        -8178.355             0.306            0.103
Chain 1:   1200        -8131.879             0.127            0.043
Chain 1:   1300        -7896.440             0.051            0.031
Chain 1:   1400        -7719.928             0.041            0.030
Chain 1:   1500        -7975.931             0.042            0.031
Chain 1:   1600        -8276.827             0.033            0.031
Chain 1:   1700        -8024.821             0.026            0.031
Chain 1:   1800        -7832.679             0.024            0.030
Chain 1:   1900        -7855.353             0.021            0.025
Chain 1:   2000        -7798.723             0.021            0.025
Chain 1:   2100        -7839.209             0.020            0.025
Chain 1:   2200        -8047.355             0.022            0.026
Chain 1:   2300        -7710.887             0.023            0.026
Chain 1:   2400        -7783.273             0.022            0.026
Chain 1:   2500        -7783.857             0.019            0.025
Chain 1:   2600        -7719.760             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003177 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86498.313             1.000            1.000
Chain 1:    200       -14930.478             2.897            4.793
Chain 1:    300       -11038.091             2.049            1.000
Chain 1:    400       -13585.644             1.583            1.000
Chain 1:    500        -9350.306             1.357            0.453
Chain 1:    600        -9472.668             1.133            0.453
Chain 1:    700        -9435.707             0.972            0.353
Chain 1:    800        -9116.899             0.855            0.353
Chain 1:    900        -9187.583             0.761            0.188
Chain 1:   1000        -9970.139             0.692            0.188
Chain 1:   1100        -9437.759             0.598            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10041.083             0.125            0.060
Chain 1:   1300        -9209.622             0.099            0.060
Chain 1:   1400        -9369.657             0.081            0.056
Chain 1:   1500        -9321.557             0.037            0.035
Chain 1:   1600        -9279.102             0.036            0.035
Chain 1:   1700        -9146.112             0.037            0.035
Chain 1:   1800        -9184.306             0.034            0.017
Chain 1:   1900        -9197.942             0.033            0.017
Chain 1:   2000        -9362.908             0.027            0.017
Chain 1:   2100        -9205.747             0.023            0.017
Chain 1:   2200        -9126.785             0.018            0.015
Chain 1:   2300        -9330.446             0.011            0.015
Chain 1:   2400        -9063.013             0.012            0.015
Chain 1:   2500        -9147.767             0.013            0.015
Chain 1:   2600        -9050.341             0.013            0.015
Chain 1:   2700        -9068.155             0.012            0.011
Chain 1:   2800        -8926.609             0.013            0.016
Chain 1:   2900        -9115.484             0.015            0.017
Chain 1:   3000        -9023.766             0.015            0.016
Chain 1:   3100        -9121.979             0.014            0.011
Chain 1:   3200        -8988.354             0.015            0.015
Chain 1:   3300        -9257.634             0.015            0.015
Chain 1:   3400        -9327.626             0.013            0.011
Chain 1:   3500        -9137.137             0.014            0.015
Chain 1:   3600        -8940.749             0.015            0.016
Chain 1:   3700        -9110.098             0.017            0.019
Chain 1:   3800        -8944.682             0.017            0.019
Chain 1:   3900        -9170.364             0.018            0.019
Chain 1:   4000        -9171.632             0.017            0.019
Chain 1:   4100        -8955.642             0.018            0.021
Chain 1:   4200        -8940.643             0.017            0.021
Chain 1:   4300        -8942.060             0.014            0.019
Chain 1:   4400        -8896.113             0.014            0.019
Chain 1:   4500        -9036.799             0.013            0.018
Chain 1:   4600        -9063.878             0.011            0.016
Chain 1:   4700        -9181.772             0.011            0.013
Chain 1:   4800        -9004.560             0.011            0.013
Chain 1:   4900        -9030.055             0.009            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003136 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8357465.244             1.000            1.000
Chain 1:    200     -1579099.165             2.646            4.293
Chain 1:    300      -892530.956             2.021            1.000
Chain 1:    400      -459610.694             1.751            1.000
Chain 1:    500      -360639.332             1.456            0.942
Chain 1:    600      -235724.185             1.301            0.942
Chain 1:    700      -121390.273             1.250            0.942
Chain 1:    800       -88430.998             1.140            0.942
Chain 1:    900       -68671.463             1.046            0.769
Chain 1:   1000       -53387.315             0.970            0.769
Chain 1:   1100       -40766.719             0.901            0.530   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39944.112             0.473            0.373
Chain 1:   1300       -27780.305             0.440            0.373
Chain 1:   1400       -27494.374             0.347            0.310
Chain 1:   1500       -24048.378             0.334            0.310
Chain 1:   1600       -23256.501             0.284            0.288
Chain 1:   1700       -22114.702             0.195            0.286
Chain 1:   1800       -22055.988             0.158            0.143
Chain 1:   1900       -22383.396             0.131            0.052
Chain 1:   2000       -20883.549             0.110            0.052
Chain 1:   2100       -21122.813             0.080            0.034
Chain 1:   2200       -21351.381             0.079            0.034
Chain 1:   2300       -20966.351             0.037            0.018
Chain 1:   2400       -20737.794             0.037            0.018
Chain 1:   2500       -20540.154             0.024            0.015
Chain 1:   2600       -20168.740             0.022            0.015
Chain 1:   2700       -20125.125             0.017            0.011
Chain 1:   2800       -19841.545             0.018            0.014
Chain 1:   2900       -20123.558             0.018            0.014
Chain 1:   3000       -20109.660             0.011            0.011
Chain 1:   3100       -20194.844             0.010            0.011
Chain 1:   3200       -19884.556             0.011            0.014
Chain 1:   3300       -20089.997             0.010            0.011
Chain 1:   3400       -19563.312             0.012            0.014
Chain 1:   3500       -20177.796             0.014            0.014
Chain 1:   3600       -19481.131             0.015            0.014
Chain 1:   3700       -19870.507             0.017            0.016
Chain 1:   3800       -18825.098             0.021            0.020
Chain 1:   3900       -18821.143             0.020            0.020
Chain 1:   4000       -18938.415             0.020            0.020
Chain 1:   4100       -18851.959             0.021            0.020
Chain 1:   4200       -18667.040             0.020            0.020
Chain 1:   4300       -18806.234             0.020            0.020
Chain 1:   4400       -18762.144             0.017            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001342 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12109.271             1.000            1.000
Chain 1:    200        -8987.153             0.674            1.000
Chain 1:    300        -7849.894             0.497            0.347
Chain 1:    400        -7949.971             0.376            0.347
Chain 1:    500        -7833.614             0.304            0.145
Chain 1:    600        -7781.302             0.254            0.145
Chain 1:    700        -7699.248             0.220            0.015
Chain 1:    800        -7747.869             0.193            0.015
Chain 1:    900        -7775.304             0.172            0.013
Chain 1:   1000        -7726.695             0.155            0.013
Chain 1:   1100        -7807.330             0.056            0.011
Chain 1:   1200        -7720.352             0.023            0.011
Chain 1:   1300        -7669.182             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001499 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56897.100             1.000            1.000
Chain 1:    200       -17138.248             1.660            2.320
Chain 1:    300        -8613.149             1.437            1.000
Chain 1:    400        -7890.247             1.100            1.000
Chain 1:    500        -8516.622             0.895            0.990
Chain 1:    600        -8645.504             0.748            0.990
Chain 1:    700        -8312.196             0.647            0.092
Chain 1:    800        -8129.920             0.569            0.092
Chain 1:    900        -7835.696             0.510            0.074
Chain 1:   1000        -7746.724             0.460            0.074
Chain 1:   1100        -7721.445             0.360            0.040
Chain 1:   1200        -7623.913             0.130            0.038
Chain 1:   1300        -7720.145             0.032            0.022
Chain 1:   1400        -7666.718             0.024            0.015
Chain 1:   1500        -7599.219             0.017            0.013
Chain 1:   1600        -7547.073             0.016            0.012
Chain 1:   1700        -7535.487             0.012            0.011
Chain 1:   1800        -7569.318             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003778 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85735.434             1.000            1.000
Chain 1:    200       -13183.907             3.252            5.503
Chain 1:    300        -9613.720             2.291            1.000
Chain 1:    400       -10652.169             1.743            1.000
Chain 1:    500        -8562.810             1.443            0.371
Chain 1:    600        -8110.130             1.212            0.371
Chain 1:    700        -8323.231             1.042            0.244
Chain 1:    800        -8844.904             0.920            0.244
Chain 1:    900        -8426.742             0.823            0.097
Chain 1:   1000        -8156.265             0.744            0.097
Chain 1:   1100        -8491.110             0.648            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8046.431             0.103            0.056
Chain 1:   1300        -8328.110             0.069            0.055
Chain 1:   1400        -8322.528             0.060            0.050
Chain 1:   1500        -8230.482             0.036            0.039
Chain 1:   1600        -8332.982             0.032            0.034
Chain 1:   1700        -8416.560             0.030            0.034
Chain 1:   1800        -8022.252             0.029            0.034
Chain 1:   1900        -8123.583             0.026            0.033
Chain 1:   2000        -8094.291             0.023            0.012
Chain 1:   2100        -8216.619             0.020            0.012
Chain 1:   2200        -7997.561             0.018            0.012
Chain 1:   2300        -8152.426             0.016            0.012
Chain 1:   2400        -8166.189             0.016            0.012
Chain 1:   2500        -8135.765             0.015            0.012
Chain 1:   2600        -8138.420             0.014            0.012
Chain 1:   2700        -8044.649             0.014            0.012
Chain 1:   2800        -8015.710             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00362 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8419483.212             1.000            1.000
Chain 1:    200     -1587300.996             2.652            4.304
Chain 1:    300      -890764.816             2.029            1.000
Chain 1:    400      -457320.986             1.759            1.000
Chain 1:    500      -357422.703             1.463            0.948
Chain 1:    600      -232248.418             1.309            0.948
Chain 1:    700      -118636.365             1.259            0.948
Chain 1:    800       -85956.684             1.149            0.948
Chain 1:    900       -66333.908             1.054            0.782
Chain 1:   1000       -51166.134             0.978            0.782
Chain 1:   1100       -38681.326             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37857.799             0.482            0.380
Chain 1:   1300       -25849.281             0.451            0.380
Chain 1:   1400       -25570.556             0.357            0.323
Chain 1:   1500       -22168.575             0.344            0.323
Chain 1:   1600       -21388.382             0.294            0.296
Chain 1:   1700       -20266.061             0.204            0.296
Chain 1:   1800       -20211.093             0.166            0.153
Chain 1:   1900       -20536.954             0.138            0.055
Chain 1:   2000       -19051.116             0.116            0.055
Chain 1:   2100       -19289.092             0.085            0.036
Chain 1:   2200       -19515.267             0.084            0.036
Chain 1:   2300       -19132.819             0.040            0.020
Chain 1:   2400       -18905.064             0.040            0.020
Chain 1:   2500       -18707.193             0.025            0.016
Chain 1:   2600       -18337.656             0.024            0.016
Chain 1:   2700       -18294.669             0.019            0.012
Chain 1:   2800       -18011.785             0.020            0.016
Chain 1:   2900       -18292.769             0.020            0.015
Chain 1:   3000       -18278.928             0.012            0.012
Chain 1:   3100       -18363.945             0.011            0.012
Chain 1:   3200       -18054.803             0.012            0.015
Chain 1:   3300       -18259.369             0.011            0.012
Chain 1:   3400       -17734.739             0.013            0.015
Chain 1:   3500       -18345.985             0.015            0.016
Chain 1:   3600       -17653.389             0.017            0.016
Chain 1:   3700       -18039.671             0.019            0.017
Chain 1:   3800       -17000.617             0.023            0.021
Chain 1:   3900       -16996.802             0.022            0.021
Chain 1:   4000       -17114.079             0.022            0.021
Chain 1:   4100       -17027.977             0.023            0.021
Chain 1:   4200       -16844.452             0.022            0.021
Chain 1:   4300       -16982.657             0.022            0.021
Chain 1:   4400       -16939.673             0.019            0.011
Chain 1:   4500       -16842.266             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13033.937             1.000            1.000
Chain 1:    200        -9903.118             0.658            1.000
Chain 1:    300        -8523.378             0.493            0.316
Chain 1:    400        -8740.476             0.376            0.316
Chain 1:    500        -8611.761             0.304            0.162
Chain 1:    600        -8460.335             0.256            0.162
Chain 1:    700        -8569.367             0.221            0.025
Chain 1:    800        -8397.399             0.196            0.025
Chain 1:    900        -8432.743             0.175            0.020
Chain 1:   1000        -8399.440             0.158            0.020
Chain 1:   1100        -8502.836             0.059            0.018
Chain 1:   1200        -8383.218             0.029            0.015
Chain 1:   1300        -8326.962             0.013            0.014
Chain 1:   1400        -8342.787             0.011            0.013
Chain 1:   1500        -8440.060             0.011            0.012
Chain 1:   1600        -8354.453             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001543 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50095.123             1.000            1.000
Chain 1:    200       -16701.629             1.500            1.999
Chain 1:    300        -9086.907             1.279            1.000
Chain 1:    400        -8395.251             0.980            1.000
Chain 1:    500        -8053.182             0.792            0.838
Chain 1:    600        -9441.456             0.685            0.838
Chain 1:    700        -8816.927             0.597            0.147
Chain 1:    800        -8514.499             0.527            0.147
Chain 1:    900        -7967.443             0.476            0.082
Chain 1:   1000        -8017.392             0.429            0.082
Chain 1:   1100        -8075.678             0.330            0.071
Chain 1:   1200        -7768.198             0.134            0.069
Chain 1:   1300        -7837.878             0.051            0.042
Chain 1:   1400        -7735.216             0.044            0.040
Chain 1:   1500        -7605.500             0.041            0.036
Chain 1:   1600        -7793.445             0.029            0.024
Chain 1:   1700        -7544.726             0.025            0.024
Chain 1:   1800        -7707.575             0.024            0.021
Chain 1:   1900        -7815.924             0.018            0.017
Chain 1:   2000        -7827.742             0.018            0.017
Chain 1:   2100        -7667.497             0.019            0.021
Chain 1:   2200        -7926.517             0.019            0.021
Chain 1:   2300        -7708.554             0.021            0.021
Chain 1:   2400        -7701.917             0.019            0.021
Chain 1:   2500        -7639.577             0.018            0.021
Chain 1:   2600        -7628.079             0.016            0.021
Chain 1:   2700        -7527.349             0.014            0.014
Chain 1:   2800        -7755.428             0.015            0.014
Chain 1:   2900        -7451.684             0.018            0.021
Chain 1:   3000        -7607.260             0.020            0.021
Chain 1:   3100        -7607.547             0.018            0.020
Chain 1:   3200        -7806.426             0.017            0.020
Chain 1:   3300        -7505.113             0.018            0.020
Chain 1:   3400        -7740.315             0.021            0.025
Chain 1:   3500        -7513.601             0.023            0.029
Chain 1:   3600        -7583.713             0.024            0.029
Chain 1:   3700        -7538.332             0.023            0.029
Chain 1:   3800        -7509.938             0.021            0.025
Chain 1:   3900        -7483.902             0.017            0.020
Chain 1:   4000        -7480.191             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003235 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86862.588             1.000            1.000
Chain 1:    200       -14241.374             3.050            5.099
Chain 1:    300       -10486.226             2.152            1.000
Chain 1:    400       -12113.465             1.648            1.000
Chain 1:    500        -9200.270             1.382            0.358
Chain 1:    600        -9865.559             1.163            0.358
Chain 1:    700        -9013.535             1.010            0.317
Chain 1:    800        -9743.317             0.893            0.317
Chain 1:    900        -9188.648             0.801            0.134
Chain 1:   1000        -9183.724             0.721            0.134
Chain 1:   1100        -9237.761             0.621            0.095   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8791.062             0.116            0.075
Chain 1:   1300        -9110.845             0.084            0.067
Chain 1:   1400        -9138.963             0.071            0.060
Chain 1:   1500        -8972.261             0.041            0.051
Chain 1:   1600        -9086.342             0.036            0.035
Chain 1:   1700        -9139.815             0.027            0.019
Chain 1:   1800        -8691.127             0.024            0.019
Chain 1:   1900        -8800.323             0.020            0.013
Chain 1:   2000        -8783.542             0.020            0.013
Chain 1:   2100        -8923.038             0.021            0.016
Chain 1:   2200        -8695.357             0.018            0.016
Chain 1:   2300        -8795.167             0.016            0.013
Chain 1:   2400        -8867.768             0.016            0.013
Chain 1:   2500        -8807.498             0.015            0.012
Chain 1:   2600        -8824.512             0.014            0.011
Chain 1:   2700        -8730.671             0.015            0.011
Chain 1:   2800        -8676.456             0.010            0.011
Chain 1:   2900        -8782.446             0.010            0.011
Chain 1:   3000        -8620.552             0.012            0.011
Chain 1:   3100        -8760.769             0.012            0.011
Chain 1:   3200        -8630.145             0.011            0.011
Chain 1:   3300        -8858.414             0.012            0.012
Chain 1:   3400        -8875.570             0.012            0.012
Chain 1:   3500        -8732.533             0.012            0.015
Chain 1:   3600        -8587.422             0.014            0.016
Chain 1:   3700        -8734.445             0.015            0.016
Chain 1:   3800        -8590.135             0.016            0.017
Chain 1:   3900        -8522.017             0.015            0.017
Chain 1:   4000        -8631.379             0.015            0.016
Chain 1:   4100        -8597.184             0.013            0.016
Chain 1:   4200        -8582.990             0.012            0.016
Chain 1:   4300        -8616.442             0.010            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003439 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387364.357             1.000            1.000
Chain 1:    200     -1585177.612             2.646            4.291
Chain 1:    300      -892690.087             2.022            1.000
Chain 1:    400      -458823.593             1.753            1.000
Chain 1:    500      -359373.394             1.458            0.946
Chain 1:    600      -234109.737             1.304            0.946
Chain 1:    700      -120169.777             1.253            0.946
Chain 1:    800       -87310.549             1.144            0.946
Chain 1:    900       -67635.438             1.049            0.776
Chain 1:   1000       -52423.894             0.973            0.776
Chain 1:   1100       -39877.242             0.904            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39059.312             0.477            0.376
Chain 1:   1300       -26983.957             0.445            0.376
Chain 1:   1400       -26702.967             0.351            0.315
Chain 1:   1500       -23281.105             0.338            0.315
Chain 1:   1600       -22495.393             0.288            0.291
Chain 1:   1700       -21365.133             0.199            0.290
Chain 1:   1800       -21308.702             0.161            0.147
Chain 1:   1900       -21635.447             0.134            0.053
Chain 1:   2000       -20143.148             0.112            0.053
Chain 1:   2100       -20381.840             0.082            0.035
Chain 1:   2200       -20608.990             0.081            0.035
Chain 1:   2300       -20225.444             0.038            0.019
Chain 1:   2400       -19997.292             0.038            0.019
Chain 1:   2500       -19799.294             0.024            0.015
Chain 1:   2600       -19428.885             0.023            0.015
Chain 1:   2700       -19385.676             0.018            0.012
Chain 1:   2800       -19102.226             0.019            0.015
Chain 1:   2900       -19383.845             0.019            0.015
Chain 1:   3000       -19369.998             0.011            0.012
Chain 1:   3100       -19455.047             0.011            0.011
Chain 1:   3200       -19145.325             0.011            0.015
Chain 1:   3300       -19350.378             0.010            0.011
Chain 1:   3400       -18824.533             0.012            0.015
Chain 1:   3500       -19437.546             0.014            0.015
Chain 1:   3600       -18742.839             0.016            0.015
Chain 1:   3700       -19130.679             0.018            0.016
Chain 1:   3800       -18088.138             0.022            0.020
Chain 1:   3900       -18084.235             0.021            0.020
Chain 1:   4000       -18201.549             0.021            0.020
Chain 1:   4100       -18115.170             0.021            0.020
Chain 1:   4200       -17930.946             0.021            0.020
Chain 1:   4300       -18069.673             0.020            0.020
Chain 1:   4400       -18026.106             0.018            0.010
Chain 1:   4500       -17928.579             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00141 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13078.804             1.000            1.000
Chain 1:    200        -9811.081             0.667            1.000
Chain 1:    300        -8492.678             0.496            0.333
Chain 1:    400        -8663.046             0.377            0.333
Chain 1:    500        -8488.095             0.306            0.155
Chain 1:    600        -8385.590             0.257            0.155
Chain 1:    700        -8261.659             0.222            0.021
Chain 1:    800        -8259.532             0.195            0.021
Chain 1:    900        -8284.976             0.173            0.020
Chain 1:   1000        -8358.589             0.157            0.020
Chain 1:   1100        -8398.276             0.057            0.015
Chain 1:   1200        -8309.632             0.025            0.012
Chain 1:   1300        -8231.743             0.010            0.011
Chain 1:   1400        -8253.572             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001617 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.17 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57631.344             1.000            1.000
Chain 1:    200       -18152.048             1.587            2.175
Chain 1:    300        -9107.327             1.389            1.000
Chain 1:    400        -8368.823             1.064            1.000
Chain 1:    500        -8471.635             0.854            0.993
Chain 1:    600        -8806.573             0.718            0.993
Chain 1:    700        -8272.193             0.624            0.088
Chain 1:    800        -8274.255             0.546            0.088
Chain 1:    900        -8230.278             0.486            0.065
Chain 1:   1000        -8222.409             0.438            0.065
Chain 1:   1100        -7774.183             0.344            0.058
Chain 1:   1200        -8180.410             0.131            0.050
Chain 1:   1300        -7983.928             0.034            0.038
Chain 1:   1400        -7934.391             0.026            0.025
Chain 1:   1500        -7642.924             0.029            0.038
Chain 1:   1600        -7918.152             0.028            0.035
Chain 1:   1700        -7514.875             0.027            0.035
Chain 1:   1800        -7751.376             0.030            0.035
Chain 1:   1900        -7658.506             0.031            0.035
Chain 1:   2000        -7802.583             0.033            0.035
Chain 1:   2100        -7688.436             0.028            0.031
Chain 1:   2200        -7885.463             0.026            0.025
Chain 1:   2300        -7730.923             0.025            0.025
Chain 1:   2400        -7819.169             0.026            0.025
Chain 1:   2500        -7709.723             0.023            0.020
Chain 1:   2600        -7622.019             0.021            0.018
Chain 1:   2700        -7613.249             0.016            0.015
Chain 1:   2800        -7744.641             0.015            0.015
Chain 1:   2900        -7469.858             0.017            0.017
Chain 1:   3000        -7628.036             0.017            0.017
Chain 1:   3100        -7630.498             0.016            0.017
Chain 1:   3200        -7824.124             0.016            0.017
Chain 1:   3300        -7533.037             0.018            0.017
Chain 1:   3400        -7629.796             0.018            0.017
Chain 1:   3500        -7590.845             0.017            0.017
Chain 1:   3600        -7557.994             0.016            0.017
Chain 1:   3700        -7546.260             0.016            0.017
Chain 1:   3800        -7520.436             0.015            0.013
Chain 1:   3900        -7497.852             0.011            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00415 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86729.898             1.000            1.000
Chain 1:    200       -14256.967             3.042            5.083
Chain 1:    300       -10461.465             2.149            1.000
Chain 1:    400       -12379.396             1.650            1.000
Chain 1:    500        -8869.479             1.399            0.396
Chain 1:    600        -8759.173             1.168            0.396
Chain 1:    700        -8819.424             1.002            0.363
Chain 1:    800        -9141.696             0.881            0.363
Chain 1:    900        -9308.102             0.785            0.155
Chain 1:   1000        -8776.146             0.713            0.155
Chain 1:   1100        -9172.365             0.617            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8795.741             0.113            0.043
Chain 1:   1300        -9078.590             0.080            0.043
Chain 1:   1400        -8904.495             0.067            0.035
Chain 1:   1500        -8923.246             0.027            0.031
Chain 1:   1600        -9000.919             0.027            0.031
Chain 1:   1700        -9064.417             0.027            0.031
Chain 1:   1800        -8612.434             0.029            0.031
Chain 1:   1900        -8722.178             0.028            0.031
Chain 1:   2000        -8738.357             0.022            0.020
Chain 1:   2100        -8826.135             0.019            0.013
Chain 1:   2200        -8610.372             0.017            0.013
Chain 1:   2300        -8773.450             0.016            0.013
Chain 1:   2400        -8618.560             0.016            0.013
Chain 1:   2500        -8692.441             0.016            0.013
Chain 1:   2600        -8603.197             0.016            0.013
Chain 1:   2700        -8637.218             0.016            0.013
Chain 1:   2800        -8588.368             0.011            0.010
Chain 1:   2900        -8703.073             0.012            0.010
Chain 1:   3000        -8616.577             0.012            0.010
Chain 1:   3100        -8580.490             0.012            0.010
Chain 1:   3200        -8552.507             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003194 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403543.759             1.000            1.000
Chain 1:    200     -1583718.024             2.653            4.306
Chain 1:    300      -891291.036             2.028            1.000
Chain 1:    400      -458263.415             1.757            1.000
Chain 1:    500      -358710.022             1.461            0.945
Chain 1:    600      -233654.384             1.307            0.945
Chain 1:    700      -119956.596             1.256            0.945
Chain 1:    800       -87185.906             1.146            0.945
Chain 1:    900       -67550.002             1.051            0.777
Chain 1:   1000       -52373.468             0.974            0.777
Chain 1:   1100       -39861.399             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39049.736             0.477            0.376
Chain 1:   1300       -26999.896             0.444            0.376
Chain 1:   1400       -26722.758             0.351            0.314
Chain 1:   1500       -23307.609             0.338            0.314
Chain 1:   1600       -22524.613             0.288            0.291
Chain 1:   1700       -21396.726             0.198            0.290
Chain 1:   1800       -21341.213             0.161            0.147
Chain 1:   1900       -21668.198             0.133            0.053
Chain 1:   2000       -20177.007             0.112            0.053
Chain 1:   2100       -20415.535             0.081            0.035
Chain 1:   2200       -20642.674             0.080            0.035
Chain 1:   2300       -20259.102             0.038            0.019
Chain 1:   2400       -20030.911             0.038            0.019
Chain 1:   2500       -19832.875             0.024            0.015
Chain 1:   2600       -19462.169             0.023            0.015
Chain 1:   2700       -19418.980             0.018            0.012
Chain 1:   2800       -19135.401             0.019            0.015
Chain 1:   2900       -19417.087             0.019            0.015
Chain 1:   3000       -19403.228             0.011            0.012
Chain 1:   3100       -19488.298             0.011            0.011
Chain 1:   3200       -19178.429             0.011            0.015
Chain 1:   3300       -19383.636             0.010            0.011
Chain 1:   3400       -18857.500             0.012            0.015
Chain 1:   3500       -19470.895             0.014            0.015
Chain 1:   3600       -18775.697             0.016            0.015
Chain 1:   3700       -19163.853             0.018            0.016
Chain 1:   3800       -18120.528             0.022            0.020
Chain 1:   3900       -18116.617             0.021            0.020
Chain 1:   4000       -18233.931             0.021            0.020
Chain 1:   4100       -18147.476             0.021            0.020
Chain 1:   4200       -17963.142             0.021            0.020
Chain 1:   4300       -18101.958             0.020            0.020
Chain 1:   4400       -18058.236             0.018            0.010
Chain 1:   4500       -17960.698             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001361 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12329.146             1.000            1.000
Chain 1:    200        -9343.657             0.660            1.000
Chain 1:    300        -7915.145             0.500            0.320
Chain 1:    400        -8002.988             0.378            0.320
Chain 1:    500        -7897.225             0.305            0.180
Chain 1:    600        -7823.815             0.256            0.180
Chain 1:    700        -7709.832             0.221            0.015
Chain 1:    800        -7713.704             0.194            0.015
Chain 1:    900        -7728.356             0.172            0.013
Chain 1:   1000        -7931.141             0.158            0.015
Chain 1:   1100        -7839.529             0.059            0.013
Chain 1:   1200        -7737.666             0.028            0.013
Chain 1:   1300        -7682.796             0.011            0.012
Chain 1:   1400        -7707.133             0.010            0.012
Chain 1:   1500        -7794.601             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001515 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61545.489             1.000            1.000
Chain 1:    200       -17870.073             1.722            2.444
Chain 1:    300        -8861.776             1.487            1.017
Chain 1:    400        -9533.755             1.133            1.017
Chain 1:    500        -8658.767             0.926            1.000
Chain 1:    600        -8796.065             0.775            1.000
Chain 1:    700        -8306.514             0.672            0.101
Chain 1:    800        -8313.833             0.588            0.101
Chain 1:    900        -7598.459             0.534            0.094
Chain 1:   1000        -7863.144             0.484            0.094
Chain 1:   1100        -7699.071             0.386            0.070
Chain 1:   1200        -7563.041             0.143            0.059
Chain 1:   1300        -7717.531             0.043            0.034
Chain 1:   1400        -7649.289             0.037            0.021
Chain 1:   1500        -7554.589             0.028            0.020
Chain 1:   1600        -7787.463             0.030            0.021
Chain 1:   1700        -7454.008             0.028            0.021
Chain 1:   1800        -7632.523             0.031            0.023
Chain 1:   1900        -7649.906             0.021            0.021
Chain 1:   2000        -7657.962             0.018            0.020
Chain 1:   2100        -7608.622             0.017            0.018
Chain 1:   2200        -7713.091             0.016            0.014
Chain 1:   2300        -7605.649             0.016            0.014
Chain 1:   2400        -7656.928             0.015            0.014
Chain 1:   2500        -7750.854             0.015            0.014
Chain 1:   2600        -7523.687             0.015            0.014
Chain 1:   2700        -7565.195             0.012            0.012
Chain 1:   2800        -7629.009             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003649 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86617.010             1.000            1.000
Chain 1:    200       -13505.475             3.207            5.413
Chain 1:    300        -9788.364             2.264            1.000
Chain 1:    400       -11172.529             1.729            1.000
Chain 1:    500        -8716.544             1.440            0.380
Chain 1:    600        -8193.998             1.210            0.380
Chain 1:    700        -8569.366             1.044            0.282
Chain 1:    800        -9312.639             0.923            0.282
Chain 1:    900        -8522.696             0.831            0.124
Chain 1:   1000        -8593.312             0.749            0.124
Chain 1:   1100        -8538.146             0.649            0.093   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8054.986             0.114            0.080
Chain 1:   1300        -8434.150             0.081            0.064
Chain 1:   1400        -8434.072             0.068            0.060
Chain 1:   1500        -8310.447             0.041            0.045
Chain 1:   1600        -8421.472             0.036            0.044
Chain 1:   1700        -8482.198             0.033            0.015
Chain 1:   1800        -8047.717             0.030            0.015
Chain 1:   1900        -8151.258             0.022            0.013
Chain 1:   2000        -8126.569             0.022            0.013
Chain 1:   2100        -8272.536             0.023            0.015
Chain 1:   2200        -8058.501             0.019            0.015
Chain 1:   2300        -8214.689             0.017            0.015
Chain 1:   2400        -8053.850             0.019            0.018
Chain 1:   2500        -8124.746             0.018            0.018
Chain 1:   2600        -8036.996             0.018            0.018
Chain 1:   2700        -8071.012             0.018            0.018
Chain 1:   2800        -8031.167             0.013            0.013
Chain 1:   2900        -8124.305             0.013            0.011
Chain 1:   3000        -7956.183             0.014            0.018
Chain 1:   3100        -8113.854             0.015            0.019
Chain 1:   3200        -7985.850             0.014            0.016
Chain 1:   3300        -7993.557             0.012            0.011
Chain 1:   3400        -8152.019             0.012            0.011
Chain 1:   3500        -8157.592             0.011            0.011
Chain 1:   3600        -7942.460             0.013            0.016
Chain 1:   3700        -8087.980             0.014            0.018
Chain 1:   3800        -7949.047             0.015            0.018
Chain 1:   3900        -7883.703             0.015            0.018
Chain 1:   4000        -7958.796             0.014            0.017
Chain 1:   4100        -7949.507             0.012            0.016
Chain 1:   4200        -7939.368             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003542 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8408180.848             1.000            1.000
Chain 1:    200     -1582197.592             2.657            4.314
Chain 1:    300      -890865.625             2.030            1.000
Chain 1:    400      -457838.936             1.759            1.000
Chain 1:    500      -358529.904             1.463            0.946
Chain 1:    600      -233385.920             1.308            0.946
Chain 1:    700      -119459.133             1.258            0.946
Chain 1:    800       -86631.385             1.148            0.946
Chain 1:    900       -66927.264             1.053            0.776
Chain 1:   1000       -51697.638             0.977            0.776
Chain 1:   1100       -39146.437             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38320.904             0.480            0.379
Chain 1:   1300       -26240.143             0.448            0.379
Chain 1:   1400       -25956.445             0.355            0.321
Chain 1:   1500       -22534.110             0.342            0.321
Chain 1:   1600       -21748.370             0.292            0.295
Chain 1:   1700       -20617.320             0.202            0.294
Chain 1:   1800       -20560.449             0.165            0.152
Chain 1:   1900       -20887.048             0.137            0.055
Chain 1:   2000       -19394.894             0.115            0.055
Chain 1:   2100       -19633.460             0.084            0.036
Chain 1:   2200       -19860.656             0.083            0.036
Chain 1:   2300       -19477.081             0.039            0.020
Chain 1:   2400       -19249.007             0.039            0.020
Chain 1:   2500       -19051.167             0.025            0.016
Chain 1:   2600       -18680.860             0.024            0.016
Chain 1:   2700       -18637.619             0.018            0.012
Chain 1:   2800       -18354.441             0.020            0.015
Chain 1:   2900       -18635.860             0.020            0.015
Chain 1:   3000       -18621.952             0.012            0.012
Chain 1:   3100       -18707.063             0.011            0.012
Chain 1:   3200       -18397.416             0.012            0.015
Chain 1:   3300       -18602.373             0.011            0.012
Chain 1:   3400       -18076.847             0.013            0.015
Chain 1:   3500       -18689.468             0.015            0.015
Chain 1:   3600       -17995.137             0.017            0.015
Chain 1:   3700       -18382.790             0.019            0.017
Chain 1:   3800       -17340.967             0.023            0.021
Chain 1:   3900       -17337.085             0.021            0.021
Chain 1:   4000       -17454.355             0.022            0.021
Chain 1:   4100       -17368.106             0.022            0.021
Chain 1:   4200       -17183.978             0.022            0.021
Chain 1:   4300       -17322.618             0.021            0.021
Chain 1:   4400       -17279.193             0.019            0.011
Chain 1:   4500       -17181.666             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001678 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12689.661             1.000            1.000
Chain 1:    200        -9375.297             0.677            1.000
Chain 1:    300        -7940.222             0.511            0.354
Chain 1:    400        -8052.121             0.387            0.354
Chain 1:    500        -7965.523             0.312            0.181
Chain 1:    600        -7841.529             0.262            0.181
Chain 1:    700        -7790.570             0.226            0.016
Chain 1:    800        -7772.214             0.198            0.016
Chain 1:    900        -7849.979             0.177            0.014
Chain 1:   1000        -7842.829             0.159            0.014
Chain 1:   1100        -7891.685             0.060            0.011
Chain 1:   1200        -7799.020             0.026            0.011
Chain 1:   1300        -7765.905             0.008            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00165 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57859.557             1.000            1.000
Chain 1:    200       -17382.692             1.664            2.329
Chain 1:    300        -8541.482             1.455            1.035
Chain 1:    400        -8163.042             1.103            1.035
Chain 1:    500        -7940.485             0.888            1.000
Chain 1:    600        -8639.445             0.753            1.000
Chain 1:    700        -7732.182             0.662            0.117
Chain 1:    800        -7979.806             0.583            0.117
Chain 1:    900        -7898.522             0.520            0.081
Chain 1:   1000        -7854.502             0.468            0.081
Chain 1:   1100        -7696.497             0.370            0.046
Chain 1:   1200        -7572.278             0.139            0.031
Chain 1:   1300        -7683.424             0.037            0.028
Chain 1:   1400        -7701.335             0.033            0.021
Chain 1:   1500        -7576.865             0.032            0.016
Chain 1:   1600        -7522.171             0.024            0.016
Chain 1:   1700        -7506.925             0.013            0.014
Chain 1:   1800        -7530.153             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003293 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86173.612             1.000            1.000
Chain 1:    200       -13198.463             3.265            5.529
Chain 1:    300        -9653.456             2.299            1.000
Chain 1:    400       -10604.697             1.746            1.000
Chain 1:    500        -8585.164             1.444            0.367
Chain 1:    600        -8218.464             1.211            0.367
Chain 1:    700        -8400.922             1.041            0.235
Chain 1:    800        -8706.784             0.915            0.235
Chain 1:    900        -8543.004             0.816            0.090
Chain 1:   1000        -8227.104             0.738            0.090
Chain 1:   1100        -8568.122             0.642            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8239.432             0.093            0.040
Chain 1:   1300        -8280.926             0.057            0.040
Chain 1:   1400        -8427.610             0.050            0.038
Chain 1:   1500        -8288.714             0.028            0.035
Chain 1:   1600        -8398.006             0.025            0.022
Chain 1:   1700        -8480.393             0.023            0.019
Chain 1:   1800        -8095.814             0.025            0.019
Chain 1:   1900        -8198.351             0.024            0.017
Chain 1:   2000        -8168.003             0.021            0.017
Chain 1:   2100        -8302.795             0.018            0.016
Chain 1:   2200        -8087.146             0.017            0.016
Chain 1:   2300        -8228.287             0.018            0.017
Chain 1:   2400        -8239.212             0.016            0.016
Chain 1:   2500        -8207.570             0.015            0.013
Chain 1:   2600        -8205.565             0.014            0.013
Chain 1:   2700        -8114.864             0.014            0.013
Chain 1:   2800        -8093.007             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002945 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422295.585             1.000            1.000
Chain 1:    200     -1589767.769             2.649            4.298
Chain 1:    300      -890660.681             2.028            1.000
Chain 1:    400      -457266.708             1.758            1.000
Chain 1:    500      -357143.673             1.462            0.948
Chain 1:    600      -232071.469             1.308            0.948
Chain 1:    700      -118593.705             1.258            0.948
Chain 1:    800       -85856.046             1.148            0.948
Chain 1:    900       -66259.521             1.054            0.785
Chain 1:   1000       -51100.350             0.978            0.785
Chain 1:   1100       -38625.317             0.910            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37801.700             0.483            0.381
Chain 1:   1300       -25823.594             0.451            0.381
Chain 1:   1400       -25545.228             0.357            0.323
Chain 1:   1500       -22149.759             0.344            0.323
Chain 1:   1600       -21370.241             0.294            0.297
Chain 1:   1700       -20252.856             0.204            0.296
Chain 1:   1800       -20198.589             0.166            0.153
Chain 1:   1900       -20524.191             0.138            0.055
Chain 1:   2000       -19040.804             0.116            0.055
Chain 1:   2100       -19278.946             0.085            0.036
Chain 1:   2200       -19504.236             0.084            0.036
Chain 1:   2300       -19122.585             0.040            0.020
Chain 1:   2400       -18894.977             0.040            0.020
Chain 1:   2500       -18696.634             0.025            0.016
Chain 1:   2600       -18327.842             0.024            0.016
Chain 1:   2700       -18285.045             0.019            0.012
Chain 1:   2800       -18002.029             0.020            0.016
Chain 1:   2900       -18282.911             0.020            0.015
Chain 1:   3000       -18269.220             0.012            0.012
Chain 1:   3100       -18354.123             0.011            0.012
Chain 1:   3200       -18045.286             0.012            0.015
Chain 1:   3300       -18249.602             0.011            0.012
Chain 1:   3400       -17725.262             0.013            0.015
Chain 1:   3500       -18335.927             0.015            0.016
Chain 1:   3600       -17644.138             0.017            0.016
Chain 1:   3700       -18029.787             0.019            0.017
Chain 1:   3800       -16991.787             0.023            0.021
Chain 1:   3900       -16987.927             0.022            0.021
Chain 1:   4000       -17105.291             0.022            0.021
Chain 1:   4100       -17019.165             0.023            0.021
Chain 1:   4200       -16835.879             0.022            0.021
Chain 1:   4300       -16973.987             0.022            0.021
Chain 1:   4400       -16931.244             0.019            0.011
Chain 1:   4500       -16833.791             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001288 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12673.279             1.000            1.000
Chain 1:    200        -9521.116             0.666            1.000
Chain 1:    300        -8169.164             0.499            0.331
Chain 1:    400        -8273.883             0.377            0.331
Chain 1:    500        -8200.269             0.304            0.165
Chain 1:    600        -8115.792             0.255            0.165
Chain 1:    700        -8020.038             0.220            0.013
Chain 1:    800        -8058.940             0.193            0.013
Chain 1:    900        -8198.460             0.174            0.013
Chain 1:   1000        -8075.661             0.158            0.015
Chain 1:   1100        -8113.512             0.058            0.013
Chain 1:   1200        -8061.293             0.026            0.012
Chain 1:   1300        -7988.778             0.010            0.010
Chain 1:   1400        -8013.056             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002225 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 22.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46532.665             1.000            1.000
Chain 1:    200       -15696.297             1.482            1.965
Chain 1:    300        -8766.993             1.252            1.000
Chain 1:    400        -8800.452             0.940            1.000
Chain 1:    500        -7686.071             0.781            0.790
Chain 1:    600        -8821.937             0.672            0.790
Chain 1:    700        -8149.773             0.588            0.145
Chain 1:    800        -8176.838             0.515            0.145
Chain 1:    900        -8062.684             0.459            0.129
Chain 1:   1000        -7938.316             0.415            0.129
Chain 1:   1100        -7912.710             0.315            0.082
Chain 1:   1200        -7784.365             0.120            0.016
Chain 1:   1300        -7811.900             0.042            0.016
Chain 1:   1400        -7816.279             0.041            0.016
Chain 1:   1500        -7682.554             0.029            0.016
Chain 1:   1600        -7685.236             0.016            0.014
Chain 1:   1700        -7606.261             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004363 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86174.740             1.000            1.000
Chain 1:    200       -13598.272             3.169            5.337
Chain 1:    300        -9976.986             2.233            1.000
Chain 1:    400       -10693.155             1.692            1.000
Chain 1:    500        -8963.672             1.392            0.363
Chain 1:    600        -8471.949             1.170            0.363
Chain 1:    700        -8848.962             1.009            0.193
Chain 1:    800        -9321.608             0.889            0.193
Chain 1:    900        -8740.825             0.798            0.067
Chain 1:   1000        -8609.782             0.719            0.067
Chain 1:   1100        -8845.778             0.622            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8380.055             0.094            0.058
Chain 1:   1300        -8695.992             0.061            0.056
Chain 1:   1400        -8695.218             0.054            0.051
Chain 1:   1500        -8568.788             0.037            0.043
Chain 1:   1600        -8675.998             0.032            0.036
Chain 1:   1700        -8762.311             0.029            0.027
Chain 1:   1800        -8355.805             0.029            0.027
Chain 1:   1900        -8452.635             0.023            0.015
Chain 1:   2000        -8424.757             0.022            0.015
Chain 1:   2100        -8545.249             0.021            0.014
Chain 1:   2200        -8355.879             0.017            0.014
Chain 1:   2300        -8492.359             0.015            0.014
Chain 1:   2400        -8499.676             0.015            0.014
Chain 1:   2500        -8465.920             0.014            0.012
Chain 1:   2600        -8463.929             0.013            0.011
Chain 1:   2700        -8377.955             0.013            0.011
Chain 1:   2800        -8343.174             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003551 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.51 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8368633.612             1.000            1.000
Chain 1:    200     -1579479.029             2.649            4.298
Chain 1:    300      -890515.966             2.024            1.000
Chain 1:    400      -457408.074             1.755            1.000
Chain 1:    500      -358205.013             1.459            0.947
Chain 1:    600      -233391.905             1.305            0.947
Chain 1:    700      -119529.429             1.255            0.947
Chain 1:    800       -86677.291             1.145            0.947
Chain 1:    900       -66996.854             1.051            0.774
Chain 1:   1000       -51763.010             0.975            0.774
Chain 1:   1100       -39204.676             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38377.263             0.479            0.379
Chain 1:   1300       -26301.632             0.448            0.379
Chain 1:   1400       -26017.365             0.354            0.320
Chain 1:   1500       -22595.381             0.342            0.320
Chain 1:   1600       -21808.603             0.292            0.294
Chain 1:   1700       -20678.854             0.202            0.294
Chain 1:   1800       -20622.031             0.164            0.151
Chain 1:   1900       -20948.047             0.137            0.055
Chain 1:   2000       -19457.197             0.115            0.055
Chain 1:   2100       -19695.916             0.084            0.036
Chain 1:   2200       -19922.473             0.083            0.036
Chain 1:   2300       -19539.571             0.039            0.020
Chain 1:   2400       -19311.630             0.039            0.020
Chain 1:   2500       -19113.689             0.025            0.016
Chain 1:   2600       -18744.118             0.023            0.016
Chain 1:   2700       -18701.076             0.018            0.012
Chain 1:   2800       -18418.017             0.019            0.015
Chain 1:   2900       -18699.231             0.019            0.015
Chain 1:   3000       -18685.483             0.012            0.012
Chain 1:   3100       -18770.440             0.011            0.012
Chain 1:   3200       -18461.241             0.012            0.015
Chain 1:   3300       -18665.834             0.011            0.012
Chain 1:   3400       -18140.985             0.012            0.015
Chain 1:   3500       -18752.613             0.015            0.015
Chain 1:   3600       -18059.608             0.017            0.015
Chain 1:   3700       -18446.214             0.018            0.017
Chain 1:   3800       -17406.451             0.023            0.021
Chain 1:   3900       -17402.587             0.021            0.021
Chain 1:   4000       -17519.898             0.022            0.021
Chain 1:   4100       -17433.705             0.022            0.021
Chain 1:   4200       -17250.019             0.021            0.021
Chain 1:   4300       -17388.378             0.021            0.021
Chain 1:   4400       -17345.310             0.018            0.011
Chain 1:   4500       -17247.824             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13444.875             1.000            1.000
Chain 1:    200       -10323.179             0.651            1.000
Chain 1:    300        -8768.874             0.493            0.302
Chain 1:    400        -9024.054             0.377            0.302
Chain 1:    500        -8805.285             0.307            0.177
Chain 1:    600        -8667.509             0.258            0.177
Chain 1:    700        -8541.210             0.223            0.028
Chain 1:    800        -8638.098             0.197            0.028
Chain 1:    900        -8544.680             0.176            0.025
Chain 1:   1000        -8635.354             0.160            0.025
Chain 1:   1100        -8612.839             0.060            0.016
Chain 1:   1200        -8608.501             0.030            0.015
Chain 1:   1300        -8536.695             0.013            0.011
Chain 1:   1400        -8547.582             0.010            0.011
Chain 1:   1500        -8621.588             0.008            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001638 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -65113.257             1.000            1.000
Chain 1:    200       -19551.555             1.665            2.330
Chain 1:    300        -9487.607             1.464            1.061
Chain 1:    400        -8560.099             1.125            1.061
Chain 1:    500        -8366.048             0.905            1.000
Chain 1:    600        -8529.812             0.757            1.000
Chain 1:    700        -9277.426             0.660            0.108
Chain 1:    800        -7737.927             0.603            0.199
Chain 1:    900        -9221.325             0.554            0.161
Chain 1:   1000        -8026.105             0.513            0.161
Chain 1:   1100        -8078.655             0.414            0.149
Chain 1:   1200        -7920.251             0.183            0.108
Chain 1:   1300        -7974.384             0.077            0.081
Chain 1:   1400        -7934.356             0.067            0.023
Chain 1:   1500        -7664.967             0.068            0.035
Chain 1:   1600        -7868.110             0.069            0.035
Chain 1:   1700        -7752.078             0.062            0.026
Chain 1:   1800        -7688.899             0.043            0.020
Chain 1:   1900        -7695.905             0.027            0.015
Chain 1:   2000        -7700.882             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003153 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87404.355             1.000            1.000
Chain 1:    200       -14715.356             2.970            4.940
Chain 1:    300       -10843.404             2.099            1.000
Chain 1:    400       -13216.666             1.619            1.000
Chain 1:    500        -9170.605             1.384            0.441
Chain 1:    600        -9464.180             1.158            0.441
Chain 1:    700        -9290.904             0.995            0.357
Chain 1:    800        -9426.218             0.873            0.357
Chain 1:    900        -9569.729             0.777            0.180
Chain 1:   1000        -9019.273             0.706            0.180
Chain 1:   1100        -9243.992             0.608            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8878.197             0.118            0.041
Chain 1:   1300        -9353.956             0.088            0.041
Chain 1:   1400        -9223.267             0.071            0.031
Chain 1:   1500        -9268.018             0.028            0.024
Chain 1:   1600        -9266.993             0.024            0.019
Chain 1:   1700        -9384.645             0.024            0.015
Chain 1:   1800        -8900.394             0.028            0.024
Chain 1:   1900        -9024.964             0.028            0.024
Chain 1:   2000        -9033.959             0.022            0.014
Chain 1:   2100        -9155.080             0.021            0.014
Chain 1:   2200        -8895.496             0.019            0.014
Chain 1:   2300        -8987.275             0.015            0.013
Chain 1:   2400        -9075.792             0.015            0.013
Chain 1:   2500        -8995.923             0.015            0.013
Chain 1:   2600        -9019.857             0.016            0.013
Chain 1:   2700        -8933.949             0.015            0.010
Chain 1:   2800        -8894.440             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003747 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401028.145             1.000            1.000
Chain 1:    200     -1582968.148             2.654            4.307
Chain 1:    300      -891776.379             2.027            1.000
Chain 1:    400      -459090.355             1.756            1.000
Chain 1:    500      -359737.073             1.460            0.942
Chain 1:    600      -234583.744             1.306            0.942
Chain 1:    700      -120654.044             1.254            0.942
Chain 1:    800       -87864.817             1.144            0.942
Chain 1:    900       -68175.089             1.049            0.775
Chain 1:   1000       -52962.007             0.973            0.775
Chain 1:   1100       -40417.793             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39601.830             0.475            0.373
Chain 1:   1300       -27509.861             0.442            0.373
Chain 1:   1400       -27230.044             0.348            0.310
Chain 1:   1500       -23804.868             0.335            0.310
Chain 1:   1600       -23019.971             0.285            0.289
Chain 1:   1700       -21886.058             0.196            0.287
Chain 1:   1800       -21829.490             0.159            0.144
Chain 1:   1900       -22156.837             0.132            0.052
Chain 1:   2000       -20662.121             0.110            0.052
Chain 1:   2100       -20900.686             0.080            0.034
Chain 1:   2200       -21128.783             0.079            0.034
Chain 1:   2300       -20744.279             0.037            0.019
Chain 1:   2400       -20515.881             0.037            0.019
Chain 1:   2500       -20318.227             0.024            0.015
Chain 1:   2600       -19946.832             0.022            0.015
Chain 1:   2700       -19903.312             0.017            0.011
Chain 1:   2800       -19619.778             0.018            0.014
Chain 1:   2900       -19901.671             0.018            0.014
Chain 1:   3000       -19887.644             0.011            0.011
Chain 1:   3100       -19972.885             0.010            0.011
Chain 1:   3200       -19662.646             0.011            0.014
Chain 1:   3300       -19868.107             0.010            0.011
Chain 1:   3400       -19341.538             0.012            0.014
Chain 1:   3500       -19955.743             0.014            0.014
Chain 1:   3600       -19259.386             0.016            0.014
Chain 1:   3700       -19648.494             0.017            0.016
Chain 1:   3800       -18603.594             0.022            0.020
Chain 1:   3900       -18599.673             0.020            0.020
Chain 1:   4000       -18716.929             0.021            0.020
Chain 1:   4100       -18630.510             0.021            0.020
Chain 1:   4200       -18445.728             0.020            0.020
Chain 1:   4300       -18584.806             0.020            0.020
Chain 1:   4400       -18540.787             0.017            0.010
Chain 1:   4500       -18443.224             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49430.328             1.000            1.000
Chain 1:    200       -20853.478             1.185            1.370
Chain 1:    300       -21748.481             0.804            1.000
Chain 1:    400       -13032.291             0.770            1.000
Chain 1:    500       -16256.824             0.656            0.669
Chain 1:    600       -13222.191             0.585            0.669
Chain 1:    700       -12860.521             0.505            0.230
Chain 1:    800       -11826.830             0.453            0.230
Chain 1:    900       -11713.919             0.404            0.198
Chain 1:   1000       -12477.159             0.369            0.198
Chain 1:   1100       -28835.360             0.326            0.198
Chain 1:   1200       -10766.220             0.357            0.198
Chain 1:   1300       -10229.897             0.358            0.198
Chain 1:   1400       -11020.783             0.298            0.087
Chain 1:   1500        -9777.952             0.291            0.087
Chain 1:   1600       -15210.548             0.304            0.087
Chain 1:   1700       -21088.802             0.329            0.127
Chain 1:   1800       -12784.597             0.385            0.279
Chain 1:   1900       -10316.164             0.408            0.279
Chain 1:   2000       -19234.455             0.449            0.357
Chain 1:   2100       -11556.156             0.458            0.357
Chain 1:   2200       -10641.282             0.299            0.279
Chain 1:   2300       -17787.044             0.334            0.357
Chain 1:   2400        -9651.597             0.411            0.402
Chain 1:   2500        -9911.322             0.401            0.402
Chain 1:   2600       -13881.871             0.394            0.402
Chain 1:   2700        -9831.040             0.407            0.412
Chain 1:   2800       -17180.817             0.385            0.412
Chain 1:   2900        -9941.857             0.434            0.428
Chain 1:   3000       -11029.678             0.397            0.412
Chain 1:   3100        -9927.386             0.342            0.402
Chain 1:   3200       -10609.627             0.340            0.402
Chain 1:   3300       -17052.754             0.337            0.378
Chain 1:   3400        -9210.861             0.338            0.378
Chain 1:   3500        -9269.399             0.336            0.378
Chain 1:   3600        -9199.470             0.309            0.378
Chain 1:   3700        -9379.232             0.269            0.111
Chain 1:   3800        -9340.581             0.227            0.099
Chain 1:   3900       -10657.891             0.166            0.099
Chain 1:   4000        -9004.502             0.175            0.111
Chain 1:   4100        -8941.209             0.165            0.064
Chain 1:   4200       -10047.746             0.169            0.110
Chain 1:   4300        -9980.291             0.132            0.019
Chain 1:   4400        -8975.443             0.058            0.019
Chain 1:   4500        -9162.218             0.059            0.020
Chain 1:   4600       -13790.867             0.092            0.110
Chain 1:   4700        -8811.909             0.147            0.112
Chain 1:   4800        -8781.332             0.147            0.112
Chain 1:   4900       -14527.768             0.174            0.112
Chain 1:   5000        -9682.848             0.206            0.112
Chain 1:   5100       -13163.535             0.231            0.264
Chain 1:   5200       -13725.465             0.224            0.264
Chain 1:   5300        -8951.856             0.277            0.336
Chain 1:   5400       -16734.311             0.312            0.396
Chain 1:   5500       -13434.008             0.335            0.396
Chain 1:   5600        -8717.973             0.355            0.465
Chain 1:   5700       -10219.325             0.314            0.396
Chain 1:   5800       -11265.069             0.323            0.396
Chain 1:   5900       -15047.252             0.308            0.264
Chain 1:   6000        -9733.269             0.313            0.264
Chain 1:   6100        -9882.423             0.288            0.251
Chain 1:   6200        -9084.033             0.292            0.251
Chain 1:   6300        -8636.490             0.244            0.246
Chain 1:   6400        -8430.586             0.200            0.147
Chain 1:   6500        -9398.322             0.186            0.103
Chain 1:   6600        -8778.953             0.139            0.093
Chain 1:   6700        -8672.568             0.126            0.088
Chain 1:   6800        -8651.609             0.116            0.071
Chain 1:   6900        -8602.641             0.092            0.052
Chain 1:   7000       -12204.857             0.067            0.052
Chain 1:   7100        -8792.493             0.104            0.071
Chain 1:   7200        -8940.121             0.097            0.052
Chain 1:   7300        -8715.585             0.094            0.026
Chain 1:   7400        -9008.455             0.095            0.033
Chain 1:   7500        -8554.468             0.090            0.033
Chain 1:   7600        -8776.855             0.086            0.026
Chain 1:   7700        -8371.717             0.089            0.033
Chain 1:   7800        -8850.632             0.094            0.048
Chain 1:   7900        -8429.340             0.099            0.050
Chain 1:   8000        -8757.330             0.073            0.048
Chain 1:   8100        -8711.487             0.035            0.037
Chain 1:   8200        -9822.605             0.044            0.048
Chain 1:   8300        -8549.024             0.057            0.050
Chain 1:   8400        -9015.202             0.059            0.052
Chain 1:   8500        -8432.423             0.060            0.052
Chain 1:   8600        -9422.246             0.068            0.054
Chain 1:   8700       -10522.686             0.074            0.069
Chain 1:   8800        -8588.733             0.091            0.105
Chain 1:   8900        -8971.879             0.090            0.105
Chain 1:   9000        -8542.457             0.092            0.105
Chain 1:   9100        -8718.645             0.093            0.105
Chain 1:   9200        -8411.279             0.085            0.069
Chain 1:   9300        -8567.176             0.072            0.052
Chain 1:   9400       -10194.422             0.083            0.069
Chain 1:   9500        -9112.888             0.088            0.105
Chain 1:   9600       -10546.554             0.091            0.105
Chain 1:   9700        -8643.806             0.103            0.119
Chain 1:   9800       -13127.194             0.114            0.119
Chain 1:   9900        -8378.418             0.167            0.136
Chain 1:   10000       -11081.526             0.186            0.160
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00149 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58937.213             1.000            1.000
Chain 1:    200       -18191.287             1.620            2.240
Chain 1:    300        -8908.107             1.427            1.042
Chain 1:    400        -8170.422             1.093            1.042
Chain 1:    500        -8761.635             0.888            1.000
Chain 1:    600        -8125.829             0.753            1.000
Chain 1:    700        -8337.442             0.649            0.090
Chain 1:    800        -8469.111             0.570            0.090
Chain 1:    900        -7737.235             0.517            0.090
Chain 1:   1000        -7983.583             0.468            0.090
Chain 1:   1100        -7740.785             0.372            0.078
Chain 1:   1200        -7719.399             0.148            0.067
Chain 1:   1300        -7795.589             0.045            0.031
Chain 1:   1400        -7681.052             0.037            0.031
Chain 1:   1500        -7594.697             0.031            0.025
Chain 1:   1600        -7737.093             0.025            0.018
Chain 1:   1700        -7615.438             0.025            0.016
Chain 1:   1800        -7757.499             0.025            0.018
Chain 1:   1900        -7637.454             0.017            0.016
Chain 1:   2000        -7715.465             0.015            0.016
Chain 1:   2100        -7607.653             0.013            0.015
Chain 1:   2200        -7830.243             0.016            0.016
Chain 1:   2300        -7573.691             0.018            0.016
Chain 1:   2400        -7592.343             0.017            0.016
Chain 1:   2500        -7654.139             0.017            0.016
Chain 1:   2600        -7574.293             0.016            0.016
Chain 1:   2700        -7497.908             0.015            0.014
Chain 1:   2800        -7530.946             0.014            0.011
Chain 1:   2900        -7454.204             0.013            0.010
Chain 1:   3000        -7592.514             0.014            0.011
Chain 1:   3100        -7573.971             0.013            0.010
Chain 1:   3200        -7778.887             0.013            0.010
Chain 1:   3300        -7494.650             0.013            0.010
Chain 1:   3400        -7729.499             0.016            0.011
Chain 1:   3500        -7481.403             0.018            0.018
Chain 1:   3600        -7546.085             0.018            0.018
Chain 1:   3700        -7497.197             0.018            0.018
Chain 1:   3800        -7497.072             0.017            0.018
Chain 1:   3900        -7456.788             0.017            0.018
Chain 1:   4000        -7448.700             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003267 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.67 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86276.716             1.000            1.000
Chain 1:    200       -13916.310             3.100            5.200
Chain 1:    300       -10160.063             2.190            1.000
Chain 1:    400       -11666.719             1.675            1.000
Chain 1:    500        -8918.737             1.401            0.370
Chain 1:    600        -8580.429             1.174            0.370
Chain 1:    700        -8489.898             1.008            0.308
Chain 1:    800        -8908.477             0.888            0.308
Chain 1:    900        -8889.511             0.790            0.129
Chain 1:   1000        -9108.223             0.713            0.129
Chain 1:   1100        -8805.186             0.616            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8484.403             0.100            0.039
Chain 1:   1300        -8779.864             0.067            0.038
Chain 1:   1400        -8606.330             0.056            0.034
Chain 1:   1500        -8634.530             0.025            0.034
Chain 1:   1600        -8742.856             0.023            0.024
Chain 1:   1700        -8794.076             0.022            0.024
Chain 1:   1800        -8341.829             0.023            0.024
Chain 1:   1900        -8450.616             0.024            0.024
Chain 1:   2000        -8450.306             0.021            0.020
Chain 1:   2100        -8617.338             0.020            0.019
Chain 1:   2200        -8346.637             0.019            0.019
Chain 1:   2300        -8526.656             0.018            0.019
Chain 1:   2400        -8346.250             0.018            0.019
Chain 1:   2500        -8422.882             0.019            0.019
Chain 1:   2600        -8333.524             0.019            0.019
Chain 1:   2700        -8367.046             0.019            0.019
Chain 1:   2800        -8318.746             0.014            0.013
Chain 1:   2900        -8430.284             0.014            0.013
Chain 1:   3000        -8367.322             0.014            0.013
Chain 1:   3100        -8311.033             0.013            0.011
Chain 1:   3200        -8284.065             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003597 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403003.436             1.000            1.000
Chain 1:    200     -1584668.866             2.651            4.303
Chain 1:    300      -890843.215             2.027            1.000
Chain 1:    400      -458166.513             1.756            1.000
Chain 1:    500      -358639.302             1.461            0.944
Chain 1:    600      -233478.222             1.307            0.944
Chain 1:    700      -119692.722             1.256            0.944
Chain 1:    800       -86915.330             1.146            0.944
Chain 1:    900       -67256.486             1.051            0.779
Chain 1:   1000       -52061.868             0.975            0.779
Chain 1:   1100       -39540.729             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38722.281             0.479            0.377
Chain 1:   1300       -26668.192             0.446            0.377
Chain 1:   1400       -26388.527             0.353            0.317
Chain 1:   1500       -22973.325             0.340            0.317
Chain 1:   1600       -22190.240             0.290            0.292
Chain 1:   1700       -21061.974             0.200            0.292
Chain 1:   1800       -21006.200             0.162            0.149
Chain 1:   1900       -21333.051             0.135            0.054
Chain 1:   2000       -19841.953             0.113            0.054
Chain 1:   2100       -20080.393             0.083            0.035
Chain 1:   2200       -20307.608             0.082            0.035
Chain 1:   2300       -19923.969             0.038            0.019
Chain 1:   2400       -19695.822             0.038            0.019
Chain 1:   2500       -19497.898             0.025            0.015
Chain 1:   2600       -19127.318             0.023            0.015
Chain 1:   2700       -19084.014             0.018            0.012
Chain 1:   2800       -18800.622             0.019            0.015
Chain 1:   2900       -19082.205             0.019            0.015
Chain 1:   3000       -19068.266             0.012            0.012
Chain 1:   3100       -19153.417             0.011            0.012
Chain 1:   3200       -18843.603             0.011            0.015
Chain 1:   3300       -19048.712             0.011            0.012
Chain 1:   3400       -18522.788             0.012            0.015
Chain 1:   3500       -19135.976             0.014            0.015
Chain 1:   3600       -18440.918             0.016            0.015
Chain 1:   3700       -18829.042             0.018            0.016
Chain 1:   3800       -17786.109             0.022            0.021
Chain 1:   3900       -17782.204             0.021            0.021
Chain 1:   4000       -17899.488             0.022            0.021
Chain 1:   4100       -17813.161             0.022            0.021
Chain 1:   4200       -17628.803             0.021            0.021
Chain 1:   4300       -17767.615             0.021            0.021
Chain 1:   4400       -17723.963             0.018            0.010
Chain 1:   4500       -17626.425             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001198 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -11950.309             1.000            1.000
Chain 1:    200        -8908.292             0.671            1.000
Chain 1:    300        -7955.638             0.487            0.341
Chain 1:    400        -8045.048             0.368            0.341
Chain 1:    500        -7855.076             0.299            0.120
Chain 1:    600        -7790.837             0.251            0.120
Chain 1:    700        -7731.234             0.216            0.024
Chain 1:    800        -7746.022             0.189            0.024
Chain 1:    900        -7814.442             0.169            0.011
Chain 1:   1000        -7771.170             0.153            0.011
Chain 1:   1100        -7834.849             0.054            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56850.893             1.000            1.000
Chain 1:    200       -16994.501             1.673            2.345
Chain 1:    300        -8514.635             1.447            1.000
Chain 1:    400        -8696.547             1.091            1.000
Chain 1:    500        -8396.931             0.880            0.996
Chain 1:    600        -8441.804             0.734            0.996
Chain 1:    700        -8217.046             0.633            0.036
Chain 1:    800        -8052.964             0.556            0.036
Chain 1:    900        -7840.678             0.498            0.027
Chain 1:   1000        -7730.262             0.449            0.027
Chain 1:   1100        -7751.061             0.349            0.027
Chain 1:   1200        -7598.379             0.117            0.021
Chain 1:   1300        -7639.026             0.018            0.020
Chain 1:   1400        -7862.545             0.019            0.020
Chain 1:   1500        -7596.357             0.019            0.020
Chain 1:   1600        -7495.963             0.019            0.020
Chain 1:   1700        -7481.471             0.017            0.020
Chain 1:   1800        -7506.798             0.015            0.014
Chain 1:   1900        -7562.858             0.013            0.013
Chain 1:   2000        -7564.115             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002632 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85551.442             1.000            1.000
Chain 1:    200       -13015.102             3.287            5.573
Chain 1:    300        -9526.701             2.313            1.000
Chain 1:    400       -10175.454             1.751            1.000
Chain 1:    500        -8427.445             1.442            0.366
Chain 1:    600        -8432.742             1.202            0.366
Chain 1:    700        -8459.944             1.031            0.207
Chain 1:    800        -8640.262             0.904            0.207
Chain 1:    900        -8433.028             0.807            0.064
Chain 1:   1000        -8165.339             0.729            0.064
Chain 1:   1100        -8329.631             0.631            0.033   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8109.623             0.077            0.027
Chain 1:   1300        -8185.705             0.041            0.025
Chain 1:   1400        -8313.887             0.036            0.021
Chain 1:   1500        -8219.463             0.017            0.020
Chain 1:   1600        -8302.273             0.017            0.020
Chain 1:   1700        -8401.281             0.018            0.020
Chain 1:   1800        -8025.316             0.021            0.020
Chain 1:   1900        -8122.016             0.020            0.015
Chain 1:   2000        -8092.662             0.017            0.012
Chain 1:   2100        -8240.009             0.017            0.012
Chain 1:   2200        -8016.324             0.017            0.012
Chain 1:   2300        -8105.405             0.017            0.012
Chain 1:   2400        -8169.831             0.016            0.012
Chain 1:   2500        -8129.592             0.015            0.012
Chain 1:   2600        -8122.484             0.014            0.012
Chain 1:   2700        -8035.174             0.014            0.011
Chain 1:   2800        -8019.886             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004813 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 48.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406559.686             1.000            1.000
Chain 1:    200     -1586011.718             2.650            4.300
Chain 1:    300      -890914.533             2.027            1.000
Chain 1:    400      -457419.558             1.757            1.000
Chain 1:    500      -357531.942             1.462            0.948
Chain 1:    600      -232424.168             1.308            0.948
Chain 1:    700      -118662.506             1.258            0.948
Chain 1:    800       -85882.862             1.148            0.948
Chain 1:    900       -66226.315             1.054            0.780
Chain 1:   1000       -51017.751             0.978            0.780
Chain 1:   1100       -38501.239             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37668.305             0.483            0.382
Chain 1:   1300       -25648.719             0.452            0.382
Chain 1:   1400       -25364.632             0.358            0.325
Chain 1:   1500       -21959.711             0.346            0.325
Chain 1:   1600       -21177.072             0.295            0.298
Chain 1:   1700       -20054.843             0.205            0.297
Chain 1:   1800       -19999.287             0.167            0.155
Chain 1:   1900       -20324.503             0.139            0.056
Chain 1:   2000       -18839.524             0.117            0.056
Chain 1:   2100       -19077.500             0.086            0.037
Chain 1:   2200       -19303.135             0.085            0.037
Chain 1:   2300       -18921.332             0.040            0.020
Chain 1:   2400       -18693.817             0.040            0.020
Chain 1:   2500       -18495.799             0.026            0.016
Chain 1:   2600       -18126.976             0.024            0.016
Chain 1:   2700       -18084.197             0.019            0.012
Chain 1:   2800       -17801.501             0.020            0.016
Chain 1:   2900       -18082.274             0.020            0.016
Chain 1:   3000       -18068.493             0.012            0.012
Chain 1:   3100       -18153.368             0.011            0.012
Chain 1:   3200       -17844.672             0.012            0.016
Chain 1:   3300       -18048.897             0.011            0.012
Chain 1:   3400       -17524.948             0.013            0.016
Chain 1:   3500       -18135.120             0.015            0.016
Chain 1:   3600       -17444.018             0.017            0.016
Chain 1:   3700       -17829.180             0.019            0.017
Chain 1:   3800       -16792.332             0.024            0.022
Chain 1:   3900       -16788.575             0.022            0.022
Chain 1:   4000       -16905.864             0.023            0.022
Chain 1:   4100       -16819.820             0.023            0.022
Chain 1:   4200       -16636.803             0.022            0.022
Chain 1:   4300       -16774.673             0.022            0.022
Chain 1:   4400       -16732.109             0.019            0.011
Chain 1:   4500       -16634.765             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12663.776             1.000            1.000
Chain 1:    200        -9598.379             0.660            1.000
Chain 1:    300        -8236.391             0.495            0.319
Chain 1:    400        -8470.562             0.378            0.319
Chain 1:    500        -8322.799             0.306            0.165
Chain 1:    600        -8181.659             0.258            0.165
Chain 1:    700        -8081.743             0.223            0.028
Chain 1:    800        -8085.250             0.195            0.028
Chain 1:    900        -8012.615             0.174            0.018
Chain 1:   1000        -8205.944             0.159            0.024
Chain 1:   1100        -8231.210             0.060            0.018
Chain 1:   1200        -8098.466             0.029            0.017
Chain 1:   1300        -8067.037             0.013            0.016
Chain 1:   1400        -8075.422             0.010            0.012
Chain 1:   1500        -8164.993             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001487 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57398.923             1.000            1.000
Chain 1:    200       -17752.220             1.617            2.233
Chain 1:    300        -8857.660             1.413            1.004
Chain 1:    400        -8149.910             1.081            1.004
Chain 1:    500        -8443.883             0.872            1.000
Chain 1:    600        -8723.537             0.732            1.000
Chain 1:    700        -7952.707             0.641            0.097
Chain 1:    800        -8447.304             0.568            0.097
Chain 1:    900        -7971.551             0.512            0.087
Chain 1:   1000        -7654.899             0.465            0.087
Chain 1:   1100        -7893.912             0.368            0.060
Chain 1:   1200        -8040.879             0.146            0.059
Chain 1:   1300        -7698.269             0.050            0.045
Chain 1:   1400        -7832.182             0.043            0.041
Chain 1:   1500        -7598.059             0.043            0.041
Chain 1:   1600        -7764.994             0.042            0.041
Chain 1:   1700        -7550.066             0.035            0.031
Chain 1:   1800        -7582.766             0.030            0.030
Chain 1:   1900        -7601.567             0.024            0.028
Chain 1:   2000        -7756.694             0.022            0.021
Chain 1:   2100        -7596.375             0.021            0.021
Chain 1:   2200        -7848.842             0.022            0.021
Chain 1:   2300        -7545.695             0.022            0.021
Chain 1:   2400        -7608.125             0.021            0.021
Chain 1:   2500        -7628.562             0.018            0.021
Chain 1:   2600        -7526.553             0.017            0.020
Chain 1:   2700        -7514.244             0.015            0.014
Chain 1:   2800        -7502.663             0.014            0.014
Chain 1:   2900        -7408.652             0.015            0.014
Chain 1:   3000        -7542.528             0.015            0.014
Chain 1:   3100        -7532.264             0.013            0.013
Chain 1:   3200        -7733.606             0.013            0.013
Chain 1:   3300        -7454.268             0.012            0.013
Chain 1:   3400        -7681.837             0.014            0.014
Chain 1:   3500        -7438.637             0.017            0.018
Chain 1:   3600        -7504.833             0.017            0.018
Chain 1:   3700        -7454.185             0.017            0.018
Chain 1:   3800        -7452.393             0.017            0.018
Chain 1:   3900        -7417.998             0.017            0.018
Chain 1:   4000        -7414.291             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003058 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86840.627             1.000            1.000
Chain 1:    200       -13826.548             3.140            5.281
Chain 1:    300       -10129.794             2.215            1.000
Chain 1:    400       -11343.884             1.688            1.000
Chain 1:    500        -9129.483             1.399            0.365
Chain 1:    600        -8696.847             1.174            0.365
Chain 1:    700        -8531.769             1.009            0.243
Chain 1:    800        -9570.529             0.897            0.243
Chain 1:    900        -8821.658             0.806            0.109
Chain 1:   1000        -8799.869             0.726            0.109
Chain 1:   1100        -8853.019             0.627            0.107   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8523.099             0.102            0.085
Chain 1:   1300        -8803.920             0.069            0.050
Chain 1:   1400        -8807.308             0.058            0.039
Chain 1:   1500        -8653.631             0.036            0.032
Chain 1:   1600        -8768.779             0.032            0.019
Chain 1:   1700        -8837.145             0.031            0.018
Chain 1:   1800        -8404.678             0.025            0.018
Chain 1:   1900        -8508.739             0.018            0.013
Chain 1:   2000        -8484.179             0.018            0.013
Chain 1:   2100        -8460.843             0.018            0.013
Chain 1:   2200        -8426.586             0.014            0.012
Chain 1:   2300        -8556.369             0.013            0.012
Chain 1:   2400        -8411.446             0.014            0.013
Chain 1:   2500        -8480.375             0.013            0.012
Chain 1:   2600        -8399.541             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003034 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8406859.334             1.000            1.000
Chain 1:    200     -1584848.073             2.652            4.305
Chain 1:    300      -890859.035             2.028            1.000
Chain 1:    400      -457945.266             1.757            1.000
Chain 1:    500      -358472.129             1.461            0.945
Chain 1:    600      -233317.620             1.307            0.945
Chain 1:    700      -119533.997             1.256            0.945
Chain 1:    800       -86800.292             1.146            0.945
Chain 1:    900       -67138.551             1.052            0.779
Chain 1:   1000       -51939.912             0.976            0.779
Chain 1:   1100       -39421.301             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38597.826             0.479            0.377
Chain 1:   1300       -26544.725             0.447            0.377
Chain 1:   1400       -26264.163             0.353            0.318
Chain 1:   1500       -22849.983             0.340            0.318
Chain 1:   1600       -22067.112             0.290            0.293
Chain 1:   1700       -20938.910             0.201            0.293
Chain 1:   1800       -20882.823             0.163            0.149
Chain 1:   1900       -21209.348             0.135            0.054
Chain 1:   2000       -19719.174             0.114            0.054
Chain 1:   2100       -19957.443             0.083            0.035
Chain 1:   2200       -20184.532             0.082            0.035
Chain 1:   2300       -19801.066             0.039            0.019
Chain 1:   2400       -19573.039             0.039            0.019
Chain 1:   2500       -19375.304             0.025            0.015
Chain 1:   2600       -19005.004             0.023            0.015
Chain 1:   2700       -18961.758             0.018            0.012
Chain 1:   2800       -18678.682             0.019            0.015
Chain 1:   2900       -18959.996             0.019            0.015
Chain 1:   3000       -18946.071             0.012            0.012
Chain 1:   3100       -19031.198             0.011            0.012
Chain 1:   3200       -18721.612             0.011            0.015
Chain 1:   3300       -18926.518             0.011            0.012
Chain 1:   3400       -18401.135             0.012            0.015
Chain 1:   3500       -19013.606             0.015            0.015
Chain 1:   3600       -18319.403             0.016            0.015
Chain 1:   3700       -18706.922             0.018            0.017
Chain 1:   3800       -17665.435             0.023            0.021
Chain 1:   3900       -17661.567             0.021            0.021
Chain 1:   4000       -17778.816             0.022            0.021
Chain 1:   4100       -17692.638             0.022            0.021
Chain 1:   4200       -17508.536             0.021            0.021
Chain 1:   4300       -17647.130             0.021            0.021
Chain 1:   4400       -17603.731             0.018            0.011
Chain 1:   4500       -17506.237             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49647.186             1.000            1.000
Chain 1:    200       -19213.032             1.292            1.584
Chain 1:    300       -13758.477             0.993            1.000
Chain 1:    400       -48829.236             0.925            1.000
Chain 1:    500       -20102.928             1.026            1.000
Chain 1:    600       -27008.781             0.897            1.000
Chain 1:    700       -12210.073             0.942            1.000
Chain 1:    800       -13617.352             0.837            1.000
Chain 1:    900       -14195.548             0.749            0.718
Chain 1:   1000       -12848.179             0.684            0.718
Chain 1:   1100       -11301.333             0.598            0.396   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12577.915             0.450            0.256
Chain 1:   1300       -14504.655             0.424            0.137
Chain 1:   1400       -14145.431             0.354            0.133
Chain 1:   1500       -12536.720             0.224            0.128
Chain 1:   1600       -20354.493             0.237            0.128
Chain 1:   1700       -10661.538             0.207            0.128
Chain 1:   1800       -10351.579             0.199            0.128
Chain 1:   1900       -19570.619             0.242            0.133
Chain 1:   2000       -13909.121             0.273            0.137
Chain 1:   2100       -19058.128             0.286            0.270
Chain 1:   2200       -12166.176             0.332            0.384
Chain 1:   2300       -10600.295             0.334            0.384
Chain 1:   2400       -10220.154             0.335            0.384
Chain 1:   2500       -10145.432             0.323            0.384
Chain 1:   2600       -10171.721             0.285            0.270
Chain 1:   2700       -10497.114             0.197            0.148
Chain 1:   2800       -11506.027             0.203            0.148
Chain 1:   2900        -9770.363             0.173            0.148
Chain 1:   3000       -10771.102             0.142            0.093
Chain 1:   3100       -10102.567             0.122            0.088
Chain 1:   3200       -10356.493             0.067            0.066
Chain 1:   3300        -9720.552             0.059            0.065
Chain 1:   3400       -17118.354             0.099            0.066
Chain 1:   3500       -10117.856             0.167            0.088
Chain 1:   3600       -11965.505             0.182            0.093
Chain 1:   3700        -9831.549             0.201            0.154
Chain 1:   3800       -18183.587             0.238            0.178
Chain 1:   3900        -9733.322             0.307            0.217
Chain 1:   4000        -9683.277             0.298            0.217
Chain 1:   4100       -10434.944             0.299            0.217
Chain 1:   4200       -17103.026             0.336            0.390
Chain 1:   4300       -10961.398             0.385            0.432
Chain 1:   4400        -9541.052             0.357            0.390
Chain 1:   4500        -9926.962             0.291            0.217
Chain 1:   4600        -9245.240             0.283            0.217
Chain 1:   4700        -9558.937             0.265            0.149
Chain 1:   4800        -9068.164             0.224            0.074
Chain 1:   4900        -9943.830             0.146            0.074
Chain 1:   5000        -9725.419             0.148            0.074
Chain 1:   5100       -12394.512             0.162            0.088
Chain 1:   5200       -13630.888             0.133            0.088
Chain 1:   5300        -9557.655             0.119            0.088
Chain 1:   5400        -9115.898             0.109            0.074
Chain 1:   5500        -9973.455             0.114            0.086
Chain 1:   5600       -11779.975             0.122            0.088
Chain 1:   5700       -16766.047             0.148            0.091
Chain 1:   5800        -9818.706             0.214            0.153
Chain 1:   5900        -9992.817             0.206            0.153
Chain 1:   6000       -12749.446             0.226            0.215
Chain 1:   6100        -9938.753             0.233            0.216
Chain 1:   6200        -9991.250             0.224            0.216
Chain 1:   6300       -13574.975             0.208            0.216
Chain 1:   6400       -13559.868             0.203            0.216
Chain 1:   6500       -12759.163             0.201            0.216
Chain 1:   6600        -9262.863             0.223            0.264
Chain 1:   6700       -13209.948             0.223            0.264
Chain 1:   6800       -14205.855             0.160            0.216
Chain 1:   6900        -9081.817             0.214            0.264
Chain 1:   7000       -14933.315             0.232            0.283
Chain 1:   7100        -8778.797             0.274            0.299
Chain 1:   7200       -11254.295             0.295            0.299
Chain 1:   7300       -10111.363             0.280            0.299
Chain 1:   7400        -8801.585             0.295            0.299
Chain 1:   7500        -9004.029             0.291            0.299
Chain 1:   7600        -8942.459             0.254            0.220
Chain 1:   7700       -10121.221             0.235            0.149
Chain 1:   7800       -13692.998             0.255            0.220
Chain 1:   7900        -8882.160             0.252            0.220
Chain 1:   8000        -8809.694             0.214            0.149
Chain 1:   8100       -11277.767             0.166            0.149
Chain 1:   8200        -8861.180             0.171            0.149
Chain 1:   8300        -8754.782             0.161            0.149
Chain 1:   8400       -13117.185             0.179            0.219
Chain 1:   8500        -9153.231             0.220            0.261
Chain 1:   8600        -9795.084             0.226            0.261
Chain 1:   8700        -9964.348             0.216            0.261
Chain 1:   8800       -12063.540             0.208            0.219
Chain 1:   8900        -9817.424             0.176            0.219
Chain 1:   9000       -10711.738             0.184            0.219
Chain 1:   9100        -9380.888             0.176            0.174
Chain 1:   9200        -8665.792             0.157            0.142
Chain 1:   9300        -9116.016             0.161            0.142
Chain 1:   9400        -9518.173             0.132            0.083
Chain 1:   9500       -12986.465             0.115            0.083
Chain 1:   9600       -11449.109             0.122            0.134
Chain 1:   9700        -8789.194             0.151            0.142
Chain 1:   9800        -9442.742             0.140            0.134
Chain 1:   9900       -11116.284             0.132            0.134
Chain 1:   10000        -9416.861             0.142            0.142
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002141 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 21.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62821.644             1.000            1.000
Chain 1:    200       -18848.536             1.666            2.333
Chain 1:    300        -9398.980             1.446            1.005
Chain 1:    400        -8665.041             1.106            1.005
Chain 1:    500        -9455.366             0.901            1.000
Chain 1:    600        -8273.626             0.775            1.000
Chain 1:    700        -9166.804             0.678            0.143
Chain 1:    800        -7931.448             0.613            0.156
Chain 1:    900        -8038.636             0.546            0.143
Chain 1:   1000        -8379.252             0.496            0.143
Chain 1:   1100        -8097.664             0.399            0.097
Chain 1:   1200        -7612.392             0.172            0.085
Chain 1:   1300        -7727.827             0.073            0.084
Chain 1:   1400        -8109.011             0.069            0.064
Chain 1:   1500        -7670.325             0.067            0.057
Chain 1:   1600        -7880.684             0.055            0.047
Chain 1:   1700        -7550.675             0.050            0.044
Chain 1:   1800        -7685.163             0.036            0.041
Chain 1:   1900        -7659.592             0.035            0.041
Chain 1:   2000        -7830.175             0.033            0.035
Chain 1:   2100        -7722.361             0.031            0.027
Chain 1:   2200        -7902.376             0.027            0.023
Chain 1:   2300        -7721.554             0.028            0.023
Chain 1:   2400        -7782.273             0.024            0.023
Chain 1:   2500        -7627.759             0.020            0.022
Chain 1:   2600        -7636.267             0.018            0.020
Chain 1:   2700        -7643.996             0.013            0.017
Chain 1:   2800        -7743.346             0.013            0.014
Chain 1:   2900        -7470.079             0.016            0.020
Chain 1:   3000        -7646.872             0.016            0.020
Chain 1:   3100        -7624.169             0.015            0.020
Chain 1:   3200        -7842.184             0.016            0.020
Chain 1:   3300        -7497.942             0.018            0.020
Chain 1:   3400        -7623.924             0.019            0.020
Chain 1:   3500        -7551.297             0.018            0.017
Chain 1:   3600        -7565.055             0.018            0.017
Chain 1:   3700        -7547.962             0.018            0.017
Chain 1:   3800        -7494.803             0.017            0.017
Chain 1:   3900        -7491.161             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003277 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86021.087             1.000            1.000
Chain 1:    200       -14359.336             2.995            4.991
Chain 1:    300       -10560.321             2.117            1.000
Chain 1:    400       -12576.510             1.628            1.000
Chain 1:    500        -9230.062             1.375            0.363
Chain 1:    600        -9700.423             1.154            0.363
Chain 1:    700        -8872.296             1.002            0.360
Chain 1:    800        -9704.257             0.888            0.360
Chain 1:    900        -9244.994             0.794            0.160
Chain 1:   1000        -9554.691             0.718            0.160
Chain 1:   1100        -9360.197             0.620            0.093   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8909.915             0.126            0.086
Chain 1:   1300        -9158.787             0.093            0.051
Chain 1:   1400        -8997.729             0.079            0.050
Chain 1:   1500        -9025.763             0.043            0.048
Chain 1:   1600        -9116.760             0.039            0.032
Chain 1:   1700        -9150.592             0.030            0.027
Chain 1:   1800        -8694.878             0.027            0.027
Chain 1:   1900        -8806.681             0.023            0.021
Chain 1:   2000        -8826.705             0.020            0.018
Chain 1:   2100        -8911.119             0.019            0.013
Chain 1:   2200        -8689.580             0.016            0.013
Chain 1:   2300        -8898.027             0.016            0.013
Chain 1:   2400        -8697.622             0.017            0.013
Chain 1:   2500        -8775.315             0.017            0.013
Chain 1:   2600        -8682.934             0.017            0.013
Chain 1:   2700        -8720.301             0.017            0.013
Chain 1:   2800        -8672.078             0.013            0.011
Chain 1:   2900        -8786.213             0.013            0.011
Chain 1:   3000        -8695.534             0.013            0.011
Chain 1:   3100        -8662.809             0.013            0.011
Chain 1:   3200        -8633.849             0.011            0.010
Chain 1:   3300        -8897.314             0.011            0.010
Chain 1:   3400        -8943.804             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002861 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8378518.665             1.000            1.000
Chain 1:    200     -1579452.845             2.652            4.305
Chain 1:    300      -892437.846             2.025            1.000
Chain 1:    400      -459734.019             1.754            1.000
Chain 1:    500      -360512.108             1.458            0.941
Chain 1:    600      -235208.432             1.304            0.941
Chain 1:    700      -120787.055             1.253            0.941
Chain 1:    800       -87864.060             1.143            0.941
Chain 1:    900       -68077.469             1.048            0.770
Chain 1:   1000       -52784.723             0.973            0.770
Chain 1:   1100       -40170.595             0.904            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39341.061             0.476            0.375
Chain 1:   1300       -27185.474             0.443            0.375
Chain 1:   1400       -26898.017             0.350            0.314
Chain 1:   1500       -23456.514             0.337            0.314
Chain 1:   1600       -22665.936             0.288            0.291
Chain 1:   1700       -21525.001             0.198            0.290
Chain 1:   1800       -21466.331             0.161            0.147
Chain 1:   1900       -21793.346             0.133            0.053
Chain 1:   2000       -20295.330             0.112            0.053
Chain 1:   2100       -20534.053             0.082            0.035
Chain 1:   2200       -20762.594             0.081            0.035
Chain 1:   2300       -20377.773             0.038            0.019
Chain 1:   2400       -20149.390             0.038            0.019
Chain 1:   2500       -19952.003             0.024            0.015
Chain 1:   2600       -19580.699             0.023            0.015
Chain 1:   2700       -19537.136             0.018            0.012
Chain 1:   2800       -19253.903             0.019            0.015
Chain 1:   2900       -19535.682             0.019            0.014
Chain 1:   3000       -19521.665             0.011            0.012
Chain 1:   3100       -19606.868             0.011            0.011
Chain 1:   3200       -19296.776             0.011            0.014
Chain 1:   3300       -19502.067             0.010            0.011
Chain 1:   3400       -18975.888             0.012            0.014
Chain 1:   3500       -19589.669             0.014            0.015
Chain 1:   3600       -18893.869             0.016            0.015
Chain 1:   3700       -19282.631             0.018            0.016
Chain 1:   3800       -18238.678             0.022            0.020
Chain 1:   3900       -18234.809             0.021            0.020
Chain 1:   4000       -18352.026             0.021            0.020
Chain 1:   4100       -18265.714             0.021            0.020
Chain 1:   4200       -18081.081             0.021            0.020
Chain 1:   4300       -18220.011             0.020            0.020
Chain 1:   4400       -18176.160             0.018            0.010
Chain 1:   4500       -18078.656             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00162 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49367.616             1.000            1.000
Chain 1:    200       -19307.544             1.278            1.557
Chain 1:    300       -44460.798             1.041            1.000
Chain 1:    400       -19484.853             1.101            1.282
Chain 1:    500       -13944.468             0.960            1.000
Chain 1:    600       -12840.473             0.815            1.000
Chain 1:    700       -18743.711             0.743            0.566
Chain 1:    800       -12937.575             0.706            0.566
Chain 1:    900       -14305.723             0.639            0.449
Chain 1:   1000       -11100.828             0.604            0.449
Chain 1:   1100       -16831.009             0.538            0.397   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -16744.097             0.382            0.340
Chain 1:   1300       -11506.578             0.371            0.340
Chain 1:   1400       -10602.066             0.252            0.315
Chain 1:   1500       -13140.203             0.231            0.289
Chain 1:   1600       -15886.200             0.240            0.289
Chain 1:   1700       -14902.563             0.215            0.193
Chain 1:   1800       -11651.698             0.198            0.193
Chain 1:   1900       -19556.609             0.229            0.279
Chain 1:   2000       -25432.263             0.223            0.231
Chain 1:   2100       -11208.389             0.316            0.231
Chain 1:   2200       -11330.252             0.317            0.231
Chain 1:   2300       -13006.998             0.284            0.193
Chain 1:   2400       -10153.015             0.304            0.231
Chain 1:   2500       -16848.029             0.324            0.279
Chain 1:   2600       -10458.050             0.368            0.281
Chain 1:   2700        -9467.352             0.372            0.281
Chain 1:   2800       -10972.544             0.358            0.281
Chain 1:   2900       -10091.458             0.326            0.231
Chain 1:   3000        -9560.271             0.308            0.137
Chain 1:   3100       -14028.288             0.213            0.137
Chain 1:   3200       -13849.478             0.213            0.137
Chain 1:   3300       -12210.983             0.214            0.137
Chain 1:   3400        -9749.027             0.211            0.137
Chain 1:   3500       -11294.731             0.185            0.137
Chain 1:   3600       -11304.015             0.124            0.134
Chain 1:   3700        -8910.127             0.140            0.137
Chain 1:   3800        -9039.185             0.128            0.134
Chain 1:   3900        -9509.121             0.124            0.134
Chain 1:   4000        -9245.304             0.122            0.134
Chain 1:   4100       -10067.929             0.098            0.082
Chain 1:   4200        -9846.063             0.099            0.082
Chain 1:   4300       -10406.334             0.091            0.054
Chain 1:   4400        -9091.291             0.080            0.054
Chain 1:   4500        -9254.691             0.068            0.049
Chain 1:   4600       -10893.463             0.083            0.054
Chain 1:   4700        -9534.400             0.071            0.054
Chain 1:   4800        -9297.178             0.072            0.054
Chain 1:   4900        -9553.865             0.069            0.054
Chain 1:   5000       -12314.824             0.089            0.082
Chain 1:   5100       -11810.238             0.085            0.054
Chain 1:   5200       -10171.951             0.099            0.143
Chain 1:   5300       -11455.921             0.105            0.143
Chain 1:   5400        -9548.213             0.110            0.143
Chain 1:   5500       -13010.092             0.135            0.150
Chain 1:   5600        -9356.816             0.159            0.161
Chain 1:   5700        -9340.197             0.145            0.161
Chain 1:   5800        -9375.968             0.143            0.161
Chain 1:   5900       -13492.843             0.171            0.200
Chain 1:   6000        -9313.728             0.193            0.200
Chain 1:   6100       -10344.659             0.199            0.200
Chain 1:   6200        -9235.436             0.195            0.200
Chain 1:   6300       -11364.261             0.202            0.200
Chain 1:   6400       -10490.438             0.191            0.187
Chain 1:   6500        -9405.486             0.176            0.120
Chain 1:   6600        -8730.129             0.144            0.115
Chain 1:   6700        -9470.239             0.152            0.115
Chain 1:   6800        -9173.120             0.155            0.115
Chain 1:   6900        -8740.022             0.129            0.100
Chain 1:   7000        -8780.542             0.085            0.083
Chain 1:   7100        -9632.638             0.084            0.083
Chain 1:   7200        -8910.834             0.080            0.081
Chain 1:   7300       -12061.213             0.087            0.081
Chain 1:   7400       -13111.049             0.087            0.080
Chain 1:   7500        -8716.708             0.126            0.080
Chain 1:   7600       -11764.912             0.144            0.081
Chain 1:   7700       -12706.498             0.143            0.081
Chain 1:   7800        -8714.745             0.186            0.088
Chain 1:   7900        -8757.695             0.182            0.088
Chain 1:   8000        -8730.920             0.181            0.088
Chain 1:   8100       -10755.573             0.191            0.188
Chain 1:   8200        -8890.714             0.204            0.210
Chain 1:   8300        -8635.263             0.181            0.188
Chain 1:   8400        -8451.930             0.175            0.188
Chain 1:   8500        -8789.420             0.129            0.074
Chain 1:   8600        -9310.537             0.108            0.056
Chain 1:   8700        -9067.072             0.104            0.038
Chain 1:   8800        -8574.289             0.064            0.038
Chain 1:   8900        -8790.367             0.066            0.038
Chain 1:   9000       -11557.967             0.089            0.056
Chain 1:   9100        -9753.530             0.089            0.056
Chain 1:   9200        -8974.893             0.077            0.056
Chain 1:   9300        -8481.425             0.079            0.057
Chain 1:   9400        -8608.644             0.079            0.057
Chain 1:   9500       -11697.920             0.101            0.058
Chain 1:   9600        -8664.531             0.131            0.087
Chain 1:   9700        -8431.748             0.131            0.087
Chain 1:   9800       -11140.581             0.149            0.185
Chain 1:   9900        -9641.983             0.162            0.185
Chain 1:   10000        -8596.468             0.151            0.155
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001644 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57953.227             1.000            1.000
Chain 1:    200       -18165.153             1.595            2.190
Chain 1:    300        -9060.987             1.398            1.005
Chain 1:    400        -8224.519             1.074            1.005
Chain 1:    500        -8602.314             0.868            1.000
Chain 1:    600        -9182.322             0.734            1.000
Chain 1:    700        -9211.175             0.630            0.102
Chain 1:    800        -8383.710             0.563            0.102
Chain 1:    900        -8210.064             0.503            0.099
Chain 1:   1000        -8240.273             0.453            0.099
Chain 1:   1100        -7874.540             0.358            0.063
Chain 1:   1200        -7719.071             0.141            0.046
Chain 1:   1300        -7737.073             0.040            0.044
Chain 1:   1400        -7786.893             0.031            0.021
Chain 1:   1500        -7689.229             0.028            0.020
Chain 1:   1600        -7780.552             0.023            0.013
Chain 1:   1700        -7818.994             0.023            0.013
Chain 1:   1800        -7676.921             0.015            0.013
Chain 1:   1900        -7651.650             0.013            0.012
Chain 1:   2000        -7876.895             0.016            0.013
Chain 1:   2100        -7687.875             0.013            0.013
Chain 1:   2200        -7810.934             0.013            0.013
Chain 1:   2300        -7581.513             0.016            0.016
Chain 1:   2400        -7660.712             0.016            0.016
Chain 1:   2500        -7615.382             0.015            0.016
Chain 1:   2600        -7591.092             0.015            0.016
Chain 1:   2700        -7498.876             0.015            0.016
Chain 1:   2800        -7717.891             0.016            0.016
Chain 1:   2900        -7465.670             0.019            0.025
Chain 1:   3000        -7594.925             0.018            0.017
Chain 1:   3100        -7594.705             0.016            0.016
Chain 1:   3200        -7800.348             0.017            0.017
Chain 1:   3300        -7499.721             0.018            0.017
Chain 1:   3400        -7719.456             0.020            0.026
Chain 1:   3500        -7496.331             0.022            0.028
Chain 1:   3600        -7561.512             0.022            0.028
Chain 1:   3700        -7513.084             0.022            0.028
Chain 1:   3800        -7487.601             0.019            0.026
Chain 1:   3900        -7463.091             0.016            0.017
Chain 1:   4000        -7459.512             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003362 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86260.577             1.000            1.000
Chain 1:    200       -14129.249             3.053            5.105
Chain 1:    300       -10352.887             2.157            1.000
Chain 1:    400       -11961.999             1.651            1.000
Chain 1:    500        -9178.615             1.382            0.365
Chain 1:    600        -8776.252             1.159            0.365
Chain 1:    700        -8612.447             0.996            0.303
Chain 1:    800        -9362.638             0.882            0.303
Chain 1:    900        -8995.367             0.788            0.135
Chain 1:   1000        -9345.028             0.713            0.135
Chain 1:   1100        -9052.417             0.616            0.080   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8650.978             0.110            0.046
Chain 1:   1300        -8958.756             0.077            0.046
Chain 1:   1400        -8893.322             0.065            0.041
Chain 1:   1500        -8841.421             0.035            0.037
Chain 1:   1600        -8919.690             0.031            0.034
Chain 1:   1700        -8970.694             0.030            0.034
Chain 1:   1800        -8513.707             0.027            0.034
Chain 1:   1900        -8623.004             0.024            0.032
Chain 1:   2000        -8635.472             0.021            0.013
Chain 1:   2100        -8727.192             0.019            0.011
Chain 1:   2200        -8512.742             0.017            0.011
Chain 1:   2300        -8713.199             0.015            0.011
Chain 1:   2400        -8518.508             0.017            0.013
Chain 1:   2500        -8594.462             0.017            0.013
Chain 1:   2600        -8504.448             0.017            0.013
Chain 1:   2700        -8537.506             0.017            0.013
Chain 1:   2800        -8488.644             0.012            0.011
Chain 1:   2900        -8602.884             0.013            0.011
Chain 1:   3000        -8520.593             0.013            0.011
Chain 1:   3100        -8480.868             0.013            0.011
Chain 1:   3200        -8453.272             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0032 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8371918.593             1.000            1.000
Chain 1:    200     -1578242.662             2.652            4.305
Chain 1:    300      -890614.018             2.026            1.000
Chain 1:    400      -458126.056             1.755            1.000
Chain 1:    500      -359243.926             1.459            0.944
Chain 1:    600      -234215.903             1.305            0.944
Chain 1:    700      -120207.896             1.254            0.944
Chain 1:    800       -87387.348             1.144            0.944
Chain 1:    900       -67673.800             1.049            0.772
Chain 1:   1000       -52432.990             0.974            0.772
Chain 1:   1100       -39862.927             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39040.350             0.477            0.376
Chain 1:   1300       -26924.754             0.445            0.376
Chain 1:   1400       -26641.123             0.351            0.315
Chain 1:   1500       -23209.742             0.338            0.315
Chain 1:   1600       -22422.491             0.289            0.291
Chain 1:   1700       -21286.101             0.199            0.291
Chain 1:   1800       -21228.598             0.162            0.148
Chain 1:   1900       -21555.625             0.134            0.053
Chain 1:   2000       -20060.184             0.113            0.053
Chain 1:   2100       -20298.790             0.082            0.035
Chain 1:   2200       -20526.864             0.081            0.035
Chain 1:   2300       -20142.450             0.038            0.019
Chain 1:   2400       -19914.150             0.038            0.019
Chain 1:   2500       -19716.634             0.024            0.015
Chain 1:   2600       -19345.561             0.023            0.015
Chain 1:   2700       -19302.136             0.018            0.012
Chain 1:   2800       -19018.862             0.019            0.015
Chain 1:   2900       -19300.573             0.019            0.015
Chain 1:   3000       -19286.547             0.012            0.012
Chain 1:   3100       -19371.730             0.011            0.011
Chain 1:   3200       -19061.768             0.011            0.015
Chain 1:   3300       -19267.006             0.010            0.011
Chain 1:   3400       -18740.964             0.012            0.015
Chain 1:   3500       -19354.490             0.014            0.015
Chain 1:   3600       -18659.022             0.016            0.015
Chain 1:   3700       -19047.503             0.018            0.016
Chain 1:   3800       -18004.040             0.022            0.020
Chain 1:   3900       -18000.181             0.021            0.020
Chain 1:   4000       -18117.399             0.021            0.020
Chain 1:   4100       -18031.084             0.021            0.020
Chain 1:   4200       -17846.606             0.021            0.020
Chain 1:   4300       -17985.456             0.020            0.020
Chain 1:   4400       -17941.700             0.018            0.010
Chain 1:   4500       -17844.185             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13485.905             1.000            1.000
Chain 1:    200       -10274.168             0.656            1.000
Chain 1:    300        -8663.582             0.500            0.313
Chain 1:    400        -8889.214             0.381            0.313
Chain 1:    500        -8861.708             0.305            0.186
Chain 1:    600        -8543.504             0.261            0.186
Chain 1:    700        -8469.841             0.225            0.037
Chain 1:    800        -8485.648             0.197            0.037
Chain 1:    900        -8478.849             0.175            0.025
Chain 1:   1000        -8570.879             0.159            0.025
Chain 1:   1100        -8535.841             0.059            0.011
Chain 1:   1200        -8510.311             0.028            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001571 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47480.734             1.000            1.000
Chain 1:    200       -16812.639             1.412            1.824
Chain 1:    300        -9387.854             1.205            1.000
Chain 1:    400        -8320.834             0.936            1.000
Chain 1:    500        -8952.255             0.763            0.791
Chain 1:    600        -9150.525             0.639            0.791
Chain 1:    700        -8214.323             0.564            0.128
Chain 1:    800        -8840.478             0.503            0.128
Chain 1:    900        -7860.448             0.461            0.125
Chain 1:   1000        -7995.745             0.416            0.125
Chain 1:   1100        -7916.101             0.317            0.114
Chain 1:   1200        -7880.490             0.135            0.071
Chain 1:   1300        -8062.863             0.058            0.071
Chain 1:   1400        -7959.356             0.047            0.023
Chain 1:   1500        -7685.207             0.043            0.023
Chain 1:   1600        -7851.484             0.043            0.023
Chain 1:   1700        -7783.840             0.033            0.021
Chain 1:   1800        -7715.878             0.027            0.017
Chain 1:   1900        -7700.825             0.014            0.013
Chain 1:   2000        -7800.137             0.014            0.013
Chain 1:   2100        -7672.955             0.015            0.013
Chain 1:   2200        -8058.281             0.019            0.017
Chain 1:   2300        -7745.929             0.021            0.017
Chain 1:   2400        -7723.389             0.020            0.017
Chain 1:   2500        -7672.510             0.017            0.013
Chain 1:   2600        -7762.398             0.016            0.012
Chain 1:   2700        -7553.345             0.018            0.013
Chain 1:   2800        -7640.650             0.018            0.013
Chain 1:   2900        -7473.741             0.020            0.017
Chain 1:   3000        -7712.157             0.022            0.022
Chain 1:   3100        -7627.162             0.021            0.022
Chain 1:   3200        -7741.725             0.018            0.015
Chain 1:   3300        -7489.084             0.017            0.015
Chain 1:   3400        -7886.495             0.022            0.022
Chain 1:   3500        -7548.085             0.026            0.028
Chain 1:   3600        -7693.736             0.027            0.028
Chain 1:   3700        -7494.050             0.027            0.027
Chain 1:   3800        -7601.762             0.027            0.027
Chain 1:   3900        -7506.880             0.026            0.027
Chain 1:   4000        -7496.768             0.023            0.019
Chain 1:   4100        -7498.999             0.022            0.019
Chain 1:   4200        -7704.651             0.023            0.027
Chain 1:   4300        -7484.971             0.023            0.027
Chain 1:   4400        -7539.070             0.018            0.019
Chain 1:   4500        -7671.089             0.015            0.017
Chain 1:   4600        -7559.253             0.015            0.015
Chain 1:   4700        -7551.055             0.012            0.014
Chain 1:   4800        -7514.665             0.012            0.013
Chain 1:   4900        -7785.496             0.014            0.015
Chain 1:   5000        -7704.600             0.015            0.015
Chain 1:   5100        -7578.995             0.016            0.017
Chain 1:   5200        -7598.266             0.014            0.015
Chain 1:   5300        -7585.537             0.011            0.010
Chain 1:   5400        -7547.774             0.011            0.010
Chain 1:   5500        -7484.569             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0036 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87810.250             1.000            1.000
Chain 1:    200       -14716.658             2.983            4.967
Chain 1:    300       -10818.810             2.109            1.000
Chain 1:    400       -13283.097             1.628            1.000
Chain 1:    500        -9145.000             1.393            0.452
Chain 1:    600        -9562.432             1.168            0.452
Chain 1:    700        -9515.944             1.002            0.360
Chain 1:    800        -8925.540             0.885            0.360
Chain 1:    900        -8948.139             0.787            0.186
Chain 1:   1000        -9800.758             0.717            0.186
Chain 1:   1100        -9292.357             0.622            0.087   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9597.169             0.129            0.066
Chain 1:   1300        -8949.732             0.100            0.066
Chain 1:   1400        -9021.377             0.082            0.055
Chain 1:   1500        -9126.631             0.038            0.044
Chain 1:   1600        -9053.015             0.035            0.032
Chain 1:   1700        -8917.940             0.036            0.032
Chain 1:   1800        -8958.796             0.030            0.015
Chain 1:   1900        -8954.754             0.029            0.015
Chain 1:   2000        -9198.870             0.023            0.015
Chain 1:   2100        -8899.318             0.021            0.015
Chain 1:   2200        -8862.709             0.018            0.012
Chain 1:   2300        -9120.045             0.014            0.012
Chain 1:   2400        -8840.079             0.016            0.015
Chain 1:   2500        -8920.004             0.016            0.015
Chain 1:   2600        -8824.790             0.016            0.015
Chain 1:   2700        -8843.031             0.015            0.011
Chain 1:   2800        -8702.731             0.016            0.016
Chain 1:   2900        -8889.832             0.018            0.021
Chain 1:   3000        -8797.579             0.017            0.016
Chain 1:   3100        -8896.043             0.014            0.011
Chain 1:   3200        -8762.314             0.016            0.015
Chain 1:   3300        -9030.874             0.016            0.015
Chain 1:   3400        -9100.411             0.013            0.011
Chain 1:   3500        -8908.685             0.015            0.015
Chain 1:   3600        -8714.786             0.016            0.016
Chain 1:   3700        -8884.364             0.017            0.019
Chain 1:   3800        -8718.634             0.018            0.019
Chain 1:   3900        -8944.082             0.018            0.019
Chain 1:   4000        -8945.841             0.017            0.019
Chain 1:   4100        -8729.651             0.018            0.022
Chain 1:   4200        -8714.644             0.017            0.022
Chain 1:   4300        -8716.031             0.014            0.019
Chain 1:   4400        -8670.068             0.014            0.019
Chain 1:   4500        -8810.804             0.013            0.019
Chain 1:   4600        -8837.869             0.011            0.016
Chain 1:   4700        -8955.838             0.011            0.013
Chain 1:   4800        -8778.519             0.011            0.013
Chain 1:   4900        -8804.270             0.009            0.005   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004106 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415537.539             1.000            1.000
Chain 1:    200     -1587904.242             2.650            4.300
Chain 1:    300      -893094.756             2.026            1.000
Chain 1:    400      -459302.957             1.756            1.000
Chain 1:    500      -359413.808             1.460            0.944
Chain 1:    600      -234348.606             1.306            0.944
Chain 1:    700      -120531.765             1.254            0.944
Chain 1:    800       -87718.312             1.144            0.944
Chain 1:    900       -68077.725             1.049            0.778
Chain 1:   1000       -52893.508             0.973            0.778
Chain 1:   1100       -40368.934             0.904            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39559.225             0.476            0.374
Chain 1:   1300       -27492.327             0.442            0.374
Chain 1:   1400       -27215.249             0.349            0.310
Chain 1:   1500       -23795.345             0.335            0.310
Chain 1:   1600       -23011.340             0.285            0.289
Chain 1:   1700       -21881.106             0.196            0.287
Chain 1:   1800       -21825.105             0.159            0.144
Chain 1:   1900       -22152.588             0.131            0.052
Chain 1:   2000       -20659.192             0.110            0.052
Chain 1:   2100       -20897.920             0.080            0.034
Chain 1:   2200       -21125.545             0.079            0.034
Chain 1:   2300       -20741.404             0.037            0.019
Chain 1:   2400       -20513.027             0.037            0.019
Chain 1:   2500       -20314.974             0.024            0.015
Chain 1:   2600       -19943.806             0.022            0.015
Chain 1:   2700       -19900.414             0.017            0.011
Chain 1:   2800       -19616.645             0.018            0.014
Chain 1:   2900       -19898.564             0.018            0.014
Chain 1:   3000       -19884.706             0.011            0.011
Chain 1:   3100       -19969.843             0.010            0.011
Chain 1:   3200       -19659.616             0.011            0.014
Chain 1:   3300       -19865.086             0.010            0.011
Chain 1:   3400       -19338.338             0.012            0.014
Chain 1:   3500       -19952.627             0.014            0.014
Chain 1:   3600       -19256.271             0.016            0.014
Chain 1:   3700       -19645.312             0.017            0.016
Chain 1:   3800       -18600.152             0.022            0.020
Chain 1:   3900       -18596.178             0.020            0.020
Chain 1:   4000       -18713.512             0.021            0.020
Chain 1:   4100       -18626.976             0.021            0.020
Chain 1:   4200       -18442.205             0.020            0.020
Chain 1:   4300       -18581.331             0.020            0.020
Chain 1:   4400       -18537.292             0.017            0.010
Chain 1:   4500       -18439.682             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49033.578             1.000            1.000
Chain 1:    200       -15317.652             1.601            2.201
Chain 1:    300       -40312.725             1.274            1.000
Chain 1:    400       -20541.841             1.196            1.000
Chain 1:    500       -18250.214             0.982            0.962
Chain 1:    600       -27355.850             0.874            0.962
Chain 1:    700       -15537.873             0.858            0.761
Chain 1:    800       -12422.909             0.782            0.761
Chain 1:    900       -15594.259             0.717            0.620
Chain 1:   1000       -10216.901             0.698            0.620
Chain 1:   1100       -10333.703             0.599            0.526   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12299.175             0.395            0.333
Chain 1:   1300       -10092.829             0.355            0.251
Chain 1:   1400       -10765.747             0.265            0.219
Chain 1:   1500        -9953.331             0.261            0.219
Chain 1:   1600        -9915.489             0.228            0.203
Chain 1:   1700       -11450.518             0.165            0.160
Chain 1:   1800       -14438.830             0.161            0.160
Chain 1:   1900       -14991.828             0.144            0.134
Chain 1:   2000       -10209.498             0.138            0.134
Chain 1:   2100        -9743.422             0.142            0.134
Chain 1:   2200       -17986.022             0.172            0.134
Chain 1:   2300       -11312.215             0.209            0.134
Chain 1:   2400        -9352.430             0.224            0.207
Chain 1:   2500       -13622.213             0.247            0.210
Chain 1:   2600       -15657.942             0.260            0.210
Chain 1:   2700        -9401.296             0.313            0.313
Chain 1:   2800       -19970.564             0.345            0.458
Chain 1:   2900       -17278.377             0.357            0.458
Chain 1:   3000        -9168.905             0.398            0.458
Chain 1:   3100        -8669.545             0.399            0.458
Chain 1:   3200        -9311.327             0.360            0.313
Chain 1:   3300        -9983.320             0.308            0.210
Chain 1:   3400       -13224.062             0.312            0.245
Chain 1:   3500        -9769.572             0.316            0.245
Chain 1:   3600        -9275.973             0.308            0.245
Chain 1:   3700        -9210.837             0.242            0.156
Chain 1:   3800        -9137.678             0.190            0.069
Chain 1:   3900        -9005.091             0.176            0.067
Chain 1:   4000        -8965.534             0.088            0.058
Chain 1:   4100        -8804.605             0.084            0.053
Chain 1:   4200       -10764.062             0.095            0.053
Chain 1:   4300       -15579.211             0.120            0.053
Chain 1:   4400       -13750.780             0.108            0.053
Chain 1:   4500        -8931.705             0.127            0.053
Chain 1:   4600        -8785.715             0.123            0.018
Chain 1:   4700       -13062.665             0.155            0.133
Chain 1:   4800        -9158.824             0.197            0.182
Chain 1:   4900        -9819.009             0.202            0.182
Chain 1:   5000        -8538.042             0.217            0.182
Chain 1:   5100       -12611.533             0.247            0.309
Chain 1:   5200       -13600.423             0.236            0.309
Chain 1:   5300       -10756.359             0.232            0.264
Chain 1:   5400        -8602.836             0.244            0.264
Chain 1:   5500        -8701.959             0.191            0.250
Chain 1:   5600        -8661.865             0.190            0.250
Chain 1:   5700       -10866.471             0.177            0.203
Chain 1:   5800        -8930.200             0.156            0.203
Chain 1:   5900        -8750.492             0.152            0.203
Chain 1:   6000        -9918.583             0.148            0.203
Chain 1:   6100       -10339.674             0.120            0.118
Chain 1:   6200        -9671.375             0.120            0.118
Chain 1:   6300        -8662.858             0.105            0.116
Chain 1:   6400       -11478.307             0.105            0.116
Chain 1:   6500        -8650.413             0.136            0.118
Chain 1:   6600       -10430.856             0.153            0.171
Chain 1:   6700        -8444.483             0.156            0.171
Chain 1:   6800        -8565.786             0.136            0.118
Chain 1:   6900        -8591.227             0.134            0.118
Chain 1:   7000       -13337.581             0.158            0.171
Chain 1:   7100        -8219.727             0.216            0.235
Chain 1:   7200        -8273.923             0.210            0.235
Chain 1:   7300        -8224.368             0.199            0.235
Chain 1:   7400        -8931.125             0.182            0.171
Chain 1:   7500        -9302.089             0.153            0.079
Chain 1:   7600        -8771.710             0.142            0.060
Chain 1:   7700        -8434.524             0.123            0.040
Chain 1:   7800       -13018.288             0.157            0.060
Chain 1:   7900        -8392.939             0.211            0.079
Chain 1:   8000        -8223.276             0.178            0.060
Chain 1:   8100        -8249.379             0.116            0.040
Chain 1:   8200        -8423.052             0.117            0.040
Chain 1:   8300        -9872.282             0.131            0.060
Chain 1:   8400        -8314.319             0.142            0.060
Chain 1:   8500        -8844.196             0.144            0.060
Chain 1:   8600        -8996.570             0.140            0.060
Chain 1:   8700        -8649.523             0.140            0.060
Chain 1:   8800        -8484.561             0.107            0.040
Chain 1:   8900        -8315.326             0.054            0.021
Chain 1:   9000        -9219.963             0.061            0.040
Chain 1:   9100        -8294.086             0.072            0.060
Chain 1:   9200       -11674.583             0.099            0.098
Chain 1:   9300        -8671.808             0.119            0.098
Chain 1:   9400        -8400.640             0.103            0.060
Chain 1:   9500        -8555.513             0.099            0.040
Chain 1:   9600        -9173.940             0.104            0.067
Chain 1:   9700        -8213.339             0.112            0.098
Chain 1:   9800       -11581.859             0.139            0.112
Chain 1:   9900        -9661.816             0.157            0.117
Chain 1:   10000        -8407.274             0.162            0.149
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001421 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57962.502             1.000            1.000
Chain 1:    200       -17721.744             1.635            2.271
Chain 1:    300        -8727.270             1.434            1.031
Chain 1:    400        -8208.415             1.091            1.031
Chain 1:    500        -8468.724             0.879            1.000
Chain 1:    600        -8355.838             0.735            1.000
Chain 1:    700        -7878.070             0.638            0.063
Chain 1:    800        -8209.057             0.564            0.063
Chain 1:    900        -7871.289             0.506            0.061
Chain 1:   1000        -8074.266             0.458            0.061
Chain 1:   1100        -7752.530             0.362            0.043
Chain 1:   1200        -7703.819             0.135            0.042
Chain 1:   1300        -7718.750             0.033            0.040
Chain 1:   1400        -7980.745             0.030            0.033
Chain 1:   1500        -7607.736             0.031            0.040
Chain 1:   1600        -7821.230             0.033            0.040
Chain 1:   1700        -7560.228             0.030            0.035
Chain 1:   1800        -7658.808             0.027            0.033
Chain 1:   1900        -7589.737             0.024            0.027
Chain 1:   2000        -7648.321             0.022            0.027
Chain 1:   2100        -7619.633             0.019            0.013
Chain 1:   2200        -7742.151             0.019            0.016
Chain 1:   2300        -7646.157             0.021            0.016
Chain 1:   2400        -7688.879             0.018            0.013
Chain 1:   2500        -7602.127             0.014            0.013
Chain 1:   2600        -7567.983             0.012            0.011
Chain 1:   2700        -7536.972             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003244 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.44 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86433.861             1.000            1.000
Chain 1:    200       -13553.921             3.189            5.377
Chain 1:    300        -9907.369             2.248            1.000
Chain 1:    400       -10695.368             1.705            1.000
Chain 1:    500        -8901.461             1.404            0.368
Chain 1:    600        -8367.210             1.181            0.368
Chain 1:    700        -8547.365             1.015            0.202
Chain 1:    800        -8939.730             0.894            0.202
Chain 1:    900        -8690.118             0.798            0.074
Chain 1:   1000        -8558.926             0.719            0.074
Chain 1:   1100        -8721.751             0.621            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8238.439             0.089            0.059
Chain 1:   1300        -8605.030             0.057            0.044
Chain 1:   1400        -8593.708             0.050            0.043
Chain 1:   1500        -8463.518             0.031            0.029
Chain 1:   1600        -8570.639             0.026            0.021
Chain 1:   1700        -8649.802             0.025            0.019
Chain 1:   1800        -8229.072             0.025            0.019
Chain 1:   1900        -8328.287             0.024            0.015
Chain 1:   2000        -8302.494             0.022            0.015
Chain 1:   2100        -8427.199             0.022            0.015
Chain 1:   2200        -8235.189             0.019            0.015
Chain 1:   2300        -8323.007             0.015            0.012
Chain 1:   2400        -8392.231             0.016            0.012
Chain 1:   2500        -8338.328             0.015            0.012
Chain 1:   2600        -8339.095             0.014            0.011
Chain 1:   2700        -8256.088             0.014            0.011
Chain 1:   2800        -8216.870             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003436 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8424692.962             1.000            1.000
Chain 1:    200     -1590748.966             2.648            4.296
Chain 1:    300      -890922.571             2.027            1.000
Chain 1:    400      -457296.324             1.757            1.000
Chain 1:    500      -357035.046             1.462            0.948
Chain 1:    600      -232049.215             1.308            0.948
Chain 1:    700      -118781.307             1.258            0.948
Chain 1:    800       -86107.361             1.148            0.948
Chain 1:    900       -66554.249             1.053            0.786
Chain 1:   1000       -51437.985             0.977            0.786
Chain 1:   1100       -38991.271             0.909            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38179.398             0.481            0.379
Chain 1:   1300       -26210.403             0.449            0.379
Chain 1:   1400       -25937.263             0.355            0.319
Chain 1:   1500       -22543.235             0.342            0.319
Chain 1:   1600       -21765.658             0.291            0.294
Chain 1:   1700       -20648.091             0.202            0.294
Chain 1:   1800       -20594.423             0.164            0.151
Chain 1:   1900       -20920.693             0.136            0.054
Chain 1:   2000       -19436.205             0.114            0.054
Chain 1:   2100       -19674.428             0.084            0.036
Chain 1:   2200       -19900.144             0.083            0.036
Chain 1:   2300       -19517.966             0.039            0.020
Chain 1:   2400       -19290.093             0.039            0.020
Chain 1:   2500       -19091.788             0.025            0.016
Chain 1:   2600       -18722.266             0.023            0.016
Chain 1:   2700       -18679.387             0.018            0.012
Chain 1:   2800       -18396.014             0.019            0.015
Chain 1:   2900       -18677.214             0.019            0.015
Chain 1:   3000       -18663.516             0.012            0.012
Chain 1:   3100       -18748.468             0.011            0.012
Chain 1:   3200       -18439.237             0.012            0.015
Chain 1:   3300       -18643.917             0.011            0.012
Chain 1:   3400       -18118.789             0.012            0.015
Chain 1:   3500       -18730.616             0.015            0.015
Chain 1:   3600       -18037.361             0.017            0.015
Chain 1:   3700       -18424.008             0.018            0.017
Chain 1:   3800       -17383.748             0.023            0.021
Chain 1:   3900       -17379.845             0.021            0.021
Chain 1:   4000       -17497.212             0.022            0.021
Chain 1:   4100       -17410.908             0.022            0.021
Chain 1:   4200       -17227.205             0.021            0.021
Chain 1:   4300       -17365.620             0.021            0.021
Chain 1:   4400       -17322.449             0.018            0.011
Chain 1:   4500       -17224.934             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001448 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49491.608             1.000            1.000
Chain 1:    200       -14749.378             1.678            2.356
Chain 1:    300       -20788.481             1.215            1.000
Chain 1:    400       -17617.051             0.957            1.000
Chain 1:    500       -19403.462             0.784            0.291
Chain 1:    600       -12217.098             0.751            0.588
Chain 1:    700       -15261.798             0.672            0.291
Chain 1:    800       -15120.728             0.589            0.291
Chain 1:    900       -19917.827             0.551            0.241
Chain 1:   1000       -11742.565             0.565            0.291
Chain 1:   1100       -14819.790             0.486            0.241
Chain 1:   1200       -21887.615             0.283            0.241
Chain 1:   1300       -11198.247             0.349            0.241
Chain 1:   1400       -12221.266             0.339            0.241
Chain 1:   1500       -13193.407             0.338            0.241
Chain 1:   1600       -10161.145             0.309            0.241
Chain 1:   1700       -19942.747             0.338            0.298
Chain 1:   1800       -10365.432             0.429            0.323
Chain 1:   1900       -10763.200             0.409            0.323
Chain 1:   2000       -11208.541             0.343            0.298
Chain 1:   2100       -10652.350             0.328            0.298
Chain 1:   2200        -9997.728             0.302            0.084
Chain 1:   2300       -11848.730             0.222            0.084
Chain 1:   2400       -12318.444             0.218            0.074
Chain 1:   2500       -14659.911             0.226            0.156
Chain 1:   2600        -9576.468             0.249            0.156
Chain 1:   2700       -10071.108             0.205            0.065
Chain 1:   2800        -9245.288             0.122            0.065
Chain 1:   2900       -12565.100             0.144            0.089
Chain 1:   3000       -13006.130             0.144            0.089
Chain 1:   3100       -11310.301             0.154            0.150
Chain 1:   3200       -11533.943             0.149            0.150
Chain 1:   3300        -9243.371             0.158            0.150
Chain 1:   3400        -9633.935             0.158            0.150
Chain 1:   3500       -13135.554             0.169            0.150
Chain 1:   3600        -9020.033             0.162            0.150
Chain 1:   3700        -9608.385             0.163            0.150
Chain 1:   3800        -9012.036             0.161            0.150
Chain 1:   3900        -9111.399             0.135            0.066
Chain 1:   4000        -9744.623             0.138            0.066
Chain 1:   4100        -9014.141             0.131            0.066
Chain 1:   4200       -13960.819             0.165            0.081
Chain 1:   4300        -9833.825             0.182            0.081
Chain 1:   4400       -11730.668             0.194            0.162
Chain 1:   4500       -12391.337             0.173            0.081
Chain 1:   4600        -9178.539             0.162            0.081
Chain 1:   4700        -9381.464             0.158            0.081
Chain 1:   4800       -10251.294             0.160            0.085
Chain 1:   4900        -9365.577             0.169            0.095
Chain 1:   5000       -12933.333             0.190            0.162
Chain 1:   5100        -8642.981             0.231            0.276
Chain 1:   5200       -10612.574             0.214            0.186
Chain 1:   5300        -8664.810             0.195            0.186
Chain 1:   5400       -11851.105             0.206            0.225
Chain 1:   5500        -9243.311             0.228            0.269
Chain 1:   5600        -8791.675             0.199            0.225
Chain 1:   5700       -14980.755             0.238            0.269
Chain 1:   5800        -9232.892             0.292            0.276
Chain 1:   5900        -8379.725             0.292            0.276
Chain 1:   6000        -9242.872             0.274            0.269
Chain 1:   6100        -8856.059             0.229            0.225
Chain 1:   6200        -8481.369             0.215            0.225
Chain 1:   6300       -13075.237             0.227            0.269
Chain 1:   6400       -15549.069             0.216            0.159
Chain 1:   6500        -9162.657             0.258            0.159
Chain 1:   6600        -9776.712             0.259            0.159
Chain 1:   6700       -12467.809             0.239            0.159
Chain 1:   6800        -9611.627             0.207            0.159
Chain 1:   6900        -9911.547             0.199            0.159
Chain 1:   7000        -8602.887             0.205            0.159
Chain 1:   7100        -9611.808             0.211            0.159
Chain 1:   7200       -10594.886             0.216            0.159
Chain 1:   7300        -8670.458             0.203            0.159
Chain 1:   7400        -9457.551             0.196            0.152
Chain 1:   7500        -8354.252             0.139            0.132
Chain 1:   7600        -8671.664             0.137            0.132
Chain 1:   7700        -9214.127             0.121            0.105
Chain 1:   7800       -10097.439             0.100            0.093
Chain 1:   7900        -8249.109             0.119            0.105
Chain 1:   8000       -10422.212             0.125            0.105
Chain 1:   8100        -8462.585             0.138            0.132
Chain 1:   8200        -8986.665             0.134            0.132
Chain 1:   8300        -8328.653             0.120            0.087
Chain 1:   8400        -8408.076             0.113            0.087
Chain 1:   8500       -11488.716             0.126            0.087
Chain 1:   8600        -9625.544             0.142            0.194
Chain 1:   8700        -8559.393             0.148            0.194
Chain 1:   8800        -8196.222             0.144            0.194
Chain 1:   8900       -10647.147             0.145            0.194
Chain 1:   9000        -9379.160             0.137            0.135
Chain 1:   9100        -9339.605             0.115            0.125
Chain 1:   9200        -8448.489             0.119            0.125
Chain 1:   9300        -8741.840             0.115            0.125
Chain 1:   9400       -10831.924             0.133            0.135
Chain 1:   9500        -8408.072             0.135            0.135
Chain 1:   9600       -10210.825             0.134            0.135
Chain 1:   9700        -8593.327             0.140            0.177
Chain 1:   9800        -8920.171             0.139            0.177
Chain 1:   9900        -8315.647             0.123            0.135
Chain 1:   10000        -8139.242             0.112            0.105
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001857 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57249.588             1.000            1.000
Chain 1:    200       -17775.554             1.610            2.221
Chain 1:    300        -8868.457             1.408            1.004
Chain 1:    400        -8201.968             1.077            1.004
Chain 1:    500        -9225.938             0.883            1.000
Chain 1:    600        -8863.802             0.743            1.000
Chain 1:    700        -8586.042             0.642            0.111
Chain 1:    800        -8130.749             0.568            0.111
Chain 1:    900        -7871.603             0.509            0.081
Chain 1:   1000        -7663.582             0.461            0.081
Chain 1:   1100        -7697.449             0.361            0.056
Chain 1:   1200        -7646.655             0.140            0.041
Chain 1:   1300        -7729.139             0.040            0.033
Chain 1:   1400        -7861.446             0.034            0.032
Chain 1:   1500        -7521.424             0.027            0.032
Chain 1:   1600        -7705.795             0.026            0.027
Chain 1:   1700        -7432.279             0.026            0.027
Chain 1:   1800        -7657.358             0.023            0.027
Chain 1:   1900        -7718.026             0.021            0.024
Chain 1:   2000        -7690.137             0.019            0.017
Chain 1:   2100        -7570.517             0.020            0.017
Chain 1:   2200        -7792.709             0.022            0.024
Chain 1:   2300        -7506.834             0.025            0.029
Chain 1:   2400        -7552.470             0.024            0.029
Chain 1:   2500        -7545.445             0.019            0.024
Chain 1:   2600        -7498.189             0.017            0.016
Chain 1:   2700        -7453.293             0.014            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003645 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87109.163             1.000            1.000
Chain 1:    200       -13810.024             3.154            5.308
Chain 1:    300       -10018.127             2.229            1.000
Chain 1:    400       -11746.118             1.708            1.000
Chain 1:    500        -8514.408             1.443            0.380
Chain 1:    600        -8277.745             1.207            0.380
Chain 1:    700        -8777.783             1.043            0.379
Chain 1:    800        -9098.504             0.917            0.379
Chain 1:    900        -8709.753             0.820            0.147
Chain 1:   1000        -8577.129             0.739            0.147
Chain 1:   1100        -8799.626             0.642            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8285.654             0.117            0.057
Chain 1:   1300        -8612.201             0.083            0.045
Chain 1:   1400        -8488.472             0.070            0.038
Chain 1:   1500        -8492.593             0.032            0.035
Chain 1:   1600        -8577.880             0.030            0.035
Chain 1:   1700        -8627.459             0.025            0.025
Chain 1:   1800        -8169.709             0.027            0.025
Chain 1:   1900        -8279.887             0.024            0.015
Chain 1:   2000        -8296.374             0.023            0.015
Chain 1:   2100        -8423.775             0.022            0.015
Chain 1:   2200        -8174.823             0.019            0.015
Chain 1:   2300        -8358.207             0.017            0.015
Chain 1:   2400        -8174.480             0.018            0.015
Chain 1:   2500        -8250.398             0.019            0.015
Chain 1:   2600        -8159.503             0.019            0.015
Chain 1:   2700        -8193.983             0.019            0.015
Chain 1:   2800        -8144.856             0.014            0.013
Chain 1:   2900        -8259.488             0.014            0.014
Chain 1:   3000        -8173.258             0.015            0.014
Chain 1:   3100        -8137.037             0.013            0.011
Chain 1:   3200        -8108.881             0.011            0.011
Chain 1:   3300        -8368.683             0.012            0.011
Chain 1:   3400        -8410.004             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003062 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418607.244             1.000            1.000
Chain 1:    200     -1588693.889             2.650            4.299
Chain 1:    300      -891540.864             2.027            1.000
Chain 1:    400      -458194.208             1.757            1.000
Chain 1:    500      -358203.594             1.461            0.946
Chain 1:    600      -233010.548             1.307            0.946
Chain 1:    700      -119360.781             1.256            0.946
Chain 1:    800       -86654.005             1.147            0.946
Chain 1:    900       -67030.103             1.052            0.782
Chain 1:   1000       -51870.762             0.976            0.782
Chain 1:   1100       -39383.372             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38570.893             0.480            0.377
Chain 1:   1300       -26542.150             0.447            0.377
Chain 1:   1400       -26267.285             0.353            0.317
Chain 1:   1500       -22858.633             0.340            0.317
Chain 1:   1600       -22077.985             0.290            0.293
Chain 1:   1700       -20952.124             0.200            0.292
Chain 1:   1800       -20897.100             0.163            0.149
Chain 1:   1900       -21224.145             0.135            0.054
Chain 1:   2000       -19734.202             0.113            0.054
Chain 1:   2100       -19972.579             0.083            0.035
Chain 1:   2200       -20199.704             0.082            0.035
Chain 1:   2300       -19816.118             0.038            0.019
Chain 1:   2400       -19587.888             0.039            0.019
Chain 1:   2500       -19389.966             0.025            0.015
Chain 1:   2600       -19019.229             0.023            0.015
Chain 1:   2700       -18975.958             0.018            0.012
Chain 1:   2800       -18692.476             0.019            0.015
Chain 1:   2900       -18974.096             0.019            0.015
Chain 1:   3000       -18960.178             0.012            0.012
Chain 1:   3100       -19045.310             0.011            0.012
Chain 1:   3200       -18735.427             0.011            0.015
Chain 1:   3300       -18940.615             0.011            0.012
Chain 1:   3400       -18414.527             0.012            0.015
Chain 1:   3500       -19027.918             0.015            0.015
Chain 1:   3600       -18332.602             0.016            0.015
Chain 1:   3700       -18720.863             0.018            0.017
Chain 1:   3800       -17677.496             0.023            0.021
Chain 1:   3900       -17673.558             0.021            0.021
Chain 1:   4000       -17790.874             0.022            0.021
Chain 1:   4100       -17704.490             0.022            0.021
Chain 1:   4200       -17520.047             0.021            0.021
Chain 1:   4300       -17658.910             0.021            0.021
Chain 1:   4400       -17615.163             0.018            0.011
Chain 1:   4500       -17517.604             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001542 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12478.725             1.000            1.000
Chain 1:    200        -9082.667             0.687            1.000
Chain 1:    300        -7934.756             0.506            0.374
Chain 1:    400        -8105.732             0.385            0.374
Chain 1:    500        -8014.493             0.310            0.145
Chain 1:    600        -7883.697             0.261            0.145
Chain 1:    700        -7809.005             0.225            0.021
Chain 1:    800        -7783.734             0.198            0.021
Chain 1:    900        -7700.403             0.177            0.017
Chain 1:   1000        -7867.058             0.161            0.021
Chain 1:   1100        -7915.099             0.062            0.017
Chain 1:   1200        -7849.856             0.025            0.011
Chain 1:   1300        -7776.506             0.012            0.011
Chain 1:   1400        -7779.891             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001657 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51356.814             1.000            1.000
Chain 1:    200       -16461.228             1.560            2.120
Chain 1:    300        -8563.184             1.347            1.000
Chain 1:    400        -8371.518             1.016            1.000
Chain 1:    500        -8577.804             0.818            0.922
Chain 1:    600        -8326.706             0.687            0.922
Chain 1:    700        -8332.660             0.589            0.030
Chain 1:    800        -7939.270             0.521            0.050
Chain 1:    900        -7901.978             0.464            0.030
Chain 1:   1000        -7677.947             0.420            0.030
Chain 1:   1100        -7682.362             0.320            0.029
Chain 1:   1200        -7606.749             0.109            0.024
Chain 1:   1300        -7623.367             0.017            0.023
Chain 1:   1400        -7836.468             0.018            0.024
Chain 1:   1500        -7519.223             0.020            0.027
Chain 1:   1600        -7733.382             0.019            0.027
Chain 1:   1700        -7484.146             0.023            0.028
Chain 1:   1800        -7527.080             0.018            0.027
Chain 1:   1900        -7527.669             0.018            0.027
Chain 1:   2000        -7583.076             0.016            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85814.503             1.000            1.000
Chain 1:    200       -13467.323             3.186            5.372
Chain 1:    300        -9808.793             2.248            1.000
Chain 1:    400       -10684.933             1.707            1.000
Chain 1:    500        -8791.532             1.408            0.373
Chain 1:    600        -8405.759             1.181            0.373
Chain 1:    700        -8536.518             1.015            0.215
Chain 1:    800        -8588.678             0.889            0.215
Chain 1:    900        -8689.663             0.791            0.082
Chain 1:   1000        -8434.324             0.715            0.082
Chain 1:   1100        -8589.091             0.617            0.046   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8142.834             0.085            0.046
Chain 1:   1300        -8458.382             0.052            0.037
Chain 1:   1400        -8503.669             0.044            0.030
Chain 1:   1500        -8360.841             0.024            0.018
Chain 1:   1600        -8476.004             0.021            0.017
Chain 1:   1700        -8550.542             0.020            0.017
Chain 1:   1800        -8128.918             0.025            0.018
Chain 1:   1900        -8228.490             0.025            0.018
Chain 1:   2000        -8203.146             0.022            0.017
Chain 1:   2100        -8328.435             0.022            0.015
Chain 1:   2200        -8133.189             0.019            0.015
Chain 1:   2300        -8223.612             0.016            0.014
Chain 1:   2400        -8292.570             0.016            0.014
Chain 1:   2500        -8238.742             0.015            0.012
Chain 1:   2600        -8239.842             0.014            0.011
Chain 1:   2700        -8156.711             0.014            0.011
Chain 1:   2800        -8116.945             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003858 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.58 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397949.715             1.000            1.000
Chain 1:    200     -1585313.105             2.649            4.297
Chain 1:    300      -891292.738             2.025            1.000
Chain 1:    400      -457567.496             1.756            1.000
Chain 1:    500      -357853.864             1.461            0.948
Chain 1:    600      -232957.894             1.306            0.948
Chain 1:    700      -119213.977             1.256            0.948
Chain 1:    800       -86412.200             1.147            0.948
Chain 1:    900       -66759.440             1.052            0.779
Chain 1:   1000       -51561.326             0.976            0.779
Chain 1:   1100       -39036.152             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38216.113             0.481            0.380
Chain 1:   1300       -26169.610             0.449            0.380
Chain 1:   1400       -25889.680             0.355            0.321
Chain 1:   1500       -22475.309             0.342            0.321
Chain 1:   1600       -21691.481             0.292            0.295
Chain 1:   1700       -20564.842             0.203            0.294
Chain 1:   1800       -20509.133             0.165            0.152
Chain 1:   1900       -20835.330             0.137            0.055
Chain 1:   2000       -19345.997             0.115            0.055
Chain 1:   2100       -19584.523             0.084            0.036
Chain 1:   2200       -19810.972             0.083            0.036
Chain 1:   2300       -19428.183             0.039            0.020
Chain 1:   2400       -19200.218             0.039            0.020
Chain 1:   2500       -19002.177             0.025            0.016
Chain 1:   2600       -18632.335             0.024            0.016
Chain 1:   2700       -18589.325             0.018            0.012
Chain 1:   2800       -18306.035             0.020            0.015
Chain 1:   2900       -18587.402             0.020            0.015
Chain 1:   3000       -18573.635             0.012            0.012
Chain 1:   3100       -18658.585             0.011            0.012
Chain 1:   3200       -18349.227             0.012            0.015
Chain 1:   3300       -18554.002             0.011            0.012
Chain 1:   3400       -18028.778             0.013            0.015
Chain 1:   3500       -18640.819             0.015            0.015
Chain 1:   3600       -17947.357             0.017            0.015
Chain 1:   3700       -18334.226             0.019            0.017
Chain 1:   3800       -17293.643             0.023            0.021
Chain 1:   3900       -17289.768             0.022            0.021
Chain 1:   4000       -17407.105             0.022            0.021
Chain 1:   4100       -17320.783             0.022            0.021
Chain 1:   4200       -17137.012             0.022            0.021
Chain 1:   4300       -17275.441             0.021            0.021
Chain 1:   4400       -17232.214             0.019            0.011
Chain 1:   4500       -17134.738             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001972 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49595.396             1.000            1.000
Chain 1:    200       -19159.137             1.294            1.589
Chain 1:    300       -21153.884             0.894            1.000
Chain 1:    400       -17517.318             0.723            1.000
Chain 1:    500       -12803.073             0.652            0.368
Chain 1:    600       -12160.038             0.552            0.368
Chain 1:    700       -16689.060             0.512            0.271
Chain 1:    800       -12183.652             0.494            0.368
Chain 1:    900       -11411.720             0.447            0.271
Chain 1:   1000       -10742.081             0.408            0.271
Chain 1:   1100       -18068.745             0.349            0.271
Chain 1:   1200       -12758.276             0.232            0.271
Chain 1:   1300       -11487.192             0.233            0.271
Chain 1:   1400       -10865.347             0.218            0.271
Chain 1:   1500       -11020.768             0.183            0.111
Chain 1:   1600       -10516.645             0.182            0.111
Chain 1:   1700       -10225.345             0.158            0.068
Chain 1:   1800       -11782.332             0.134            0.068
Chain 1:   1900       -18820.159             0.165            0.111
Chain 1:   2000       -10976.252             0.230            0.132
Chain 1:   2100       -11621.070             0.195            0.111
Chain 1:   2200       -10802.546             0.161            0.076
Chain 1:   2300        -9749.883             0.161            0.076
Chain 1:   2400        -9453.789             0.158            0.076
Chain 1:   2500        -9686.140             0.159            0.076
Chain 1:   2600        -9413.251             0.157            0.076
Chain 1:   2700       -12861.918             0.181            0.108
Chain 1:   2800       -19476.489             0.202            0.108
Chain 1:   2900       -17663.365             0.175            0.103
Chain 1:   3000       -14786.036             0.123            0.103
Chain 1:   3100       -12157.867             0.139            0.108
Chain 1:   3200        -9209.951             0.163            0.195
Chain 1:   3300       -10291.395             0.163            0.195
Chain 1:   3400       -14415.979             0.189            0.216
Chain 1:   3500        -9547.413             0.237            0.268
Chain 1:   3600        -9014.218             0.240            0.268
Chain 1:   3700       -11522.750             0.235            0.218
Chain 1:   3800       -14564.889             0.222            0.216
Chain 1:   3900        -9843.440             0.260            0.218
Chain 1:   4000        -8817.840             0.252            0.218
Chain 1:   4100        -9798.428             0.240            0.218
Chain 1:   4200       -15722.790             0.246            0.218
Chain 1:   4300       -12932.291             0.257            0.218
Chain 1:   4400       -10188.855             0.255            0.218
Chain 1:   4500       -11487.248             0.216            0.216
Chain 1:   4600       -16537.527             0.240            0.218
Chain 1:   4700        -9169.102             0.299            0.269
Chain 1:   4800        -8623.271             0.284            0.269
Chain 1:   4900        -9089.886             0.241            0.216
Chain 1:   5000       -14556.667             0.267            0.269
Chain 1:   5100        -8771.941             0.323            0.305
Chain 1:   5200       -12099.176             0.313            0.275
Chain 1:   5300        -9819.289             0.315            0.275
Chain 1:   5400       -13686.382             0.316            0.283
Chain 1:   5500       -11332.314             0.326            0.283
Chain 1:   5600       -13113.881             0.309            0.275
Chain 1:   5700       -10930.503             0.248            0.232
Chain 1:   5800        -9013.866             0.263            0.232
Chain 1:   5900       -10362.709             0.271            0.232
Chain 1:   6000        -9239.052             0.246            0.213
Chain 1:   6100       -12427.083             0.205            0.213
Chain 1:   6200        -8604.875             0.222            0.213
Chain 1:   6300       -13900.356             0.237            0.213
Chain 1:   6400        -8915.976             0.265            0.213
Chain 1:   6500       -10299.107             0.258            0.213
Chain 1:   6600        -9586.530             0.251            0.213
Chain 1:   6700       -14396.297             0.265            0.257
Chain 1:   6800        -8741.202             0.308            0.334
Chain 1:   6900       -12826.338             0.327            0.334
Chain 1:   7000        -9059.693             0.356            0.381
Chain 1:   7100        -8428.289             0.338            0.381
Chain 1:   7200       -10239.430             0.312            0.334
Chain 1:   7300        -8295.452             0.297            0.318
Chain 1:   7400        -8601.444             0.245            0.234
Chain 1:   7500        -8937.592             0.235            0.234
Chain 1:   7600       -10796.488             0.245            0.234
Chain 1:   7700        -9998.969             0.219            0.177
Chain 1:   7800        -9327.703             0.162            0.172
Chain 1:   7900        -8309.574             0.142            0.123
Chain 1:   8000       -12410.171             0.134            0.123
Chain 1:   8100       -13236.964             0.132            0.123
Chain 1:   8200        -9021.148             0.161            0.123
Chain 1:   8300        -8330.445             0.146            0.083
Chain 1:   8400        -8859.630             0.149            0.083
Chain 1:   8500        -8373.129             0.151            0.083
Chain 1:   8600       -10628.528             0.155            0.083
Chain 1:   8700        -8225.208             0.176            0.123
Chain 1:   8800        -8620.327             0.173            0.123
Chain 1:   8900        -8997.398             0.165            0.083
Chain 1:   9000        -9672.607             0.139            0.070
Chain 1:   9100        -8636.464             0.145            0.083
Chain 1:   9200        -9010.631             0.102            0.070
Chain 1:   9300       -10896.962             0.111            0.070
Chain 1:   9400       -12053.347             0.115            0.096
Chain 1:   9500       -13070.144             0.117            0.096
Chain 1:   9600        -9267.770             0.137            0.096
Chain 1:   9700        -8211.321             0.120            0.096
Chain 1:   9800        -8340.084             0.117            0.096
Chain 1:   9900        -8775.451             0.118            0.096
Chain 1:   10000        -8375.628             0.116            0.096
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003269 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58654.290             1.000            1.000
Chain 1:    200       -18061.844             1.624            2.247
Chain 1:    300        -8837.398             1.430            1.044
Chain 1:    400        -8090.918             1.096            1.044
Chain 1:    500        -8710.594             0.891            1.000
Chain 1:    600        -8664.537             0.743            1.000
Chain 1:    700        -8532.531             0.639            0.092
Chain 1:    800        -8246.950             0.564            0.092
Chain 1:    900        -7952.516             0.505            0.071
Chain 1:   1000        -7707.633             0.458            0.071
Chain 1:   1100        -7843.451             0.360            0.037
Chain 1:   1200        -7818.713             0.135            0.035
Chain 1:   1300        -7582.917             0.034            0.032
Chain 1:   1400        -7609.564             0.025            0.031
Chain 1:   1500        -7548.400             0.019            0.017
Chain 1:   1600        -7735.864             0.021            0.024
Chain 1:   1700        -7652.864             0.020            0.024
Chain 1:   1800        -7548.832             0.018            0.017
Chain 1:   1900        -7581.960             0.015            0.014
Chain 1:   2000        -7667.370             0.013            0.011
Chain 1:   2100        -7624.511             0.012            0.011
Chain 1:   2200        -7732.376             0.013            0.011
Chain 1:   2300        -7534.305             0.012            0.011
Chain 1:   2400        -7530.094             0.012            0.011
Chain 1:   2500        -7615.766             0.012            0.011
Chain 1:   2600        -7519.824             0.011            0.011
Chain 1:   2700        -7506.117             0.010            0.011
Chain 1:   2800        -7500.056             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003513 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85467.591             1.000            1.000
Chain 1:    200       -13824.692             3.091            5.182
Chain 1:    300       -10060.753             2.185            1.000
Chain 1:    400       -11419.854             1.669            1.000
Chain 1:    500        -9054.485             1.387            0.374
Chain 1:    600        -9570.474             1.165            0.374
Chain 1:    700        -8380.014             1.019            0.261
Chain 1:    800        -8784.803             0.897            0.261
Chain 1:    900        -8964.074             0.800            0.142
Chain 1:   1000        -8445.419             0.726            0.142
Chain 1:   1100        -8779.173             0.630            0.119   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8368.102             0.116            0.061
Chain 1:   1300        -8676.592             0.083            0.054
Chain 1:   1400        -8472.017             0.073            0.049
Chain 1:   1500        -8528.863             0.048            0.046
Chain 1:   1600        -8630.413             0.043            0.038
Chain 1:   1700        -8681.917             0.030            0.036
Chain 1:   1800        -8226.089             0.031            0.036
Chain 1:   1900        -8336.912             0.030            0.036
Chain 1:   2000        -8344.079             0.024            0.024
Chain 1:   2100        -8285.460             0.021            0.013
Chain 1:   2200        -8262.344             0.016            0.012
Chain 1:   2300        -8442.551             0.015            0.012
Chain 1:   2400        -8232.017             0.015            0.012
Chain 1:   2500        -8305.782             0.015            0.012
Chain 1:   2600        -8221.399             0.015            0.010
Chain 1:   2700        -8254.010             0.015            0.010
Chain 1:   2800        -8205.249             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004692 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 46.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8381498.618             1.000            1.000
Chain 1:    200     -1581946.137             2.649            4.298
Chain 1:    300      -890591.167             2.025            1.000
Chain 1:    400      -457813.286             1.755            1.000
Chain 1:    500      -358383.053             1.459            0.945
Chain 1:    600      -233606.154             1.305            0.945
Chain 1:    700      -119752.997             1.255            0.945
Chain 1:    800       -86924.720             1.145            0.945
Chain 1:    900       -67249.292             1.050            0.776
Chain 1:   1000       -52035.332             0.974            0.776
Chain 1:   1100       -39489.279             0.906            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38673.999             0.479            0.378
Chain 1:   1300       -26592.140             0.446            0.378
Chain 1:   1400       -26312.745             0.353            0.318
Chain 1:   1500       -22888.221             0.340            0.318
Chain 1:   1600       -22102.752             0.290            0.293
Chain 1:   1700       -20970.695             0.201            0.292
Chain 1:   1800       -20914.348             0.163            0.150
Chain 1:   1900       -21241.206             0.135            0.054
Chain 1:   2000       -19748.025             0.114            0.054
Chain 1:   2100       -19986.817             0.083            0.036
Chain 1:   2200       -20214.075             0.082            0.036
Chain 1:   2300       -19830.385             0.039            0.019
Chain 1:   2400       -19602.117             0.039            0.019
Chain 1:   2500       -19404.279             0.025            0.015
Chain 1:   2600       -19033.584             0.023            0.015
Chain 1:   2700       -18990.380             0.018            0.012
Chain 1:   2800       -18706.815             0.019            0.015
Chain 1:   2900       -18988.572             0.019            0.015
Chain 1:   3000       -18974.733             0.012            0.012
Chain 1:   3100       -19059.767             0.011            0.012
Chain 1:   3200       -18749.974             0.011            0.015
Chain 1:   3300       -18955.135             0.011            0.012
Chain 1:   3400       -18429.128             0.012            0.015
Chain 1:   3500       -19042.402             0.015            0.015
Chain 1:   3600       -18347.361             0.016            0.015
Chain 1:   3700       -18735.384             0.018            0.017
Chain 1:   3800       -17692.411             0.023            0.021
Chain 1:   3900       -17688.518             0.021            0.021
Chain 1:   4000       -17805.818             0.022            0.021
Chain 1:   4100       -17719.343             0.022            0.021
Chain 1:   4200       -17535.104             0.021            0.021
Chain 1:   4300       -17673.857             0.021            0.021
Chain 1:   4400       -17630.192             0.018            0.011
Chain 1:   4500       -17532.671             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001619 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48778.821             1.000            1.000
Chain 1:    200       -19213.058             1.269            1.539
Chain 1:    300       -15759.684             0.919            1.000
Chain 1:    400       -18273.892             0.724            1.000
Chain 1:    500       -12126.308             0.681            0.507
Chain 1:    600       -27204.702             0.659            0.554
Chain 1:    700       -22946.719             0.592            0.507
Chain 1:    800       -14007.261             0.598            0.554
Chain 1:    900       -10672.077             0.566            0.507
Chain 1:   1000       -11099.862             0.513            0.507
Chain 1:   1100       -11003.319             0.414            0.313
Chain 1:   1200       -10292.577             0.267            0.219
Chain 1:   1300       -14211.203             0.273            0.276
Chain 1:   1400       -10497.353             0.294            0.313
Chain 1:   1500        -9801.959             0.251            0.276
Chain 1:   1600       -14787.116             0.229            0.276
Chain 1:   1700        -9902.769             0.260            0.313
Chain 1:   1800       -14856.342             0.229            0.313
Chain 1:   1900       -10238.654             0.243            0.333
Chain 1:   2000       -10946.220             0.246            0.333
Chain 1:   2100       -13437.706             0.263            0.333
Chain 1:   2200        -9498.563             0.298            0.337
Chain 1:   2300       -11533.621             0.288            0.337
Chain 1:   2400        -9362.852             0.276            0.333
Chain 1:   2500        -9314.865             0.269            0.333
Chain 1:   2600       -15544.477             0.276            0.333
Chain 1:   2700        -9246.383             0.294            0.333
Chain 1:   2800        -9468.614             0.263            0.232
Chain 1:   2900        -9188.633             0.221            0.185
Chain 1:   3000        -9141.029             0.215            0.185
Chain 1:   3100       -13239.579             0.228            0.232
Chain 1:   3200       -12650.499             0.191            0.176
Chain 1:   3300       -12472.921             0.175            0.047
Chain 1:   3400        -9044.207             0.190            0.047
Chain 1:   3500        -9042.237             0.189            0.047
Chain 1:   3600       -14843.841             0.188            0.047
Chain 1:   3700       -18840.108             0.141            0.047
Chain 1:   3800       -12089.460             0.195            0.212
Chain 1:   3900       -10685.623             0.205            0.212
Chain 1:   4000        -9976.223             0.211            0.212
Chain 1:   4100        -9469.779             0.186            0.131
Chain 1:   4200        -8725.532             0.190            0.131
Chain 1:   4300        -9157.534             0.193            0.131
Chain 1:   4400       -10890.027             0.171            0.131
Chain 1:   4500        -9032.831             0.191            0.159
Chain 1:   4600       -14507.443             0.190            0.159
Chain 1:   4700        -8704.016             0.236            0.159
Chain 1:   4800        -8543.716             0.182            0.131
Chain 1:   4900        -8896.959             0.172            0.085
Chain 1:   5000       -11017.169             0.185            0.159
Chain 1:   5100        -8569.842             0.208            0.192
Chain 1:   5200        -8707.474             0.201            0.192
Chain 1:   5300       -12966.202             0.229            0.206
Chain 1:   5400       -10503.415             0.236            0.234
Chain 1:   5500        -9065.393             0.232            0.234
Chain 1:   5600        -9239.128             0.196            0.192
Chain 1:   5700        -9128.129             0.130            0.159
Chain 1:   5800        -8590.371             0.135            0.159
Chain 1:   5900       -11110.996             0.154            0.192
Chain 1:   6000       -11837.936             0.140            0.159
Chain 1:   6100       -11769.316             0.113            0.063
Chain 1:   6200        -8426.582             0.151            0.159
Chain 1:   6300        -9049.151             0.125            0.069
Chain 1:   6400       -13321.238             0.133            0.069
Chain 1:   6500       -12287.277             0.126            0.069
Chain 1:   6600        -8606.074             0.167            0.084
Chain 1:   6700        -9102.579             0.171            0.084
Chain 1:   6800       -10166.462             0.175            0.105
Chain 1:   6900       -10444.479             0.155            0.084
Chain 1:   7000        -8544.786             0.171            0.105
Chain 1:   7100        -8552.698             0.171            0.105
Chain 1:   7200        -8840.987             0.134            0.084
Chain 1:   7300        -8724.227             0.129            0.084
Chain 1:   7400       -10223.370             0.111            0.084
Chain 1:   7500        -8961.780             0.117            0.105
Chain 1:   7600       -13155.951             0.106            0.105
Chain 1:   7700       -12014.900             0.110            0.105
Chain 1:   7800       -11848.205             0.101            0.095
Chain 1:   7900        -9670.997             0.121            0.141
Chain 1:   8000        -8563.058             0.112            0.129
Chain 1:   8100        -8305.542             0.115            0.129
Chain 1:   8200        -9824.604             0.127            0.141
Chain 1:   8300        -8347.568             0.143            0.147
Chain 1:   8400       -10309.352             0.148            0.155
Chain 1:   8500        -8177.544             0.160            0.177
Chain 1:   8600       -10810.634             0.152            0.177
Chain 1:   8700        -8631.471             0.168            0.190
Chain 1:   8800        -8279.126             0.171            0.190
Chain 1:   8900        -9317.369             0.159            0.177
Chain 1:   9000        -8422.249             0.157            0.177
Chain 1:   9100        -8219.523             0.156            0.177
Chain 1:   9200        -8480.121             0.144            0.177
Chain 1:   9300        -9078.020             0.133            0.111
Chain 1:   9400        -8583.943             0.120            0.106
Chain 1:   9500        -9536.367             0.103            0.100
Chain 1:   9600        -8994.195             0.085            0.066
Chain 1:   9700        -9588.590             0.066            0.062
Chain 1:   9800        -8357.519             0.077            0.066
Chain 1:   9900       -10502.007             0.086            0.066
Chain 1:   10000        -8217.332             0.103            0.066
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001566 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56779.860             1.000            1.000
Chain 1:    200       -17398.885             1.632            2.263
Chain 1:    300        -8744.748             1.418            1.000
Chain 1:    400        -8384.475             1.074            1.000
Chain 1:    500        -8640.689             0.865            0.990
Chain 1:    600        -8392.766             0.726            0.990
Chain 1:    700        -8183.139             0.626            0.043
Chain 1:    800        -8159.252             0.548            0.043
Chain 1:    900        -7953.967             0.490            0.030
Chain 1:   1000        -7636.267             0.445            0.042
Chain 1:   1100        -7807.690             0.347            0.030
Chain 1:   1200        -7665.574             0.123            0.030
Chain 1:   1300        -7639.639             0.024            0.026
Chain 1:   1400        -7662.000             0.020            0.026
Chain 1:   1500        -7641.525             0.017            0.022
Chain 1:   1600        -7740.686             0.016            0.019
Chain 1:   1700        -7465.687             0.017            0.019
Chain 1:   1800        -7626.839             0.019            0.021
Chain 1:   1900        -7603.148             0.016            0.019
Chain 1:   2000        -7640.632             0.013            0.013
Chain 1:   2100        -7577.839             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005349 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 53.49 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86679.522             1.000            1.000
Chain 1:    200       -13537.238             3.202            5.403
Chain 1:    300        -9948.102             2.255            1.000
Chain 1:    400       -10710.049             1.709            1.000
Chain 1:    500        -8898.970             1.408            0.361
Chain 1:    600        -8463.953             1.182            0.361
Chain 1:    700        -8498.102             1.013            0.204
Chain 1:    800        -8709.740             0.890            0.204
Chain 1:    900        -8794.248             0.792            0.071
Chain 1:   1000        -8595.940             0.715            0.071
Chain 1:   1100        -8812.092             0.618            0.051   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8462.780             0.081            0.041
Chain 1:   1300        -8658.159             0.048            0.025
Chain 1:   1400        -8659.493             0.040            0.024
Chain 1:   1500        -8556.154             0.021            0.023
Chain 1:   1600        -8657.852             0.017            0.023
Chain 1:   1700        -8746.158             0.018            0.023
Chain 1:   1800        -8346.749             0.020            0.023
Chain 1:   1900        -8447.414             0.021            0.023
Chain 1:   2000        -8418.347             0.019            0.012
Chain 1:   2100        -8538.905             0.018            0.012
Chain 1:   2200        -8315.463             0.016            0.012
Chain 1:   2300        -8476.547             0.016            0.012
Chain 1:   2400        -8482.729             0.016            0.012
Chain 1:   2500        -8461.230             0.015            0.012
Chain 1:   2600        -8462.984             0.014            0.012
Chain 1:   2700        -8368.985             0.014            0.012
Chain 1:   2800        -8339.059             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 43.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390741.885             1.000            1.000
Chain 1:    200     -1586069.231             2.645            4.290
Chain 1:    300      -891121.032             2.023            1.000
Chain 1:    400      -457189.607             1.755            1.000
Chain 1:    500      -357199.521             1.460            0.949
Chain 1:    600      -232286.050             1.306            0.949
Chain 1:    700      -118924.822             1.256            0.949
Chain 1:    800       -86174.607             1.146            0.949
Chain 1:    900       -66602.627             1.052            0.780
Chain 1:   1000       -51455.696             0.976            0.780
Chain 1:   1100       -38979.854             0.908            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38163.337             0.481            0.380
Chain 1:   1300       -26176.993             0.449            0.380
Chain 1:   1400       -25900.420             0.355            0.320
Chain 1:   1500       -22500.739             0.342            0.320
Chain 1:   1600       -21720.513             0.292            0.294
Chain 1:   1700       -20601.717             0.202            0.294
Chain 1:   1800       -20547.450             0.164            0.151
Chain 1:   1900       -20873.287             0.136            0.054
Chain 1:   2000       -19388.419             0.115            0.054
Chain 1:   2100       -19626.905             0.084            0.036
Chain 1:   2200       -19852.276             0.083            0.036
Chain 1:   2300       -19470.458             0.039            0.020
Chain 1:   2400       -19242.709             0.039            0.020
Chain 1:   2500       -19044.242             0.025            0.016
Chain 1:   2600       -18675.260             0.023            0.016
Chain 1:   2700       -18632.509             0.018            0.012
Chain 1:   2800       -18349.239             0.020            0.015
Chain 1:   2900       -18630.268             0.019            0.015
Chain 1:   3000       -18616.694             0.012            0.012
Chain 1:   3100       -18701.558             0.011            0.012
Chain 1:   3200       -18392.578             0.012            0.015
Chain 1:   3300       -18597.045             0.011            0.012
Chain 1:   3400       -18072.339             0.013            0.015
Chain 1:   3500       -18683.524             0.015            0.015
Chain 1:   3600       -17991.141             0.017            0.015
Chain 1:   3700       -18377.138             0.018            0.017
Chain 1:   3800       -17338.190             0.023            0.021
Chain 1:   3900       -17334.306             0.021            0.021
Chain 1:   4000       -17451.679             0.022            0.021
Chain 1:   4100       -17365.416             0.022            0.021
Chain 1:   4200       -17182.024             0.021            0.021
Chain 1:   4300       -17320.257             0.021            0.021
Chain 1:   4400       -17277.341             0.019            0.011
Chain 1:   4500       -17179.841             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48824.901             1.000            1.000
Chain 1:    200       -23049.514             1.059            1.118
Chain 1:    300       -18956.438             0.778            1.000
Chain 1:    400       -12010.122             0.728            1.000
Chain 1:    500       -14713.546             0.619            0.578
Chain 1:    600       -18966.242             0.553            0.578
Chain 1:    700       -12491.648             0.548            0.518
Chain 1:    800       -11500.327             0.491            0.518
Chain 1:    900       -15597.393             0.465            0.263
Chain 1:   1000       -10436.924             0.468            0.494
Chain 1:   1100       -11145.506             0.375            0.263
Chain 1:   1200       -10669.990             0.267            0.224
Chain 1:   1300       -13107.958             0.264            0.224
Chain 1:   1400       -15938.495             0.224            0.186
Chain 1:   1500       -13898.121             0.220            0.186
Chain 1:   1600       -12343.456             0.211            0.178
Chain 1:   1700       -10908.170             0.172            0.147
Chain 1:   1800        -9890.300             0.174            0.147
Chain 1:   1900       -10151.395             0.150            0.132
Chain 1:   2000       -12900.073             0.122            0.132
Chain 1:   2100       -10889.356             0.134            0.147
Chain 1:   2200        -9761.601             0.141            0.147
Chain 1:   2300        -9474.672             0.125            0.132
Chain 1:   2400       -10286.254             0.116            0.126
Chain 1:   2500       -12486.518             0.118            0.126
Chain 1:   2600       -14049.940             0.117            0.116
Chain 1:   2700       -13894.291             0.105            0.111
Chain 1:   2800       -11071.723             0.120            0.116
Chain 1:   2900        -9372.186             0.136            0.176
Chain 1:   3000       -18508.642             0.164            0.176
Chain 1:   3100        -9318.379             0.244            0.176
Chain 1:   3200        -9358.266             0.233            0.176
Chain 1:   3300       -16256.303             0.272            0.181
Chain 1:   3400        -9227.255             0.341            0.255
Chain 1:   3500        -9075.401             0.325            0.255
Chain 1:   3600       -11130.816             0.332            0.255
Chain 1:   3700        -9694.955             0.346            0.255
Chain 1:   3800        -9825.274             0.321            0.185
Chain 1:   3900       -12345.768             0.324            0.204
Chain 1:   4000       -15301.127             0.294            0.193
Chain 1:   4100        -9362.923             0.258            0.193
Chain 1:   4200       -12168.243             0.281            0.204
Chain 1:   4300        -9688.547             0.264            0.204
Chain 1:   4400        -9444.248             0.191            0.193
Chain 1:   4500        -9546.312             0.190            0.193
Chain 1:   4600       -13396.907             0.200            0.204
Chain 1:   4700        -9003.485             0.234            0.231
Chain 1:   4800       -10772.731             0.249            0.231
Chain 1:   4900        -9151.652             0.247            0.231
Chain 1:   5000        -8728.646             0.232            0.231
Chain 1:   5100       -17294.345             0.218            0.231
Chain 1:   5200       -14106.171             0.218            0.226
Chain 1:   5300       -14416.182             0.194            0.177
Chain 1:   5400       -12122.495             0.211            0.189
Chain 1:   5500        -8677.623             0.249            0.226
Chain 1:   5600        -8599.775             0.222            0.189
Chain 1:   5700       -11958.380             0.201            0.189
Chain 1:   5800        -8587.350             0.224            0.226
Chain 1:   5900        -8575.651             0.206            0.226
Chain 1:   6000        -8692.639             0.203            0.226
Chain 1:   6100       -10587.918             0.171            0.189
Chain 1:   6200        -8958.132             0.167            0.182
Chain 1:   6300        -9452.431             0.170            0.182
Chain 1:   6400       -11859.292             0.171            0.182
Chain 1:   6500        -9263.771             0.159            0.182
Chain 1:   6600       -12125.819             0.182            0.203
Chain 1:   6700        -8515.274             0.196            0.203
Chain 1:   6800        -8612.266             0.158            0.182
Chain 1:   6900       -12390.773             0.189            0.203
Chain 1:   7000        -8382.773             0.235            0.236
Chain 1:   7100        -9350.083             0.228            0.236
Chain 1:   7200        -8442.540             0.220            0.236
Chain 1:   7300       -11421.017             0.241            0.261
Chain 1:   7400        -8586.950             0.254            0.280
Chain 1:   7500        -9889.319             0.239            0.261
Chain 1:   7600        -9375.657             0.221            0.261
Chain 1:   7700        -8581.886             0.188            0.132
Chain 1:   7800        -8681.276             0.188            0.132
Chain 1:   7900        -8312.400             0.161            0.107
Chain 1:   8000        -9874.850             0.129            0.107
Chain 1:   8100        -8557.013             0.135            0.132
Chain 1:   8200        -9044.973             0.129            0.132
Chain 1:   8300        -8242.747             0.113            0.097
Chain 1:   8400        -9206.494             0.090            0.097
Chain 1:   8500        -8441.656             0.086            0.092
Chain 1:   8600        -8281.947             0.083            0.092
Chain 1:   8700        -8708.223             0.078            0.091
Chain 1:   8800        -9288.976             0.083            0.091
Chain 1:   8900       -11452.777             0.098            0.097
Chain 1:   9000       -10525.027             0.091            0.091
Chain 1:   9100       -10860.875             0.079            0.088
Chain 1:   9200        -8788.124             0.097            0.091
Chain 1:   9300        -8349.265             0.092            0.088
Chain 1:   9400        -8245.534             0.083            0.063
Chain 1:   9500        -8240.925             0.074            0.053
Chain 1:   9600        -8297.276             0.073            0.053
Chain 1:   9700        -8622.896             0.072            0.053
Chain 1:   9800       -10448.383             0.083            0.053
Chain 1:   9900       -10029.813             0.068            0.042
Chain 1:   10000        -8826.292             0.073            0.042
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001593 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56870.675             1.000            1.000
Chain 1:    200       -17338.894             1.640            2.280
Chain 1:    300        -8730.204             1.422            1.000
Chain 1:    400        -8424.440             1.076            1.000
Chain 1:    500        -8192.699             0.866            0.986
Chain 1:    600        -8414.627             0.726            0.986
Chain 1:    700        -7950.368             0.631            0.058
Chain 1:    800        -8066.496             0.554            0.058
Chain 1:    900        -8043.006             0.493            0.036
Chain 1:   1000        -7858.683             0.446            0.036
Chain 1:   1100        -7678.960             0.348            0.028
Chain 1:   1200        -7660.637             0.120            0.026
Chain 1:   1300        -7721.741             0.022            0.023
Chain 1:   1400        -7859.550             0.021            0.023
Chain 1:   1500        -7666.682             0.020            0.023
Chain 1:   1600        -7779.086             0.019            0.018
Chain 1:   1700        -7551.289             0.016            0.018
Chain 1:   1800        -7594.481             0.015            0.018
Chain 1:   1900        -7600.805             0.015            0.018
Chain 1:   2000        -7671.358             0.014            0.014
Chain 1:   2100        -7633.153             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003223 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85826.547             1.000            1.000
Chain 1:    200       -13444.107             3.192            5.384
Chain 1:    300        -9896.684             2.247            1.000
Chain 1:    400       -10631.494             1.703            1.000
Chain 1:    500        -8833.635             1.403            0.358
Chain 1:    600        -8400.291             1.178            0.358
Chain 1:    700        -8802.688             1.016            0.204
Chain 1:    800        -9257.217             0.895            0.204
Chain 1:    900        -8702.169             0.803            0.069
Chain 1:   1000        -8476.917             0.725            0.069
Chain 1:   1100        -8624.053             0.627            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8493.383             0.090            0.052
Chain 1:   1300        -8613.883             0.056            0.049
Chain 1:   1400        -8623.424             0.049            0.046
Chain 1:   1500        -8521.194             0.030            0.027
Chain 1:   1600        -8619.227             0.026            0.017
Chain 1:   1700        -8711.458             0.022            0.015
Chain 1:   1800        -8323.029             0.022            0.015
Chain 1:   1900        -8425.548             0.017            0.014
Chain 1:   2000        -8395.488             0.014            0.012
Chain 1:   2100        -8527.446             0.014            0.012
Chain 1:   2200        -8313.198             0.015            0.012
Chain 1:   2300        -8454.843             0.016            0.012
Chain 1:   2400        -8467.168             0.016            0.012
Chain 1:   2500        -8435.265             0.015            0.012
Chain 1:   2600        -8434.893             0.014            0.012
Chain 1:   2700        -8343.188             0.014            0.012
Chain 1:   2800        -8319.372             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005534 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 55.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8381422.476             1.000            1.000
Chain 1:    200     -1582640.562             2.648            4.296
Chain 1:    300      -890791.788             2.024            1.000
Chain 1:    400      -457450.724             1.755            1.000
Chain 1:    500      -357743.644             1.460            0.947
Chain 1:    600      -232914.592             1.306            0.947
Chain 1:    700      -119196.732             1.255            0.947
Chain 1:    800       -86375.281             1.146            0.947
Chain 1:    900       -66722.978             1.051            0.777
Chain 1:   1000       -51513.272             0.976            0.777
Chain 1:   1100       -38980.059             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38155.811             0.481            0.380
Chain 1:   1300       -26112.285             0.449            0.380
Chain 1:   1400       -25829.897             0.355            0.322
Chain 1:   1500       -22416.379             0.343            0.322
Chain 1:   1600       -21631.941             0.293            0.295
Chain 1:   1700       -20506.290             0.203            0.295
Chain 1:   1800       -20450.432             0.165            0.152
Chain 1:   1900       -20776.028             0.137            0.055
Chain 1:   2000       -19288.398             0.115            0.055
Chain 1:   2100       -19526.780             0.084            0.036
Chain 1:   2200       -19752.674             0.083            0.036
Chain 1:   2300       -19370.534             0.039            0.020
Chain 1:   2400       -19142.828             0.039            0.020
Chain 1:   2500       -18944.761             0.025            0.016
Chain 1:   2600       -18575.549             0.024            0.016
Chain 1:   2700       -18532.767             0.018            0.012
Chain 1:   2800       -18249.738             0.020            0.016
Chain 1:   2900       -18530.834             0.020            0.015
Chain 1:   3000       -18517.127             0.012            0.012
Chain 1:   3100       -18601.959             0.011            0.012
Chain 1:   3200       -18293.018             0.012            0.015
Chain 1:   3300       -18497.501             0.011            0.012
Chain 1:   3400       -17973.013             0.013            0.015
Chain 1:   3500       -18583.893             0.015            0.016
Chain 1:   3600       -17892.010             0.017            0.016
Chain 1:   3700       -18277.703             0.019            0.017
Chain 1:   3800       -17239.496             0.023            0.021
Chain 1:   3900       -17235.713             0.022            0.021
Chain 1:   4000       -17353.039             0.022            0.021
Chain 1:   4100       -17266.788             0.022            0.021
Chain 1:   4200       -17083.605             0.022            0.021
Chain 1:   4300       -17221.636             0.021            0.021
Chain 1:   4400       -17178.848             0.019            0.011
Chain 1:   4500       -17081.463             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001583 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13193.614             1.000            1.000
Chain 1:    200        -9784.922             0.674            1.000
Chain 1:    300        -7953.889             0.526            0.348
Chain 1:    400        -8148.259             0.401            0.348
Chain 1:    500        -8084.773             0.322            0.230
Chain 1:    600        -7877.819             0.273            0.230
Chain 1:    700        -7864.561             0.234            0.026
Chain 1:    800        -7938.023             0.206            0.026
Chain 1:    900        -8009.352             0.184            0.024
Chain 1:   1000        -7939.563             0.167            0.024
Chain 1:   1100        -8054.671             0.068            0.014
Chain 1:   1200        -7906.389             0.035            0.014
Chain 1:   1300        -7815.463             0.013            0.012
Chain 1:   1400        -7838.246             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001475 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58099.481             1.000            1.000
Chain 1:    200       -17830.526             1.629            2.258
Chain 1:    300        -8729.382             1.434            1.043
Chain 1:    400        -8103.753             1.095            1.043
Chain 1:    500        -8632.623             0.888            1.000
Chain 1:    600        -8772.946             0.743            1.000
Chain 1:    700        -8215.271             0.646            0.077
Chain 1:    800        -8267.719             0.566            0.077
Chain 1:    900        -7840.123             0.509            0.068
Chain 1:   1000        -7825.525             0.459            0.068
Chain 1:   1100        -7788.614             0.359            0.061
Chain 1:   1200        -7609.163             0.136            0.055
Chain 1:   1300        -7578.052             0.032            0.024
Chain 1:   1400        -7892.830             0.028            0.024
Chain 1:   1500        -7544.645             0.027            0.024
Chain 1:   1600        -7732.333             0.027            0.024
Chain 1:   1700        -7730.154             0.021            0.024
Chain 1:   1800        -7646.301             0.021            0.024
Chain 1:   1900        -7605.156             0.016            0.011
Chain 1:   2000        -7630.498             0.016            0.011
Chain 1:   2100        -7545.921             0.017            0.011
Chain 1:   2200        -7696.787             0.017            0.011
Chain 1:   2300        -7563.252             0.018            0.018
Chain 1:   2400        -7634.263             0.015            0.011
Chain 1:   2500        -7541.266             0.011            0.011
Chain 1:   2600        -7498.872             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003193 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.93 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86263.700             1.000            1.000
Chain 1:    200       -13622.737             3.166            5.332
Chain 1:    300        -9924.977             2.235            1.000
Chain 1:    400       -11006.931             1.701            1.000
Chain 1:    500        -8927.454             1.407            0.373
Chain 1:    600        -8570.384             1.180            0.373
Chain 1:    700        -8525.651             1.012            0.233
Chain 1:    800        -9405.995             0.897            0.233
Chain 1:    900        -8763.817             0.806            0.098
Chain 1:   1000        -8588.651             0.727            0.098
Chain 1:   1100        -8732.385             0.629            0.094   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8177.891             0.102            0.073
Chain 1:   1300        -8609.073             0.070            0.068
Chain 1:   1400        -8556.352             0.061            0.050
Chain 1:   1500        -8456.939             0.039            0.042
Chain 1:   1600        -8562.677             0.036            0.020
Chain 1:   1700        -8627.812             0.036            0.020
Chain 1:   1800        -8194.438             0.032            0.020
Chain 1:   1900        -8298.850             0.026            0.016
Chain 1:   2000        -8274.250             0.024            0.013
Chain 1:   2100        -8251.884             0.023            0.012
Chain 1:   2200        -8216.921             0.016            0.012
Chain 1:   2300        -8346.118             0.013            0.012
Chain 1:   2400        -8201.117             0.014            0.012
Chain 1:   2500        -8269.878             0.014            0.012
Chain 1:   2600        -8188.959             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004574 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 45.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398110.376             1.000            1.000
Chain 1:    200     -1584715.187             2.650            4.299
Chain 1:    300      -890689.975             2.026            1.000
Chain 1:    400      -457606.575             1.756            1.000
Chain 1:    500      -357883.270             1.461            0.946
Chain 1:    600      -233039.367             1.307            0.946
Chain 1:    700      -119351.345             1.256            0.946
Chain 1:    800       -86532.398             1.146            0.946
Chain 1:    900       -66896.894             1.052            0.779
Chain 1:   1000       -51708.008             0.976            0.779
Chain 1:   1100       -39189.780             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38370.583             0.480            0.379
Chain 1:   1300       -26332.352             0.448            0.379
Chain 1:   1400       -26053.236             0.354            0.319
Chain 1:   1500       -22640.146             0.341            0.319
Chain 1:   1600       -21856.685             0.291            0.294
Chain 1:   1700       -20731.134             0.202            0.294
Chain 1:   1800       -20675.531             0.164            0.151
Chain 1:   1900       -21002.009             0.136            0.054
Chain 1:   2000       -19512.464             0.114            0.054
Chain 1:   2100       -19751.187             0.084            0.036
Chain 1:   2200       -19977.600             0.083            0.036
Chain 1:   2300       -19594.696             0.039            0.020
Chain 1:   2400       -19366.659             0.039            0.020
Chain 1:   2500       -19168.438             0.025            0.016
Chain 1:   2600       -18798.608             0.023            0.016
Chain 1:   2700       -18755.527             0.018            0.012
Chain 1:   2800       -18472.120             0.019            0.015
Chain 1:   2900       -18753.496             0.019            0.015
Chain 1:   3000       -18739.823             0.012            0.012
Chain 1:   3100       -18824.807             0.011            0.012
Chain 1:   3200       -18515.349             0.012            0.015
Chain 1:   3300       -18720.156             0.011            0.012
Chain 1:   3400       -18194.709             0.012            0.015
Chain 1:   3500       -18807.096             0.015            0.015
Chain 1:   3600       -18113.112             0.017            0.015
Chain 1:   3700       -18500.385             0.018            0.017
Chain 1:   3800       -17458.991             0.023            0.021
Chain 1:   3900       -17455.051             0.021            0.021
Chain 1:   4000       -17572.419             0.022            0.021
Chain 1:   4100       -17486.087             0.022            0.021
Chain 1:   4200       -17302.084             0.021            0.021
Chain 1:   4300       -17440.712             0.021            0.021
Chain 1:   4400       -17397.364             0.018            0.011
Chain 1:   4500       -17299.795             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49259.891             1.000            1.000
Chain 1:    200       -15525.056             1.586            2.173
Chain 1:    300       -29312.744             1.214            1.000
Chain 1:    400       -14979.928             1.150            1.000
Chain 1:    500       -16487.700             0.938            0.957
Chain 1:    600       -18224.103             0.798            0.957
Chain 1:    700       -12646.273             0.747            0.470
Chain 1:    800       -15694.896             0.678            0.470
Chain 1:    900       -13871.620             0.617            0.441
Chain 1:   1000       -13479.099             0.558            0.441
Chain 1:   1100       -11655.846             0.474            0.194
Chain 1:   1200       -18675.941             0.294            0.194
Chain 1:   1300       -11732.140             0.306            0.194
Chain 1:   1400       -12355.656             0.216            0.156
Chain 1:   1500       -10392.406             0.225            0.189
Chain 1:   1600       -11751.668             0.228            0.189
Chain 1:   1700       -17397.396             0.216            0.189
Chain 1:   1800       -13574.772             0.225            0.189
Chain 1:   1900       -11265.653             0.232            0.205
Chain 1:   2000       -15911.936             0.258            0.282
Chain 1:   2100       -11025.951             0.287            0.292
Chain 1:   2200       -10138.345             0.258            0.282
Chain 1:   2300       -12387.876             0.217            0.205
Chain 1:   2400        -9667.268             0.240            0.281
Chain 1:   2500       -17876.720             0.267            0.282
Chain 1:   2600        -9701.167             0.340            0.292
Chain 1:   2700       -10111.621             0.311            0.282
Chain 1:   2800       -11395.146             0.295            0.281
Chain 1:   2900        -9898.398             0.289            0.281
Chain 1:   3000       -10391.253             0.265            0.182
Chain 1:   3100       -10274.603             0.222            0.151
Chain 1:   3200       -18783.129             0.258            0.182
Chain 1:   3300       -16539.243             0.254            0.151
Chain 1:   3400        -9904.050             0.292            0.151
Chain 1:   3500       -10552.174             0.253            0.136
Chain 1:   3600       -17459.575             0.208            0.136
Chain 1:   3700        -9816.926             0.282            0.151
Chain 1:   3800       -14075.854             0.301            0.303
Chain 1:   3900       -10380.933             0.321            0.356
Chain 1:   4000        -9979.403             0.320            0.356
Chain 1:   4100        -9803.373             0.321            0.356
Chain 1:   4200       -10708.433             0.284            0.303
Chain 1:   4300       -10142.795             0.276            0.303
Chain 1:   4400       -10085.673             0.210            0.085
Chain 1:   4500        -9261.671             0.213            0.089
Chain 1:   4600        -9168.507             0.174            0.085
Chain 1:   4700        -9220.379             0.097            0.056
Chain 1:   4800        -8927.352             0.070            0.040
Chain 1:   4900        -9074.989             0.036            0.033
Chain 1:   5000       -14142.985             0.068            0.033
Chain 1:   5100        -8874.207             0.125            0.056
Chain 1:   5200       -12375.428             0.145            0.056
Chain 1:   5300       -12082.784             0.142            0.033
Chain 1:   5400        -8931.967             0.177            0.089
Chain 1:   5500        -9040.922             0.169            0.033
Chain 1:   5600        -8987.072             0.168            0.033
Chain 1:   5700        -9110.103             0.169            0.033
Chain 1:   5800        -9246.899             0.167            0.024
Chain 1:   5900       -15874.793             0.208            0.283
Chain 1:   6000       -12662.357             0.197            0.254
Chain 1:   6100        -9067.201             0.177            0.254
Chain 1:   6200        -9935.308             0.158            0.087
Chain 1:   6300        -8891.178             0.167            0.117
Chain 1:   6400        -9190.684             0.135            0.087
Chain 1:   6500        -8875.220             0.137            0.087
Chain 1:   6600        -8950.874             0.138            0.087
Chain 1:   6700        -9994.878             0.147            0.104
Chain 1:   6800        -8854.767             0.158            0.117
Chain 1:   6900        -8993.508             0.118            0.104
Chain 1:   7000        -9763.371             0.101            0.087
Chain 1:   7100        -8709.595             0.073            0.087
Chain 1:   7200        -9381.129             0.071            0.079
Chain 1:   7300       -10762.651             0.073            0.079
Chain 1:   7400        -9478.768             0.083            0.104
Chain 1:   7500       -11193.782             0.095            0.121
Chain 1:   7600        -9845.188             0.107            0.128
Chain 1:   7700        -9036.503             0.106            0.128
Chain 1:   7800        -9090.079             0.094            0.121
Chain 1:   7900        -8914.562             0.094            0.121
Chain 1:   8000       -12338.027             0.114            0.128
Chain 1:   8100        -9656.510             0.130            0.135
Chain 1:   8200       -11466.564             0.138            0.137
Chain 1:   8300        -8624.436             0.158            0.153
Chain 1:   8400        -8648.644             0.145            0.153
Chain 1:   8500        -8659.231             0.130            0.137
Chain 1:   8600        -8589.219             0.117            0.089
Chain 1:   8700        -9064.842             0.113            0.052
Chain 1:   8800        -8809.684             0.116            0.052
Chain 1:   8900        -9324.765             0.119            0.055
Chain 1:   9000        -9929.257             0.097            0.055
Chain 1:   9100        -8916.797             0.081            0.055
Chain 1:   9200        -8762.317             0.067            0.052
Chain 1:   9300       -13076.874             0.067            0.052
Chain 1:   9400        -8679.625             0.117            0.055
Chain 1:   9500       -11170.419             0.140            0.061
Chain 1:   9600       -10776.992             0.142            0.061
Chain 1:   9700        -8839.136             0.159            0.114
Chain 1:   9800        -8823.746             0.156            0.114
Chain 1:   9900       -10245.082             0.165            0.139
Chain 1:   10000        -8888.402             0.174            0.153
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001895 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57679.285             1.000            1.000
Chain 1:    200       -18052.499             1.598            2.195
Chain 1:    300        -9042.248             1.397            1.000
Chain 1:    400        -8462.690             1.065            1.000
Chain 1:    500        -8637.818             0.856            0.996
Chain 1:    600        -8670.708             0.714            0.996
Chain 1:    700        -8189.784             0.620            0.068
Chain 1:    800        -8455.108             0.547            0.068
Chain 1:    900        -8076.702             0.491            0.059
Chain 1:   1000        -7900.531             0.444            0.059
Chain 1:   1100        -7986.475             0.345            0.047
Chain 1:   1200        -7789.167             0.128            0.031
Chain 1:   1300        -8022.458             0.032            0.029
Chain 1:   1400        -7890.615             0.027            0.025
Chain 1:   1500        -7675.198             0.027            0.028
Chain 1:   1600        -7847.852             0.029            0.028
Chain 1:   1700        -7712.604             0.025            0.025
Chain 1:   1800        -7829.630             0.023            0.022
Chain 1:   1900        -7691.992             0.020            0.022
Chain 1:   2000        -7801.288             0.020            0.018
Chain 1:   2100        -7683.289             0.020            0.018
Chain 1:   2200        -7875.523             0.020            0.018
Chain 1:   2300        -7652.029             0.020            0.018
Chain 1:   2400        -7730.784             0.019            0.018
Chain 1:   2500        -7701.707             0.017            0.018
Chain 1:   2600        -7621.440             0.016            0.015
Chain 1:   2700        -7609.482             0.014            0.015
Chain 1:   2800        -7613.945             0.013            0.014
Chain 1:   2900        -7473.951             0.013            0.014
Chain 1:   3000        -7625.116             0.013            0.015
Chain 1:   3100        -7622.700             0.012            0.011
Chain 1:   3200        -7839.413             0.012            0.011
Chain 1:   3300        -7562.023             0.013            0.011
Chain 1:   3400        -7793.905             0.015            0.019
Chain 1:   3500        -7533.859             0.018            0.020
Chain 1:   3600        -7601.412             0.018            0.020
Chain 1:   3700        -7550.820             0.018            0.020
Chain 1:   3800        -7551.836             0.018            0.020
Chain 1:   3900        -7510.636             0.017            0.020
Chain 1:   4000        -7502.653             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003286 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87195.448             1.000            1.000
Chain 1:    200       -14028.318             3.108            5.216
Chain 1:    300       -10320.308             2.192            1.000
Chain 1:    400       -11491.074             1.669            1.000
Chain 1:    500        -9327.530             1.382            0.359
Chain 1:    600        -8837.521             1.161            0.359
Chain 1:    700        -9253.716             1.001            0.232
Chain 1:    800        -9450.338             0.879            0.232
Chain 1:    900        -9097.597             0.785            0.102
Chain 1:   1000        -9074.135             0.707            0.102
Chain 1:   1100        -8922.046             0.609            0.055   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8714.396             0.090            0.045
Chain 1:   1300        -9005.749             0.057            0.039
Chain 1:   1400        -8959.802             0.047            0.032
Chain 1:   1500        -8853.611             0.025            0.024
Chain 1:   1600        -8959.697             0.021            0.021
Chain 1:   1700        -9035.113             0.017            0.017
Chain 1:   1800        -8603.068             0.020            0.017
Chain 1:   1900        -8707.155             0.018            0.012
Chain 1:   2000        -8682.584             0.018            0.012
Chain 1:   2100        -8653.278             0.016            0.012
Chain 1:   2200        -8625.246             0.014            0.012
Chain 1:   2300        -8753.629             0.012            0.012
Chain 1:   2400        -8610.599             0.014            0.012
Chain 1:   2500        -8678.116             0.013            0.012
Chain 1:   2600        -8598.484             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004794 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 47.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8391493.286             1.000            1.000
Chain 1:    200     -1582783.377             2.651            4.302
Chain 1:    300      -891464.389             2.026            1.000
Chain 1:    400      -458702.093             1.755            1.000
Chain 1:    500      -358954.863             1.460            0.943
Chain 1:    600      -233836.098             1.306            0.943
Chain 1:    700      -119915.482             1.255            0.943
Chain 1:    800       -87068.641             1.145            0.943
Chain 1:    900       -67393.213             1.050            0.775
Chain 1:   1000       -52171.957             0.974            0.775
Chain 1:   1100       -39630.836             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38806.893             0.478            0.377
Chain 1:   1300       -26744.183             0.446            0.377
Chain 1:   1400       -26462.489             0.352            0.316
Chain 1:   1500       -23044.009             0.339            0.316
Chain 1:   1600       -22259.044             0.289            0.292
Chain 1:   1700       -21130.389             0.200            0.292
Chain 1:   1800       -21074.075             0.162            0.148
Chain 1:   1900       -21400.485             0.135            0.053
Chain 1:   2000       -19909.613             0.113            0.053
Chain 1:   2100       -20148.268             0.082            0.035
Chain 1:   2200       -20375.062             0.081            0.035
Chain 1:   2300       -19991.856             0.038            0.019
Chain 1:   2400       -19763.805             0.038            0.019
Chain 1:   2500       -19565.812             0.025            0.015
Chain 1:   2600       -19195.784             0.023            0.015
Chain 1:   2700       -19152.629             0.018            0.012
Chain 1:   2800       -18869.386             0.019            0.015
Chain 1:   2900       -19150.752             0.019            0.015
Chain 1:   3000       -19136.958             0.012            0.012
Chain 1:   3100       -19222.000             0.011            0.012
Chain 1:   3200       -18912.512             0.011            0.015
Chain 1:   3300       -19117.336             0.011            0.012
Chain 1:   3400       -18591.956             0.012            0.015
Chain 1:   3500       -19204.347             0.014            0.015
Chain 1:   3600       -18510.330             0.016            0.015
Chain 1:   3700       -18897.669             0.018            0.016
Chain 1:   3800       -17856.334             0.022            0.020
Chain 1:   3900       -17852.433             0.021            0.020
Chain 1:   4000       -17969.744             0.021            0.020
Chain 1:   4100       -17883.479             0.022            0.020
Chain 1:   4200       -17699.445             0.021            0.020
Chain 1:   4300       -17838.049             0.021            0.020
Chain 1:   4400       -17794.696             0.018            0.010
Chain 1:   4500       -17697.156             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00136 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12658.746             1.000            1.000
Chain 1:    200        -9589.283             0.660            1.000
Chain 1:    300        -8355.508             0.489            0.320
Chain 1:    400        -8561.564             0.373            0.320
Chain 1:    500        -8454.833             0.301            0.148
Chain 1:    600        -8303.055             0.254            0.148
Chain 1:    700        -8225.205             0.219            0.024
Chain 1:    800        -8233.517             0.192            0.024
Chain 1:    900        -8140.559             0.172            0.018
Chain 1:   1000        -8247.373             0.156            0.018
Chain 1:   1100        -8368.056             0.057            0.014
Chain 1:   1200        -8260.040             0.026            0.013
Chain 1:   1300        -8197.797             0.012            0.013
Chain 1:   1400        -8220.935             0.010            0.013
Chain 1:   1500        -8308.675             0.010            0.011
Chain 1:   1600        -8280.461             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002983 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58188.110             1.000            1.000
Chain 1:    200       -17895.120             1.626            2.252
Chain 1:    300        -8800.260             1.428            1.033
Chain 1:    400        -8256.469             1.088            1.033
Chain 1:    500        -8553.247             0.877            1.000
Chain 1:    600        -8314.519             0.736            1.000
Chain 1:    700        -8352.584             0.631            0.066
Chain 1:    800        -8458.866             0.554            0.066
Chain 1:    900        -8033.692             0.498            0.053
Chain 1:   1000        -8064.744             0.449            0.053
Chain 1:   1100        -7751.484             0.353            0.040
Chain 1:   1200        -7823.479             0.129            0.035
Chain 1:   1300        -7762.866             0.026            0.029
Chain 1:   1400        -7905.781             0.021            0.018
Chain 1:   1500        -7691.496             0.021            0.018
Chain 1:   1600        -7828.176             0.019            0.017
Chain 1:   1700        -7577.741             0.022            0.018
Chain 1:   1800        -7687.783             0.022            0.018
Chain 1:   1900        -7620.216             0.018            0.017
Chain 1:   2000        -7699.341             0.019            0.017
Chain 1:   2100        -7653.177             0.015            0.014
Chain 1:   2200        -7773.431             0.016            0.015
Chain 1:   2300        -7669.237             0.016            0.015
Chain 1:   2400        -7712.465             0.015            0.014
Chain 1:   2500        -7624.683             0.014            0.014
Chain 1:   2600        -7587.252             0.012            0.012
Chain 1:   2700        -7574.975             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86898.573             1.000            1.000
Chain 1:    200       -13718.172             3.167            5.335
Chain 1:    300       -10130.319             2.230            1.000
Chain 1:    400       -10858.668             1.689            1.000
Chain 1:    500        -9084.034             1.390            0.354
Chain 1:    600        -8882.328             1.162            0.354
Chain 1:    700        -8620.186             1.001            0.195
Chain 1:    800        -9141.984             0.883            0.195
Chain 1:    900        -8963.781             0.787            0.067
Chain 1:   1000        -8735.932             0.711            0.067
Chain 1:   1100        -8999.629             0.614            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8618.374             0.085            0.044
Chain 1:   1300        -8857.126             0.052            0.030
Chain 1:   1400        -8834.020             0.045            0.029
Chain 1:   1500        -8738.580             0.027            0.027
Chain 1:   1600        -8840.860             0.026            0.027
Chain 1:   1700        -8927.545             0.024            0.026
Chain 1:   1800        -8531.070             0.023            0.026
Chain 1:   1900        -8632.291             0.022            0.026
Chain 1:   2000        -8603.085             0.020            0.012
Chain 1:   2100        -8724.649             0.018            0.012
Chain 1:   2200        -8504.187             0.016            0.012
Chain 1:   2300        -8661.093             0.015            0.012
Chain 1:   2400        -8674.506             0.015            0.012
Chain 1:   2500        -8644.706             0.015            0.012
Chain 1:   2600        -8647.386             0.013            0.012
Chain 1:   2700        -8553.596             0.014            0.012
Chain 1:   2800        -8524.310             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.005679 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 56.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8414515.243             1.000            1.000
Chain 1:    200     -1586103.253             2.653            4.305
Chain 1:    300      -891434.179             2.028            1.000
Chain 1:    400      -458475.831             1.757            1.000
Chain 1:    500      -358589.291             1.461            0.944
Chain 1:    600      -233448.421             1.307            0.944
Chain 1:    700      -119544.210             1.257            0.944
Chain 1:    800       -86700.050             1.147            0.944
Chain 1:    900       -67022.666             1.052            0.779
Chain 1:   1000       -51797.436             0.976            0.779
Chain 1:   1100       -39261.215             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38432.054             0.480            0.379
Chain 1:   1300       -26387.376             0.448            0.379
Chain 1:   1400       -26104.179             0.354            0.319
Chain 1:   1500       -22691.307             0.341            0.319
Chain 1:   1600       -21906.835             0.291            0.294
Chain 1:   1700       -20781.160             0.201            0.294
Chain 1:   1800       -20725.058             0.164            0.150
Chain 1:   1900       -21050.841             0.136            0.054
Chain 1:   2000       -19562.799             0.114            0.054
Chain 1:   2100       -19801.188             0.084            0.036
Chain 1:   2200       -20027.334             0.083            0.036
Chain 1:   2300       -19644.888             0.039            0.019
Chain 1:   2400       -19417.098             0.039            0.019
Chain 1:   2500       -19219.070             0.025            0.015
Chain 1:   2600       -18849.752             0.023            0.015
Chain 1:   2700       -18806.786             0.018            0.012
Chain 1:   2800       -18523.820             0.019            0.015
Chain 1:   2900       -18804.896             0.019            0.015
Chain 1:   3000       -18791.120             0.012            0.012
Chain 1:   3100       -18876.063             0.011            0.012
Chain 1:   3200       -18567.020             0.012            0.015
Chain 1:   3300       -18771.489             0.011            0.012
Chain 1:   3400       -18246.901             0.012            0.015
Chain 1:   3500       -18858.062             0.015            0.015
Chain 1:   3600       -18165.650             0.016            0.015
Chain 1:   3700       -18551.817             0.018            0.017
Chain 1:   3800       -17512.918             0.023            0.021
Chain 1:   3900       -17509.070             0.021            0.021
Chain 1:   4000       -17626.396             0.022            0.021
Chain 1:   4100       -17540.246             0.022            0.021
Chain 1:   4200       -17356.739             0.021            0.021
Chain 1:   4300       -17494.965             0.021            0.021
Chain 1:   4400       -17452.057             0.018            0.011
Chain 1:   4500       -17354.598             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001199 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48786.038             1.000            1.000
Chain 1:    200       -20090.218             1.214            1.428
Chain 1:    300       -22690.208             0.848            1.000
Chain 1:    400       -34947.279             0.723            1.000
Chain 1:    500       -12851.678             0.923            1.000
Chain 1:    600       -14902.381             0.792            1.000
Chain 1:    700       -14723.711             0.680            0.351
Chain 1:    800       -13325.617             0.608            0.351
Chain 1:    900       -16284.176             0.561            0.182
Chain 1:   1000       -13485.276             0.526            0.208
Chain 1:   1100       -15021.756             0.436            0.182
Chain 1:   1200       -10771.500             0.333            0.182
Chain 1:   1300       -11854.833             0.330            0.182
Chain 1:   1400       -11555.752             0.298            0.138
Chain 1:   1500       -12347.887             0.132            0.105
Chain 1:   1600       -10871.673             0.132            0.105
Chain 1:   1700       -10396.226             0.135            0.105
Chain 1:   1800       -11406.254             0.134            0.102
Chain 1:   1900        -9632.918             0.134            0.102
Chain 1:   2000       -11334.062             0.128            0.102
Chain 1:   2100        -9335.453             0.139            0.136
Chain 1:   2200        -9010.176             0.104            0.091
Chain 1:   2300        -9504.580             0.100            0.089
Chain 1:   2400        -8988.548             0.103            0.089
Chain 1:   2500        -8986.671             0.096            0.089
Chain 1:   2600        -9616.373             0.089            0.065
Chain 1:   2700       -15832.986             0.124            0.089
Chain 1:   2800       -10049.126             0.173            0.150
Chain 1:   2900       -10999.653             0.163            0.086
Chain 1:   3000       -10198.448             0.156            0.079
Chain 1:   3100        -8938.712             0.149            0.079
Chain 1:   3200        -8866.064             0.146            0.079
Chain 1:   3300        -8966.368             0.142            0.079
Chain 1:   3400        -9107.722             0.137            0.079
Chain 1:   3500       -10624.654             0.152            0.086
Chain 1:   3600       -12333.063             0.159            0.139
Chain 1:   3700        -9703.250             0.147            0.139
Chain 1:   3800       -15902.621             0.128            0.139
Chain 1:   3900       -11752.103             0.155            0.141
Chain 1:   4000        -8555.030             0.184            0.143
Chain 1:   4100        -9062.539             0.176            0.143
Chain 1:   4200       -12553.671             0.203            0.271
Chain 1:   4300        -8763.675             0.245            0.278
Chain 1:   4400        -9415.100             0.250            0.278
Chain 1:   4500        -9330.455             0.237            0.278
Chain 1:   4600        -8705.177             0.230            0.278
Chain 1:   4700       -10525.095             0.221            0.278
Chain 1:   4800        -8355.731             0.208            0.260
Chain 1:   4900        -8369.894             0.172            0.173
Chain 1:   5000       -11839.785             0.164            0.173
Chain 1:   5100        -8311.712             0.201            0.260
Chain 1:   5200        -8906.255             0.180            0.173
Chain 1:   5300        -9285.399             0.141            0.072
Chain 1:   5400        -8578.687             0.142            0.082
Chain 1:   5500       -13036.241             0.176            0.173
Chain 1:   5600        -9002.494             0.213            0.260
Chain 1:   5700       -13077.263             0.227            0.293
Chain 1:   5800        -9106.406             0.245            0.312
Chain 1:   5900        -8427.046             0.253            0.312
Chain 1:   6000        -8955.942             0.229            0.312
Chain 1:   6100        -9114.745             0.188            0.082
Chain 1:   6200        -8537.420             0.189            0.082
Chain 1:   6300        -8342.696             0.187            0.082
Chain 1:   6400       -10588.275             0.200            0.212
Chain 1:   6500        -8490.637             0.190            0.212
Chain 1:   6600        -8297.543             0.148            0.081
Chain 1:   6700       -12555.743             0.151            0.081
Chain 1:   6800        -8538.776             0.154            0.081
Chain 1:   6900        -8190.047             0.150            0.068
Chain 1:   7000        -8820.998             0.151            0.072
Chain 1:   7100        -8534.749             0.153            0.072
Chain 1:   7200       -12086.528             0.176            0.212
Chain 1:   7300        -8736.631             0.212            0.247
Chain 1:   7400       -12194.633             0.219            0.284
Chain 1:   7500       -10344.776             0.212            0.284
Chain 1:   7600        -8662.729             0.229            0.284
Chain 1:   7700        -8597.490             0.196            0.194
Chain 1:   7800       -10230.299             0.165            0.179
Chain 1:   7900        -8696.921             0.178            0.179
Chain 1:   8000        -9857.956             0.183            0.179
Chain 1:   8100        -8825.829             0.191            0.179
Chain 1:   8200       -10993.489             0.182            0.179
Chain 1:   8300        -8174.486             0.178            0.179
Chain 1:   8400        -8113.369             0.150            0.176
Chain 1:   8500       -12468.470             0.167            0.176
Chain 1:   8600        -8352.522             0.197            0.176
Chain 1:   8700       -10849.374             0.219            0.197
Chain 1:   8800        -8562.531             0.230            0.230
Chain 1:   8900        -8144.635             0.217            0.230
Chain 1:   9000        -9741.826             0.222            0.230
Chain 1:   9100       -11207.115             0.223            0.230
Chain 1:   9200        -8429.849             0.237            0.267
Chain 1:   9300        -8102.012             0.206            0.230
Chain 1:   9400        -8229.274             0.207            0.230
Chain 1:   9500        -8200.776             0.172            0.164
Chain 1:   9600        -9600.395             0.138            0.146
Chain 1:   9700       -10300.352             0.122            0.131
Chain 1:   9800        -8071.023             0.122            0.131
Chain 1:   9900        -8667.326             0.124            0.131
Chain 1:   10000        -8033.595             0.116            0.079
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001422 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56539.293             1.000            1.000
Chain 1:    200       -17085.357             1.655            2.309
Chain 1:    300        -8594.462             1.432            1.000
Chain 1:    400        -7909.664             1.096            1.000
Chain 1:    500        -8306.587             0.886            0.988
Chain 1:    600        -8237.822             0.740            0.988
Chain 1:    700        -7687.695             0.644            0.087
Chain 1:    800        -8075.138             0.570            0.087
Chain 1:    900        -7757.051             0.511            0.072
Chain 1:   1000        -7670.068             0.461            0.072
Chain 1:   1100        -7620.653             0.362            0.048
Chain 1:   1200        -7566.663             0.132            0.048
Chain 1:   1300        -7583.910             0.033            0.041
Chain 1:   1400        -7817.254             0.027            0.030
Chain 1:   1500        -7584.615             0.026            0.030
Chain 1:   1600        -7485.931             0.026            0.030
Chain 1:   1700        -7482.105             0.019            0.013
Chain 1:   1800        -7550.450             0.015            0.011
Chain 1:   1900        -7562.184             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85637.148             1.000            1.000
Chain 1:    200       -13225.286             3.238            5.475
Chain 1:    300        -9665.274             2.281            1.000
Chain 1:    400       -10450.845             1.730            1.000
Chain 1:    500        -8577.051             1.427            0.368
Chain 1:    600        -8210.250             1.197            0.368
Chain 1:    700        -8363.551             1.029            0.218
Chain 1:    800        -8748.714             0.906            0.218
Chain 1:    900        -8517.610             0.808            0.075
Chain 1:   1000        -8266.201             0.730            0.075
Chain 1:   1100        -8458.533             0.632            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8175.980             0.088            0.044
Chain 1:   1300        -8234.208             0.052            0.035
Chain 1:   1400        -8222.504             0.045            0.030
Chain 1:   1500        -8260.301             0.023            0.027
Chain 1:   1600        -8270.199             0.019            0.023
Chain 1:   1700        -8195.164             0.018            0.023
Chain 1:   1800        -8081.641             0.015            0.014
Chain 1:   1900        -8200.284             0.014            0.014
Chain 1:   2000        -8160.215             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006036 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 60.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8387636.068             1.000            1.000
Chain 1:    200     -1578809.311             2.656            4.313
Chain 1:    300      -889687.540             2.029            1.000
Chain 1:    400      -456848.691             1.759            1.000
Chain 1:    500      -357390.412             1.463            0.947
Chain 1:    600      -232542.229             1.308            0.947
Chain 1:    700      -118919.067             1.258            0.947
Chain 1:    800       -86112.395             1.148            0.947
Chain 1:    900       -66473.169             1.054            0.775
Chain 1:   1000       -51268.783             0.978            0.775
Chain 1:   1100       -38748.778             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37925.216             0.481            0.381
Chain 1:   1300       -25892.338             0.450            0.381
Chain 1:   1400       -25610.624             0.356            0.323
Chain 1:   1500       -22199.931             0.344            0.323
Chain 1:   1600       -21416.594             0.294            0.297
Chain 1:   1700       -20292.134             0.204            0.295
Chain 1:   1800       -20236.701             0.166            0.154
Chain 1:   1900       -20562.317             0.138            0.055
Chain 1:   2000       -19075.426             0.116            0.055
Chain 1:   2100       -19313.629             0.085            0.037
Chain 1:   2200       -19539.483             0.084            0.037
Chain 1:   2300       -19157.404             0.040            0.020
Chain 1:   2400       -18929.716             0.040            0.020
Chain 1:   2500       -18731.591             0.025            0.016
Chain 1:   2600       -18362.277             0.024            0.016
Chain 1:   2700       -18319.573             0.019            0.012
Chain 1:   2800       -18036.505             0.020            0.016
Chain 1:   2900       -18317.587             0.020            0.015
Chain 1:   3000       -18303.847             0.012            0.012
Chain 1:   3100       -18388.681             0.011            0.012
Chain 1:   3200       -18079.736             0.012            0.015
Chain 1:   3300       -18284.249             0.011            0.012
Chain 1:   3400       -17759.690             0.013            0.015
Chain 1:   3500       -18370.673             0.015            0.016
Chain 1:   3600       -17678.665             0.017            0.016
Chain 1:   3700       -18064.402             0.019            0.017
Chain 1:   3800       -17026.020             0.023            0.021
Chain 1:   3900       -17022.257             0.022            0.021
Chain 1:   4000       -17139.554             0.022            0.021
Chain 1:   4100       -17053.281             0.022            0.021
Chain 1:   4200       -16870.112             0.022            0.021
Chain 1:   4300       -17008.141             0.022            0.021
Chain 1:   4400       -16965.312             0.019            0.011
Chain 1:   4500       -16867.943             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001235 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48870.028             1.000            1.000
Chain 1:    200       -14896.216             1.640            2.281
Chain 1:    300       -12913.505             1.145            1.000
Chain 1:    400       -41814.033             1.031            1.000
Chain 1:    500       -12129.784             1.315            1.000
Chain 1:    600       -16328.674             1.138            1.000
Chain 1:    700       -12666.339             1.017            0.691
Chain 1:    800       -14187.502             0.903            0.691
Chain 1:    900       -13372.848             0.810            0.289
Chain 1:   1000       -11979.768             0.740            0.289
Chain 1:   1100       -10919.919             0.650            0.257   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10502.840             0.426            0.154
Chain 1:   1300       -19360.971             0.456            0.257
Chain 1:   1400       -13276.497             0.433            0.257
Chain 1:   1500       -12983.166             0.191            0.116
Chain 1:   1600       -10241.365             0.192            0.116
Chain 1:   1700       -10326.719             0.164            0.107
Chain 1:   1800       -11733.511             0.165            0.116
Chain 1:   1900       -10338.515             0.172            0.120
Chain 1:   2000        -9920.317             0.165            0.120
Chain 1:   2100       -10937.460             0.164            0.120
Chain 1:   2200       -10702.161             0.163            0.120
Chain 1:   2300       -13485.482             0.138            0.120
Chain 1:   2400        -9296.614             0.137            0.120
Chain 1:   2500       -17192.982             0.180            0.135
Chain 1:   2600       -16181.735             0.160            0.120
Chain 1:   2700        -8991.123             0.239            0.135
Chain 1:   2800       -10154.838             0.239            0.135
Chain 1:   2900       -15315.643             0.259            0.206
Chain 1:   3000        -9046.511             0.324            0.337
Chain 1:   3100       -11325.154             0.335            0.337
Chain 1:   3200        -8813.450             0.361            0.337
Chain 1:   3300       -10158.787             0.354            0.337
Chain 1:   3400       -16415.895             0.347            0.337
Chain 1:   3500        -9542.108             0.373            0.337
Chain 1:   3600       -13497.225             0.396            0.337
Chain 1:   3700        -8722.068             0.371            0.337
Chain 1:   3800        -8720.060             0.359            0.337
Chain 1:   3900       -10176.486             0.340            0.293
Chain 1:   4000        -8846.185             0.285            0.285
Chain 1:   4100        -9866.848             0.276            0.285
Chain 1:   4200       -14749.312             0.280            0.293
Chain 1:   4300        -9267.584             0.326            0.331
Chain 1:   4400        -8967.654             0.291            0.293
Chain 1:   4500        -8860.196             0.221            0.150
Chain 1:   4600       -13119.295             0.224            0.150
Chain 1:   4700        -8595.554             0.222            0.150
Chain 1:   4800       -13114.443             0.256            0.325
Chain 1:   4900        -8935.682             0.289            0.331
Chain 1:   5000        -9469.175             0.279            0.331
Chain 1:   5100        -8731.646             0.277            0.331
Chain 1:   5200       -16576.792             0.291            0.345
Chain 1:   5300       -10192.443             0.295            0.345
Chain 1:   5400        -8860.277             0.307            0.345
Chain 1:   5500       -13017.459             0.337            0.345
Chain 1:   5600       -13164.137             0.306            0.345
Chain 1:   5700       -13319.581             0.255            0.319
Chain 1:   5800        -9202.164             0.265            0.319
Chain 1:   5900        -9598.721             0.222            0.150
Chain 1:   6000        -8955.653             0.224            0.150
Chain 1:   6100        -9103.824             0.217            0.150
Chain 1:   6200       -13421.881             0.202            0.150
Chain 1:   6300        -8670.069             0.194            0.150
Chain 1:   6400        -8318.504             0.183            0.072
Chain 1:   6500        -9326.573             0.162            0.072
Chain 1:   6600        -8431.238             0.171            0.106
Chain 1:   6700        -8424.166             0.170            0.106
Chain 1:   6800        -8420.856             0.126            0.072
Chain 1:   6900       -12056.033             0.152            0.106
Chain 1:   7000       -12938.937             0.151            0.106
Chain 1:   7100       -13309.028             0.153            0.106
Chain 1:   7200        -8492.095             0.177            0.106
Chain 1:   7300       -10920.442             0.144            0.106
Chain 1:   7400       -14172.256             0.163            0.108
Chain 1:   7500        -8232.645             0.225            0.222
Chain 1:   7600        -8488.203             0.217            0.222
Chain 1:   7700        -8829.217             0.221            0.222
Chain 1:   7800        -9037.713             0.223            0.222
Chain 1:   7900        -8316.207             0.202            0.087
Chain 1:   8000        -8466.997             0.196            0.087
Chain 1:   8100        -9717.236             0.207            0.129
Chain 1:   8200        -8291.774             0.167            0.129
Chain 1:   8300        -8064.356             0.148            0.087
Chain 1:   8400       -13580.440             0.165            0.087
Chain 1:   8500        -8674.731             0.150            0.087
Chain 1:   8600       -11528.593             0.171            0.129
Chain 1:   8700        -8337.643             0.206            0.172
Chain 1:   8800        -8449.415             0.205            0.172
Chain 1:   8900       -10490.403             0.216            0.195
Chain 1:   9000        -8255.270             0.241            0.248
Chain 1:   9100        -8634.650             0.232            0.248
Chain 1:   9200        -8471.143             0.217            0.248
Chain 1:   9300        -8402.519             0.215            0.248
Chain 1:   9400        -8052.071             0.179            0.195
Chain 1:   9500        -7928.095             0.124            0.044
Chain 1:   9600        -8351.336             0.104            0.044
Chain 1:   9700        -9371.983             0.077            0.044
Chain 1:   9800       -11133.254             0.091            0.051
Chain 1:   9900        -8286.585             0.106            0.051
Chain 1:   10000        -8311.475             0.079            0.044
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001448 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46553.884             1.000            1.000
Chain 1:    200       -15634.378             1.489            1.978
Chain 1:    300        -8754.569             1.255            1.000
Chain 1:    400        -8503.910             0.948            1.000
Chain 1:    500        -8760.661             0.764            0.786
Chain 1:    600        -8774.876             0.637            0.786
Chain 1:    700        -8116.958             0.558            0.081
Chain 1:    800        -8249.598             0.490            0.081
Chain 1:    900        -7965.662             0.440            0.036
Chain 1:   1000        -7858.761             0.397            0.036
Chain 1:   1100        -7805.073             0.298            0.029
Chain 1:   1200        -7614.124             0.102            0.029
Chain 1:   1300        -7785.959             0.026            0.025
Chain 1:   1400        -7974.398             0.025            0.024
Chain 1:   1500        -7617.535             0.027            0.024
Chain 1:   1600        -7830.550             0.030            0.025
Chain 1:   1700        -7555.841             0.025            0.025
Chain 1:   1800        -7620.635             0.025            0.025
Chain 1:   1900        -7638.468             0.021            0.024
Chain 1:   2000        -7666.471             0.020            0.024
Chain 1:   2100        -7633.314             0.020            0.024
Chain 1:   2200        -7739.767             0.019            0.022
Chain 1:   2300        -7606.292             0.018            0.018
Chain 1:   2400        -7679.578             0.017            0.014
Chain 1:   2500        -7657.706             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003821 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86200.737             1.000            1.000
Chain 1:    200       -13564.662             3.177            5.355
Chain 1:    300        -9851.628             2.244            1.000
Chain 1:    400       -11113.353             1.711            1.000
Chain 1:    500        -8852.156             1.420            0.377
Chain 1:    600        -8964.038             1.186            0.377
Chain 1:    700        -8637.079             1.022            0.255
Chain 1:    800        -8174.201             0.901            0.255
Chain 1:    900        -8262.174             0.802            0.114
Chain 1:   1000        -8511.132             0.725            0.114
Chain 1:   1100        -8647.118             0.626            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8222.339             0.096            0.052
Chain 1:   1300        -8534.479             0.062            0.038
Chain 1:   1400        -8454.331             0.052            0.037
Chain 1:   1500        -8369.072             0.027            0.029
Chain 1:   1600        -8479.597             0.027            0.029
Chain 1:   1700        -8546.598             0.024            0.016
Chain 1:   1800        -8110.675             0.024            0.016
Chain 1:   1900        -8215.214             0.024            0.016
Chain 1:   2000        -8191.141             0.021            0.013
Chain 1:   2100        -8335.807             0.022            0.013
Chain 1:   2200        -8122.855             0.019            0.013
Chain 1:   2300        -8278.403             0.017            0.013
Chain 1:   2400        -8118.417             0.018            0.017
Chain 1:   2500        -8189.242             0.018            0.017
Chain 1:   2600        -8101.529             0.018            0.017
Chain 1:   2700        -8135.481             0.018            0.017
Chain 1:   2800        -8095.704             0.013            0.013
Chain 1:   2900        -8188.773             0.012            0.011
Chain 1:   3000        -8020.375             0.014            0.017
Chain 1:   3100        -8178.328             0.014            0.019
Chain 1:   3200        -8050.403             0.013            0.016
Chain 1:   3300        -8058.152             0.012            0.011
Chain 1:   3400        -8216.185             0.012            0.011
Chain 1:   3500        -8221.052             0.011            0.011
Chain 1:   3600        -8007.038             0.012            0.016
Chain 1:   3700        -8152.413             0.014            0.018
Chain 1:   3800        -8013.647             0.015            0.018
Chain 1:   3900        -7948.338             0.015            0.018
Chain 1:   4000        -8023.319             0.014            0.017
Chain 1:   4100        -8014.220             0.012            0.016
Chain 1:   4200        -8003.903             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00378 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380957.220             1.000            1.000
Chain 1:    200     -1581251.602             2.650            4.300
Chain 1:    300      -890377.455             2.025            1.000
Chain 1:    400      -457508.870             1.756            1.000
Chain 1:    500      -358267.624             1.460            0.946
Chain 1:    600      -233348.673             1.306            0.946
Chain 1:    700      -119458.637             1.255            0.946
Chain 1:    800       -86623.695             1.146            0.946
Chain 1:    900       -66947.758             1.051            0.776
Chain 1:   1000       -51730.264             0.976            0.776
Chain 1:   1100       -39186.210             0.908            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38363.570             0.480            0.379
Chain 1:   1300       -26294.892             0.448            0.379
Chain 1:   1400       -26013.154             0.354            0.320
Chain 1:   1500       -22593.051             0.342            0.320
Chain 1:   1600       -21807.584             0.292            0.294
Chain 1:   1700       -20678.119             0.202            0.294
Chain 1:   1800       -20621.702             0.164            0.151
Chain 1:   1900       -20948.215             0.137            0.055
Chain 1:   2000       -19456.748             0.115            0.055
Chain 1:   2100       -19695.451             0.084            0.036
Chain 1:   2200       -19922.340             0.083            0.036
Chain 1:   2300       -19539.022             0.039            0.020
Chain 1:   2400       -19310.933             0.039            0.020
Chain 1:   2500       -19112.934             0.025            0.016
Chain 1:   2600       -18742.822             0.023            0.016
Chain 1:   2700       -18699.632             0.018            0.012
Chain 1:   2800       -18416.342             0.019            0.015
Chain 1:   2900       -18697.773             0.019            0.015
Chain 1:   3000       -18683.992             0.012            0.012
Chain 1:   3100       -18769.030             0.011            0.012
Chain 1:   3200       -18459.460             0.012            0.015
Chain 1:   3300       -18664.350             0.011            0.012
Chain 1:   3400       -18138.841             0.012            0.015
Chain 1:   3500       -18751.397             0.015            0.015
Chain 1:   3600       -18057.170             0.017            0.015
Chain 1:   3700       -18444.678             0.018            0.017
Chain 1:   3800       -17402.981             0.023            0.021
Chain 1:   3900       -17399.064             0.021            0.021
Chain 1:   4000       -17516.390             0.022            0.021
Chain 1:   4100       -17430.091             0.022            0.021
Chain 1:   4200       -17245.997             0.021            0.021
Chain 1:   4300       -17384.652             0.021            0.021
Chain 1:   4400       -17341.229             0.019            0.011
Chain 1:   4500       -17243.694             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001426 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48351.930             1.000            1.000
Chain 1:    200       -43027.722             0.562            1.000
Chain 1:    300       -20691.889             0.734            1.000
Chain 1:    400       -17347.467             0.599            1.000
Chain 1:    500       -21822.449             0.520            0.205
Chain 1:    600       -11470.150             0.584            0.903
Chain 1:    700       -11266.237             0.503            0.205
Chain 1:    800       -12440.994             0.452            0.205
Chain 1:    900       -10936.381             0.417            0.193
Chain 1:   1000       -10348.467             0.381            0.193
Chain 1:   1100       -15576.963             0.315            0.193
Chain 1:   1200       -18315.750             0.317            0.193
Chain 1:   1300       -11075.652             0.275            0.193
Chain 1:   1400       -13744.987             0.275            0.194
Chain 1:   1500       -23610.488             0.296            0.194
Chain 1:   1600       -10229.342             0.337            0.194
Chain 1:   1700       -10707.855             0.339            0.194
Chain 1:   1800        -9529.535             0.342            0.194
Chain 1:   1900       -10389.513             0.337            0.194
Chain 1:   2000       -15229.982             0.363            0.318
Chain 1:   2100        -9571.743             0.388            0.318
Chain 1:   2200        -9230.260             0.377            0.318
Chain 1:   2300        -8691.408             0.318            0.194
Chain 1:   2400        -8669.194             0.299            0.124
Chain 1:   2500        -9852.523             0.269            0.120
Chain 1:   2600        -9582.615             0.141            0.083
Chain 1:   2700       -19416.587             0.187            0.120
Chain 1:   2800        -9029.266             0.290            0.120
Chain 1:   2900        -8842.865             0.284            0.120
Chain 1:   3000        -9005.285             0.254            0.062
Chain 1:   3100        -8661.930             0.199            0.040
Chain 1:   3200        -8229.785             0.200            0.053
Chain 1:   3300       -10793.029             0.218            0.053
Chain 1:   3400       -14617.300             0.244            0.120
Chain 1:   3500        -8863.221             0.296            0.237
Chain 1:   3600       -10356.256             0.308            0.237
Chain 1:   3700        -9555.109             0.266            0.144
Chain 1:   3800        -9633.908             0.152            0.084
Chain 1:   3900        -8903.364             0.158            0.084
Chain 1:   4000        -9033.290             0.157            0.084
Chain 1:   4100        -9666.933             0.160            0.084
Chain 1:   4200       -12803.697             0.179            0.144
Chain 1:   4300        -9053.957             0.197            0.144
Chain 1:   4400        -8506.915             0.177            0.084
Chain 1:   4500        -9077.991             0.118            0.082
Chain 1:   4600       -11786.940             0.127            0.082
Chain 1:   4700       -10521.977             0.131            0.082
Chain 1:   4800        -8438.248             0.155            0.120
Chain 1:   4900       -12948.632             0.181            0.230
Chain 1:   5000        -9207.842             0.220            0.245
Chain 1:   5100        -8288.838             0.225            0.245
Chain 1:   5200        -9007.805             0.208            0.230
Chain 1:   5300        -8737.572             0.170            0.120
Chain 1:   5400        -8110.126             0.171            0.120
Chain 1:   5500        -9053.215             0.175            0.120
Chain 1:   5600        -9472.370             0.157            0.111
Chain 1:   5700        -8251.744             0.160            0.111
Chain 1:   5800        -8820.531             0.141            0.104
Chain 1:   5900       -10908.379             0.126            0.104
Chain 1:   6000        -8836.256             0.109            0.104
Chain 1:   6100        -9396.152             0.103            0.080
Chain 1:   6200        -8490.815             0.106            0.104
Chain 1:   6300       -10958.434             0.126            0.107
Chain 1:   6400       -10211.942             0.125            0.107
Chain 1:   6500       -12172.829             0.131            0.148
Chain 1:   6600        -8064.784             0.177            0.161
Chain 1:   6700       -10381.757             0.185            0.191
Chain 1:   6800       -11721.009             0.190            0.191
Chain 1:   6900        -8505.755             0.208            0.223
Chain 1:   7000       -14191.052             0.225            0.223
Chain 1:   7100        -8416.194             0.288            0.225
Chain 1:   7200        -8144.727             0.280            0.225
Chain 1:   7300        -9247.417             0.270            0.223
Chain 1:   7400        -8220.582             0.275            0.223
Chain 1:   7500       -10297.179             0.279            0.223
Chain 1:   7600        -8605.472             0.248            0.202
Chain 1:   7700        -7920.290             0.234            0.197
Chain 1:   7800        -8208.057             0.226            0.197
Chain 1:   7900        -8162.264             0.189            0.125
Chain 1:   8000        -7944.445             0.152            0.119
Chain 1:   8100       -10648.763             0.108            0.119
Chain 1:   8200        -8711.500             0.127            0.125
Chain 1:   8300        -8053.205             0.124            0.125
Chain 1:   8400        -8135.763             0.112            0.087
Chain 1:   8500        -8353.188             0.095            0.082
Chain 1:   8600        -9166.569             0.084            0.082
Chain 1:   8700       -10846.816             0.091            0.082
Chain 1:   8800        -9267.880             0.104            0.089
Chain 1:   8900        -8677.082             0.110            0.089
Chain 1:   9000        -9971.584             0.121            0.130
Chain 1:   9100        -7884.552             0.122            0.130
Chain 1:   9200       -10075.442             0.121            0.130
Chain 1:   9300        -7996.348             0.139            0.155
Chain 1:   9400       -10206.435             0.160            0.170
Chain 1:   9500       -11835.450             0.171            0.170
Chain 1:   9600        -8053.848             0.209            0.217
Chain 1:   9700        -8590.341             0.200            0.217
Chain 1:   9800       -10725.868             0.203            0.217
Chain 1:   9900        -8303.339             0.225            0.217
Chain 1:   10000        -8305.204             0.212            0.217
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001483 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57456.147             1.000            1.000
Chain 1:    200       -17145.384             1.676            2.351
Chain 1:    300        -8480.785             1.458            1.022
Chain 1:    400        -7869.778             1.113            1.022
Chain 1:    500        -8324.894             0.901            1.000
Chain 1:    600        -8412.185             0.753            1.000
Chain 1:    700        -7961.530             0.653            0.078
Chain 1:    800        -7960.093             0.572            0.078
Chain 1:    900        -7698.383             0.512            0.057
Chain 1:   1000        -7806.456             0.462            0.057
Chain 1:   1100        -7643.353             0.364            0.055
Chain 1:   1200        -7604.737             0.130            0.034
Chain 1:   1300        -7684.334             0.028            0.021
Chain 1:   1400        -7899.485             0.023            0.021
Chain 1:   1500        -7642.294             0.021            0.021
Chain 1:   1600        -7582.589             0.021            0.021
Chain 1:   1700        -7525.196             0.016            0.014
Chain 1:   1800        -7555.552             0.017            0.014
Chain 1:   1900        -7565.868             0.013            0.010
Chain 1:   2000        -7607.804             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003225 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86381.799             1.000            1.000
Chain 1:    200       -12978.212             3.328            5.656
Chain 1:    300        -9461.888             2.343            1.000
Chain 1:    400       -10306.444             1.777            1.000
Chain 1:    500        -8344.207             1.469            0.372
Chain 1:    600        -8060.243             1.230            0.372
Chain 1:    700        -8375.933             1.060            0.235
Chain 1:    800        -8603.577             0.931            0.235
Chain 1:    900        -8366.796             0.830            0.082
Chain 1:   1000        -8105.574             0.750            0.082
Chain 1:   1100        -8393.081             0.654            0.038   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8141.866             0.091            0.035
Chain 1:   1300        -8071.102             0.055            0.034
Chain 1:   1400        -8104.312             0.047            0.032
Chain 1:   1500        -8110.149             0.024            0.031
Chain 1:   1600        -8114.332             0.020            0.028
Chain 1:   1700        -8056.561             0.017            0.026
Chain 1:   1800        -7933.677             0.016            0.015
Chain 1:   1900        -8047.897             0.015            0.014
Chain 1:   2000        -8009.049             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004145 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422919.672             1.000            1.000
Chain 1:    200     -1589821.425             2.649            4.298
Chain 1:    300      -891393.273             2.027            1.000
Chain 1:    400      -457470.417             1.758            1.000
Chain 1:    500      -357328.866             1.462            0.949
Chain 1:    600      -232095.338             1.308            0.949
Chain 1:    700      -118470.403             1.258            0.949
Chain 1:    800       -85712.043             1.149            0.949
Chain 1:    900       -66090.168             1.054            0.784
Chain 1:   1000       -50910.015             0.979            0.784
Chain 1:   1100       -38418.513             0.911            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37589.157             0.484            0.382
Chain 1:   1300       -25596.016             0.452            0.382
Chain 1:   1400       -25314.800             0.358            0.325
Chain 1:   1500       -21916.097             0.346            0.325
Chain 1:   1600       -21135.166             0.296            0.298
Chain 1:   1700       -20016.326             0.205            0.297
Chain 1:   1800       -19961.454             0.167            0.155
Chain 1:   1900       -20286.837             0.139            0.056
Chain 1:   2000       -18803.072             0.117            0.056
Chain 1:   2100       -19041.157             0.086            0.037
Chain 1:   2200       -19266.510             0.085            0.037
Chain 1:   2300       -18884.859             0.040            0.020
Chain 1:   2400       -18657.321             0.040            0.020
Chain 1:   2500       -18459.059             0.026            0.016
Chain 1:   2600       -18090.404             0.024            0.016
Chain 1:   2700       -18047.627             0.019            0.013
Chain 1:   2800       -17764.794             0.020            0.016
Chain 1:   2900       -18045.549             0.020            0.016
Chain 1:   3000       -18031.847             0.012            0.013
Chain 1:   3100       -18116.732             0.011            0.012
Chain 1:   3200       -17808.016             0.012            0.016
Chain 1:   3300       -18012.220             0.011            0.012
Chain 1:   3400       -17488.153             0.013            0.016
Chain 1:   3500       -18098.479             0.015            0.016
Chain 1:   3600       -17407.123             0.017            0.016
Chain 1:   3700       -17792.463             0.019            0.017
Chain 1:   3800       -16755.194             0.024            0.022
Chain 1:   3900       -16751.364             0.022            0.022
Chain 1:   4000       -16868.697             0.023            0.022
Chain 1:   4100       -16782.654             0.023            0.022
Chain 1:   4200       -16599.495             0.022            0.022
Chain 1:   4300       -16737.490             0.022            0.022
Chain 1:   4400       -16694.867             0.019            0.011
Chain 1:   4500       -16597.451             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001478 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12316.205             1.000            1.000
Chain 1:    200        -9209.914             0.669            1.000
Chain 1:    300        -7986.440             0.497            0.337
Chain 1:    400        -8112.587             0.377            0.337
Chain 1:    500        -8104.425             0.301            0.153
Chain 1:    600        -7917.792             0.255            0.153
Chain 1:    700        -7853.414             0.220            0.024
Chain 1:    800        -7868.525             0.193            0.024
Chain 1:    900        -7732.209             0.173            0.018
Chain 1:   1000        -7872.385             0.158            0.018
Chain 1:   1100        -7857.895             0.058            0.018
Chain 1:   1200        -7850.992             0.024            0.016
Chain 1:   1300        -7800.917             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0018 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58371.495             1.000            1.000
Chain 1:    200       -17669.819             1.652            2.303
Chain 1:    300        -8632.818             1.450            1.047
Chain 1:    400        -8175.249             1.102            1.047
Chain 1:    500        -8302.283             0.884            1.000
Chain 1:    600        -8685.920             0.744            1.000
Chain 1:    700        -7701.758             0.656            0.128
Chain 1:    800        -7972.107             0.578            0.128
Chain 1:    900        -7811.319             0.516            0.056
Chain 1:   1000        -7987.220             0.467            0.056
Chain 1:   1100        -7609.320             0.372            0.050
Chain 1:   1200        -7527.951             0.143            0.044
Chain 1:   1300        -7581.680             0.039            0.034
Chain 1:   1400        -7768.652             0.036            0.024
Chain 1:   1500        -7548.404             0.037            0.029
Chain 1:   1600        -7689.434             0.034            0.024
Chain 1:   1700        -7443.262             0.025            0.024
Chain 1:   1800        -7520.954             0.023            0.022
Chain 1:   1900        -7503.408             0.021            0.022
Chain 1:   2000        -7544.765             0.019            0.018
Chain 1:   2100        -7515.255             0.014            0.011
Chain 1:   2200        -7638.504             0.015            0.016
Chain 1:   2300        -7539.222             0.016            0.016
Chain 1:   2400        -7579.274             0.014            0.013
Chain 1:   2500        -7520.055             0.012            0.010
Chain 1:   2600        -7492.884             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003308 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85843.838             1.000            1.000
Chain 1:    200       -13429.831             3.196            5.392
Chain 1:    300        -9808.169             2.254            1.000
Chain 1:    400       -10630.889             1.710            1.000
Chain 1:    500        -8784.523             1.410            0.369
Chain 1:    600        -8236.351             1.186            0.369
Chain 1:    700        -8469.536             1.020            0.210
Chain 1:    800        -9237.269             0.903            0.210
Chain 1:    900        -8613.285             0.811            0.083
Chain 1:   1000        -8457.650             0.732            0.083
Chain 1:   1100        -8582.896             0.633            0.077   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8160.003             0.099            0.072
Chain 1:   1300        -8491.676             0.066            0.067
Chain 1:   1400        -8501.054             0.058            0.052
Chain 1:   1500        -8385.123             0.039            0.039
Chain 1:   1600        -8494.578             0.033            0.028
Chain 1:   1700        -8570.793             0.032            0.018
Chain 1:   1800        -8159.474             0.028            0.018
Chain 1:   1900        -8255.304             0.022            0.015
Chain 1:   2000        -8228.445             0.021            0.014
Chain 1:   2100        -8350.980             0.021            0.014
Chain 1:   2200        -8170.856             0.018            0.014
Chain 1:   2300        -8250.438             0.015            0.013
Chain 1:   2400        -8320.042             0.016            0.013
Chain 1:   2500        -8265.356             0.015            0.012
Chain 1:   2600        -8264.662             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003477 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422774.670             1.000            1.000
Chain 1:    200     -1583010.236             2.660            4.321
Chain 1:    300      -889331.865             2.034            1.000
Chain 1:    400      -457003.367             1.762            1.000
Chain 1:    500      -357453.058             1.465            0.946
Chain 1:    600      -232624.365             1.310            0.946
Chain 1:    700      -119012.823             1.259            0.946
Chain 1:    800       -86294.090             1.149            0.946
Chain 1:    900       -66657.662             1.054            0.780
Chain 1:   1000       -51473.459             0.979            0.780
Chain 1:   1100       -38972.911             0.911            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38148.201             0.481            0.379
Chain 1:   1300       -26121.943             0.449            0.379
Chain 1:   1400       -25841.981             0.355            0.321
Chain 1:   1500       -22435.111             0.343            0.321
Chain 1:   1600       -21653.712             0.292            0.295
Chain 1:   1700       -20529.222             0.203            0.295
Chain 1:   1800       -20473.849             0.165            0.152
Chain 1:   1900       -20800.002             0.137            0.055
Chain 1:   2000       -19312.470             0.115            0.055
Chain 1:   2100       -19550.479             0.084            0.036
Chain 1:   2200       -19777.027             0.083            0.036
Chain 1:   2300       -19394.216             0.039            0.020
Chain 1:   2400       -19166.360             0.039            0.020
Chain 1:   2500       -18968.477             0.025            0.016
Chain 1:   2600       -18598.586             0.024            0.016
Chain 1:   2700       -18555.523             0.018            0.012
Chain 1:   2800       -18272.506             0.020            0.015
Chain 1:   2900       -18553.679             0.020            0.015
Chain 1:   3000       -18539.787             0.012            0.012
Chain 1:   3100       -18624.821             0.011            0.012
Chain 1:   3200       -18315.498             0.012            0.015
Chain 1:   3300       -18520.213             0.011            0.012
Chain 1:   3400       -17995.206             0.013            0.015
Chain 1:   3500       -18607.014             0.015            0.015
Chain 1:   3600       -17913.729             0.017            0.015
Chain 1:   3700       -18300.533             0.019            0.017
Chain 1:   3800       -17260.359             0.023            0.021
Chain 1:   3900       -17256.525             0.022            0.021
Chain 1:   4000       -17373.801             0.022            0.021
Chain 1:   4100       -17287.630             0.022            0.021
Chain 1:   4200       -17103.864             0.022            0.021
Chain 1:   4300       -17242.248             0.021            0.021
Chain 1:   4400       -17199.080             0.019            0.011
Chain 1:   4500       -17101.634             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001528 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.28 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12745.895             1.000            1.000
Chain 1:    200        -9592.634             0.664            1.000
Chain 1:    300        -8331.691             0.493            0.329
Chain 1:    400        -8517.204             0.375            0.329
Chain 1:    500        -8372.723             0.304            0.151
Chain 1:    600        -8229.997             0.256            0.151
Chain 1:    700        -8308.898             0.221            0.022
Chain 1:    800        -8161.735             0.195            0.022
Chain 1:    900        -8218.357             0.175            0.018
Chain 1:   1000        -8090.535             0.159            0.018
Chain 1:   1100        -8219.011             0.060            0.017
Chain 1:   1200        -8141.079             0.028            0.017
Chain 1:   1300        -8074.361             0.014            0.016
Chain 1:   1400        -8104.040             0.012            0.016
Chain 1:   1500        -8202.291             0.012            0.012
Chain 1:   1600        -8131.042             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004155 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62161.127             1.000            1.000
Chain 1:    200       -18381.767             1.691            2.382
Chain 1:    300        -9081.251             1.469            1.024
Chain 1:    400        -8854.741             1.108            1.024
Chain 1:    500        -8533.995             0.894            1.000
Chain 1:    600        -8488.206             0.746            1.000
Chain 1:    700        -8352.597             0.642            0.038
Chain 1:    800        -8112.599             0.565            0.038
Chain 1:    900        -7505.835             0.511            0.038
Chain 1:   1000        -8096.611             0.467            0.073
Chain 1:   1100        -7812.485             0.371            0.038
Chain 1:   1200        -7848.246             0.133            0.036
Chain 1:   1300        -7547.944             0.035            0.036
Chain 1:   1400        -7893.026             0.037            0.038
Chain 1:   1500        -7493.766             0.038            0.040
Chain 1:   1600        -7564.373             0.039            0.040
Chain 1:   1700        -7541.978             0.037            0.040
Chain 1:   1800        -7618.917             0.035            0.040
Chain 1:   1900        -7553.770             0.028            0.036
Chain 1:   2000        -7668.965             0.022            0.015
Chain 1:   2100        -7558.301             0.020            0.015
Chain 1:   2200        -7732.712             0.022            0.015
Chain 1:   2300        -7515.888             0.021            0.015
Chain 1:   2400        -7549.660             0.017            0.015
Chain 1:   2500        -7550.370             0.012            0.010
Chain 1:   2600        -7499.223             0.011            0.010
Chain 1:   2700        -7466.613             0.012            0.010
Chain 1:   2800        -7466.238             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003165 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86247.768             1.000            1.000
Chain 1:    200       -13980.403             3.085            5.169
Chain 1:    300       -10234.740             2.178            1.000
Chain 1:    400       -11976.837             1.670            1.000
Chain 1:    500        -8882.266             1.406            0.366
Chain 1:    600        -8581.259             1.177            0.366
Chain 1:    700        -8973.401             1.015            0.348
Chain 1:    800        -8853.529             0.890            0.348
Chain 1:    900        -8951.791             0.792            0.145
Chain 1:   1000        -8898.391             0.714            0.145
Chain 1:   1100        -9067.447             0.616            0.044   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8549.141             0.105            0.044
Chain 1:   1300        -8778.643             0.071            0.035
Chain 1:   1400        -8887.783             0.058            0.026
Chain 1:   1500        -8727.679             0.025            0.019
Chain 1:   1600        -8840.469             0.022            0.018
Chain 1:   1700        -8900.117             0.019            0.014
Chain 1:   1800        -8453.821             0.023            0.018
Chain 1:   1900        -8563.071             0.023            0.018
Chain 1:   2000        -8548.094             0.022            0.018
Chain 1:   2100        -8669.679             0.022            0.014
Chain 1:   2200        -8459.081             0.018            0.014
Chain 1:   2300        -8558.125             0.017            0.013
Chain 1:   2400        -8622.455             0.016            0.013
Chain 1:   2500        -8572.370             0.015            0.013
Chain 1:   2600        -8585.835             0.014            0.012
Chain 1:   2700        -8492.811             0.014            0.012
Chain 1:   2800        -8439.150             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004238 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 42.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8390855.070             1.000            1.000
Chain 1:    200     -1584809.471             2.647            4.295
Chain 1:    300      -892997.883             2.023            1.000
Chain 1:    400      -459523.150             1.753            1.000
Chain 1:    500      -359789.243             1.458            0.943
Chain 1:    600      -234416.074             1.304            0.943
Chain 1:    700      -120151.802             1.254            0.943
Chain 1:    800       -87268.864             1.144            0.943
Chain 1:    900       -67528.083             1.049            0.775
Chain 1:   1000       -52270.641             0.974            0.775
Chain 1:   1100       -39693.404             0.905            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38867.913             0.478            0.377
Chain 1:   1300       -26755.732             0.446            0.377
Chain 1:   1400       -26471.721             0.353            0.317
Chain 1:   1500       -23041.182             0.340            0.317
Chain 1:   1600       -22253.556             0.290            0.292
Chain 1:   1700       -21118.113             0.200            0.292
Chain 1:   1800       -21060.595             0.163            0.149
Chain 1:   1900       -21387.293             0.135            0.054
Chain 1:   2000       -19892.820             0.113            0.054
Chain 1:   2100       -20131.415             0.083            0.035
Chain 1:   2200       -20359.153             0.082            0.035
Chain 1:   2300       -19975.096             0.038            0.019
Chain 1:   2400       -19746.886             0.039            0.019
Chain 1:   2500       -19549.307             0.025            0.015
Chain 1:   2600       -19178.524             0.023            0.015
Chain 1:   2700       -19135.194             0.018            0.012
Chain 1:   2800       -18851.962             0.019            0.015
Chain 1:   2900       -19133.573             0.019            0.015
Chain 1:   3000       -19119.629             0.012            0.012
Chain 1:   3100       -19204.733             0.011            0.012
Chain 1:   3200       -18894.941             0.011            0.015
Chain 1:   3300       -19100.041             0.011            0.012
Chain 1:   3400       -18574.263             0.012            0.015
Chain 1:   3500       -19187.339             0.014            0.015
Chain 1:   3600       -18492.489             0.016            0.015
Chain 1:   3700       -18880.466             0.018            0.016
Chain 1:   3800       -17837.913             0.022            0.021
Chain 1:   3900       -17834.056             0.021            0.021
Chain 1:   4000       -17951.301             0.022            0.021
Chain 1:   4100       -17864.994             0.022            0.021
Chain 1:   4200       -17680.724             0.021            0.021
Chain 1:   4300       -17819.419             0.021            0.021
Chain 1:   4400       -17775.825             0.018            0.010
Chain 1:   4500       -17678.350             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12421.068             1.000            1.000
Chain 1:    200        -9422.632             0.659            1.000
Chain 1:    300        -8256.780             0.486            0.318
Chain 1:    400        -8370.771             0.368            0.318
Chain 1:    500        -8340.227             0.295            0.141
Chain 1:    600        -8314.816             0.247            0.141
Chain 1:    700        -8094.531             0.215            0.027
Chain 1:    800        -8107.939             0.189            0.027
Chain 1:    900        -8241.407             0.169            0.016
Chain 1:   1000        -8116.510             0.154            0.016
Chain 1:   1100        -8157.998             0.055            0.015
Chain 1:   1200        -8106.508             0.023            0.014
Chain 1:   1300        -8040.354             0.010            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001447 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -62060.883             1.000            1.000
Chain 1:    200       -17994.235             1.724            2.449
Chain 1:    300        -8882.323             1.492            1.026
Chain 1:    400        -9586.158             1.137            1.026
Chain 1:    500        -8129.285             0.945            1.000
Chain 1:    600        -8265.462             0.791            1.000
Chain 1:    700        -8102.605             0.681            0.179
Chain 1:    800        -7775.888             0.601            0.179
Chain 1:    900        -7977.740             0.537            0.073
Chain 1:   1000        -7898.018             0.484            0.073
Chain 1:   1100        -7589.653             0.388            0.042
Chain 1:   1200        -7789.334             0.146            0.041
Chain 1:   1300        -7540.959             0.047            0.033
Chain 1:   1400        -7587.881             0.040            0.026
Chain 1:   1500        -7492.974             0.023            0.025
Chain 1:   1600        -7660.059             0.024            0.025
Chain 1:   1700        -7484.878             0.024            0.025
Chain 1:   1800        -7561.468             0.021            0.023
Chain 1:   1900        -7552.440             0.018            0.022
Chain 1:   2000        -7537.810             0.018            0.022
Chain 1:   2100        -7523.465             0.014            0.013
Chain 1:   2200        -7659.242             0.013            0.013
Chain 1:   2300        -7540.569             0.011            0.013
Chain 1:   2400        -7569.465             0.011            0.013
Chain 1:   2500        -7500.619             0.011            0.010
Chain 1:   2600        -7468.093             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003537 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85862.503             1.000            1.000
Chain 1:    200       -13620.880             3.152            5.304
Chain 1:    300       -10028.350             2.221            1.000
Chain 1:    400       -10776.388             1.683            1.000
Chain 1:    500        -8953.516             1.387            0.358
Chain 1:    600        -8667.231             1.161            0.358
Chain 1:    700        -8857.058             0.998            0.204
Chain 1:    800        -9369.562             0.881            0.204
Chain 1:    900        -8875.471             0.789            0.069
Chain 1:   1000        -8536.082             0.714            0.069
Chain 1:   1100        -8873.026             0.618            0.056   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8551.515             0.091            0.055
Chain 1:   1300        -8741.159             0.057            0.040
Chain 1:   1400        -8741.378             0.051            0.038
Chain 1:   1500        -8605.453             0.032            0.038
Chain 1:   1600        -8715.139             0.030            0.038
Chain 1:   1700        -8803.666             0.029            0.038
Chain 1:   1800        -8400.359             0.028            0.038
Chain 1:   1900        -8497.869             0.023            0.022
Chain 1:   2000        -8469.637             0.020            0.016
Chain 1:   2100        -8589.517             0.017            0.014
Chain 1:   2200        -8393.077             0.016            0.014
Chain 1:   2300        -8533.533             0.016            0.014
Chain 1:   2400        -8536.842             0.016            0.014
Chain 1:   2500        -8512.100             0.014            0.013
Chain 1:   2600        -8511.617             0.013            0.011
Chain 1:   2700        -8422.096             0.013            0.011
Chain 1:   2800        -8388.826             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8369384.943             1.000            1.000
Chain 1:    200     -1577790.545             2.652            4.304
Chain 1:    300      -890167.535             2.026            1.000
Chain 1:    400      -457503.552             1.756            1.000
Chain 1:    500      -358632.689             1.460            0.946
Chain 1:    600      -233745.180             1.305            0.946
Chain 1:    700      -119714.689             1.255            0.946
Chain 1:    800       -86853.102             1.145            0.946
Chain 1:    900       -67126.437             1.051            0.772
Chain 1:   1000       -51862.759             0.975            0.772
Chain 1:   1100       -39280.884             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38448.802             0.479            0.378
Chain 1:   1300       -26344.521             0.448            0.378
Chain 1:   1400       -26057.015             0.354            0.320
Chain 1:   1500       -22628.963             0.342            0.320
Chain 1:   1600       -21840.805             0.292            0.294
Chain 1:   1700       -20707.229             0.202            0.294
Chain 1:   1800       -20649.653             0.165            0.151
Chain 1:   1900       -20975.622             0.137            0.055
Chain 1:   2000       -19483.466             0.115            0.055
Chain 1:   2100       -19721.855             0.084            0.036
Chain 1:   2200       -19948.902             0.083            0.036
Chain 1:   2300       -19565.675             0.039            0.020
Chain 1:   2400       -19337.775             0.039            0.020
Chain 1:   2500       -19140.131             0.025            0.016
Chain 1:   2600       -18770.191             0.023            0.016
Chain 1:   2700       -18727.132             0.018            0.012
Chain 1:   2800       -18444.220             0.019            0.015
Chain 1:   2900       -18725.500             0.019            0.015
Chain 1:   3000       -18711.581             0.012            0.012
Chain 1:   3100       -18796.546             0.011            0.012
Chain 1:   3200       -18487.302             0.012            0.015
Chain 1:   3300       -18691.993             0.011            0.012
Chain 1:   3400       -18167.151             0.012            0.015
Chain 1:   3500       -18778.795             0.015            0.015
Chain 1:   3600       -18085.858             0.017            0.015
Chain 1:   3700       -18472.431             0.018            0.017
Chain 1:   3800       -17432.784             0.023            0.021
Chain 1:   3900       -17429.016             0.021            0.021
Chain 1:   4000       -17546.249             0.022            0.021
Chain 1:   4100       -17460.063             0.022            0.021
Chain 1:   4200       -17276.462             0.021            0.021
Chain 1:   4300       -17414.706             0.021            0.021
Chain 1:   4400       -17371.652             0.018            0.011
Chain 1:   4500       -17274.263             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00133 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.3 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12663.427             1.000            1.000
Chain 1:    200        -9546.448             0.663            1.000
Chain 1:    300        -8103.980             0.502            0.327
Chain 1:    400        -8326.028             0.383            0.327
Chain 1:    500        -8185.996             0.310            0.178
Chain 1:    600        -8041.239             0.261            0.178
Chain 1:    700        -8164.650             0.226            0.027
Chain 1:    800        -7981.242             0.201            0.027
Chain 1:    900        -7892.427             0.180            0.023
Chain 1:   1000        -7955.182             0.162            0.023
Chain 1:   1100        -8075.344             0.064            0.018
Chain 1:   1200        -7963.141             0.033            0.017
Chain 1:   1300        -7908.946             0.015            0.015
Chain 1:   1400        -7916.358             0.013            0.015
Chain 1:   1500        -8019.109             0.012            0.014
Chain 1:   1600        -7919.150             0.012            0.013
Chain 1:   1700        -7893.366             0.011            0.013
Chain 1:   1800        -7866.050             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46589.255             1.000            1.000
Chain 1:    200       -15878.983             1.467            1.934
Chain 1:    300        -8827.590             1.244            1.000
Chain 1:    400        -8400.816             0.946            1.000
Chain 1:    500        -8313.766             0.759            0.799
Chain 1:    600        -8412.196             0.634            0.799
Chain 1:    700        -8572.246             0.546            0.051
Chain 1:    800        -8293.666             0.482            0.051
Chain 1:    900        -8047.205             0.432            0.034
Chain 1:   1000        -7889.721             0.391            0.034
Chain 1:   1100        -7645.315             0.294            0.032
Chain 1:   1200        -8138.771             0.107            0.032
Chain 1:   1300        -7572.299             0.034            0.032
Chain 1:   1400        -7649.436             0.030            0.031
Chain 1:   1500        -7525.429             0.031            0.031
Chain 1:   1600        -7757.975             0.033            0.031
Chain 1:   1700        -7518.757             0.034            0.032
Chain 1:   1800        -7560.284             0.031            0.031
Chain 1:   1900        -7543.738             0.028            0.030
Chain 1:   2000        -7612.620             0.027            0.030
Chain 1:   2100        -7541.681             0.025            0.016
Chain 1:   2200        -7729.041             0.021            0.016
Chain 1:   2300        -7494.559             0.017            0.016
Chain 1:   2400        -7536.568             0.017            0.016
Chain 1:   2500        -7578.865             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003285 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86253.611             1.000            1.000
Chain 1:    200       -13831.965             3.118            5.236
Chain 1:    300       -10069.228             2.203            1.000
Chain 1:    400       -11745.339             1.688            1.000
Chain 1:    500        -8703.204             1.420            0.374
Chain 1:    600        -8677.620             1.184            0.374
Chain 1:    700        -8427.437             1.019            0.350
Chain 1:    800        -9039.869             0.900            0.350
Chain 1:    900        -8812.632             0.803            0.143
Chain 1:   1000        -8669.818             0.724            0.143
Chain 1:   1100        -8719.723             0.625            0.068   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8358.085             0.106            0.043
Chain 1:   1300        -8817.927             0.074            0.043
Chain 1:   1400        -8558.824             0.062            0.030
Chain 1:   1500        -8557.434             0.027            0.030
Chain 1:   1600        -8650.249             0.028            0.030
Chain 1:   1700        -8710.083             0.026            0.026
Chain 1:   1800        -8267.556             0.024            0.026
Chain 1:   1900        -8377.044             0.023            0.016
Chain 1:   2000        -8366.232             0.022            0.013
Chain 1:   2100        -8540.564             0.023            0.020
Chain 1:   2200        -8272.348             0.022            0.020
Chain 1:   2300        -8456.093             0.019            0.020
Chain 1:   2400        -8272.672             0.018            0.020
Chain 1:   2500        -8350.215             0.019            0.020
Chain 1:   2600        -8266.810             0.019            0.020
Chain 1:   2700        -8294.277             0.019            0.020
Chain 1:   2800        -8247.587             0.014            0.013
Chain 1:   2900        -8355.358             0.014            0.013
Chain 1:   3000        -8306.169             0.014            0.013
Chain 1:   3100        -8239.012             0.013            0.010
Chain 1:   3200        -8212.124             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00362 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389048.532             1.000            1.000
Chain 1:    200     -1585354.500             2.646            4.292
Chain 1:    300      -892362.522             2.023            1.000
Chain 1:    400      -458280.387             1.754            1.000
Chain 1:    500      -358798.652             1.459            0.947
Chain 1:    600      -233700.249             1.305            0.947
Chain 1:    700      -119778.146             1.254            0.947
Chain 1:    800       -86919.796             1.145            0.947
Chain 1:    900       -67247.402             1.050            0.777
Chain 1:   1000       -52035.326             0.974            0.777
Chain 1:   1100       -39487.032             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38668.628             0.479            0.378
Chain 1:   1300       -26589.648             0.447            0.378
Chain 1:   1400       -26308.802             0.353            0.318
Chain 1:   1500       -22885.564             0.340            0.318
Chain 1:   1600       -22099.550             0.290            0.293
Chain 1:   1700       -20968.731             0.201            0.292
Chain 1:   1800       -20912.144             0.163            0.150
Chain 1:   1900       -21238.944             0.135            0.054
Chain 1:   2000       -19746.070             0.114            0.054
Chain 1:   2100       -19984.901             0.083            0.036
Chain 1:   2200       -20212.115             0.082            0.036
Chain 1:   2300       -19828.469             0.039            0.019
Chain 1:   2400       -19600.251             0.039            0.019
Chain 1:   2500       -19402.275             0.025            0.015
Chain 1:   2600       -19031.857             0.023            0.015
Chain 1:   2700       -18988.605             0.018            0.012
Chain 1:   2800       -18705.146             0.019            0.015
Chain 1:   2900       -18986.759             0.019            0.015
Chain 1:   3000       -18972.960             0.012            0.012
Chain 1:   3100       -19058.026             0.011            0.012
Chain 1:   3200       -18748.271             0.011            0.015
Chain 1:   3300       -18953.325             0.011            0.012
Chain 1:   3400       -18427.441             0.012            0.015
Chain 1:   3500       -19040.553             0.014            0.015
Chain 1:   3600       -18345.671             0.016            0.015
Chain 1:   3700       -18733.647             0.018            0.017
Chain 1:   3800       -17690.897             0.023            0.021
Chain 1:   3900       -17686.971             0.021            0.021
Chain 1:   4000       -17804.294             0.022            0.021
Chain 1:   4100       -17717.917             0.022            0.021
Chain 1:   4200       -17533.623             0.021            0.021
Chain 1:   4300       -17672.411             0.021            0.021
Chain 1:   4400       -17628.804             0.018            0.011
Chain 1:   4500       -17531.245             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001587 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48999.815             1.000            1.000
Chain 1:    200       -20155.331             1.216            1.431
Chain 1:    300       -16571.538             0.882            1.000
Chain 1:    400       -19039.101             0.694            1.000
Chain 1:    500       -21951.159             0.582            0.216
Chain 1:    600       -12223.576             0.618            0.796
Chain 1:    700       -14819.099             0.554            0.216
Chain 1:    800       -12507.126             0.508            0.216
Chain 1:    900       -10856.175             0.469            0.185
Chain 1:   1000       -10719.453             0.423            0.185
Chain 1:   1100       -10414.338             0.326            0.175
Chain 1:   1200       -16383.001             0.219            0.175
Chain 1:   1300       -13606.690             0.218            0.175
Chain 1:   1400       -25324.920             0.251            0.185
Chain 1:   1500       -10179.522             0.387            0.204
Chain 1:   1600       -11168.818             0.316            0.185
Chain 1:   1700        -9555.196             0.316            0.185
Chain 1:   1800       -19286.016             0.348            0.204
Chain 1:   1900       -10562.546             0.415            0.364
Chain 1:   2000        -9942.268             0.420            0.364
Chain 1:   2100        -9584.972             0.421            0.364
Chain 1:   2200       -10547.470             0.393            0.204
Chain 1:   2300       -14731.173             0.401            0.284
Chain 1:   2400        -8904.341             0.421            0.284
Chain 1:   2500       -10435.324             0.286            0.169
Chain 1:   2600        -9419.000             0.288            0.169
Chain 1:   2700       -14211.806             0.305            0.284
Chain 1:   2800       -10421.257             0.291            0.284
Chain 1:   2900        -9825.400             0.215            0.147
Chain 1:   3000        -8687.861             0.221            0.147
Chain 1:   3100        -9446.811             0.226            0.147
Chain 1:   3200       -15900.689             0.257            0.284
Chain 1:   3300       -16102.849             0.230            0.147
Chain 1:   3400       -10051.407             0.225            0.147
Chain 1:   3500        -9646.728             0.214            0.131
Chain 1:   3600        -9631.004             0.204            0.131
Chain 1:   3700        -9472.746             0.172            0.080
Chain 1:   3800        -9261.228             0.138            0.061
Chain 1:   3900       -15493.040             0.172            0.080
Chain 1:   4000        -8566.018             0.239            0.080
Chain 1:   4100        -8754.720             0.234            0.042
Chain 1:   4200       -10629.186             0.211            0.042
Chain 1:   4300       -10180.792             0.214            0.044
Chain 1:   4400        -9004.176             0.167            0.044
Chain 1:   4500       -10490.483             0.177            0.131
Chain 1:   4600        -9027.167             0.193            0.142
Chain 1:   4700        -9802.257             0.199            0.142
Chain 1:   4800        -8500.176             0.212            0.153
Chain 1:   4900       -10102.438             0.188            0.153
Chain 1:   5000       -13807.380             0.134            0.153
Chain 1:   5100       -11085.932             0.156            0.159
Chain 1:   5200        -9216.082             0.159            0.159
Chain 1:   5300       -11347.898             0.173            0.162
Chain 1:   5400        -8367.318             0.196            0.188
Chain 1:   5500       -14006.834             0.222            0.203
Chain 1:   5600       -10518.438             0.239            0.245
Chain 1:   5700        -8400.887             0.256            0.252
Chain 1:   5800       -10523.930             0.261            0.252
Chain 1:   5900       -10425.158             0.246            0.252
Chain 1:   6000       -11262.977             0.226            0.245
Chain 1:   6100       -10337.073             0.211            0.203
Chain 1:   6200        -8499.686             0.212            0.216
Chain 1:   6300       -10141.378             0.210            0.216
Chain 1:   6400       -10041.560             0.175            0.202
Chain 1:   6500        -9131.653             0.145            0.162
Chain 1:   6600        -8427.690             0.120            0.100
Chain 1:   6700       -10044.278             0.111            0.100
Chain 1:   6800        -8902.611             0.103            0.100
Chain 1:   6900        -9396.554             0.108            0.100
Chain 1:   7000       -11893.727             0.121            0.128
Chain 1:   7100       -12736.235             0.119            0.128
Chain 1:   7200        -8508.308             0.147            0.128
Chain 1:   7300        -9354.045             0.140            0.100
Chain 1:   7400        -8459.704             0.149            0.106
Chain 1:   7500        -8966.508             0.145            0.106
Chain 1:   7600        -9229.020             0.140            0.106
Chain 1:   7700        -8441.920             0.133            0.093
Chain 1:   7800        -9089.222             0.127            0.090
Chain 1:   7900        -8594.304             0.128            0.090
Chain 1:   8000        -8175.388             0.112            0.071
Chain 1:   8100        -8377.597             0.108            0.071
Chain 1:   8200       -10925.910             0.081            0.071
Chain 1:   8300        -8178.343             0.106            0.071
Chain 1:   8400        -8026.834             0.097            0.058
Chain 1:   8500        -8255.601             0.094            0.058
Chain 1:   8600       -10226.589             0.111            0.071
Chain 1:   8700        -8272.353             0.125            0.071
Chain 1:   8800        -8489.418             0.120            0.058
Chain 1:   8900       -10894.193             0.137            0.193
Chain 1:   9000        -8053.500             0.167            0.221
Chain 1:   9100       -10492.733             0.188            0.232
Chain 1:   9200        -9915.011             0.170            0.221
Chain 1:   9300        -9292.824             0.143            0.193
Chain 1:   9400        -8349.315             0.153            0.193
Chain 1:   9500        -8094.163             0.153            0.193
Chain 1:   9600        -8229.503             0.135            0.113
Chain 1:   9700        -8382.433             0.114            0.067
Chain 1:   9800        -9278.279             0.121            0.097
Chain 1:   9900       -10149.964             0.107            0.086
Chain 1:   10000        -8139.440             0.097            0.086
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00195 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63152.200             1.000            1.000
Chain 1:    200       -18077.278             1.747            2.493
Chain 1:    300        -8724.161             1.522            1.072
Chain 1:    400        -8408.803             1.151            1.072
Chain 1:    500        -8706.216             0.927            1.000
Chain 1:    600        -8639.582             0.774            1.000
Chain 1:    700        -7943.913             0.676            0.088
Chain 1:    800        -7979.047             0.592            0.088
Chain 1:    900        -7892.730             0.528            0.038
Chain 1:   1000        -7769.419             0.476            0.038
Chain 1:   1100        -7683.821             0.377            0.034
Chain 1:   1200        -7608.927             0.129            0.016
Chain 1:   1300        -7793.369             0.024            0.016
Chain 1:   1400        -7877.834             0.022            0.011
Chain 1:   1500        -7617.471             0.022            0.011
Chain 1:   1600        -7744.540             0.022            0.016
Chain 1:   1700        -7512.553             0.017            0.016
Chain 1:   1800        -7558.918             0.017            0.016
Chain 1:   1900        -7560.394             0.016            0.016
Chain 1:   2000        -7593.125             0.015            0.011
Chain 1:   2100        -7595.006             0.014            0.011
Chain 1:   2200        -7692.731             0.014            0.013
Chain 1:   2300        -7606.206             0.013            0.011
Chain 1:   2400        -7637.847             0.012            0.011
Chain 1:   2500        -7561.751             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003765 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86315.182             1.000            1.000
Chain 1:    200       -13350.945             3.233            5.465
Chain 1:    300        -9782.098             2.277            1.000
Chain 1:    400       -10593.201             1.727            1.000
Chain 1:    500        -8684.380             1.425            0.365
Chain 1:    600        -8425.371             1.193            0.365
Chain 1:    700        -8492.044             1.024            0.220
Chain 1:    800        -8750.440             0.899            0.220
Chain 1:    900        -8657.824             0.801            0.077
Chain 1:   1000        -8327.070             0.724            0.077
Chain 1:   1100        -8675.926             0.629            0.040   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8394.987             0.085            0.040
Chain 1:   1300        -8524.006             0.050            0.033
Chain 1:   1400        -8515.548             0.043            0.031
Chain 1:   1500        -8390.647             0.022            0.030
Chain 1:   1600        -8497.720             0.021            0.015
Chain 1:   1700        -8583.414             0.021            0.015
Chain 1:   1800        -8191.074             0.023            0.015
Chain 1:   1900        -8292.693             0.023            0.015
Chain 1:   2000        -8263.114             0.019            0.015
Chain 1:   2100        -8387.797             0.017            0.015
Chain 1:   2200        -8172.160             0.016            0.015
Chain 1:   2300        -8321.419             0.016            0.015
Chain 1:   2400        -8336.462             0.016            0.015
Chain 1:   2500        -8304.018             0.015            0.013
Chain 1:   2600        -8306.198             0.014            0.012
Chain 1:   2700        -8212.800             0.014            0.012
Chain 1:   2800        -8185.101             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003774 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8426765.073             1.000            1.000
Chain 1:    200     -1587546.039             2.654            4.308
Chain 1:    300      -891009.034             2.030            1.000
Chain 1:    400      -457641.000             1.759            1.000
Chain 1:    500      -357674.166             1.463            0.947
Chain 1:    600      -232578.173             1.309            0.947
Chain 1:    700      -118917.118             1.259            0.947
Chain 1:    800       -86166.317             1.149            0.947
Chain 1:    900       -66528.647             1.054            0.782
Chain 1:   1000       -51343.316             0.978            0.782
Chain 1:   1100       -38844.099             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38017.901             0.482            0.380
Chain 1:   1300       -26006.146             0.450            0.380
Chain 1:   1400       -25725.576             0.356            0.322
Chain 1:   1500       -22322.362             0.343            0.322
Chain 1:   1600       -21541.164             0.293            0.296
Chain 1:   1700       -20419.201             0.203            0.295
Chain 1:   1800       -20364.112             0.165            0.152
Chain 1:   1900       -20689.856             0.137            0.055
Chain 1:   2000       -19204.261             0.116            0.055
Chain 1:   2100       -19442.207             0.085            0.036
Chain 1:   2200       -19668.159             0.084            0.036
Chain 1:   2300       -19285.978             0.039            0.020
Chain 1:   2400       -19058.287             0.039            0.020
Chain 1:   2500       -18860.227             0.025            0.016
Chain 1:   2600       -18490.849             0.024            0.016
Chain 1:   2700       -18447.976             0.018            0.012
Chain 1:   2800       -18164.991             0.020            0.016
Chain 1:   2900       -18446.037             0.020            0.015
Chain 1:   3000       -18432.222             0.012            0.012
Chain 1:   3100       -18517.142             0.011            0.012
Chain 1:   3200       -18208.107             0.012            0.015
Chain 1:   3300       -18412.631             0.011            0.012
Chain 1:   3400       -17888.016             0.013            0.015
Chain 1:   3500       -18499.114             0.015            0.016
Chain 1:   3600       -17806.844             0.017            0.016
Chain 1:   3700       -18192.844             0.019            0.017
Chain 1:   3800       -17154.111             0.023            0.021
Chain 1:   3900       -17150.306             0.022            0.021
Chain 1:   4000       -17267.618             0.022            0.021
Chain 1:   4100       -17181.441             0.022            0.021
Chain 1:   4200       -16998.046             0.022            0.021
Chain 1:   4300       -17136.187             0.021            0.021
Chain 1:   4400       -17093.288             0.019            0.011
Chain 1:   4500       -16995.889             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001656 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.56 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49077.044             1.000            1.000
Chain 1:    200       -19172.134             1.280            1.560
Chain 1:    300       -20020.665             0.867            1.000
Chain 1:    400       -13279.185             0.777            1.000
Chain 1:    500       -18745.905             0.680            0.508
Chain 1:    600       -17341.980             0.580            0.508
Chain 1:    700       -11764.803             0.565            0.474
Chain 1:    800       -23116.290             0.556            0.491
Chain 1:    900       -12943.619             0.581            0.491
Chain 1:   1000       -12233.526             0.529            0.491
Chain 1:   1100       -14045.021             0.442            0.474
Chain 1:   1200       -11070.771             0.313            0.292
Chain 1:   1300       -13508.143             0.327            0.292
Chain 1:   1400       -16646.454             0.295            0.269
Chain 1:   1500        -9989.267             0.332            0.269
Chain 1:   1600       -10030.408             0.325            0.269
Chain 1:   1700       -17576.006             0.320            0.269
Chain 1:   1800       -19922.060             0.283            0.189
Chain 1:   1900       -11002.752             0.285            0.189
Chain 1:   2000       -12400.733             0.291            0.189
Chain 1:   2100       -11245.045             0.288            0.189
Chain 1:   2200        -9883.839             0.275            0.180
Chain 1:   2300        -9404.289             0.262            0.138
Chain 1:   2400       -17998.799             0.291            0.138
Chain 1:   2500        -9554.754             0.313            0.138
Chain 1:   2600        -9738.051             0.314            0.138
Chain 1:   2700        -9826.724             0.272            0.118
Chain 1:   2800        -9254.829             0.267            0.113
Chain 1:   2900        -9241.856             0.186            0.103
Chain 1:   3000        -9050.913             0.176            0.062
Chain 1:   3100       -11749.930             0.189            0.062
Chain 1:   3200       -20208.394             0.217            0.062
Chain 1:   3300       -16430.099             0.235            0.230
Chain 1:   3400       -15985.342             0.190            0.062
Chain 1:   3500        -9560.861             0.169            0.062
Chain 1:   3600        -9161.231             0.171            0.062
Chain 1:   3700        -9823.711             0.177            0.067
Chain 1:   3800        -9184.712             0.178            0.070
Chain 1:   3900       -14859.673             0.216            0.230
Chain 1:   4000        -9096.789             0.277            0.230
Chain 1:   4100       -11904.575             0.278            0.236
Chain 1:   4200        -8938.323             0.269            0.236
Chain 1:   4300       -12366.820             0.274            0.277
Chain 1:   4400        -8980.186             0.309            0.332
Chain 1:   4500       -15003.969             0.282            0.332
Chain 1:   4600       -10237.118             0.324            0.377
Chain 1:   4700        -9942.468             0.320            0.377
Chain 1:   4800       -13499.844             0.340            0.377
Chain 1:   4900        -9678.561             0.341            0.377
Chain 1:   5000       -10156.493             0.282            0.332
Chain 1:   5100        -8736.926             0.275            0.332
Chain 1:   5200       -16323.914             0.288            0.377
Chain 1:   5300       -14390.448             0.274            0.377
Chain 1:   5400       -15346.702             0.243            0.264
Chain 1:   5500        -8667.596             0.280            0.264
Chain 1:   5600        -9550.219             0.242            0.162
Chain 1:   5700       -12309.389             0.262            0.224
Chain 1:   5800        -8872.179             0.274            0.224
Chain 1:   5900       -14472.086             0.273            0.224
Chain 1:   6000        -9279.657             0.324            0.387
Chain 1:   6100        -9633.699             0.312            0.387
Chain 1:   6200        -8550.901             0.278            0.224
Chain 1:   6300        -9586.430             0.275            0.224
Chain 1:   6400        -9051.488             0.275            0.224
Chain 1:   6500       -12918.766             0.228            0.224
Chain 1:   6600       -11093.482             0.235            0.224
Chain 1:   6700        -8476.733             0.244            0.299
Chain 1:   6800        -9042.523             0.211            0.165
Chain 1:   6900       -10406.344             0.186            0.131
Chain 1:   7000        -8564.730             0.151            0.131
Chain 1:   7100        -8457.639             0.149            0.131
Chain 1:   7200        -9600.564             0.148            0.131
Chain 1:   7300       -10864.263             0.149            0.131
Chain 1:   7400        -8820.661             0.166            0.165
Chain 1:   7500        -8344.465             0.142            0.131
Chain 1:   7600        -8548.960             0.128            0.119
Chain 1:   7700       -11346.372             0.122            0.119
Chain 1:   7800       -11260.742             0.116            0.119
Chain 1:   7900        -8816.962             0.131            0.119
Chain 1:   8000        -8436.190             0.114            0.116
Chain 1:   8100        -8561.716             0.114            0.116
Chain 1:   8200        -9100.038             0.108            0.059
Chain 1:   8300       -10735.069             0.112            0.059
Chain 1:   8400        -8871.939             0.109            0.059
Chain 1:   8500        -8529.166             0.108            0.059
Chain 1:   8600       -10161.562             0.121            0.152
Chain 1:   8700        -8418.265             0.117            0.152
Chain 1:   8800        -8295.527             0.118            0.152
Chain 1:   8900        -8681.013             0.095            0.059
Chain 1:   9000       -11712.341             0.116            0.152
Chain 1:   9100        -8280.405             0.156            0.161
Chain 1:   9200        -8837.418             0.157            0.161
Chain 1:   9300        -8390.051             0.147            0.161
Chain 1:   9400        -8375.554             0.126            0.063
Chain 1:   9500        -8413.684             0.122            0.063
Chain 1:   9600        -8504.720             0.107            0.053
Chain 1:   9700        -8296.031             0.089            0.044
Chain 1:   9800        -9906.181             0.104            0.053
Chain 1:   9900       -11141.468             0.111            0.063
Chain 1:   10000        -8247.649             0.120            0.063
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001399 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61860.791             1.000            1.000
Chain 1:    200       -18099.470             1.709            2.418
Chain 1:    300        -8964.902             1.479            1.019
Chain 1:    400        -9343.124             1.119            1.019
Chain 1:    500        -7955.357             0.930            1.000
Chain 1:    600        -8704.289             0.790            1.000
Chain 1:    700        -7883.390             0.692            0.174
Chain 1:    800        -8526.447             0.615            0.174
Chain 1:    900        -7871.285             0.556            0.104
Chain 1:   1000        -7893.039             0.500            0.104
Chain 1:   1100        -7945.231             0.401            0.086
Chain 1:   1200        -7699.525             0.162            0.083
Chain 1:   1300        -7747.943             0.061            0.075
Chain 1:   1400        -7697.211             0.058            0.075
Chain 1:   1500        -7638.110             0.041            0.032
Chain 1:   1600        -7821.808             0.035            0.023
Chain 1:   1700        -7716.681             0.026            0.014
Chain 1:   1800        -7713.899             0.018            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85111.378             1.000            1.000
Chain 1:    200       -13684.879             3.110            5.219
Chain 1:    300       -10068.350             2.193            1.000
Chain 1:    400       -10875.089             1.663            1.000
Chain 1:    500        -9042.007             1.371            0.359
Chain 1:    600        -8518.968             1.153            0.359
Chain 1:    700        -8593.093             0.989            0.203
Chain 1:    800        -8762.653             0.868            0.203
Chain 1:    900        -8892.091             0.773            0.074
Chain 1:   1000        -8698.496             0.698            0.074
Chain 1:   1100        -8862.615             0.600            0.061   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8545.639             0.082            0.037
Chain 1:   1300        -8775.547             0.048            0.026
Chain 1:   1400        -8761.258             0.041            0.022
Chain 1:   1500        -8624.930             0.023            0.019
Chain 1:   1600        -8735.506             0.018            0.019
Chain 1:   1700        -8820.368             0.018            0.019
Chain 1:   1800        -8407.306             0.021            0.019
Chain 1:   1900        -8503.445             0.020            0.019
Chain 1:   2000        -8476.732             0.019            0.016
Chain 1:   2100        -8599.285             0.018            0.014
Chain 1:   2200        -8419.454             0.017            0.014
Chain 1:   2300        -8498.399             0.015            0.013
Chain 1:   2400        -8568.104             0.015            0.013
Chain 1:   2500        -8513.521             0.015            0.011
Chain 1:   2600        -8512.925             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.007243 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 72.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8376387.049             1.000            1.000
Chain 1:    200     -1579658.030             2.651            4.303
Chain 1:    300      -891058.256             2.025            1.000
Chain 1:    400      -458162.589             1.755            1.000
Chain 1:    500      -359025.974             1.459            0.945
Chain 1:    600      -234098.149             1.305            0.945
Chain 1:    700      -119882.300             1.255            0.945
Chain 1:    800       -86990.167             1.145            0.945
Chain 1:    900       -67243.880             1.051            0.773
Chain 1:   1000       -51971.217             0.975            0.773
Chain 1:   1100       -39380.018             0.907            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38546.791             0.479            0.378
Chain 1:   1300       -26431.180             0.447            0.378
Chain 1:   1400       -26143.689             0.354            0.320
Chain 1:   1500       -22712.369             0.341            0.320
Chain 1:   1600       -21923.318             0.292            0.294
Chain 1:   1700       -20788.340             0.202            0.294
Chain 1:   1800       -20730.416             0.164            0.151
Chain 1:   1900       -21056.513             0.136            0.055
Chain 1:   2000       -19563.164             0.115            0.055
Chain 1:   2100       -19801.711             0.084            0.036
Chain 1:   2200       -20028.984             0.083            0.036
Chain 1:   2300       -19645.491             0.039            0.020
Chain 1:   2400       -19417.502             0.039            0.020
Chain 1:   2500       -19219.896             0.025            0.015
Chain 1:   2600       -18849.792             0.023            0.015
Chain 1:   2700       -18806.623             0.018            0.012
Chain 1:   2800       -18523.687             0.019            0.015
Chain 1:   2900       -18804.974             0.019            0.015
Chain 1:   3000       -18791.134             0.012            0.012
Chain 1:   3100       -18876.140             0.011            0.012
Chain 1:   3200       -18566.748             0.012            0.015
Chain 1:   3300       -18771.495             0.011            0.012
Chain 1:   3400       -18246.467             0.012            0.015
Chain 1:   3500       -18858.439             0.015            0.015
Chain 1:   3600       -18165.009             0.016            0.015
Chain 1:   3700       -18551.961             0.018            0.017
Chain 1:   3800       -17511.620             0.023            0.021
Chain 1:   3900       -17507.813             0.021            0.021
Chain 1:   4000       -17625.051             0.022            0.021
Chain 1:   4100       -17538.871             0.022            0.021
Chain 1:   4200       -17355.077             0.021            0.021
Chain 1:   4300       -17493.455             0.021            0.021
Chain 1:   4400       -17450.260             0.018            0.011
Chain 1:   4500       -17352.846             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001327 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.27 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48821.292             1.000            1.000
Chain 1:    200       -39581.388             0.617            1.000
Chain 1:    300       -33164.898             0.476            0.233
Chain 1:    400       -14036.190             0.697            1.000
Chain 1:    500       -27361.771             0.655            0.487
Chain 1:    600       -11469.620             0.777            1.000
Chain 1:    700       -14819.420             0.698            0.487
Chain 1:    800       -13576.723             0.622            0.487
Chain 1:    900       -14524.139             0.561            0.233
Chain 1:   1000       -10913.879             0.538            0.331
Chain 1:   1100       -20066.718             0.483            0.331
Chain 1:   1200       -17841.541             0.472            0.331
Chain 1:   1300       -11139.183             0.513            0.456   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1400       -13457.016             0.394            0.331
Chain 1:   1500        -9996.866             0.380            0.331
Chain 1:   1600       -19644.618             0.291            0.331
Chain 1:   1700        -9572.267             0.373            0.346
Chain 1:   1800       -17210.319             0.408            0.444
Chain 1:   1900       -10157.246             0.471            0.456
Chain 1:   2000       -10703.548             0.443            0.456
Chain 1:   2100       -13806.818             0.420            0.444
Chain 1:   2200       -10974.932             0.434            0.444
Chain 1:   2300       -16472.729             0.407            0.346
Chain 1:   2400        -9691.004             0.460            0.444
Chain 1:   2500        -9328.988             0.429            0.444
Chain 1:   2600        -9450.108             0.381            0.334
Chain 1:   2700       -10146.443             0.283            0.258
Chain 1:   2800       -14526.059             0.268            0.258
Chain 1:   2900        -9771.516             0.248            0.258
Chain 1:   3000        -9137.054             0.249            0.258
Chain 1:   3100        -9298.410             0.229            0.258
Chain 1:   3200       -10107.760             0.211            0.080
Chain 1:   3300        -9589.879             0.183            0.069
Chain 1:   3400        -9833.678             0.115            0.069
Chain 1:   3500        -9299.051             0.117            0.069
Chain 1:   3600       -10365.441             0.126            0.069
Chain 1:   3700        -8860.879             0.136            0.080
Chain 1:   3800        -8958.906             0.107            0.069
Chain 1:   3900        -9884.560             0.068            0.069
Chain 1:   4000        -9795.436             0.062            0.057
Chain 1:   4100        -9182.781             0.067            0.067
Chain 1:   4200       -11775.410             0.081            0.067
Chain 1:   4300        -9660.105             0.097            0.094
Chain 1:   4400        -9524.604             0.096            0.094
Chain 1:   4500        -9550.015             0.091            0.094
Chain 1:   4600        -8686.787             0.091            0.094
Chain 1:   4700       -13800.600             0.111            0.094
Chain 1:   4800        -8666.553             0.169            0.099
Chain 1:   4900       -13069.650             0.193            0.219
Chain 1:   5000       -11204.425             0.209            0.219
Chain 1:   5100        -9068.249             0.226            0.220
Chain 1:   5200       -15257.475             0.244            0.236
Chain 1:   5300       -14303.670             0.229            0.236
Chain 1:   5400       -13537.789             0.233            0.236
Chain 1:   5500       -12294.570             0.243            0.236
Chain 1:   5600       -12387.711             0.234            0.236
Chain 1:   5700        -8744.685             0.239            0.236
Chain 1:   5800       -17538.198             0.229            0.236
Chain 1:   5900       -14976.399             0.213            0.171
Chain 1:   6000        -9281.923             0.258            0.236
Chain 1:   6100       -11456.151             0.253            0.190
Chain 1:   6200       -10095.679             0.226            0.171
Chain 1:   6300        -8997.197             0.231            0.171
Chain 1:   6400       -10098.220             0.237            0.171
Chain 1:   6500       -13282.523             0.251            0.190
Chain 1:   6600        -9395.027             0.291            0.240
Chain 1:   6700        -8404.025             0.261            0.190
Chain 1:   6800       -10834.852             0.234            0.190
Chain 1:   6900        -9098.640             0.236            0.191
Chain 1:   7000       -13794.365             0.208            0.191
Chain 1:   7100        -8276.803             0.256            0.224
Chain 1:   7200       -11161.848             0.268            0.240
Chain 1:   7300       -10349.082             0.264            0.240
Chain 1:   7400       -12028.803             0.267            0.240
Chain 1:   7500        -8789.544             0.280            0.258
Chain 1:   7600        -9744.365             0.248            0.224
Chain 1:   7700        -8374.184             0.253            0.224
Chain 1:   7800       -10245.241             0.249            0.191
Chain 1:   7900       -10066.423             0.231            0.183
Chain 1:   8000        -9314.077             0.205            0.164
Chain 1:   8100        -8427.142             0.149            0.140
Chain 1:   8200        -8374.399             0.124            0.105
Chain 1:   8300        -8888.663             0.122            0.105
Chain 1:   8400        -8294.568             0.115            0.098
Chain 1:   8500        -8228.273             0.079            0.081
Chain 1:   8600        -8783.641             0.076            0.072
Chain 1:   8700        -8256.607             0.066            0.064
Chain 1:   8800        -8366.207             0.049            0.063
Chain 1:   8900        -8997.440             0.054            0.064
Chain 1:   9000        -9100.050             0.047            0.063
Chain 1:   9100        -8196.839             0.048            0.063
Chain 1:   9200        -9469.363             0.060            0.064
Chain 1:   9300       -11491.522             0.072            0.070
Chain 1:   9400        -9474.795             0.086            0.070
Chain 1:   9500        -8192.747             0.101            0.110
Chain 1:   9600        -8485.454             0.098            0.110
Chain 1:   9700       -10224.911             0.109            0.134
Chain 1:   9800        -8625.706             0.126            0.156
Chain 1:   9900       -10439.638             0.136            0.170
Chain 1:   10000        -8212.970             0.162            0.174
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00148 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57061.758             1.000            1.000
Chain 1:    200       -17551.638             1.626            2.251
Chain 1:    300        -8790.524             1.416            1.000
Chain 1:    400        -8435.653             1.072            1.000
Chain 1:    500        -7883.044             0.872            0.997
Chain 1:    600        -8221.105             0.734            0.997
Chain 1:    700        -8529.890             0.634            0.070
Chain 1:    800        -8203.487             0.560            0.070
Chain 1:    900        -7867.771             0.502            0.043
Chain 1:   1000        -7669.756             0.455            0.043
Chain 1:   1100        -7745.549             0.356            0.042
Chain 1:   1200        -7791.927             0.131            0.041
Chain 1:   1300        -7734.418             0.032            0.040
Chain 1:   1400        -7747.357             0.028            0.036
Chain 1:   1500        -7625.081             0.023            0.026
Chain 1:   1600        -7819.710             0.021            0.025
Chain 1:   1700        -7576.292             0.021            0.025
Chain 1:   1800        -7693.236             0.018            0.016
Chain 1:   1900        -7602.149             0.015            0.015
Chain 1:   2000        -7697.096             0.014            0.012
Chain 1:   2100        -7701.890             0.013            0.012
Chain 1:   2200        -7736.349             0.013            0.012
Chain 1:   2300        -7661.847             0.013            0.012
Chain 1:   2400        -7705.909             0.013            0.012
Chain 1:   2500        -7632.163             0.013            0.012
Chain 1:   2600        -7576.980             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003774 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86280.689             1.000            1.000
Chain 1:    200       -13577.889             3.177            5.354
Chain 1:    300        -9933.346             2.240            1.000
Chain 1:    400       -10941.635             1.703            1.000
Chain 1:    500        -8773.819             1.412            0.367
Chain 1:    600        -8376.323             1.185            0.367
Chain 1:    700        -8411.977             1.016            0.247
Chain 1:    800        -9084.143             0.898            0.247
Chain 1:    900        -8785.854             0.802            0.092
Chain 1:   1000        -8599.672             0.724            0.092
Chain 1:   1100        -8658.429             0.625            0.074   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8451.607             0.092            0.047
Chain 1:   1300        -8618.985             0.057            0.034
Chain 1:   1400        -8626.294             0.048            0.024
Chain 1:   1500        -8492.672             0.025            0.022
Chain 1:   1600        -8604.096             0.021            0.019
Chain 1:   1700        -8685.989             0.022            0.019
Chain 1:   1800        -8268.670             0.020            0.019
Chain 1:   1900        -8366.680             0.017            0.016
Chain 1:   2000        -8340.547             0.015            0.013
Chain 1:   2100        -8464.371             0.016            0.015
Chain 1:   2200        -8279.353             0.016            0.015
Chain 1:   2300        -8361.296             0.015            0.013
Chain 1:   2400        -8430.866             0.016            0.013
Chain 1:   2500        -8376.761             0.015            0.012
Chain 1:   2600        -8376.972             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003626 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8397198.385             1.000            1.000
Chain 1:    200     -1587771.559             2.644            4.289
Chain 1:    300      -891957.847             2.023            1.000
Chain 1:    400      -457633.337             1.754            1.000
Chain 1:    500      -357787.426             1.459            0.949
Chain 1:    600      -232692.846             1.306            0.949
Chain 1:    700      -119102.098             1.255            0.949
Chain 1:    800       -86341.892             1.146            0.949
Chain 1:    900       -66735.799             1.051            0.780
Chain 1:   1000       -51578.519             0.976            0.780
Chain 1:   1100       -39088.325             0.907            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38270.842             0.481            0.379
Chain 1:   1300       -26258.726             0.448            0.379
Chain 1:   1400       -25981.175             0.355            0.320
Chain 1:   1500       -22575.716             0.342            0.320
Chain 1:   1600       -21794.320             0.292            0.294
Chain 1:   1700       -20671.878             0.202            0.294
Chain 1:   1800       -20616.877             0.164            0.151
Chain 1:   1900       -20943.125             0.136            0.054
Chain 1:   2000       -19455.809             0.114            0.054
Chain 1:   2100       -19694.293             0.084            0.036
Chain 1:   2200       -19920.417             0.083            0.036
Chain 1:   2300       -19537.869             0.039            0.020
Chain 1:   2400       -19309.965             0.039            0.020
Chain 1:   2500       -19111.767             0.025            0.016
Chain 1:   2600       -18742.204             0.023            0.016
Chain 1:   2700       -18699.222             0.018            0.012
Chain 1:   2800       -18415.961             0.019            0.015
Chain 1:   2900       -18697.163             0.019            0.015
Chain 1:   3000       -18683.468             0.012            0.012
Chain 1:   3100       -18768.439             0.011            0.012
Chain 1:   3200       -18459.164             0.012            0.015
Chain 1:   3300       -18663.844             0.011            0.012
Chain 1:   3400       -18138.731             0.012            0.015
Chain 1:   3500       -18750.619             0.015            0.015
Chain 1:   3600       -18057.294             0.017            0.015
Chain 1:   3700       -18444.052             0.018            0.017
Chain 1:   3800       -17403.725             0.023            0.021
Chain 1:   3900       -17399.830             0.021            0.021
Chain 1:   4000       -17517.172             0.022            0.021
Chain 1:   4100       -17430.918             0.022            0.021
Chain 1:   4200       -17247.152             0.021            0.021
Chain 1:   4300       -17385.592             0.021            0.021
Chain 1:   4400       -17342.419             0.018            0.011
Chain 1:   4500       -17244.905             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001365 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12552.223             1.000            1.000
Chain 1:    200        -9246.058             0.679            1.000
Chain 1:    300        -8110.645             0.499            0.358
Chain 1:    400        -8243.772             0.378            0.358
Chain 1:    500        -8203.020             0.304            0.140
Chain 1:    600        -8004.256             0.257            0.140
Chain 1:    700        -7899.759             0.222            0.025
Chain 1:    800        -7902.832             0.195            0.025
Chain 1:    900        -7843.175             0.174            0.016
Chain 1:   1000        -8032.020             0.159            0.024
Chain 1:   1100        -8045.282             0.059            0.016
Chain 1:   1200        -7918.942             0.025            0.016
Chain 1:   1300        -7886.307             0.011            0.013
Chain 1:   1400        -7894.498             0.010            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001562 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.62 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61284.086             1.000            1.000
Chain 1:    200       -18185.314             1.685            2.370
Chain 1:    300        -8849.014             1.475            1.055
Chain 1:    400        -8114.455             1.129            1.055
Chain 1:    500        -7892.527             0.909            1.000
Chain 1:    600        -8153.682             0.763            1.000
Chain 1:    700        -8259.418             0.656            0.091
Chain 1:    800        -8238.003             0.574            0.091
Chain 1:    900        -8028.932             0.513            0.032
Chain 1:   1000        -7911.399             0.463            0.032
Chain 1:   1100        -7763.978             0.365            0.028
Chain 1:   1200        -7589.370             0.130            0.026
Chain 1:   1300        -7747.881             0.027            0.023
Chain 1:   1400        -7833.473             0.019            0.020
Chain 1:   1500        -7664.664             0.018            0.020
Chain 1:   1600        -7553.692             0.017            0.019
Chain 1:   1700        -7586.840             0.016            0.019
Chain 1:   1800        -7658.332             0.016            0.019
Chain 1:   1900        -7650.842             0.014            0.015
Chain 1:   2000        -7695.901             0.013            0.015
Chain 1:   2100        -7640.567             0.012            0.011
Chain 1:   2200        -7766.318             0.011            0.011
Chain 1:   2300        -7602.511             0.011            0.011
Chain 1:   2400        -7589.541             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003786 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86137.168             1.000            1.000
Chain 1:    200       -13697.097             3.144            5.289
Chain 1:    300       -10002.260             2.219            1.000
Chain 1:    400       -11291.441             1.693            1.000
Chain 1:    500        -8790.678             1.411            0.369
Chain 1:    600        -8693.517             1.178            0.369
Chain 1:    700        -8417.138             1.014            0.284
Chain 1:    800        -8794.183             0.893            0.284
Chain 1:    900        -8715.998             0.795            0.114
Chain 1:   1000        -8383.606             0.719            0.114
Chain 1:   1100        -8756.940             0.623            0.043   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8336.269             0.100            0.043
Chain 1:   1300        -8606.054             0.066            0.043
Chain 1:   1400        -8624.033             0.055            0.040
Chain 1:   1500        -8502.256             0.028            0.033
Chain 1:   1600        -8609.347             0.028            0.033
Chain 1:   1700        -8677.992             0.025            0.031
Chain 1:   1800        -8241.077             0.026            0.031
Chain 1:   1900        -8346.243             0.027            0.031
Chain 1:   2000        -8322.769             0.023            0.014
Chain 1:   2100        -8464.973             0.020            0.014
Chain 1:   2200        -8252.276             0.018            0.014
Chain 1:   2300        -8411.693             0.017            0.014
Chain 1:   2400        -8248.454             0.018            0.017
Chain 1:   2500        -8320.446             0.018            0.017
Chain 1:   2600        -8232.115             0.018            0.017
Chain 1:   2700        -8266.167             0.017            0.017
Chain 1:   2800        -8225.842             0.013            0.013
Chain 1:   2900        -8319.720             0.012            0.011
Chain 1:   3000        -8154.611             0.014            0.017
Chain 1:   3100        -8308.749             0.014            0.019
Chain 1:   3200        -8180.324             0.013            0.016
Chain 1:   3300        -8189.094             0.012            0.011
Chain 1:   3400        -8351.768             0.011            0.011
Chain 1:   3500        -8363.568             0.011            0.011
Chain 1:   3600        -8136.686             0.012            0.016
Chain 1:   3700        -8283.577             0.014            0.018
Chain 1:   3800        -8142.976             0.015            0.018
Chain 1:   3900        -8077.230             0.015            0.018
Chain 1:   4000        -8154.537             0.014            0.017
Chain 1:   4100        -8148.580             0.012            0.016
Chain 1:   4200        -8132.976             0.011            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00469 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 46.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8422487.890             1.000            1.000
Chain 1:    200     -1588060.493             2.652            4.304
Chain 1:    300      -891263.096             2.028            1.000
Chain 1:    400      -458440.764             1.757            1.000
Chain 1:    500      -358360.735             1.462            0.944
Chain 1:    600      -233178.911             1.308            0.944
Chain 1:    700      -119347.110             1.257            0.944
Chain 1:    800       -86616.206             1.147            0.944
Chain 1:    900       -66961.804             1.052            0.782
Chain 1:   1000       -51777.920             0.976            0.782
Chain 1:   1100       -39275.413             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38455.318             0.480            0.378
Chain 1:   1300       -26420.088             0.447            0.378
Chain 1:   1400       -26142.731             0.354            0.318
Chain 1:   1500       -22733.142             0.341            0.318
Chain 1:   1600       -21951.706             0.291            0.294
Chain 1:   1700       -20825.503             0.201            0.293
Chain 1:   1800       -20770.229             0.163            0.150
Chain 1:   1900       -21096.738             0.136            0.054
Chain 1:   2000       -19607.525             0.114            0.054
Chain 1:   2100       -19845.803             0.083            0.036
Chain 1:   2200       -20072.689             0.082            0.036
Chain 1:   2300       -19689.429             0.039            0.019
Chain 1:   2400       -19461.345             0.039            0.019
Chain 1:   2500       -19263.539             0.025            0.015
Chain 1:   2600       -18893.151             0.023            0.015
Chain 1:   2700       -18849.924             0.018            0.012
Chain 1:   2800       -18566.682             0.019            0.015
Chain 1:   2900       -18848.094             0.019            0.015
Chain 1:   3000       -18834.189             0.012            0.012
Chain 1:   3100       -18919.300             0.011            0.012
Chain 1:   3200       -18609.649             0.012            0.015
Chain 1:   3300       -18814.625             0.011            0.012
Chain 1:   3400       -18289.056             0.012            0.015
Chain 1:   3500       -18901.712             0.015            0.015
Chain 1:   3600       -18207.280             0.016            0.015
Chain 1:   3700       -18594.905             0.018            0.017
Chain 1:   3800       -17553.025             0.023            0.021
Chain 1:   3900       -17549.128             0.021            0.021
Chain 1:   4000       -17666.416             0.022            0.021
Chain 1:   4100       -17580.140             0.022            0.021
Chain 1:   4200       -17395.999             0.021            0.021
Chain 1:   4300       -17534.638             0.021            0.021
Chain 1:   4400       -17491.133             0.018            0.011
Chain 1:   4500       -17393.638             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00152 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12191.565             1.000            1.000
Chain 1:    200        -9157.276             0.666            1.000
Chain 1:    300        -7864.162             0.499            0.331
Chain 1:    400        -8019.718             0.379            0.331
Chain 1:    500        -7887.424             0.306            0.164
Chain 1:    600        -7819.204             0.257            0.164
Chain 1:    700        -7734.005             0.222            0.019
Chain 1:    800        -7775.288             0.195            0.019
Chain 1:    900        -7891.163             0.175            0.017
Chain 1:   1000        -7800.429             0.158            0.017
Chain 1:   1100        -7811.081             0.058            0.015
Chain 1:   1200        -7742.494             0.026            0.012
Chain 1:   1300        -7710.550             0.010            0.011
Chain 1:   1400        -7724.722             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001568 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57546.694             1.000            1.000
Chain 1:    200       -17428.022             1.651            2.302
Chain 1:    300        -8569.674             1.445            1.034
Chain 1:    400        -8173.171             1.096            1.034
Chain 1:    500        -7928.928             0.883            1.000
Chain 1:    600        -8470.502             0.746            1.000
Chain 1:    700        -7790.778             0.652            0.087
Chain 1:    800        -8115.562             0.576            0.087
Chain 1:    900        -7945.636             0.514            0.064
Chain 1:   1000        -7724.843             0.466            0.064
Chain 1:   1100        -7786.884             0.366            0.049
Chain 1:   1200        -7567.655             0.139            0.040
Chain 1:   1300        -7780.454             0.038            0.031
Chain 1:   1400        -7909.755             0.035            0.029
Chain 1:   1500        -7568.299             0.037            0.029
Chain 1:   1600        -7579.530             0.030            0.029
Chain 1:   1700        -7512.232             0.023            0.027
Chain 1:   1800        -7553.101             0.019            0.021
Chain 1:   1900        -7611.623             0.018            0.016
Chain 1:   2000        -7574.723             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.47 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86547.392             1.000            1.000
Chain 1:    200       -13263.618             3.263            5.525
Chain 1:    300        -9665.403             2.299            1.000
Chain 1:    400       -10388.670             1.742            1.000
Chain 1:    500        -8618.114             1.435            0.372
Chain 1:    600        -8190.292             1.204            0.372
Chain 1:    700        -8145.075             1.033            0.205
Chain 1:    800        -8647.253             0.911            0.205
Chain 1:    900        -8457.769             0.812            0.070
Chain 1:   1000        -8302.433             0.733            0.070
Chain 1:   1100        -8488.195             0.635            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8058.347             0.088            0.053
Chain 1:   1300        -8366.317             0.054            0.052
Chain 1:   1400        -8375.850             0.048            0.037
Chain 1:   1500        -8257.098             0.028            0.022
Chain 1:   1600        -8362.569             0.024            0.022
Chain 1:   1700        -8448.979             0.025            0.022
Chain 1:   1800        -8049.019             0.024            0.022
Chain 1:   1900        -8148.874             0.023            0.019
Chain 1:   2000        -8119.966             0.022            0.014
Chain 1:   2100        -8239.974             0.021            0.014
Chain 1:   2200        -8026.581             0.018            0.014
Chain 1:   2300        -8179.725             0.016            0.014
Chain 1:   2400        -8061.314             0.018            0.015
Chain 1:   2500        -8124.337             0.017            0.015
Chain 1:   2600        -8145.558             0.016            0.015
Chain 1:   2700        -8064.723             0.016            0.015
Chain 1:   2800        -8038.881             0.011            0.012
Chain 1:   2900        -8094.265             0.011            0.010
Chain 1:   3000        -7978.590             0.012            0.014
Chain 1:   3100        -8116.171             0.012            0.014
Chain 1:   3200        -7996.248             0.011            0.014
Chain 1:   3300        -8017.620             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003586 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395329.879             1.000            1.000
Chain 1:    200     -1583206.273             2.651            4.303
Chain 1:    300      -890314.156             2.027            1.000
Chain 1:    400      -457392.847             1.757            1.000
Chain 1:    500      -357994.273             1.461            0.946
Chain 1:    600      -233001.741             1.307            0.946
Chain 1:    700      -119131.127             1.257            0.946
Chain 1:    800       -86275.250             1.147            0.946
Chain 1:    900       -66599.842             1.053            0.778
Chain 1:   1000       -51369.396             0.977            0.778
Chain 1:   1100       -38823.677             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37995.103             0.481            0.381
Chain 1:   1300       -25939.001             0.450            0.381
Chain 1:   1400       -25654.694             0.356            0.323
Chain 1:   1500       -22238.723             0.344            0.323
Chain 1:   1600       -21453.466             0.294            0.296
Chain 1:   1700       -20326.452             0.204            0.295
Chain 1:   1800       -20270.051             0.166            0.154
Chain 1:   1900       -20595.955             0.138            0.055
Chain 1:   2000       -19107.048             0.116            0.055
Chain 1:   2100       -19345.487             0.085            0.037
Chain 1:   2200       -19571.761             0.084            0.037
Chain 1:   2300       -19189.160             0.040            0.020
Chain 1:   2400       -18961.347             0.040            0.020
Chain 1:   2500       -18763.322             0.025            0.016
Chain 1:   2600       -18393.882             0.024            0.016
Chain 1:   2700       -18350.921             0.019            0.012
Chain 1:   2800       -18067.903             0.020            0.016
Chain 1:   2900       -18349.048             0.020            0.015
Chain 1:   3000       -18335.261             0.012            0.012
Chain 1:   3100       -18420.198             0.011            0.012
Chain 1:   3200       -18111.098             0.012            0.015
Chain 1:   3300       -18315.637             0.011            0.012
Chain 1:   3400       -17790.917             0.013            0.015
Chain 1:   3500       -18402.265             0.015            0.016
Chain 1:   3600       -17709.649             0.017            0.016
Chain 1:   3700       -18095.957             0.019            0.017
Chain 1:   3800       -17056.718             0.023            0.021
Chain 1:   3900       -17052.879             0.022            0.021
Chain 1:   4000       -17170.189             0.022            0.021
Chain 1:   4100       -17084.002             0.022            0.021
Chain 1:   4200       -16900.462             0.022            0.021
Chain 1:   4300       -17038.717             0.022            0.021
Chain 1:   4400       -16995.756             0.019            0.011
Chain 1:   4500       -16898.304             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001214 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48709.335             1.000            1.000
Chain 1:    200       -19936.501             1.222            1.443
Chain 1:    300       -22602.431             0.854            1.000
Chain 1:    400       -13033.231             0.824            1.000
Chain 1:    500       -16157.296             0.698            0.734
Chain 1:    600       -12547.288             0.629            0.734
Chain 1:    700       -14409.647             0.558            0.288
Chain 1:    800       -11349.027             0.522            0.288
Chain 1:    900       -13613.702             0.482            0.270
Chain 1:   1000       -35815.712             0.496            0.288
Chain 1:   1100       -10293.162             0.644            0.288   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -10075.262             0.502            0.270   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -10187.844             0.491            0.270
Chain 1:   1400       -11022.474             0.425            0.193
Chain 1:   1500       -10426.936             0.412            0.166
Chain 1:   1600       -11847.739             0.395            0.129
Chain 1:   1700       -17664.924             0.415            0.166
Chain 1:   1800       -10480.985             0.457            0.166
Chain 1:   1900       -12698.695             0.457            0.175
Chain 1:   2000       -11497.931             0.406            0.120
Chain 1:   2100        -9118.725             0.184            0.120
Chain 1:   2200       -11130.044             0.200            0.175
Chain 1:   2300        -8977.569             0.223            0.181
Chain 1:   2400        -9677.850             0.222            0.181
Chain 1:   2500       -15851.495             0.256            0.240
Chain 1:   2600        -9441.931             0.312            0.261
Chain 1:   2700       -18800.744             0.328            0.261
Chain 1:   2800        -9336.884             0.361            0.261
Chain 1:   2900        -9310.217             0.344            0.261
Chain 1:   3000       -10705.072             0.347            0.261
Chain 1:   3100       -15465.129             0.351            0.308
Chain 1:   3200       -10010.859             0.388            0.389
Chain 1:   3300        -9699.057             0.367            0.389
Chain 1:   3400        -9110.956             0.366            0.389
Chain 1:   3500        -9030.492             0.328            0.308
Chain 1:   3600        -9841.130             0.269            0.130
Chain 1:   3700       -17267.523             0.262            0.130
Chain 1:   3800        -9017.478             0.252            0.130
Chain 1:   3900       -10139.586             0.263            0.130
Chain 1:   4000        -9406.533             0.257            0.111
Chain 1:   4100        -8748.767             0.234            0.082
Chain 1:   4200       -14030.056             0.217            0.082
Chain 1:   4300        -9594.096             0.260            0.111
Chain 1:   4400        -9295.561             0.257            0.111
Chain 1:   4500        -8733.596             0.263            0.111
Chain 1:   4600        -9399.488             0.261            0.111
Chain 1:   4700       -14046.657             0.252            0.111
Chain 1:   4800        -8374.012             0.228            0.111
Chain 1:   4900        -8666.908             0.220            0.078
Chain 1:   5000       -14384.959             0.252            0.331
Chain 1:   5100        -8398.483             0.316            0.376
Chain 1:   5200        -8638.921             0.281            0.331
Chain 1:   5300       -12682.527             0.267            0.319
Chain 1:   5400        -8155.472             0.319            0.331
Chain 1:   5500        -8338.894             0.315            0.331
Chain 1:   5600       -10275.165             0.326            0.331
Chain 1:   5700       -13658.874             0.318            0.319
Chain 1:   5800       -11696.395             0.267            0.248
Chain 1:   5900        -9442.899             0.288            0.248
Chain 1:   6000        -8827.468             0.255            0.239
Chain 1:   6100        -8927.050             0.185            0.188
Chain 1:   6200        -9003.677             0.183            0.188
Chain 1:   6300        -8500.388             0.157            0.168
Chain 1:   6400        -9048.336             0.107            0.070
Chain 1:   6500       -11341.448             0.125            0.168
Chain 1:   6600       -12006.704             0.112            0.070
Chain 1:   6700        -8238.956             0.133            0.070
Chain 1:   6800        -8967.259             0.124            0.070
Chain 1:   6900        -9243.107             0.104            0.061
Chain 1:   7000        -8042.443             0.111            0.061
Chain 1:   7100       -12165.961             0.144            0.081
Chain 1:   7200        -8113.430             0.193            0.149
Chain 1:   7300       -10942.517             0.213            0.202
Chain 1:   7400       -11763.125             0.214            0.202
Chain 1:   7500       -11581.136             0.196            0.149
Chain 1:   7600        -8071.482             0.233            0.259
Chain 1:   7700        -8150.910             0.189            0.149
Chain 1:   7800       -13256.790             0.219            0.259
Chain 1:   7900        -8270.891             0.276            0.339
Chain 1:   8000        -8695.544             0.266            0.339
Chain 1:   8100       -10412.584             0.249            0.259
Chain 1:   8200        -9438.803             0.209            0.165
Chain 1:   8300        -8171.384             0.199            0.155
Chain 1:   8400       -11849.258             0.223            0.165
Chain 1:   8500        -8810.118             0.256            0.310
Chain 1:   8600        -8398.173             0.217            0.165
Chain 1:   8700       -11100.249             0.241            0.243
Chain 1:   8800        -8728.842             0.229            0.243
Chain 1:   8900       -12003.454             0.196            0.243
Chain 1:   9000        -9824.770             0.214            0.243
Chain 1:   9100        -9103.545             0.205            0.243
Chain 1:   9200        -8317.799             0.204            0.243
Chain 1:   9300        -8441.153             0.190            0.243
Chain 1:   9400        -8207.047             0.162            0.222
Chain 1:   9500        -8229.870             0.128            0.094
Chain 1:   9600        -8066.273             0.125            0.094
Chain 1:   9700       -10605.394             0.125            0.094
Chain 1:   9800        -9109.768             0.114            0.094
Chain 1:   9900        -8840.574             0.090            0.079
Chain 1:   10000        -8206.053             0.075            0.077
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001518 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.18 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57814.594             1.000            1.000
Chain 1:    200       -17437.600             1.658            2.316
Chain 1:    300        -8556.337             1.451            1.038
Chain 1:    400        -8151.287             1.101            1.038
Chain 1:    500        -8024.201             0.884            1.000
Chain 1:    600        -8791.660             0.751            1.000
Chain 1:    700        -8099.906             0.656            0.087
Chain 1:    800        -8129.136             0.574            0.087
Chain 1:    900        -7752.219             0.516            0.085
Chain 1:   1000        -7722.681             0.465            0.085
Chain 1:   1100        -7645.676             0.366            0.050
Chain 1:   1200        -7693.908             0.135            0.049
Chain 1:   1300        -7516.688             0.033            0.024
Chain 1:   1400        -7607.361             0.030            0.016
Chain 1:   1500        -7551.250             0.029            0.012
Chain 1:   1600        -7715.529             0.022            0.012
Chain 1:   1700        -7454.002             0.017            0.012
Chain 1:   1800        -7522.976             0.018            0.012
Chain 1:   1900        -7529.198             0.013            0.010
Chain 1:   2000        -7549.859             0.013            0.010
Chain 1:   2100        -7542.074             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.007321 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 73.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86199.903             1.000            1.000
Chain 1:    200       -13288.435             3.243            5.487
Chain 1:    300        -9672.472             2.287            1.000
Chain 1:    400       -10419.686             1.733            1.000
Chain 1:    500        -8645.612             1.428            0.374
Chain 1:    600        -8161.201             1.199            0.374
Chain 1:    700        -8375.368             1.032            0.205
Chain 1:    800        -8949.834             0.911            0.205
Chain 1:    900        -8501.053             0.815            0.072
Chain 1:   1000        -8317.443             0.736            0.072
Chain 1:   1100        -8467.220             0.638            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8201.542             0.092            0.059
Chain 1:   1300        -8347.582             0.057            0.053
Chain 1:   1400        -8360.189             0.050            0.032
Chain 1:   1500        -8252.471             0.031            0.026
Chain 1:   1600        -8359.854             0.026            0.022
Chain 1:   1700        -8445.460             0.024            0.018
Chain 1:   1800        -8039.786             0.023            0.018
Chain 1:   1900        -8136.404             0.019            0.017
Chain 1:   2000        -8108.487             0.017            0.013
Chain 1:   2100        -8229.329             0.017            0.013
Chain 1:   2200        -8046.965             0.016            0.013
Chain 1:   2300        -8175.859             0.016            0.013
Chain 1:   2400        -8186.016             0.016            0.013
Chain 1:   2500        -8148.034             0.015            0.013
Chain 1:   2600        -8146.891             0.014            0.012
Chain 1:   2700        -8061.829             0.014            0.012
Chain 1:   2800        -8026.656             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003775 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8402794.916             1.000            1.000
Chain 1:    200     -1584129.087             2.652            4.304
Chain 1:    300      -890576.516             2.028            1.000
Chain 1:    400      -457837.285             1.757            1.000
Chain 1:    500      -358090.616             1.461            0.945
Chain 1:    600      -233071.589             1.307            0.945
Chain 1:    700      -119169.203             1.257            0.945
Chain 1:    800       -86314.802             1.147            0.945
Chain 1:    900       -66633.653             1.053            0.779
Chain 1:   1000       -51404.801             0.977            0.779
Chain 1:   1100       -38862.387             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38035.487             0.481            0.381
Chain 1:   1300       -25979.537             0.450            0.381
Chain 1:   1400       -25696.518             0.356            0.323
Chain 1:   1500       -22280.333             0.344            0.323
Chain 1:   1600       -21495.535             0.294            0.296
Chain 1:   1700       -20368.153             0.204            0.295
Chain 1:   1800       -20311.847             0.166            0.153
Chain 1:   1900       -20637.895             0.138            0.055
Chain 1:   2000       -19148.680             0.116            0.055
Chain 1:   2100       -19387.083             0.085            0.037
Chain 1:   2200       -19613.505             0.084            0.037
Chain 1:   2300       -19230.762             0.040            0.020
Chain 1:   2400       -19002.893             0.040            0.020
Chain 1:   2500       -18804.910             0.025            0.016
Chain 1:   2600       -18435.242             0.024            0.016
Chain 1:   2700       -18392.253             0.018            0.012
Chain 1:   2800       -18109.152             0.020            0.016
Chain 1:   2900       -18390.392             0.020            0.015
Chain 1:   3000       -18376.593             0.012            0.012
Chain 1:   3100       -18461.534             0.011            0.012
Chain 1:   3200       -18152.323             0.012            0.015
Chain 1:   3300       -18356.973             0.011            0.012
Chain 1:   3400       -17832.050             0.013            0.015
Chain 1:   3500       -18443.671             0.015            0.016
Chain 1:   3600       -17750.730             0.017            0.016
Chain 1:   3700       -18137.265             0.019            0.017
Chain 1:   3800       -17097.488             0.023            0.021
Chain 1:   3900       -17093.649             0.022            0.021
Chain 1:   4000       -17210.959             0.022            0.021
Chain 1:   4100       -17124.726             0.022            0.021
Chain 1:   4200       -16941.088             0.022            0.021
Chain 1:   4300       -17079.404             0.021            0.021
Chain 1:   4400       -17036.351             0.019            0.011
Chain 1:   4500       -16938.891             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002315 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13069.512             1.000            1.000
Chain 1:    200        -9791.011             0.667            1.000
Chain 1:    300        -8297.521             0.505            0.335
Chain 1:    400        -8366.793             0.381            0.335
Chain 1:    500        -8317.474             0.306            0.180
Chain 1:    600        -8177.212             0.258            0.180
Chain 1:    700        -8030.747             0.223            0.018
Chain 1:    800        -8049.710             0.196            0.018
Chain 1:    900        -8142.982             0.175            0.017
Chain 1:   1000        -8184.264             0.158            0.017
Chain 1:   1100        -8184.590             0.058            0.011
Chain 1:   1200        -8096.394             0.026            0.011
Chain 1:   1300        -8023.328             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001616 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47914.764             1.000            1.000
Chain 1:    200       -16261.526             1.473            1.947
Chain 1:    300        -8778.205             1.266            1.000
Chain 1:    400        -8064.401             0.972            1.000
Chain 1:    500        -9238.238             0.803            0.852
Chain 1:    600        -8331.304             0.687            0.852
Chain 1:    700        -8572.809             0.593            0.127
Chain 1:    800        -8361.854             0.522            0.127
Chain 1:    900        -8057.816             0.468            0.109
Chain 1:   1000        -7770.090             0.425            0.109
Chain 1:   1100        -7843.764             0.326            0.089
Chain 1:   1200        -7613.352             0.134            0.038
Chain 1:   1300        -7906.303             0.053            0.037
Chain 1:   1400        -7639.960             0.048            0.037
Chain 1:   1500        -7536.367             0.036            0.035
Chain 1:   1600        -7819.149             0.029            0.035
Chain 1:   1700        -7618.150             0.029            0.035
Chain 1:   1800        -7537.658             0.027            0.035
Chain 1:   1900        -7579.534             0.024            0.030
Chain 1:   2000        -7631.801             0.021            0.026
Chain 1:   2100        -7572.663             0.021            0.026
Chain 1:   2200        -7785.094             0.021            0.026
Chain 1:   2300        -7606.427             0.019            0.023
Chain 1:   2400        -7620.074             0.016            0.014
Chain 1:   2500        -7654.103             0.015            0.011
Chain 1:   2600        -7514.677             0.013            0.011
Chain 1:   2700        -7417.912             0.012            0.011
Chain 1:   2800        -7654.030             0.014            0.013
Chain 1:   2900        -7363.981             0.017            0.019
Chain 1:   3000        -7514.891             0.019            0.020
Chain 1:   3100        -7518.357             0.018            0.020
Chain 1:   3200        -7723.884             0.018            0.020
Chain 1:   3300        -7425.587             0.020            0.020
Chain 1:   3400        -7672.931             0.023            0.027
Chain 1:   3500        -7419.741             0.026            0.031
Chain 1:   3600        -7485.804             0.025            0.031
Chain 1:   3700        -7436.721             0.024            0.031
Chain 1:   3800        -7408.571             0.021            0.027
Chain 1:   3900        -7388.229             0.018            0.020
Chain 1:   4000        -7384.182             0.016            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00395 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86565.381             1.000            1.000
Chain 1:    200       -14031.056             3.085            5.170
Chain 1:    300       -10239.349             2.180            1.000
Chain 1:    400       -12022.674             1.672            1.000
Chain 1:    500        -8694.706             1.414            0.383
Chain 1:    600        -8686.338             1.179            0.383
Chain 1:    700        -9389.680             1.021            0.370
Chain 1:    800        -9180.529             0.896            0.370
Chain 1:    900        -9057.093             0.798            0.148
Chain 1:   1000        -8631.515             0.723            0.148
Chain 1:   1100        -8882.261             0.626            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8520.576             0.113            0.049
Chain 1:   1300        -8852.583             0.080            0.042
Chain 1:   1400        -8789.749             0.066            0.038
Chain 1:   1500        -8726.097             0.028            0.028
Chain 1:   1600        -8775.135             0.029            0.028
Chain 1:   1700        -8844.881             0.022            0.023
Chain 1:   1800        -8382.784             0.025            0.028
Chain 1:   1900        -8504.475             0.025            0.028
Chain 1:   2000        -8520.596             0.021            0.014
Chain 1:   2100        -8611.320             0.019            0.011
Chain 1:   2200        -8392.474             0.017            0.011
Chain 1:   2300        -8562.513             0.016            0.011
Chain 1:   2400        -8399.359             0.017            0.014
Chain 1:   2500        -8473.877             0.017            0.014
Chain 1:   2600        -8384.532             0.017            0.014
Chain 1:   2700        -8418.494             0.017            0.014
Chain 1:   2800        -8369.571             0.012            0.011
Chain 1:   2900        -8484.257             0.012            0.011
Chain 1:   3000        -8398.028             0.013            0.011
Chain 1:   3100        -8361.802             0.012            0.011
Chain 1:   3200        -8333.765             0.010            0.010
Chain 1:   3300        -8593.562             0.011            0.010
Chain 1:   3400        -8634.679             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003594 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8383239.090             1.000            1.000
Chain 1:    200     -1582100.774             2.649            4.299
Chain 1:    300      -891682.745             2.024            1.000
Chain 1:    400      -458816.068             1.754            1.000
Chain 1:    500      -359615.459             1.458            0.943
Chain 1:    600      -234410.151             1.304            0.943
Chain 1:    700      -120229.208             1.254            0.943
Chain 1:    800       -87333.258             1.144            0.943
Chain 1:    900       -67596.590             1.049            0.774
Chain 1:   1000       -52342.007             0.974            0.774
Chain 1:   1100       -39761.333             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38938.013             0.478            0.377
Chain 1:   1300       -26822.811             0.445            0.377
Chain 1:   1400       -26538.905             0.352            0.316
Chain 1:   1500       -23106.993             0.339            0.316
Chain 1:   1600       -22318.806             0.289            0.292
Chain 1:   1700       -21183.261             0.200            0.291
Chain 1:   1800       -21125.773             0.162            0.149
Chain 1:   1900       -21452.742             0.135            0.054
Chain 1:   2000       -19957.391             0.113            0.054
Chain 1:   2100       -20196.201             0.083            0.035
Chain 1:   2200       -20424.001             0.082            0.035
Chain 1:   2300       -20039.784             0.038            0.019
Chain 1:   2400       -19811.475             0.038            0.019
Chain 1:   2500       -19613.743             0.025            0.015
Chain 1:   2600       -19242.865             0.023            0.015
Chain 1:   2700       -19199.484             0.018            0.012
Chain 1:   2800       -18916.091             0.019            0.015
Chain 1:   2900       -19197.837             0.019            0.015
Chain 1:   3000       -19183.880             0.012            0.012
Chain 1:   3100       -19269.013             0.011            0.012
Chain 1:   3200       -18959.093             0.011            0.015
Chain 1:   3300       -19164.275             0.010            0.012
Chain 1:   3400       -18638.211             0.012            0.015
Chain 1:   3500       -19251.701             0.014            0.015
Chain 1:   3600       -18556.296             0.016            0.015
Chain 1:   3700       -18944.721             0.018            0.016
Chain 1:   3800       -17901.231             0.022            0.021
Chain 1:   3900       -17897.321             0.021            0.021
Chain 1:   4000       -18014.596             0.021            0.021
Chain 1:   4100       -17928.233             0.021            0.021
Chain 1:   4200       -17743.743             0.021            0.021
Chain 1:   4300       -17882.624             0.021            0.021
Chain 1:   4400       -17838.883             0.018            0.010
Chain 1:   4500       -17741.334             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001406 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48751.381             1.000            1.000
Chain 1:    200       -43165.946             0.565            1.000
Chain 1:    300       -13548.290             1.105            1.000
Chain 1:    400       -13657.551             0.831            1.000
Chain 1:    500       -14722.578             0.679            0.129
Chain 1:    600       -22468.542             0.623            0.345
Chain 1:    700       -18800.053             0.562            0.195
Chain 1:    800       -13605.782             0.540            0.345
Chain 1:    900       -12166.938             0.493            0.195
Chain 1:   1000       -18803.815             0.479            0.345
Chain 1:   1100       -24454.752             0.402            0.231
Chain 1:   1200       -13404.967             0.471            0.345
Chain 1:   1300        -9644.174             0.292            0.345
Chain 1:   1400       -10503.300             0.299            0.345
Chain 1:   1500       -14593.205             0.320            0.345
Chain 1:   1600       -24077.603             0.325            0.353
Chain 1:   1700       -13484.548             0.384            0.382
Chain 1:   1800        -9446.179             0.389            0.390
Chain 1:   1900       -26966.534             0.442            0.394
Chain 1:   2000       -17717.382             0.459            0.428
Chain 1:   2100        -9393.959             0.524            0.522   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2200        -9159.762             0.444            0.428
Chain 1:   2300        -9113.265             0.406            0.428
Chain 1:   2400        -9680.672             0.403            0.428
Chain 1:   2500       -15921.730             0.415            0.428
Chain 1:   2600        -9019.725             0.452            0.522   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2700        -9333.905             0.377            0.428
Chain 1:   2800       -10121.104             0.342            0.392
Chain 1:   2900        -8599.098             0.294            0.177
Chain 1:   3000       -11323.429             0.266            0.177
Chain 1:   3100        -9180.982             0.201            0.177
Chain 1:   3200       -15329.010             0.238            0.233
Chain 1:   3300        -8836.479             0.311            0.241
Chain 1:   3400       -12938.560             0.337            0.317
Chain 1:   3500        -8882.015             0.344            0.317
Chain 1:   3600       -12813.704             0.298            0.307
Chain 1:   3700        -8773.094             0.341            0.317
Chain 1:   3800        -9025.472             0.336            0.317
Chain 1:   3900        -8721.414             0.321            0.317
Chain 1:   4000        -8527.949             0.300            0.317
Chain 1:   4100        -8861.984             0.280            0.317
Chain 1:   4200       -11816.617             0.265            0.307
Chain 1:   4300        -8555.423             0.230            0.307
Chain 1:   4400        -8368.769             0.200            0.250
Chain 1:   4500        -8808.925             0.159            0.050
Chain 1:   4600       -10668.577             0.146            0.050
Chain 1:   4700       -11121.058             0.104            0.041
Chain 1:   4800        -8666.751             0.130            0.050
Chain 1:   4900       -10409.771             0.143            0.167
Chain 1:   5000       -13146.341             0.161            0.174
Chain 1:   5100        -8505.804             0.212            0.208
Chain 1:   5200        -8674.315             0.189            0.174
Chain 1:   5300        -9247.009             0.157            0.167
Chain 1:   5400        -8390.509             0.165            0.167
Chain 1:   5500        -8419.910             0.161            0.167
Chain 1:   5600       -13933.899             0.183            0.167
Chain 1:   5700       -15938.682             0.191            0.167
Chain 1:   5800        -8469.774             0.251            0.167
Chain 1:   5900        -8326.615             0.236            0.126
Chain 1:   6000        -8774.847             0.220            0.102
Chain 1:   6100       -12601.391             0.196            0.102
Chain 1:   6200        -8266.494             0.247            0.126
Chain 1:   6300        -8128.969             0.242            0.126
Chain 1:   6400        -9397.858             0.246            0.135
Chain 1:   6500        -9951.148             0.251            0.135
Chain 1:   6600       -11639.508             0.226            0.135
Chain 1:   6700       -12099.680             0.217            0.135
Chain 1:   6800       -12503.605             0.132            0.056
Chain 1:   6900        -8286.605             0.181            0.135
Chain 1:   7000        -8208.931             0.177            0.135
Chain 1:   7100        -8080.253             0.148            0.056
Chain 1:   7200        -8966.704             0.106            0.056
Chain 1:   7300       -10394.674             0.118            0.099
Chain 1:   7400        -8043.474             0.133            0.099
Chain 1:   7500       -11262.311             0.156            0.137
Chain 1:   7600        -8544.018             0.174            0.137
Chain 1:   7700        -8888.607             0.174            0.137
Chain 1:   7800        -8890.223             0.171            0.137
Chain 1:   7900        -8156.859             0.129            0.099
Chain 1:   8000        -9000.205             0.137            0.099
Chain 1:   8100       -11797.734             0.159            0.137
Chain 1:   8200        -9800.094             0.170            0.204
Chain 1:   8300        -8488.399             0.171            0.204
Chain 1:   8400       -11313.826             0.167            0.204
Chain 1:   8500        -9001.092             0.164            0.204
Chain 1:   8600        -8509.412             0.138            0.155
Chain 1:   8700        -8603.908             0.135            0.155
Chain 1:   8800        -8019.142             0.143            0.155
Chain 1:   8900        -8750.545             0.142            0.155
Chain 1:   9000        -9444.885             0.140            0.155
Chain 1:   9100       -10396.932             0.126            0.092
Chain 1:   9200        -9150.901             0.119            0.092
Chain 1:   9300        -8165.028             0.115            0.092
Chain 1:   9400       -11632.022             0.120            0.092
Chain 1:   9500        -8056.126             0.139            0.092
Chain 1:   9600       -10153.521             0.154            0.121
Chain 1:   9700        -8294.541             0.175            0.136
Chain 1:   9800        -9175.377             0.177            0.136
Chain 1:   9900       -10844.744             0.184            0.154
Chain 1:   10000        -7977.418             0.213            0.207
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001432 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.32 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61366.636             1.000            1.000
Chain 1:    200       -17508.881             1.752            2.505
Chain 1:    300        -8682.101             1.507            1.017
Chain 1:    400        -8234.855             1.144            1.017
Chain 1:    500        -8285.868             0.916            1.000
Chain 1:    600        -8302.789             0.764            1.000
Chain 1:    700        -7715.315             0.666            0.076
Chain 1:    800        -7692.461             0.583            0.076
Chain 1:    900        -7587.045             0.520            0.054
Chain 1:   1000        -7727.959             0.470            0.054
Chain 1:   1100        -7636.083             0.371            0.018
Chain 1:   1200        -7514.908             0.122            0.016
Chain 1:   1300        -7619.194             0.022            0.014
Chain 1:   1400        -7565.448             0.017            0.014
Chain 1:   1500        -7555.319             0.016            0.014
Chain 1:   1600        -7457.783             0.017            0.014
Chain 1:   1700        -7451.660             0.010            0.013   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003173 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85889.717             1.000            1.000
Chain 1:    200       -13197.850             3.254            5.508
Chain 1:    300        -9665.034             2.291            1.000
Chain 1:    400       -10582.661             1.740            1.000
Chain 1:    500        -8605.206             1.438            0.366
Chain 1:    600        -8218.856             1.206            0.366
Chain 1:    700        -8493.197             1.038            0.230
Chain 1:    800        -8792.936             0.913            0.230
Chain 1:    900        -8543.559             0.815            0.087
Chain 1:   1000        -8226.733             0.737            0.087
Chain 1:   1100        -8571.430             0.641            0.047   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8244.676             0.094            0.040
Chain 1:   1300        -8241.383             0.058            0.040
Chain 1:   1400        -8236.586             0.049            0.039
Chain 1:   1500        -8268.233             0.027            0.034
Chain 1:   1600        -8277.136             0.022            0.032
Chain 1:   1700        -8204.953             0.020            0.029
Chain 1:   1800        -8091.785             0.018            0.014
Chain 1:   1900        -8208.456             0.016            0.014
Chain 1:   2000        -8168.777             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003326 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8420048.722             1.000            1.000
Chain 1:    200     -1586416.214             2.654            4.308
Chain 1:    300      -890579.532             2.030            1.000
Chain 1:    400      -457413.130             1.759            1.000
Chain 1:    500      -357410.656             1.463            0.947
Chain 1:    600      -232361.603             1.309            0.947
Chain 1:    700      -118707.468             1.259            0.947
Chain 1:    800       -85976.760             1.149            0.947
Chain 1:    900       -66343.350             1.054            0.781
Chain 1:   1000       -51165.234             0.978            0.781
Chain 1:   1100       -38672.783             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37847.512             0.482            0.381
Chain 1:   1300       -25840.448             0.451            0.381
Chain 1:   1400       -25561.024             0.357            0.323
Chain 1:   1500       -22158.760             0.344            0.323
Chain 1:   1600       -21377.944             0.294            0.297
Chain 1:   1700       -20256.313             0.204            0.296
Chain 1:   1800       -20201.423             0.166            0.154
Chain 1:   1900       -20526.961             0.138            0.055
Chain 1:   2000       -19041.982             0.116            0.055
Chain 1:   2100       -19279.955             0.085            0.037
Chain 1:   2200       -19505.694             0.084            0.037
Chain 1:   2300       -19123.730             0.040            0.020
Chain 1:   2400       -18896.082             0.040            0.020
Chain 1:   2500       -18698.089             0.025            0.016
Chain 1:   2600       -18328.844             0.024            0.016
Chain 1:   2700       -18286.071             0.019            0.012
Chain 1:   2800       -18003.151             0.020            0.016
Chain 1:   2900       -18284.111             0.020            0.015
Chain 1:   3000       -18270.316             0.012            0.012
Chain 1:   3100       -18355.197             0.011            0.012
Chain 1:   3200       -18046.294             0.012            0.015
Chain 1:   3300       -18250.733             0.011            0.012
Chain 1:   3400       -17726.362             0.013            0.015
Chain 1:   3500       -18337.124             0.015            0.016
Chain 1:   3600       -17645.285             0.017            0.016
Chain 1:   3700       -18030.922             0.019            0.017
Chain 1:   3800       -16992.918             0.023            0.021
Chain 1:   3900       -16989.135             0.022            0.021
Chain 1:   4000       -17106.424             0.022            0.021
Chain 1:   4100       -17020.263             0.023            0.021
Chain 1:   4200       -16837.061             0.022            0.021
Chain 1:   4300       -16975.064             0.022            0.021
Chain 1:   4400       -16932.274             0.019            0.011
Chain 1:   4500       -16834.906             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001573 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48741.815             1.000            1.000
Chain 1:    200       -20197.516             1.207            1.413
Chain 1:    300       -16136.888             0.888            1.000
Chain 1:    400       -11728.498             0.760            1.000
Chain 1:    500       -12049.025             0.613            0.376
Chain 1:    600       -15348.843             0.547            0.376
Chain 1:    700       -15183.772             0.470            0.252
Chain 1:    800       -23257.509             0.455            0.347
Chain 1:    900       -10857.536             0.531            0.347
Chain 1:   1000       -10434.785             0.482            0.347
Chain 1:   1100       -10188.789             0.385            0.252
Chain 1:   1200       -19699.477             0.292            0.252
Chain 1:   1300       -10251.410             0.359            0.347
Chain 1:   1400       -10424.226             0.323            0.215
Chain 1:   1500       -10255.518             0.322            0.215
Chain 1:   1600       -13442.469             0.324            0.237
Chain 1:   1700       -10281.042             0.354            0.308
Chain 1:   1800       -20627.163             0.369            0.308
Chain 1:   1900        -9550.276             0.371            0.308
Chain 1:   2000       -18805.778             0.416            0.483
Chain 1:   2100       -17194.591             0.423            0.483
Chain 1:   2200       -13373.804             0.403            0.308
Chain 1:   2300       -11071.532             0.332            0.286
Chain 1:   2400        -9223.285             0.350            0.286
Chain 1:   2500       -10224.680             0.358            0.286
Chain 1:   2600       -10239.116             0.335            0.286
Chain 1:   2700        -9712.727             0.309            0.208
Chain 1:   2800       -10866.265             0.270            0.200
Chain 1:   2900        -9744.878             0.165            0.115
Chain 1:   3000       -15087.671             0.152            0.115
Chain 1:   3100        -9034.820             0.209            0.200
Chain 1:   3200        -8767.359             0.184            0.115
Chain 1:   3300        -9429.884             0.170            0.106
Chain 1:   3400       -13344.471             0.179            0.106
Chain 1:   3500        -8882.317             0.220            0.115
Chain 1:   3600        -8936.273             0.220            0.115
Chain 1:   3700       -10004.372             0.225            0.115
Chain 1:   3800        -8671.892             0.230            0.154
Chain 1:   3900       -13254.835             0.253            0.293
Chain 1:   4000        -9932.630             0.251            0.293
Chain 1:   4100        -8873.896             0.196            0.154
Chain 1:   4200        -9696.013             0.202            0.154
Chain 1:   4300       -10462.528             0.202            0.154
Chain 1:   4400       -13791.659             0.197            0.154
Chain 1:   4500        -8959.345             0.200            0.154
Chain 1:   4600        -8694.758             0.203            0.154
Chain 1:   4700       -14263.800             0.231            0.241
Chain 1:   4800        -9590.516             0.265            0.334
Chain 1:   4900        -9136.288             0.235            0.241
Chain 1:   5000       -11084.906             0.219            0.176
Chain 1:   5100        -8602.999             0.236            0.241
Chain 1:   5200       -14892.114             0.270            0.288
Chain 1:   5300       -11623.822             0.291            0.288
Chain 1:   5400       -13461.634             0.280            0.288
Chain 1:   5500        -9189.938             0.273            0.288
Chain 1:   5600        -8723.780             0.275            0.288
Chain 1:   5700       -13821.727             0.273            0.288
Chain 1:   5800        -9347.870             0.272            0.288
Chain 1:   5900       -13128.662             0.296            0.288
Chain 1:   6000       -11437.114             0.293            0.288
Chain 1:   6100        -8529.102             0.298            0.341
Chain 1:   6200        -8282.611             0.259            0.288
Chain 1:   6300       -11622.788             0.260            0.288
Chain 1:   6400        -8759.605             0.279            0.327
Chain 1:   6500       -11316.878             0.255            0.288
Chain 1:   6600        -8394.103             0.284            0.327
Chain 1:   6700        -8371.241             0.248            0.288
Chain 1:   6800       -12017.333             0.230            0.288
Chain 1:   6900        -8697.554             0.239            0.303
Chain 1:   7000        -9110.021             0.229            0.303
Chain 1:   7100        -9840.227             0.203            0.287
Chain 1:   7200       -11038.565             0.210            0.287
Chain 1:   7300       -11952.214             0.189            0.226
Chain 1:   7400       -12332.279             0.160            0.109
Chain 1:   7500        -9987.631             0.161            0.109
Chain 1:   7600        -8817.746             0.139            0.109
Chain 1:   7700        -8373.286             0.144            0.109
Chain 1:   7800       -10439.068             0.134            0.109
Chain 1:   7900        -8252.781             0.122            0.109
Chain 1:   8000       -11281.918             0.144            0.133
Chain 1:   8100        -8533.489             0.169            0.198
Chain 1:   8200        -9442.107             0.168            0.198
Chain 1:   8300        -8820.542             0.167            0.198
Chain 1:   8400       -13414.511             0.198            0.235
Chain 1:   8500        -8843.015             0.227            0.265
Chain 1:   8600        -9121.009             0.216            0.265
Chain 1:   8700        -8563.763             0.218            0.265
Chain 1:   8800        -8175.561             0.202            0.265
Chain 1:   8900        -8507.152             0.180            0.096
Chain 1:   9000       -11927.590             0.182            0.096
Chain 1:   9100       -11212.318             0.156            0.070
Chain 1:   9200        -8550.681             0.177            0.070
Chain 1:   9300       -11385.241             0.195            0.249
Chain 1:   9400        -9719.144             0.178            0.171
Chain 1:   9500        -8095.719             0.146            0.171
Chain 1:   9600       -10375.522             0.165            0.201
Chain 1:   9700       -13170.487             0.180            0.212
Chain 1:   9800        -9367.193             0.216            0.220
Chain 1:   9900        -8304.004             0.225            0.220
Chain 1:   10000        -9731.335             0.211            0.212
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57081.275             1.000            1.000
Chain 1:    200       -17422.410             1.638            2.276
Chain 1:    300        -8681.513             1.428            1.007
Chain 1:    400        -8256.286             1.084            1.007
Chain 1:    500        -8500.620             0.873            1.000
Chain 1:    600        -9232.735             0.740            1.000
Chain 1:    700        -8638.497             0.644            0.079
Chain 1:    800        -8086.590             0.572            0.079
Chain 1:    900        -7894.148             0.512            0.069
Chain 1:   1000        -7752.762             0.462            0.069
Chain 1:   1100        -7565.274             0.365            0.068
Chain 1:   1200        -7595.454             0.137            0.052
Chain 1:   1300        -7566.300             0.037            0.029
Chain 1:   1400        -7780.551             0.035            0.028
Chain 1:   1500        -7558.961             0.035            0.028
Chain 1:   1600        -7707.098             0.029            0.025
Chain 1:   1700        -7489.643             0.025            0.025
Chain 1:   1800        -7520.758             0.018            0.024
Chain 1:   1900        -7553.355             0.016            0.019
Chain 1:   2000        -7592.863             0.015            0.019
Chain 1:   2100        -7585.148             0.013            0.005   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003721 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85851.419             1.000            1.000
Chain 1:    200       -13446.286             3.192            5.385
Chain 1:    300        -9852.643             2.250            1.000
Chain 1:    400       -10702.580             1.707            1.000
Chain 1:    500        -8763.172             1.410            0.365
Chain 1:    600        -8518.645             1.180            0.365
Chain 1:    700        -8707.124             1.014            0.221
Chain 1:    800        -9161.020             0.894            0.221
Chain 1:    900        -8651.723             0.801            0.079
Chain 1:   1000        -8490.296             0.723            0.079
Chain 1:   1100        -8606.212             0.624            0.059   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8383.652             0.088            0.050
Chain 1:   1300        -8583.057             0.054            0.029
Chain 1:   1400        -8566.132             0.046            0.027
Chain 1:   1500        -8430.242             0.026            0.023
Chain 1:   1600        -8538.727             0.024            0.022
Chain 1:   1700        -8626.268             0.023            0.019
Chain 1:   1800        -8218.634             0.023            0.019
Chain 1:   1900        -8316.505             0.018            0.016
Chain 1:   2000        -8288.381             0.017            0.013
Chain 1:   2100        -8408.762             0.017            0.014
Chain 1:   2200        -8213.124             0.017            0.014
Chain 1:   2300        -8355.044             0.016            0.014
Chain 1:   2400        -8361.894             0.016            0.014
Chain 1:   2500        -8329.836             0.015            0.013
Chain 1:   2600        -8328.165             0.013            0.012
Chain 1:   2700        -8241.474             0.014            0.012
Chain 1:   2800        -8207.035             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003411 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8392413.165             1.000            1.000
Chain 1:    200     -1582784.048             2.651            4.302
Chain 1:    300      -890037.512             2.027            1.000
Chain 1:    400      -457182.002             1.757            1.000
Chain 1:    500      -357859.079             1.461            0.947
Chain 1:    600      -232826.315             1.307            0.947
Chain 1:    700      -119126.959             1.257            0.947
Chain 1:    800       -86381.176             1.147            0.947
Chain 1:    900       -66728.692             1.052            0.778
Chain 1:   1000       -51529.219             0.977            0.778
Chain 1:   1100       -39008.081             0.909            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38182.302             0.481            0.379
Chain 1:   1300       -26139.050             0.449            0.379
Chain 1:   1400       -25856.992             0.355            0.321
Chain 1:   1500       -22445.715             0.343            0.321
Chain 1:   1600       -21662.512             0.293            0.295
Chain 1:   1700       -20536.333             0.203            0.295
Chain 1:   1800       -20480.472             0.165            0.152
Chain 1:   1900       -20806.491             0.137            0.055
Chain 1:   2000       -19318.086             0.115            0.055
Chain 1:   2100       -19556.280             0.084            0.036
Chain 1:   2200       -19782.841             0.083            0.036
Chain 1:   2300       -19400.027             0.039            0.020
Chain 1:   2400       -19172.179             0.039            0.020
Chain 1:   2500       -18974.324             0.025            0.016
Chain 1:   2600       -18604.616             0.024            0.016
Chain 1:   2700       -18561.526             0.018            0.012
Chain 1:   2800       -18278.591             0.020            0.015
Chain 1:   2900       -18559.735             0.020            0.015
Chain 1:   3000       -18545.837             0.012            0.012
Chain 1:   3100       -18630.874             0.011            0.012
Chain 1:   3200       -18321.622             0.012            0.015
Chain 1:   3300       -18526.267             0.011            0.012
Chain 1:   3400       -18001.425             0.013            0.015
Chain 1:   3500       -18613.020             0.015            0.015
Chain 1:   3600       -17920.002             0.017            0.015
Chain 1:   3700       -18306.650             0.019            0.017
Chain 1:   3800       -17266.910             0.023            0.021
Chain 1:   3900       -17263.084             0.022            0.021
Chain 1:   4000       -17380.362             0.022            0.021
Chain 1:   4100       -17294.228             0.022            0.021
Chain 1:   4200       -17110.535             0.022            0.021
Chain 1:   4300       -17248.863             0.021            0.021
Chain 1:   4400       -17205.767             0.019            0.011
Chain 1:   4500       -17108.340             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001319 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13115.407             1.000            1.000
Chain 1:    200        -9920.156             0.661            1.000
Chain 1:    300        -8600.320             0.492            0.322
Chain 1:    400        -8783.991             0.374            0.322
Chain 1:    500        -8628.405             0.303            0.153
Chain 1:    600        -8445.463             0.256            0.153
Chain 1:    700        -8404.495             0.220            0.022
Chain 1:    800        -8379.941             0.193            0.022
Chain 1:    900        -8404.632             0.172            0.021
Chain 1:   1000        -8473.958             0.156            0.021
Chain 1:   1100        -8516.286             0.056            0.018
Chain 1:   1200        -8457.319             0.024            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001487 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47311.921             1.000            1.000
Chain 1:    200       -16315.500             1.450            1.900
Chain 1:    300        -9094.382             1.231            1.000
Chain 1:    400        -8255.851             0.949            1.000
Chain 1:    500        -8087.966             0.763            0.794
Chain 1:    600        -8739.163             0.648            0.794
Chain 1:    700        -7805.571             0.573            0.120
Chain 1:    800        -8444.996             0.511            0.120
Chain 1:    900        -7915.086             0.461            0.102
Chain 1:   1000        -7927.761             0.415            0.102
Chain 1:   1100        -7589.184             0.320            0.076
Chain 1:   1200        -8002.361             0.135            0.075
Chain 1:   1300        -7889.688             0.057            0.067
Chain 1:   1400        -8100.895             0.050            0.052
Chain 1:   1500        -7621.449             0.054            0.063
Chain 1:   1600        -7696.591             0.047            0.052
Chain 1:   1700        -7830.731             0.037            0.045
Chain 1:   1800        -7716.522             0.031            0.026
Chain 1:   1900        -7694.710             0.025            0.017
Chain 1:   2000        -7733.924             0.025            0.017
Chain 1:   2100        -7618.362             0.022            0.015
Chain 1:   2200        -7819.693             0.019            0.015
Chain 1:   2300        -7682.468             0.020            0.017
Chain 1:   2400        -7627.868             0.018            0.015
Chain 1:   2500        -7735.630             0.013            0.015
Chain 1:   2600        -7586.568             0.014            0.015
Chain 1:   2700        -7611.258             0.013            0.015
Chain 1:   2800        -7552.735             0.012            0.014
Chain 1:   2900        -7496.862             0.012            0.014
Chain 1:   3000        -7577.714             0.013            0.014
Chain 1:   3100        -7592.019             0.012            0.011
Chain 1:   3200        -7818.493             0.012            0.011
Chain 1:   3300        -7501.656             0.014            0.011
Chain 1:   3400        -7732.139             0.017            0.014
Chain 1:   3500        -7481.860             0.019            0.020
Chain 1:   3600        -7559.217             0.018            0.011
Chain 1:   3700        -7514.117             0.018            0.011
Chain 1:   3800        -7519.688             0.017            0.011
Chain 1:   3900        -7462.690             0.017            0.011
Chain 1:   4000        -7459.682             0.016            0.010
Chain 1:   4100        -7465.664             0.016            0.010
Chain 1:   4200        -7548.675             0.014            0.010
Chain 1:   4300        -7447.483             0.011            0.010
Chain 1:   4400        -7489.412             0.009            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003496 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87145.144             1.000            1.000
Chain 1:    200       -14213.351             3.066            5.131
Chain 1:    300       -10504.195             2.161            1.000
Chain 1:    400       -11696.102             1.647            1.000
Chain 1:    500        -9506.137             1.363            0.353
Chain 1:    600        -8921.011             1.147            0.353
Chain 1:    700        -8921.698             0.983            0.230
Chain 1:    800        -9393.440             0.867            0.230
Chain 1:    900        -9252.202             0.772            0.102
Chain 1:   1000        -9261.319             0.695            0.102
Chain 1:   1100        -9276.918             0.595            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8872.741             0.086            0.050
Chain 1:   1300        -9165.099             0.054            0.046
Chain 1:   1400        -9110.452             0.045            0.032
Chain 1:   1500        -9004.651             0.023            0.015
Chain 1:   1600        -9119.316             0.018            0.013
Chain 1:   1700        -9183.320             0.018            0.013
Chain 1:   1800        -8746.444             0.018            0.013
Chain 1:   1900        -8850.754             0.018            0.012
Chain 1:   2000        -8827.736             0.018            0.012
Chain 1:   2100        -8968.950             0.019            0.013
Chain 1:   2200        -8756.719             0.017            0.013
Chain 1:   2300        -8915.943             0.016            0.013
Chain 1:   2400        -8754.113             0.017            0.016
Chain 1:   2500        -8825.097             0.017            0.016
Chain 1:   2600        -8737.124             0.017            0.016
Chain 1:   2700        -8770.592             0.016            0.016
Chain 1:   2800        -8730.249             0.012            0.012
Chain 1:   2900        -8824.262             0.012            0.011
Chain 1:   3000        -8659.507             0.013            0.016
Chain 1:   3100        -8813.004             0.013            0.017
Chain 1:   3200        -8684.737             0.012            0.015
Chain 1:   3300        -8693.977             0.011            0.011
Chain 1:   3400        -8857.137             0.011            0.011
Chain 1:   3500        -8869.120             0.010            0.011
Chain 1:   3600        -8641.085             0.012            0.015
Chain 1:   3700        -8788.016             0.013            0.017
Chain 1:   3800        -8647.319             0.014            0.017
Chain 1:   3900        -8581.538             0.014            0.017
Chain 1:   4000        -8659.323             0.013            0.016
Chain 1:   4100        -8652.906             0.011            0.015
Chain 1:   4200        -8637.405             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8415535.998             1.000            1.000
Chain 1:    200     -1590363.670             2.646            4.292
Chain 1:    300      -892522.327             2.024            1.000
Chain 1:    400      -458603.822             1.755            1.000
Chain 1:    500      -358436.633             1.460            0.946
Chain 1:    600      -233279.810             1.306            0.946
Chain 1:    700      -119686.436             1.255            0.946
Chain 1:    800       -86958.506             1.145            0.946
Chain 1:    900       -67353.698             1.050            0.782
Chain 1:   1000       -52205.326             0.974            0.782
Chain 1:   1100       -39727.158             0.906            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38912.344             0.479            0.376
Chain 1:   1300       -26907.850             0.445            0.376
Chain 1:   1400       -26632.922             0.351            0.314
Chain 1:   1500       -23229.585             0.338            0.314
Chain 1:   1600       -22449.360             0.288            0.291
Chain 1:   1700       -21327.356             0.198            0.290
Chain 1:   1800       -21272.794             0.161            0.147
Chain 1:   1900       -21599.344             0.133            0.053
Chain 1:   2000       -20111.641             0.112            0.053
Chain 1:   2100       -20350.203             0.081            0.035
Chain 1:   2200       -20576.535             0.080            0.035
Chain 1:   2300       -20193.691             0.038            0.019
Chain 1:   2400       -19965.626             0.038            0.019
Chain 1:   2500       -19767.426             0.024            0.015
Chain 1:   2600       -19397.519             0.023            0.015
Chain 1:   2700       -19354.359             0.018            0.012
Chain 1:   2800       -19070.952             0.019            0.015
Chain 1:   2900       -19352.292             0.019            0.015
Chain 1:   3000       -19338.576             0.011            0.012
Chain 1:   3100       -19423.644             0.011            0.011
Chain 1:   3200       -19114.087             0.011            0.015
Chain 1:   3300       -19318.948             0.010            0.011
Chain 1:   3400       -18793.400             0.012            0.015
Chain 1:   3500       -19405.949             0.014            0.015
Chain 1:   3600       -18711.655             0.016            0.015
Chain 1:   3700       -19099.149             0.018            0.016
Chain 1:   3800       -18057.382             0.022            0.020
Chain 1:   3900       -18053.413             0.021            0.020
Chain 1:   4000       -18170.783             0.021            0.020
Chain 1:   4100       -18084.483             0.021            0.020
Chain 1:   4200       -17900.347             0.021            0.020
Chain 1:   4300       -18039.051             0.020            0.020
Chain 1:   4400       -17995.597             0.018            0.010
Chain 1:   4500       -17898.016             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001248 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.48 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13421.074             1.000            1.000
Chain 1:    200        -9885.323             0.679            1.000
Chain 1:    300        -8562.258             0.504            0.358
Chain 1:    400        -8335.934             0.385            0.358
Chain 1:    500        -8117.582             0.313            0.155
Chain 1:    600        -8131.932             0.261            0.155
Chain 1:    700        -8001.667             0.226            0.027
Chain 1:    800        -8004.174             0.198            0.027
Chain 1:    900        -8086.923             0.177            0.027
Chain 1:   1000        -8058.123             0.160            0.027
Chain 1:   1100        -8037.292             0.060            0.016
Chain 1:   1200        -8041.173             0.024            0.010
Chain 1:   1300        -7965.084             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00139 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -53696.223             1.000            1.000
Chain 1:    200       -17259.358             1.556            2.111
Chain 1:    300        -8802.266             1.357            1.000
Chain 1:    400        -9142.380             1.027            1.000
Chain 1:    500        -8211.533             0.844            0.961
Chain 1:    600        -8536.635             0.710            0.961
Chain 1:    700        -7800.393             0.622            0.113
Chain 1:    800        -8333.095             0.552            0.113
Chain 1:    900        -7682.056             0.500            0.094
Chain 1:   1000        -7960.732             0.454            0.094
Chain 1:   1100        -7605.958             0.359            0.085
Chain 1:   1200        -7826.467             0.150            0.064
Chain 1:   1300        -7606.151             0.057            0.047
Chain 1:   1400        -7816.645             0.056            0.047
Chain 1:   1500        -7483.371             0.049            0.045
Chain 1:   1600        -7622.107             0.047            0.045
Chain 1:   1700        -7478.918             0.040            0.035
Chain 1:   1800        -7552.812             0.034            0.029
Chain 1:   1900        -7587.856             0.026            0.028
Chain 1:   2000        -7598.606             0.023            0.027
Chain 1:   2100        -7541.064             0.019            0.019
Chain 1:   2200        -7654.601             0.018            0.018
Chain 1:   2300        -7471.969             0.017            0.018
Chain 1:   2400        -7455.683             0.015            0.015
Chain 1:   2500        -7562.799             0.012            0.014
Chain 1:   2600        -7447.937             0.011            0.014
Chain 1:   2700        -7359.363             0.011            0.012
Chain 1:   2800        -7543.020             0.012            0.014
Chain 1:   2900        -7289.560             0.015            0.015
Chain 1:   3000        -7454.879             0.017            0.015
Chain 1:   3100        -7434.079             0.017            0.015
Chain 1:   3200        -7658.236             0.018            0.022
Chain 1:   3300        -7359.107             0.020            0.022
Chain 1:   3400        -7612.625             0.023            0.024
Chain 1:   3500        -7355.199             0.025            0.029
Chain 1:   3600        -7413.295             0.024            0.029
Chain 1:   3700        -7370.091             0.024            0.029
Chain 1:   3800        -7379.460             0.021            0.029
Chain 1:   3900        -7329.869             0.018            0.022
Chain 1:   4000        -7314.468             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003223 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86818.358             1.000            1.000
Chain 1:    200       -13907.675             3.121            5.242
Chain 1:    300       -10146.999             2.204            1.000
Chain 1:    400       -11754.237             1.687            1.000
Chain 1:    500        -8777.951             1.418            0.371
Chain 1:    600        -8606.496             1.185            0.371
Chain 1:    700        -8867.605             1.020            0.339
Chain 1:    800        -9188.379             0.897            0.339
Chain 1:    900        -8866.495             0.801            0.137
Chain 1:   1000        -9067.016             0.723            0.137
Chain 1:   1100        -8767.541             0.627            0.036   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8446.481             0.106            0.036
Chain 1:   1300        -8816.009             0.073            0.036
Chain 1:   1400        -8717.821             0.061            0.035
Chain 1:   1500        -8623.720             0.028            0.034
Chain 1:   1600        -8736.604             0.027            0.034
Chain 1:   1700        -8797.258             0.025            0.034
Chain 1:   1800        -8345.640             0.027            0.034
Chain 1:   1900        -8456.039             0.025            0.022
Chain 1:   2000        -8443.155             0.022            0.013
Chain 1:   2100        -8573.208             0.021            0.013
Chain 1:   2200        -8351.341             0.019            0.013
Chain 1:   2300        -8513.450             0.017            0.013
Chain 1:   2400        -8351.594             0.018            0.015
Chain 1:   2500        -8427.971             0.018            0.015
Chain 1:   2600        -8344.755             0.017            0.015
Chain 1:   2700        -8373.320             0.017            0.015
Chain 1:   2800        -8327.219             0.012            0.013
Chain 1:   2900        -8435.769             0.012            0.013
Chain 1:   3000        -8386.145             0.013            0.013
Chain 1:   3100        -8318.384             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003114 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8428341.447             1.000            1.000
Chain 1:    200     -1589467.669             2.651            4.303
Chain 1:    300      -892215.325             2.028            1.000
Chain 1:    400      -458375.829             1.758            1.000
Chain 1:    500      -358171.803             1.462            0.946
Chain 1:    600      -233156.773             1.308            0.946
Chain 1:    700      -119514.514             1.257            0.946
Chain 1:    800       -86723.207             1.147            0.946
Chain 1:    900       -67105.977             1.052            0.781
Chain 1:   1000       -51944.150             0.976            0.781
Chain 1:   1100       -39449.512             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38636.949             0.479            0.378
Chain 1:   1300       -26619.438             0.446            0.378
Chain 1:   1400       -26343.222             0.353            0.317
Chain 1:   1500       -22936.309             0.340            0.317
Chain 1:   1600       -22155.195             0.290            0.292
Chain 1:   1700       -21031.935             0.200            0.292
Chain 1:   1800       -20977.158             0.162            0.149
Chain 1:   1900       -21303.886             0.135            0.053
Chain 1:   2000       -19815.409             0.113            0.053
Chain 1:   2100       -20053.868             0.083            0.035
Chain 1:   2200       -20280.314             0.082            0.035
Chain 1:   2300       -19897.404             0.038            0.019
Chain 1:   2400       -19669.330             0.038            0.019
Chain 1:   2500       -19470.997             0.025            0.015
Chain 1:   2600       -19100.772             0.023            0.015
Chain 1:   2700       -19057.721             0.018            0.012
Chain 1:   2800       -18774.069             0.019            0.015
Chain 1:   2900       -19055.669             0.019            0.015
Chain 1:   3000       -19041.877             0.012            0.012
Chain 1:   3100       -19126.876             0.011            0.012
Chain 1:   3200       -18817.204             0.011            0.015
Chain 1:   3300       -19022.262             0.011            0.012
Chain 1:   3400       -18496.343             0.012            0.015
Chain 1:   3500       -19109.288             0.014            0.015
Chain 1:   3600       -18414.694             0.016            0.015
Chain 1:   3700       -18802.348             0.018            0.016
Chain 1:   3800       -17759.859             0.022            0.021
Chain 1:   3900       -17755.923             0.021            0.021
Chain 1:   4000       -17873.295             0.022            0.021
Chain 1:   4100       -17786.829             0.022            0.021
Chain 1:   4200       -17602.679             0.021            0.021
Chain 1:   4300       -17741.401             0.021            0.021
Chain 1:   4400       -17697.854             0.018            0.010
Chain 1:   4500       -17600.295             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001394 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48822.077             1.000            1.000
Chain 1:    200       -18473.809             1.321            1.643
Chain 1:    300       -38904.983             1.056            1.000
Chain 1:    400       -34402.197             0.825            1.000
Chain 1:    500       -17402.535             0.855            0.977
Chain 1:    600       -12524.138             0.778            0.977
Chain 1:    700       -16372.009             0.700            0.525
Chain 1:    800       -14393.326             0.630            0.525
Chain 1:    900       -11236.593             0.591            0.390
Chain 1:   1000       -14921.313             0.557            0.390
Chain 1:   1100       -29131.180             0.505            0.390   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -13090.838             0.464            0.390
Chain 1:   1300       -12026.348             0.420            0.281
Chain 1:   1400       -23575.629             0.456            0.390
Chain 1:   1500       -10846.901             0.475            0.390
Chain 1:   1600       -12626.223             0.451            0.281
Chain 1:   1700       -12508.114             0.428            0.281
Chain 1:   1800       -10905.015             0.429            0.281
Chain 1:   1900       -10544.894             0.404            0.247
Chain 1:   2000       -15531.458             0.412            0.321
Chain 1:   2100        -9774.439             0.422            0.321
Chain 1:   2200        -9768.160             0.299            0.147
Chain 1:   2300       -10121.575             0.294            0.147
Chain 1:   2400        -9307.043             0.254            0.141
Chain 1:   2500        -9485.735             0.138            0.088
Chain 1:   2600       -10052.265             0.130            0.056
Chain 1:   2700        -9139.321             0.139            0.088
Chain 1:   2800       -10241.945             0.135            0.088
Chain 1:   2900        -9999.455             0.134            0.088
Chain 1:   3000       -10037.235             0.102            0.056
Chain 1:   3100        -9818.520             0.046            0.035
Chain 1:   3200        -9075.012             0.054            0.056
Chain 1:   3300        -9555.549             0.055            0.056
Chain 1:   3400       -13147.377             0.074            0.056
Chain 1:   3500        -9071.882             0.117            0.082
Chain 1:   3600        -9893.822             0.120            0.083
Chain 1:   3700        -9307.856             0.116            0.082
Chain 1:   3800        -9363.830             0.106            0.063
Chain 1:   3900        -9201.873             0.105            0.063
Chain 1:   4000        -9495.481             0.108            0.063
Chain 1:   4100       -10429.950             0.114            0.082
Chain 1:   4200       -10073.031             0.110            0.063
Chain 1:   4300        -9333.987             0.113            0.079
Chain 1:   4400        -8598.797             0.094            0.079
Chain 1:   4500        -8940.508             0.053            0.063
Chain 1:   4600       -10239.926             0.057            0.063
Chain 1:   4700        -8839.842             0.067            0.079
Chain 1:   4800        -9671.114             0.075            0.085
Chain 1:   4900        -9109.231             0.079            0.085
Chain 1:   5000       -15993.489             0.119            0.086
Chain 1:   5100        -9039.339             0.187            0.086
Chain 1:   5200        -8821.298             0.186            0.086
Chain 1:   5300       -12426.063             0.207            0.127
Chain 1:   5400       -12427.535             0.199            0.127
Chain 1:   5500       -10514.179             0.213            0.158
Chain 1:   5600        -9417.949             0.212            0.158
Chain 1:   5700       -11991.076             0.218            0.182
Chain 1:   5800        -9019.099             0.242            0.215
Chain 1:   5900        -9100.232             0.237            0.215
Chain 1:   6000        -8571.538             0.200            0.182
Chain 1:   6100        -8770.956             0.125            0.116
Chain 1:   6200       -12396.094             0.152            0.182
Chain 1:   6300       -11624.164             0.129            0.116
Chain 1:   6400        -8487.469             0.166            0.182
Chain 1:   6500        -9163.445             0.156            0.116
Chain 1:   6600        -9234.666             0.145            0.074
Chain 1:   6700        -9886.707             0.130            0.066
Chain 1:   6800       -12966.069             0.121            0.066
Chain 1:   6900       -14103.791             0.128            0.074
Chain 1:   7000        -8533.603             0.187            0.081
Chain 1:   7100        -8452.202             0.186            0.081
Chain 1:   7200        -8796.030             0.160            0.074
Chain 1:   7300        -8207.825             0.161            0.074
Chain 1:   7400       -10296.370             0.144            0.074
Chain 1:   7500        -8371.902             0.160            0.081
Chain 1:   7600        -8484.707             0.160            0.081
Chain 1:   7700       -10065.334             0.169            0.157
Chain 1:   7800        -9962.388             0.147            0.081
Chain 1:   7900        -9102.474             0.148            0.094
Chain 1:   8000       -10463.013             0.096            0.094
Chain 1:   8100        -9445.636             0.106            0.108
Chain 1:   8200        -8981.763             0.107            0.108
Chain 1:   8300       -10865.856             0.117            0.130
Chain 1:   8400        -8722.045             0.121            0.130
Chain 1:   8500        -8798.986             0.099            0.108
Chain 1:   8600        -8701.127             0.099            0.108
Chain 1:   8700        -8640.625             0.084            0.094
Chain 1:   8800        -8561.900             0.084            0.094
Chain 1:   8900        -8695.866             0.076            0.052
Chain 1:   9000        -9143.971             0.068            0.049
Chain 1:   9100       -10289.457             0.068            0.049
Chain 1:   9200        -8737.194             0.081            0.049
Chain 1:   9300        -9661.827             0.073            0.049
Chain 1:   9400        -8299.776             0.065            0.049
Chain 1:   9500        -8691.557             0.069            0.049
Chain 1:   9600        -8434.044             0.071            0.049
Chain 1:   9700        -8455.693             0.070            0.049
Chain 1:   9800       -11953.951             0.098            0.096
Chain 1:   9900        -8504.426             0.137            0.111
Chain 1:   10000       -10096.756             0.148            0.158
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001373 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.73 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57018.840             1.000            1.000
Chain 1:    200       -17554.151             1.624            2.248
Chain 1:    300        -8748.123             1.418            1.007
Chain 1:    400        -8230.075             1.079            1.007
Chain 1:    500        -8585.990             0.872            1.000
Chain 1:    600        -8161.852             0.735            1.000
Chain 1:    700        -8214.081             0.631            0.063
Chain 1:    800        -8008.598             0.555            0.063
Chain 1:    900        -7826.327             0.496            0.052
Chain 1:   1000        -7647.488             0.449            0.052
Chain 1:   1100        -7672.865             0.349            0.041
Chain 1:   1200        -7686.119             0.125            0.026
Chain 1:   1300        -7605.913             0.025            0.023
Chain 1:   1400        -7801.455             0.021            0.023
Chain 1:   1500        -7599.184             0.020            0.023
Chain 1:   1600        -7657.450             0.015            0.023
Chain 1:   1700        -7522.320             0.017            0.023
Chain 1:   1800        -7574.239             0.015            0.018
Chain 1:   1900        -7586.779             0.012            0.011
Chain 1:   2000        -7623.737             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003334 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.34 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85942.358             1.000            1.000
Chain 1:    200       -13559.868             3.169            5.338
Chain 1:    300        -9953.279             2.233            1.000
Chain 1:    400       -10885.515             1.696            1.000
Chain 1:    500        -8835.263             1.404            0.362
Chain 1:    600        -8431.500             1.178            0.362
Chain 1:    700        -8594.979             1.012            0.232
Chain 1:    800        -8894.565             0.890            0.232
Chain 1:    900        -8751.593             0.793            0.086
Chain 1:   1000        -8553.225             0.716            0.086
Chain 1:   1100        -8782.845             0.618            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8416.093             0.089            0.044
Chain 1:   1300        -8636.761             0.055            0.034
Chain 1:   1400        -8642.011             0.047            0.026
Chain 1:   1500        -8534.410             0.025            0.026
Chain 1:   1600        -8637.497             0.021            0.023
Chain 1:   1700        -8725.463             0.020            0.023
Chain 1:   1800        -8317.310             0.022            0.023
Chain 1:   1900        -8413.915             0.021            0.023
Chain 1:   2000        -8386.181             0.019            0.013
Chain 1:   2100        -8506.940             0.018            0.013
Chain 1:   2200        -8325.752             0.016            0.013
Chain 1:   2300        -8453.333             0.015            0.013
Chain 1:   2400        -8463.791             0.015            0.013
Chain 1:   2500        -8425.781             0.014            0.012
Chain 1:   2600        -8424.642             0.013            0.011
Chain 1:   2700        -8339.487             0.013            0.011
Chain 1:   2800        -8304.369             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00332 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401382.913             1.000            1.000
Chain 1:    200     -1586146.944             2.648            4.297
Chain 1:    300      -891748.396             2.025            1.000
Chain 1:    400      -457827.775             1.756            1.000
Chain 1:    500      -358082.814             1.460            0.948
Chain 1:    600      -233026.254             1.306            0.948
Chain 1:    700      -119265.045             1.256            0.948
Chain 1:    800       -86478.622             1.146            0.948
Chain 1:    900       -66828.520             1.052            0.779
Chain 1:   1000       -51631.952             0.976            0.779
Chain 1:   1100       -39111.235             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38286.145             0.480            0.379
Chain 1:   1300       -26246.660             0.448            0.379
Chain 1:   1400       -25965.350             0.355            0.320
Chain 1:   1500       -22553.671             0.342            0.320
Chain 1:   1600       -21770.087             0.292            0.294
Chain 1:   1700       -20644.621             0.202            0.294
Chain 1:   1800       -20588.770             0.164            0.151
Chain 1:   1900       -20914.780             0.137            0.055
Chain 1:   2000       -19426.396             0.115            0.055
Chain 1:   2100       -19664.798             0.084            0.036
Chain 1:   2200       -19891.131             0.083            0.036
Chain 1:   2300       -19508.477             0.039            0.020
Chain 1:   2400       -19280.612             0.039            0.020
Chain 1:   2500       -19082.585             0.025            0.016
Chain 1:   2600       -18713.026             0.023            0.016
Chain 1:   2700       -18670.014             0.018            0.012
Chain 1:   2800       -18386.945             0.019            0.015
Chain 1:   2900       -18668.071             0.019            0.015
Chain 1:   3000       -18654.344             0.012            0.012
Chain 1:   3100       -18739.312             0.011            0.012
Chain 1:   3200       -18430.092             0.012            0.015
Chain 1:   3300       -18634.706             0.011            0.012
Chain 1:   3400       -18109.813             0.012            0.015
Chain 1:   3500       -18721.430             0.015            0.015
Chain 1:   3600       -18028.441             0.017            0.015
Chain 1:   3700       -18414.995             0.018            0.017
Chain 1:   3800       -17375.232             0.023            0.021
Chain 1:   3900       -17371.378             0.021            0.021
Chain 1:   4000       -17488.691             0.022            0.021
Chain 1:   4100       -17402.496             0.022            0.021
Chain 1:   4200       -17218.838             0.021            0.021
Chain 1:   4300       -17357.175             0.021            0.021
Chain 1:   4400       -17314.086             0.018            0.011
Chain 1:   4500       -17216.628             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001512 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48814.176             1.000            1.000
Chain 1:    200       -47311.413             0.516            1.000
Chain 1:    300       -39049.208             0.414            0.212
Chain 1:    400       -21766.369             0.509            0.794
Chain 1:    500       -13037.321             0.541            0.670
Chain 1:    600       -14969.517             0.473            0.670
Chain 1:    700       -21311.301             0.448            0.298
Chain 1:    800       -29252.576             0.426            0.298
Chain 1:    900       -12385.522             0.530            0.298
Chain 1:   1000       -24722.911             0.527            0.499
Chain 1:   1100       -19596.427             0.453            0.298
Chain 1:   1200       -17236.406             0.463            0.298
Chain 1:   1300       -18799.186             0.450            0.298
Chain 1:   1400       -10690.267             0.447            0.298
Chain 1:   1500        -9246.227             0.396            0.271
Chain 1:   1600       -13489.627             0.414            0.298
Chain 1:   1700       -11349.939             0.403            0.271
Chain 1:   1800       -22601.933             0.426            0.315
Chain 1:   1900       -10446.469             0.406            0.315
Chain 1:   2000       -10362.500             0.357            0.262
Chain 1:   2100        -9730.608             0.337            0.189
Chain 1:   2200       -12401.796             0.345            0.215
Chain 1:   2300       -11261.125             0.347            0.215
Chain 1:   2400        -8677.941             0.301            0.215
Chain 1:   2500       -11160.647             0.307            0.222
Chain 1:   2600       -10433.402             0.283            0.215
Chain 1:   2700       -10416.480             0.264            0.215
Chain 1:   2800        -9141.466             0.228            0.139
Chain 1:   2900        -9001.179             0.114            0.101
Chain 1:   3000       -10303.941             0.125            0.126
Chain 1:   3100       -14769.731             0.149            0.139
Chain 1:   3200        -9276.715             0.187            0.139
Chain 1:   3300        -9028.001             0.179            0.139
Chain 1:   3400       -10202.290             0.161            0.126
Chain 1:   3500        -8791.240             0.155            0.126
Chain 1:   3600        -8642.438             0.150            0.126
Chain 1:   3700        -9519.590             0.159            0.126
Chain 1:   3800       -12204.414             0.167            0.126
Chain 1:   3900        -8553.866             0.208            0.161
Chain 1:   4000        -8687.113             0.197            0.161
Chain 1:   4100        -9716.427             0.177            0.115
Chain 1:   4200       -13052.680             0.144            0.115
Chain 1:   4300       -12564.107             0.145            0.115
Chain 1:   4400       -12364.070             0.135            0.106
Chain 1:   4500        -8693.136             0.161            0.106
Chain 1:   4600        -8534.199             0.161            0.106
Chain 1:   4700       -10429.845             0.170            0.182
Chain 1:   4800        -8613.214             0.169            0.182
Chain 1:   4900        -8318.162             0.130            0.106
Chain 1:   5000       -12741.521             0.163            0.182
Chain 1:   5100        -9317.519             0.189            0.211
Chain 1:   5200        -9068.973             0.167            0.182
Chain 1:   5300       -10133.148             0.173            0.182
Chain 1:   5400        -9272.886             0.181            0.182
Chain 1:   5500        -8572.374             0.147            0.105
Chain 1:   5600       -11160.946             0.168            0.182
Chain 1:   5700        -8629.396             0.179            0.211
Chain 1:   5800        -8957.038             0.162            0.105
Chain 1:   5900        -9065.563             0.160            0.105
Chain 1:   6000        -9081.751             0.125            0.093
Chain 1:   6100        -8558.654             0.094            0.082
Chain 1:   6200        -8887.736             0.095            0.082
Chain 1:   6300       -13650.648             0.120            0.082
Chain 1:   6400       -11944.379             0.125            0.082
Chain 1:   6500        -9539.307             0.142            0.143
Chain 1:   6600       -12691.400             0.143            0.143
Chain 1:   6700        -8330.321             0.166            0.143
Chain 1:   6800       -12851.164             0.198            0.248
Chain 1:   6900        -8470.447             0.248            0.252
Chain 1:   7000       -10936.471             0.271            0.252
Chain 1:   7100        -8121.062             0.299            0.347
Chain 1:   7200        -8371.304             0.299            0.347
Chain 1:   7300        -8250.602             0.265            0.252
Chain 1:   7400        -8807.645             0.257            0.252
Chain 1:   7500        -9626.447             0.241            0.248
Chain 1:   7600        -8724.957             0.226            0.225
Chain 1:   7700       -10571.788             0.191            0.175
Chain 1:   7800        -9264.517             0.170            0.141
Chain 1:   7900        -9695.939             0.123            0.103
Chain 1:   8000       -12573.455             0.123            0.103
Chain 1:   8100        -8464.109             0.137            0.103
Chain 1:   8200        -9458.044             0.145            0.105
Chain 1:   8300        -8160.915             0.159            0.141
Chain 1:   8400        -8063.915             0.154            0.141
Chain 1:   8500        -7989.423             0.146            0.141
Chain 1:   8600        -8196.197             0.139            0.141
Chain 1:   8700        -8338.757             0.123            0.105
Chain 1:   8800        -8196.020             0.110            0.044
Chain 1:   8900        -8883.608             0.114            0.077
Chain 1:   9000       -10029.722             0.102            0.077
Chain 1:   9100        -8707.385             0.069            0.077
Chain 1:   9200        -8434.714             0.062            0.032
Chain 1:   9300        -8315.910             0.047            0.025
Chain 1:   9400       -11328.752             0.073            0.032
Chain 1:   9500        -8209.349             0.110            0.077
Chain 1:   9600        -8080.654             0.109            0.077
Chain 1:   9700        -8683.426             0.114            0.077
Chain 1:   9800        -8181.566             0.118            0.077
Chain 1:   9900        -9112.484             0.121            0.102
Chain 1:   10000        -8151.104             0.121            0.102
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001581 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.81 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57935.175             1.000            1.000
Chain 1:    200       -17492.703             1.656            2.312
Chain 1:    300        -8591.727             1.449            1.036
Chain 1:    400        -8152.643             1.100            1.036
Chain 1:    500        -8037.467             0.883            1.000
Chain 1:    600        -8587.572             0.747            1.000
Chain 1:    700        -8199.583             0.647            0.064
Chain 1:    800        -8039.522             0.568            0.064
Chain 1:    900        -7789.250             0.509            0.054
Chain 1:   1000        -7740.297             0.459            0.054
Chain 1:   1100        -7658.481             0.360            0.047
Chain 1:   1200        -7656.547             0.128            0.032
Chain 1:   1300        -7642.650             0.025            0.020
Chain 1:   1400        -7805.572             0.022            0.020
Chain 1:   1500        -7594.913             0.023            0.021
Chain 1:   1600        -7750.549             0.019            0.020
Chain 1:   1700        -7471.708             0.018            0.020
Chain 1:   1800        -7565.679             0.017            0.020
Chain 1:   1900        -7536.659             0.014            0.012
Chain 1:   2000        -7559.589             0.014            0.012
Chain 1:   2100        -7559.529             0.013            0.012
Chain 1:   2200        -7661.765             0.014            0.013
Chain 1:   2300        -7796.900             0.016            0.017
Chain 1:   2400        -7610.837             0.016            0.017
Chain 1:   2500        -7618.653             0.013            0.013
Chain 1:   2600        -7473.088             0.013            0.013
Chain 1:   2700        -7557.105             0.011            0.012
Chain 1:   2800        -7530.818             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002746 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.46 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86753.506             1.000            1.000
Chain 1:    200       -13320.780             3.256            5.513
Chain 1:    300        -9691.663             2.296            1.000
Chain 1:    400       -10687.862             1.745            1.000
Chain 1:    500        -8509.963             1.447            0.374
Chain 1:    600        -8496.161             1.206            0.374
Chain 1:    700        -8130.466             1.040            0.256
Chain 1:    800        -8427.168             0.915            0.256
Chain 1:    900        -8479.100             0.814            0.093
Chain 1:   1000        -8387.786             0.734            0.093
Chain 1:   1100        -8539.324             0.635            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8171.300             0.089            0.045
Chain 1:   1300        -8385.287             0.054            0.035
Chain 1:   1400        -8397.752             0.044            0.026
Chain 1:   1500        -8253.181             0.021            0.018
Chain 1:   1600        -8365.941             0.022            0.018
Chain 1:   1700        -8448.654             0.018            0.018
Chain 1:   1800        -8036.432             0.020            0.018
Chain 1:   1900        -8132.531             0.020            0.018
Chain 1:   2000        -8105.664             0.020            0.018
Chain 1:   2100        -8228.157             0.019            0.015
Chain 1:   2200        -8048.184             0.017            0.015
Chain 1:   2300        -8127.543             0.016            0.013
Chain 1:   2400        -8197.166             0.016            0.013
Chain 1:   2500        -8142.483             0.015            0.012
Chain 1:   2600        -8141.831             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003272 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8418601.167             1.000            1.000
Chain 1:    200     -1590568.563             2.646            4.293
Chain 1:    300      -891294.973             2.026            1.000
Chain 1:    400      -456941.860             1.757            1.000
Chain 1:    500      -356993.584             1.462            0.951
Chain 1:    600      -231810.391             1.308            0.951
Chain 1:    700      -118499.526             1.258            0.951
Chain 1:    800       -85823.790             1.148            0.951
Chain 1:    900       -66273.498             1.053            0.785
Chain 1:   1000       -51167.024             0.978            0.785
Chain 1:   1100       -38727.304             0.910            0.540   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37914.189             0.482            0.381
Chain 1:   1300       -25956.509             0.450            0.381
Chain 1:   1400       -25683.075             0.356            0.321
Chain 1:   1500       -22292.315             0.343            0.321
Chain 1:   1600       -21515.149             0.293            0.295
Chain 1:   1700       -20399.310             0.203            0.295
Chain 1:   1800       -20345.858             0.165            0.152
Chain 1:   1900       -20672.017             0.137            0.055
Chain 1:   2000       -19188.291             0.115            0.055
Chain 1:   2100       -19426.585             0.084            0.036
Chain 1:   2200       -19652.123             0.083            0.036
Chain 1:   2300       -19270.078             0.039            0.020
Chain 1:   2400       -19042.270             0.039            0.020
Chain 1:   2500       -18843.841             0.025            0.016
Chain 1:   2600       -18474.635             0.024            0.016
Chain 1:   2700       -18431.689             0.018            0.012
Chain 1:   2800       -18148.406             0.020            0.016
Chain 1:   2900       -18429.482             0.020            0.015
Chain 1:   3000       -18415.789             0.012            0.012
Chain 1:   3100       -18500.792             0.011            0.012
Chain 1:   3200       -18191.609             0.012            0.015
Chain 1:   3300       -18396.186             0.011            0.012
Chain 1:   3400       -17871.211             0.013            0.015
Chain 1:   3500       -18482.845             0.015            0.016
Chain 1:   3600       -17789.733             0.017            0.016
Chain 1:   3700       -18176.337             0.019            0.017
Chain 1:   3800       -17136.361             0.023            0.021
Chain 1:   3900       -17132.419             0.022            0.021
Chain 1:   4000       -17249.799             0.022            0.021
Chain 1:   4100       -17163.597             0.022            0.021
Chain 1:   4200       -16979.853             0.022            0.021
Chain 1:   4300       -17118.300             0.021            0.021
Chain 1:   4400       -17075.184             0.019            0.011
Chain 1:   4500       -16977.640             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12532.994             1.000            1.000
Chain 1:    200        -9446.777             0.663            1.000
Chain 1:    300        -8225.690             0.492            0.327
Chain 1:    400        -8422.391             0.375            0.327
Chain 1:    500        -8263.666             0.304            0.148
Chain 1:    600        -8142.914             0.255            0.148
Chain 1:    700        -8075.754             0.220            0.023
Chain 1:    800        -8049.986             0.193            0.023
Chain 1:    900        -7960.665             0.173            0.019
Chain 1:   1000        -8080.983             0.157            0.019
Chain 1:   1100        -8146.601             0.058            0.015
Chain 1:   1200        -8059.423             0.026            0.015
Chain 1:   1300        -8052.860             0.011            0.011
Chain 1:   1400        -8043.170             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001522 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.22 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57371.041             1.000            1.000
Chain 1:    200       -17603.026             1.630            2.259
Chain 1:    300        -8799.569             1.420            1.000
Chain 1:    400        -8136.589             1.085            1.000
Chain 1:    500        -8575.845             0.878            1.000
Chain 1:    600        -8254.233             0.739            1.000
Chain 1:    700        -8556.733             0.638            0.081
Chain 1:    800        -8386.952             0.561            0.081
Chain 1:    900        -8099.269             0.502            0.051
Chain 1:   1000        -7824.217             0.456            0.051
Chain 1:   1100        -7797.713             0.356            0.039
Chain 1:   1200        -7964.169             0.132            0.036
Chain 1:   1300        -7584.021             0.037            0.036
Chain 1:   1400        -8046.154             0.035            0.036
Chain 1:   1500        -7599.308             0.036            0.036
Chain 1:   1600        -7785.328             0.034            0.035
Chain 1:   1700        -7514.400             0.034            0.036
Chain 1:   1800        -7554.903             0.033            0.036
Chain 1:   1900        -7590.772             0.030            0.035
Chain 1:   2000        -7649.115             0.027            0.024
Chain 1:   2100        -7564.388             0.028            0.024
Chain 1:   2200        -7720.376             0.028            0.024
Chain 1:   2300        -7538.320             0.025            0.024
Chain 1:   2400        -7555.286             0.019            0.020
Chain 1:   2500        -7458.881             0.015            0.013
Chain 1:   2600        -7527.429             0.013            0.011
Chain 1:   2700        -7456.062             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003491 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86279.223             1.000            1.000
Chain 1:    200       -13677.598             3.154            5.308
Chain 1:    300       -10041.516             2.223            1.000
Chain 1:    400       -11094.049             1.691            1.000
Chain 1:    500        -8875.438             1.403            0.362
Chain 1:    600        -8914.186             1.170            0.362
Chain 1:    700        -8526.843             1.009            0.250
Chain 1:    800        -8809.342             0.887            0.250
Chain 1:    900        -8887.883             0.790            0.095
Chain 1:   1000        -8699.141             0.713            0.095
Chain 1:   1100        -8836.630             0.614            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8499.576             0.087            0.040
Chain 1:   1300        -8725.667             0.054            0.032
Chain 1:   1400        -8740.213             0.045            0.026
Chain 1:   1500        -8589.899             0.021            0.022
Chain 1:   1600        -8703.630             0.022            0.022
Chain 1:   1700        -8783.313             0.019            0.017
Chain 1:   1800        -8365.026             0.020            0.017
Chain 1:   1900        -8463.467             0.021            0.017
Chain 1:   2000        -8437.432             0.019            0.016
Chain 1:   2100        -8561.392             0.019            0.014
Chain 1:   2200        -8375.012             0.017            0.014
Chain 1:   2300        -8458.076             0.015            0.013
Chain 1:   2400        -8527.609             0.016            0.013
Chain 1:   2500        -8473.551             0.015            0.012
Chain 1:   2600        -8473.862             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002969 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 29.69 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8410824.455             1.000            1.000
Chain 1:    200     -1583814.094             2.655            4.310
Chain 1:    300      -890633.967             2.030            1.000
Chain 1:    400      -458281.048             1.758            1.000
Chain 1:    500      -358735.694             1.462            0.943
Chain 1:    600      -233567.250             1.308            0.943
Chain 1:    700      -119541.025             1.257            0.943
Chain 1:    800       -86782.895             1.147            0.943
Chain 1:    900       -67071.018             1.052            0.778
Chain 1:   1000       -51841.158             0.976            0.778
Chain 1:   1100       -39300.214             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38471.547             0.479            0.377
Chain 1:   1300       -26398.060             0.447            0.377
Chain 1:   1400       -26115.176             0.354            0.319
Chain 1:   1500       -22696.414             0.341            0.319
Chain 1:   1600       -21912.051             0.291            0.294
Chain 1:   1700       -20781.299             0.201            0.294
Chain 1:   1800       -20724.634             0.164            0.151
Chain 1:   1900       -21050.853             0.136            0.054
Chain 1:   2000       -19560.163             0.114            0.054
Chain 1:   2100       -19798.320             0.084            0.036
Chain 1:   2200       -20025.490             0.083            0.036
Chain 1:   2300       -19642.063             0.039            0.020
Chain 1:   2400       -19414.092             0.039            0.020
Chain 1:   2500       -19216.579             0.025            0.015
Chain 1:   2600       -18846.299             0.023            0.015
Chain 1:   2700       -18803.082             0.018            0.012
Chain 1:   2800       -18520.193             0.019            0.015
Chain 1:   2900       -18801.458             0.019            0.015
Chain 1:   3000       -18787.457             0.012            0.012
Chain 1:   3100       -18872.563             0.011            0.012
Chain 1:   3200       -18563.089             0.012            0.015
Chain 1:   3300       -18767.916             0.011            0.012
Chain 1:   3400       -18242.847             0.012            0.015
Chain 1:   3500       -18854.890             0.015            0.015
Chain 1:   3600       -18161.251             0.016            0.015
Chain 1:   3700       -18548.375             0.018            0.017
Chain 1:   3800       -17507.797             0.023            0.021
Chain 1:   3900       -17503.984             0.021            0.021
Chain 1:   4000       -17621.203             0.022            0.021
Chain 1:   4100       -17535.071             0.022            0.021
Chain 1:   4200       -17351.180             0.021            0.021
Chain 1:   4300       -17489.591             0.021            0.021
Chain 1:   4400       -17446.337             0.018            0.011
Chain 1:   4500       -17348.916             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.16 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12247.299             1.000            1.000
Chain 1:    200        -9209.896             0.665            1.000
Chain 1:    300        -7976.628             0.495            0.330
Chain 1:    400        -8182.420             0.377            0.330
Chain 1:    500        -8038.768             0.305            0.155
Chain 1:    600        -7896.224             0.258            0.155
Chain 1:    700        -7818.700             0.222            0.025
Chain 1:    800        -7826.051             0.195            0.025
Chain 1:    900        -7722.706             0.174            0.018
Chain 1:   1000        -7826.859             0.158            0.018
Chain 1:   1100        -7891.902             0.059            0.018
Chain 1:   1200        -7845.739             0.027            0.013
Chain 1:   1300        -7786.149             0.012            0.013
Chain 1:   1400        -7808.401             0.010            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001441 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.41 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56590.786             1.000            1.000
Chain 1:    200       -17236.769             1.642            2.283
Chain 1:    300        -8668.444             1.424            1.000
Chain 1:    400        -8165.811             1.083            1.000
Chain 1:    500        -8068.608             0.869            0.988
Chain 1:    600        -8645.804             0.735            0.988
Chain 1:    700        -7792.190             0.646            0.110
Chain 1:    800        -8114.059             0.570            0.110
Chain 1:    900        -8031.875             0.508            0.067
Chain 1:   1000        -7953.156             0.458            0.067
Chain 1:   1100        -7814.911             0.360            0.062
Chain 1:   1200        -7749.272             0.132            0.040
Chain 1:   1300        -7670.133             0.035            0.018
Chain 1:   1400        -7707.577             0.029            0.012
Chain 1:   1500        -7609.307             0.029            0.013
Chain 1:   1600        -7657.783             0.023            0.010
Chain 1:   1700        -7564.645             0.013            0.010
Chain 1:   1800        -7614.240             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003213 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86571.657             1.000            1.000
Chain 1:    200       -13374.372             3.236            5.473
Chain 1:    300        -9770.299             2.281            1.000
Chain 1:    400       -10554.979             1.729            1.000
Chain 1:    500        -8716.745             1.425            0.369
Chain 1:    600        -8288.133             1.196            0.369
Chain 1:    700        -8182.510             1.027            0.211
Chain 1:    800        -8675.565             0.906            0.211
Chain 1:    900        -8608.394             0.806            0.074
Chain 1:   1000        -8320.566             0.729            0.074
Chain 1:   1100        -8569.489             0.632            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8244.181             0.089            0.052
Chain 1:   1300        -8465.439             0.054            0.039
Chain 1:   1400        -8460.814             0.047            0.035
Chain 1:   1500        -8352.488             0.027            0.029
Chain 1:   1600        -8455.892             0.023            0.026
Chain 1:   1700        -8545.388             0.023            0.026
Chain 1:   1800        -8140.088             0.022            0.026
Chain 1:   1900        -8238.425             0.023            0.026
Chain 1:   2000        -8210.026             0.020            0.013
Chain 1:   2100        -8329.923             0.018            0.013
Chain 1:   2200        -8138.323             0.017            0.013
Chain 1:   2300        -8275.488             0.016            0.013
Chain 1:   2400        -8277.925             0.016            0.013
Chain 1:   2500        -8252.582             0.015            0.012
Chain 1:   2600        -8251.931             0.013            0.012
Chain 1:   2700        -8162.499             0.013            0.012
Chain 1:   2800        -8129.255             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003604 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8403762.973             1.000            1.000
Chain 1:    200     -1584822.164             2.651            4.303
Chain 1:    300      -891108.827             2.027            1.000
Chain 1:    400      -457851.118             1.757            1.000
Chain 1:    500      -358240.655             1.461            0.946
Chain 1:    600      -232911.672             1.307            0.946
Chain 1:    700      -119085.832             1.257            0.946
Chain 1:    800       -86284.827             1.147            0.946
Chain 1:    900       -66613.560             1.053            0.778
Chain 1:   1000       -51413.202             0.977            0.778
Chain 1:   1100       -38894.730             0.909            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38070.688             0.481            0.380
Chain 1:   1300       -26037.929             0.449            0.380
Chain 1:   1400       -25756.238             0.356            0.322
Chain 1:   1500       -22346.988             0.343            0.322
Chain 1:   1600       -21564.142             0.293            0.296
Chain 1:   1700       -20439.676             0.203            0.295
Chain 1:   1800       -20384.162             0.165            0.153
Chain 1:   1900       -20710.102             0.137            0.055
Chain 1:   2000       -19222.663             0.116            0.055
Chain 1:   2100       -19460.868             0.085            0.036
Chain 1:   2200       -19687.039             0.084            0.036
Chain 1:   2300       -19304.576             0.039            0.020
Chain 1:   2400       -19076.810             0.040            0.020
Chain 1:   2500       -18878.741             0.025            0.016
Chain 1:   2600       -18509.161             0.024            0.016
Chain 1:   2700       -18466.260             0.018            0.012
Chain 1:   2800       -18183.169             0.020            0.016
Chain 1:   2900       -18464.343             0.020            0.015
Chain 1:   3000       -18450.491             0.012            0.012
Chain 1:   3100       -18535.454             0.011            0.012
Chain 1:   3200       -18226.291             0.012            0.015
Chain 1:   3300       -18430.923             0.011            0.012
Chain 1:   3400       -17906.089             0.013            0.015
Chain 1:   3500       -18517.535             0.015            0.016
Chain 1:   3600       -17824.820             0.017            0.016
Chain 1:   3700       -18211.170             0.019            0.017
Chain 1:   3800       -17171.735             0.023            0.021
Chain 1:   3900       -17167.918             0.022            0.021
Chain 1:   4000       -17285.216             0.022            0.021
Chain 1:   4100       -17199.001             0.022            0.021
Chain 1:   4200       -17015.478             0.022            0.021
Chain 1:   4300       -17153.707             0.021            0.021
Chain 1:   4400       -17110.694             0.019            0.011
Chain 1:   4500       -17013.279             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001355 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13323.448             1.000            1.000
Chain 1:    200       -10093.291             0.660            1.000
Chain 1:    300        -8700.547             0.493            0.320
Chain 1:    400        -8886.819             0.375            0.320
Chain 1:    500        -8503.334             0.309            0.160
Chain 1:    600        -8598.479             0.260            0.160
Chain 1:    700        -8895.678             0.227            0.045
Chain 1:    800        -8509.305             0.205            0.045
Chain 1:    900        -8589.651             0.183            0.045
Chain 1:   1000        -8545.477             0.165            0.045
Chain 1:   1100        -8603.749             0.066            0.033
Chain 1:   1200        -8523.656             0.035            0.021
Chain 1:   1300        -8490.504             0.019            0.011
Chain 1:   1400        -8489.561             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00145 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.5 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -63267.017             1.000            1.000
Chain 1:    200       -18971.827             1.667            2.335
Chain 1:    300        -9498.907             1.444            1.000
Chain 1:    400        -8488.216             1.113            1.000
Chain 1:    500        -8531.926             0.891            0.997
Chain 1:    600        -9361.869             0.757            0.997
Chain 1:    700        -9741.323             0.655            0.119
Chain 1:    800        -8731.193             0.587            0.119
Chain 1:    900        -7910.422             0.534            0.116
Chain 1:   1000        -8014.085             0.482            0.116
Chain 1:   1100        -7925.428             0.383            0.104
Chain 1:   1200        -7755.984             0.151            0.089
Chain 1:   1300        -8010.794             0.055            0.039
Chain 1:   1400        -7880.493             0.045            0.032
Chain 1:   1500        -7669.844             0.047            0.032
Chain 1:   1600        -7985.958             0.042            0.032
Chain 1:   1700        -7697.552             0.042            0.032
Chain 1:   1800        -7652.349             0.031            0.027
Chain 1:   1900        -7660.913             0.021            0.022
Chain 1:   2000        -7905.798             0.022            0.027
Chain 1:   2100        -7721.969             0.024            0.027
Chain 1:   2200        -7935.163             0.024            0.027
Chain 1:   2300        -7715.044             0.024            0.027
Chain 1:   2400        -7729.428             0.022            0.027
Chain 1:   2500        -7744.328             0.020            0.027
Chain 1:   2600        -7648.979             0.017            0.024
Chain 1:   2700        -7620.794             0.014            0.012
Chain 1:   2800        -7688.588             0.014            0.012
Chain 1:   2900        -7501.682             0.016            0.024
Chain 1:   3000        -7657.970             0.015            0.020
Chain 1:   3100        -7636.755             0.013            0.012
Chain 1:   3200        -7864.718             0.013            0.012
Chain 1:   3300        -7565.339             0.015            0.012
Chain 1:   3400        -7846.830             0.018            0.020
Chain 1:   3500        -7564.051             0.021            0.025
Chain 1:   3600        -7606.657             0.021            0.025
Chain 1:   3700        -7576.016             0.021            0.025
Chain 1:   3800        -7567.160             0.020            0.025
Chain 1:   3900        -7514.973             0.018            0.020
Chain 1:   4000        -7502.996             0.016            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002601 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87496.689             1.000            1.000
Chain 1:    200       -14527.356             3.011            5.023
Chain 1:    300       -10718.415             2.126            1.000
Chain 1:    400       -12723.511             1.634            1.000
Chain 1:    500        -9105.635             1.387            0.397
Chain 1:    600        -9198.170             1.157            0.397
Chain 1:    700        -9530.892             0.997            0.355
Chain 1:    800        -9821.449             0.876            0.355
Chain 1:    900        -9594.492             0.781            0.158
Chain 1:   1000        -9732.730             0.705            0.158
Chain 1:   1100        -9402.978             0.608            0.035   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8982.930             0.110            0.035
Chain 1:   1300        -9298.937             0.078            0.035
Chain 1:   1400        -9212.263             0.063            0.034
Chain 1:   1500        -9196.342             0.024            0.030
Chain 1:   1600        -9238.558             0.023            0.030
Chain 1:   1700        -9305.255             0.021            0.024
Chain 1:   1800        -8860.958             0.023            0.024
Chain 1:   1900        -8959.372             0.021            0.014
Chain 1:   2000        -8980.691             0.020            0.011
Chain 1:   2100        -9066.968             0.018            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003397 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8436577.541             1.000            1.000
Chain 1:    200     -1586849.014             2.658            4.317
Chain 1:    300      -891080.629             2.032            1.000
Chain 1:    400      -458405.202             1.760            1.000
Chain 1:    500      -358473.423             1.464            0.944
Chain 1:    600      -233485.291             1.309            0.944
Chain 1:    700      -119976.621             1.257            0.944
Chain 1:    800       -87280.751             1.147            0.944
Chain 1:    900       -67685.786             1.052            0.781
Chain 1:   1000       -52545.556             0.975            0.781
Chain 1:   1100       -40071.304             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39261.843             0.477            0.375
Chain 1:   1300       -27249.837             0.443            0.375
Chain 1:   1400       -26976.198             0.350            0.311
Chain 1:   1500       -23571.884             0.336            0.311
Chain 1:   1600       -22792.662             0.286            0.289
Chain 1:   1700       -21669.030             0.197            0.288
Chain 1:   1800       -21614.577             0.159            0.144
Chain 1:   1900       -21941.620             0.132            0.052
Chain 1:   2000       -20452.810             0.110            0.052
Chain 1:   2100       -20691.079             0.080            0.034
Chain 1:   2200       -20917.995             0.079            0.034
Chain 1:   2300       -20534.633             0.037            0.019
Chain 1:   2400       -20306.478             0.037            0.019
Chain 1:   2500       -20108.368             0.024            0.015
Chain 1:   2600       -19737.659             0.022            0.015
Chain 1:   2700       -19694.421             0.017            0.012
Chain 1:   2800       -19410.828             0.019            0.015
Chain 1:   2900       -19692.482             0.018            0.014
Chain 1:   3000       -19678.571             0.011            0.012
Chain 1:   3100       -19763.699             0.011            0.011
Chain 1:   3200       -19453.757             0.011            0.014
Chain 1:   3300       -19659.009             0.010            0.011
Chain 1:   3400       -19132.798             0.012            0.014
Chain 1:   3500       -19746.255             0.014            0.015
Chain 1:   3600       -19050.889             0.016            0.015
Chain 1:   3700       -19439.182             0.018            0.016
Chain 1:   3800       -18395.628             0.022            0.020
Chain 1:   3900       -18391.686             0.020            0.020
Chain 1:   4000       -18509.014             0.021            0.020
Chain 1:   4100       -18422.595             0.021            0.020
Chain 1:   4200       -18238.152             0.020            0.020
Chain 1:   4300       -18377.034             0.020            0.020
Chain 1:   4400       -18333.270             0.018            0.010
Chain 1:   4500       -18235.709             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0015 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49298.331             1.000            1.000
Chain 1:    200      -113273.251             0.782            1.000
Chain 1:    300       -15819.394             2.575            1.000
Chain 1:    400       -17580.471             1.956            1.000
Chain 1:    500       -14035.422             1.616            0.565
Chain 1:    600       -16102.525             1.368            0.565
Chain 1:    700       -14312.534             1.190            0.253
Chain 1:    800       -11855.164             1.067            0.253
Chain 1:    900       -14716.289             0.970            0.207
Chain 1:   1000       -17859.761             0.891            0.207
Chain 1:   1100       -15406.983             0.807            0.194   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -12273.260             0.776            0.194   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1300       -12824.047             0.164            0.176
Chain 1:   1400       -16546.209             0.177            0.194
Chain 1:   1500       -12771.783             0.181            0.194
Chain 1:   1600       -11382.978             0.180            0.194
Chain 1:   1700       -11854.863             0.172            0.194
Chain 1:   1800       -11898.210             0.151            0.176
Chain 1:   1900       -10278.540             0.148            0.159
Chain 1:   2000       -21552.819             0.182            0.159
Chain 1:   2100       -12394.480             0.240            0.225
Chain 1:   2200       -10194.048             0.236            0.216
Chain 1:   2300       -12633.514             0.251            0.216
Chain 1:   2400        -9890.763             0.257            0.216
Chain 1:   2500       -22128.327             0.282            0.216
Chain 1:   2600        -9628.216             0.400            0.277
Chain 1:   2700        -9434.595             0.398            0.277
Chain 1:   2800       -10523.927             0.408            0.277
Chain 1:   2900        -9476.829             0.403            0.277
Chain 1:   3000       -13217.674             0.379            0.277
Chain 1:   3100        -9678.359             0.342            0.277
Chain 1:   3200        -9155.657             0.326            0.277
Chain 1:   3300        -9573.568             0.311            0.277
Chain 1:   3400       -10247.957             0.290            0.110
Chain 1:   3500        -9419.742             0.244            0.104
Chain 1:   3600        -9269.914             0.115            0.088
Chain 1:   3700        -8824.814             0.118            0.088
Chain 1:   3800       -10519.483             0.124            0.088
Chain 1:   3900       -10596.912             0.114            0.066
Chain 1:   4000       -10282.592             0.089            0.057
Chain 1:   4100        -9592.981             0.059            0.057
Chain 1:   4200        -9171.514             0.058            0.050
Chain 1:   4300        -9694.774             0.059            0.054
Chain 1:   4400        -9132.581             0.059            0.054
Chain 1:   4500        -9181.239             0.050            0.050
Chain 1:   4600       -11348.781             0.068            0.054
Chain 1:   4700       -12415.126             0.071            0.062
Chain 1:   4800        -9322.853             0.089            0.062
Chain 1:   4900        -8921.477             0.092            0.062
Chain 1:   5000       -12096.806             0.115            0.072
Chain 1:   5100        -8600.042             0.149            0.086
Chain 1:   5200       -13569.525             0.181            0.191
Chain 1:   5300        -9967.775             0.212            0.262
Chain 1:   5400        -9341.180             0.212            0.262
Chain 1:   5500        -8784.572             0.218            0.262
Chain 1:   5600        -9055.650             0.202            0.262
Chain 1:   5700        -9924.840             0.202            0.262
Chain 1:   5800        -8999.890             0.179            0.103
Chain 1:   5900       -17629.507             0.224            0.262
Chain 1:   6000        -9063.195             0.292            0.361
Chain 1:   6100       -12596.197             0.279            0.280
Chain 1:   6200        -9088.960             0.281            0.280
Chain 1:   6300        -8729.899             0.249            0.103
Chain 1:   6400       -10312.275             0.258            0.153
Chain 1:   6500        -9501.772             0.260            0.153
Chain 1:   6600        -8805.893             0.265            0.153
Chain 1:   6700       -14107.636             0.294            0.280
Chain 1:   6800       -11213.264             0.309            0.280
Chain 1:   6900       -13038.831             0.274            0.258
Chain 1:   7000        -8725.922             0.229            0.258
Chain 1:   7100        -8691.695             0.202            0.153
Chain 1:   7200        -8644.667             0.164            0.140
Chain 1:   7300       -10905.930             0.180            0.153
Chain 1:   7400        -8550.955             0.192            0.207
Chain 1:   7500       -12593.921             0.216            0.258
Chain 1:   7600        -9039.057             0.247            0.275
Chain 1:   7700        -9352.340             0.213            0.258
Chain 1:   7800        -9637.913             0.190            0.207
Chain 1:   7900        -9543.350             0.177            0.207
Chain 1:   8000        -8559.679             0.139            0.115
Chain 1:   8100        -8494.243             0.140            0.115
Chain 1:   8200        -8677.885             0.141            0.115
Chain 1:   8300       -13249.160             0.155            0.115
Chain 1:   8400        -9502.532             0.167            0.115
Chain 1:   8500        -9384.925             0.136            0.033
Chain 1:   8600        -8307.773             0.110            0.033
Chain 1:   8700        -8838.186             0.112            0.060
Chain 1:   8800       -10947.891             0.129            0.115
Chain 1:   8900       -10873.543             0.128            0.115
Chain 1:   9000        -8693.036             0.142            0.130
Chain 1:   9100        -9014.134             0.145            0.130
Chain 1:   9200       -10790.849             0.159            0.165
Chain 1:   9300        -8612.892             0.150            0.165
Chain 1:   9400       -11076.061             0.133            0.165
Chain 1:   9500        -8847.847             0.157            0.193
Chain 1:   9600        -8753.746             0.145            0.193
Chain 1:   9700        -8392.282             0.143            0.193
Chain 1:   9800        -9370.550             0.134            0.165
Chain 1:   9900       -11334.145             0.151            0.173
Chain 1:   10000        -8340.873             0.162            0.173
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00154 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57410.653             1.000            1.000
Chain 1:    200       -17875.295             1.606            2.212
Chain 1:    300        -8936.498             1.404            1.000
Chain 1:    400        -8174.915             1.076            1.000
Chain 1:    500        -8961.173             0.879            1.000
Chain 1:    600        -9305.726             0.738            1.000
Chain 1:    700        -8122.535             0.654            0.146
Chain 1:    800        -8456.329             0.577            0.146
Chain 1:    900        -7916.437             0.520            0.093
Chain 1:   1000        -7852.115             0.469            0.093
Chain 1:   1100        -8012.950             0.371            0.088
Chain 1:   1200        -7935.875             0.151            0.068
Chain 1:   1300        -7695.496             0.054            0.039
Chain 1:   1400        -7912.732             0.047            0.037
Chain 1:   1500        -7593.427             0.043            0.037
Chain 1:   1600        -7526.512             0.040            0.031
Chain 1:   1700        -7730.907             0.028            0.027
Chain 1:   1800        -7646.880             0.025            0.026
Chain 1:   1900        -7746.173             0.020            0.020
Chain 1:   2000        -7592.246             0.021            0.020
Chain 1:   2100        -7580.639             0.019            0.020
Chain 1:   2200        -7753.153             0.020            0.022
Chain 1:   2300        -7610.559             0.019            0.020
Chain 1:   2400        -7558.189             0.017            0.019
Chain 1:   2500        -7654.138             0.014            0.013
Chain 1:   2600        -7543.798             0.015            0.015
Chain 1:   2700        -7674.977             0.014            0.015
Chain 1:   2800        -7521.321             0.015            0.017
Chain 1:   2900        -7380.394             0.015            0.019
Chain 1:   3000        -7520.810             0.015            0.019
Chain 1:   3100        -7531.303             0.015            0.019
Chain 1:   3200        -7750.899             0.016            0.019
Chain 1:   3300        -7453.373             0.018            0.019
Chain 1:   3400        -7700.201             0.020            0.019
Chain 1:   3500        -7446.551             0.023            0.020
Chain 1:   3600        -7507.948             0.022            0.020
Chain 1:   3700        -7462.232             0.021            0.020
Chain 1:   3800        -7452.129             0.019            0.019
Chain 1:   3900        -7419.471             0.017            0.019
Chain 1:   4000        -7412.094             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003265 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.65 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86838.305             1.000            1.000
Chain 1:    200       -13971.125             3.108            5.216
Chain 1:    300       -10228.341             2.194            1.000
Chain 1:    400       -11807.239             1.679            1.000
Chain 1:    500        -8989.406             1.406            0.366
Chain 1:    600        -9460.013             1.180            0.366
Chain 1:    700        -8952.936             1.019            0.313
Chain 1:    800        -9706.358             0.902            0.313
Chain 1:    900        -9072.902             0.809            0.134
Chain 1:   1000        -9054.499             0.728            0.134
Chain 1:   1100        -8927.561             0.630            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8551.970             0.113            0.070
Chain 1:   1300        -8876.289             0.080            0.057
Chain 1:   1400        -8714.359             0.068            0.050
Chain 1:   1500        -8726.243             0.037            0.044
Chain 1:   1600        -8833.893             0.033            0.037
Chain 1:   1700        -8891.018             0.028            0.019
Chain 1:   1800        -8447.111             0.026            0.019
Chain 1:   1900        -8553.560             0.020            0.014
Chain 1:   2000        -8539.830             0.020            0.014
Chain 1:   2100        -8657.520             0.020            0.014
Chain 1:   2200        -8449.934             0.018            0.014
Chain 1:   2300        -8546.883             0.015            0.012
Chain 1:   2400        -8612.677             0.014            0.012
Chain 1:   2500        -8561.873             0.015            0.012
Chain 1:   2600        -8575.952             0.014            0.011
Chain 1:   2700        -8483.044             0.014            0.011
Chain 1:   2800        -8429.843             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003396 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.96 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8389208.174             1.000            1.000
Chain 1:    200     -1580478.611             2.654            4.308
Chain 1:    300      -890761.908             2.027            1.000
Chain 1:    400      -457891.613             1.757            1.000
Chain 1:    500      -358923.565             1.461            0.945
Chain 1:    600      -233892.765             1.306            0.945
Chain 1:    700      -119972.963             1.255            0.945
Chain 1:    800       -87158.563             1.146            0.945
Chain 1:    900       -67455.540             1.051            0.774
Chain 1:   1000       -52216.007             0.975            0.774
Chain 1:   1100       -39655.416             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38830.641             0.478            0.376
Chain 1:   1300       -26732.008             0.446            0.376
Chain 1:   1400       -26447.744             0.352            0.317
Chain 1:   1500       -23021.087             0.339            0.317
Chain 1:   1600       -22234.706             0.290            0.292
Chain 1:   1700       -21100.953             0.200            0.292
Chain 1:   1800       -21043.771             0.163            0.149
Chain 1:   1900       -21370.496             0.135            0.054
Chain 1:   2000       -19876.916             0.113            0.054
Chain 1:   2100       -20115.431             0.083            0.035
Chain 1:   2200       -20343.059             0.082            0.035
Chain 1:   2300       -19959.061             0.038            0.019
Chain 1:   2400       -19730.913             0.038            0.019
Chain 1:   2500       -19533.241             0.025            0.015
Chain 1:   2600       -19162.579             0.023            0.015
Chain 1:   2700       -19119.245             0.018            0.012
Chain 1:   2800       -18836.099             0.019            0.015
Chain 1:   2900       -19117.580             0.019            0.015
Chain 1:   3000       -19103.599             0.012            0.012
Chain 1:   3100       -19188.780             0.011            0.012
Chain 1:   3200       -18878.973             0.011            0.015
Chain 1:   3300       -19084.060             0.011            0.012
Chain 1:   3400       -18558.348             0.012            0.015
Chain 1:   3500       -19171.339             0.014            0.015
Chain 1:   3600       -18476.489             0.016            0.015
Chain 1:   3700       -18864.536             0.018            0.016
Chain 1:   3800       -17822.034             0.022            0.021
Chain 1:   3900       -17818.175             0.021            0.021
Chain 1:   4000       -17935.394             0.022            0.021
Chain 1:   4100       -17849.145             0.022            0.021
Chain 1:   4200       -17664.878             0.021            0.021
Chain 1:   4300       -17803.602             0.021            0.021
Chain 1:   4400       -17760.035             0.018            0.010
Chain 1:   4500       -17662.524             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48189.727             1.000            1.000
Chain 1:    200       -14672.454             1.642            2.284
Chain 1:    300       -16683.301             1.135            1.000
Chain 1:    400       -19121.600             0.883            1.000
Chain 1:    500       -14363.206             0.773            0.331
Chain 1:    600       -10772.616             0.700            0.333
Chain 1:    700       -12263.486             0.617            0.331
Chain 1:    800       -18023.801             0.580            0.331
Chain 1:    900       -10235.512             0.600            0.331
Chain 1:   1000       -12452.753             0.558            0.331
Chain 1:   1100       -10173.372             0.480            0.320
Chain 1:   1200        -9744.557             0.256            0.224
Chain 1:   1300       -13351.366             0.271            0.270
Chain 1:   1400       -10248.213             0.289            0.303
Chain 1:   1500        -9376.465             0.265            0.270
Chain 1:   1600       -13532.763             0.262            0.270
Chain 1:   1700        -9406.366             0.294            0.303
Chain 1:   1800       -12389.571             0.286            0.270
Chain 1:   1900       -10428.608             0.229            0.241
Chain 1:   2000        -9908.994             0.216            0.241
Chain 1:   2100       -11462.228             0.207            0.241
Chain 1:   2200       -10017.913             0.217            0.241
Chain 1:   2300        -9792.373             0.193            0.188
Chain 1:   2400        -8993.103             0.171            0.144
Chain 1:   2500       -11003.697             0.180            0.183
Chain 1:   2600        -9207.277             0.169            0.183
Chain 1:   2700        -9157.845             0.126            0.144
Chain 1:   2800        -8816.845             0.105            0.136
Chain 1:   2900        -9413.592             0.093            0.089
Chain 1:   3000        -9758.021             0.091            0.089
Chain 1:   3100        -8872.654             0.088            0.089
Chain 1:   3200       -17627.442             0.123            0.089
Chain 1:   3300       -15239.735             0.136            0.100
Chain 1:   3400       -15609.164             0.130            0.100
Chain 1:   3500        -9516.930             0.175            0.100
Chain 1:   3600        -9762.166             0.158            0.063
Chain 1:   3700        -8820.372             0.169            0.100
Chain 1:   3800       -15387.514             0.207            0.107
Chain 1:   3900        -9592.514             0.262            0.157
Chain 1:   4000        -8742.423             0.268            0.157
Chain 1:   4100        -9835.200             0.269            0.157
Chain 1:   4200       -13060.934             0.244            0.157
Chain 1:   4300        -9619.419             0.264            0.247
Chain 1:   4400       -10200.470             0.267            0.247
Chain 1:   4500        -8532.856             0.223            0.195
Chain 1:   4600        -9184.866             0.227            0.195
Chain 1:   4700        -8700.486             0.222            0.195
Chain 1:   4800        -8605.941             0.181            0.111
Chain 1:   4900       -15392.025             0.164            0.111
Chain 1:   5000        -9280.868             0.221            0.195
Chain 1:   5100        -8367.131             0.220            0.195
Chain 1:   5200       -11032.464             0.220            0.195
Chain 1:   5300        -8157.020             0.219            0.195
Chain 1:   5400        -8428.900             0.217            0.195
Chain 1:   5500        -9323.283             0.207            0.109
Chain 1:   5600        -9209.638             0.201            0.109
Chain 1:   5700        -9362.849             0.197            0.109
Chain 1:   5800        -8522.426             0.206            0.109
Chain 1:   5900        -9891.949             0.176            0.109
Chain 1:   6000        -8239.531             0.130            0.109
Chain 1:   6100       -11486.794             0.147            0.138
Chain 1:   6200        -8143.271             0.164            0.138
Chain 1:   6300        -8518.850             0.133            0.099
Chain 1:   6400       -11469.074             0.156            0.138
Chain 1:   6500        -8067.799             0.188            0.201
Chain 1:   6600        -9850.210             0.205            0.201
Chain 1:   6700       -10719.984             0.212            0.201
Chain 1:   6800        -9307.765             0.217            0.201
Chain 1:   6900        -8458.053             0.213            0.201
Chain 1:   7000       -11257.492             0.218            0.249
Chain 1:   7100        -8065.962             0.229            0.249
Chain 1:   7200       -13345.196             0.228            0.249
Chain 1:   7300        -8897.638             0.273            0.257
Chain 1:   7400        -7917.669             0.260            0.249
Chain 1:   7500        -7943.151             0.218            0.181
Chain 1:   7600       -11656.082             0.232            0.249
Chain 1:   7700        -7974.032             0.270            0.319
Chain 1:   7800        -8283.549             0.258            0.319
Chain 1:   7900        -8919.728             0.256            0.319
Chain 1:   8000        -9609.787             0.238            0.319
Chain 1:   8100        -8144.214             0.216            0.180
Chain 1:   8200        -7802.528             0.181            0.124
Chain 1:   8300       -11344.064             0.162            0.124
Chain 1:   8400       -10211.081             0.161            0.111
Chain 1:   8500        -8207.602             0.185            0.180
Chain 1:   8600        -7939.332             0.157            0.111
Chain 1:   8700       -10253.856             0.133            0.111
Chain 1:   8800        -7886.497             0.159            0.180
Chain 1:   8900       -11229.030             0.182            0.226
Chain 1:   9000        -7947.873             0.216            0.244
Chain 1:   9100       -10425.152             0.222            0.244
Chain 1:   9200        -8385.830             0.242            0.244
Chain 1:   9300        -8750.724             0.215            0.243
Chain 1:   9400       -10016.355             0.216            0.243
Chain 1:   9500        -7987.153             0.217            0.243
Chain 1:   9600        -8137.487             0.216            0.243
Chain 1:   9700       -10637.216             0.217            0.243
Chain 1:   9800        -8387.884             0.214            0.243
Chain 1:   9900        -9848.108             0.199            0.238
Chain 1:   10000        -8579.093             0.172            0.235
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001431 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56384.737             1.000            1.000
Chain 1:    200       -16896.536             1.669            2.337
Chain 1:    300        -8506.440             1.441            1.000
Chain 1:    400        -8624.376             1.084            1.000
Chain 1:    500        -8002.270             0.883            0.986
Chain 1:    600        -8678.454             0.749            0.986
Chain 1:    700        -7912.448             0.656            0.097
Chain 1:    800        -8135.983             0.577            0.097
Chain 1:    900        -7836.146             0.517            0.078
Chain 1:   1000        -7693.076             0.467            0.078
Chain 1:   1100        -7660.539             0.368            0.078
Chain 1:   1200        -7615.633             0.135            0.038
Chain 1:   1300        -7697.159             0.037            0.027
Chain 1:   1400        -7850.337             0.038            0.027
Chain 1:   1500        -7614.266             0.033            0.027
Chain 1:   1600        -7513.986             0.027            0.020
Chain 1:   1700        -7506.250             0.017            0.019
Chain 1:   1800        -7525.740             0.015            0.013
Chain 1:   1900        -7595.824             0.012            0.011
Chain 1:   2000        -7586.002             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002745 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86311.603             1.000            1.000
Chain 1:    200       -12997.604             3.320            5.641
Chain 1:    300        -9495.651             2.336            1.000
Chain 1:    400       -10308.609             1.772            1.000
Chain 1:    500        -8342.875             1.465            0.369
Chain 1:    600        -8115.573             1.225            0.369
Chain 1:    700        -8213.876             1.052            0.236
Chain 1:    800        -8377.314             0.923            0.236
Chain 1:    900        -8383.057             0.820            0.079
Chain 1:   1000        -8118.475             0.742            0.079
Chain 1:   1100        -8429.893             0.645            0.037   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8216.314             0.084            0.033
Chain 1:   1300        -8277.533             0.048            0.028
Chain 1:   1400        -8257.640             0.040            0.026
Chain 1:   1500        -8171.232             0.018            0.020
Chain 1:   1600        -8253.656             0.016            0.012
Chain 1:   1700        -8357.755             0.016            0.012
Chain 1:   1800        -7980.525             0.019            0.012
Chain 1:   1900        -8077.933             0.020            0.012
Chain 1:   2000        -8048.744             0.017            0.012
Chain 1:   2100        -8195.111             0.015            0.012
Chain 1:   2200        -7972.030             0.015            0.012
Chain 1:   2300        -8115.184             0.016            0.012
Chain 1:   2400        -8114.905             0.016            0.012
Chain 1:   2500        -8085.805             0.015            0.012
Chain 1:   2600        -8078.720             0.014            0.012
Chain 1:   2700        -7989.914             0.014            0.012
Chain 1:   2800        -7976.036             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002613 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.13 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8393463.066             1.000            1.000
Chain 1:    200     -1583137.560             2.651            4.302
Chain 1:    300      -890756.902             2.026            1.000
Chain 1:    400      -457156.149             1.757            1.000
Chain 1:    500      -357578.951             1.461            0.948
Chain 1:    600      -232544.851             1.307            0.948
Chain 1:    700      -118740.515             1.257            0.948
Chain 1:    800       -85905.557             1.148            0.948
Chain 1:    900       -66243.581             1.053            0.777
Chain 1:   1000       -51022.081             0.978            0.777
Chain 1:   1100       -38491.052             0.911            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37658.576             0.483            0.382
Chain 1:   1300       -25627.008             0.452            0.382
Chain 1:   1400       -25342.490             0.358            0.326
Chain 1:   1500       -21933.037             0.346            0.326
Chain 1:   1600       -21148.906             0.296            0.298
Chain 1:   1700       -20025.366             0.205            0.297
Chain 1:   1800       -19969.402             0.167            0.155
Chain 1:   1900       -20294.703             0.139            0.056
Chain 1:   2000       -18808.626             0.117            0.056
Chain 1:   2100       -19046.889             0.086            0.037
Chain 1:   2200       -19272.501             0.085            0.037
Chain 1:   2300       -18890.646             0.040            0.020
Chain 1:   2400       -18663.083             0.040            0.020
Chain 1:   2500       -18464.964             0.026            0.016
Chain 1:   2600       -18096.236             0.024            0.016
Chain 1:   2700       -18053.475             0.019            0.013
Chain 1:   2800       -17770.698             0.020            0.016
Chain 1:   2900       -18051.500             0.020            0.016
Chain 1:   3000       -18037.804             0.012            0.013
Chain 1:   3100       -18122.643             0.011            0.012
Chain 1:   3200       -17813.959             0.012            0.016
Chain 1:   3300       -18018.160             0.011            0.012
Chain 1:   3400       -17494.170             0.013            0.016
Chain 1:   3500       -18104.414             0.015            0.016
Chain 1:   3600       -17413.225             0.017            0.016
Chain 1:   3700       -17798.453             0.019            0.017
Chain 1:   3800       -16761.438             0.024            0.022
Chain 1:   3900       -16757.640             0.022            0.022
Chain 1:   4000       -16874.952             0.023            0.022
Chain 1:   4100       -16788.893             0.023            0.022
Chain 1:   4200       -16605.820             0.022            0.022
Chain 1:   4300       -16743.748             0.022            0.022
Chain 1:   4400       -16701.179             0.019            0.011
Chain 1:   4500       -16603.789             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001354 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.54 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12057.109             1.000            1.000
Chain 1:    200        -8998.119             0.670            1.000
Chain 1:    300        -8009.409             0.488            0.340
Chain 1:    400        -8108.971             0.369            0.340
Chain 1:    500        -7939.926             0.299            0.123
Chain 1:    600        -7824.512             0.252            0.123
Chain 1:    700        -7754.034             0.217            0.021
Chain 1:    800        -7721.998             0.191            0.021
Chain 1:    900        -7759.948             0.170            0.015
Chain 1:   1000        -7815.690             0.154            0.015
Chain 1:   1100        -7891.608             0.055            0.012
Chain 1:   1200        -7784.793             0.022            0.012
Chain 1:   1300        -7748.117             0.010            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51037.481             1.000            1.000
Chain 1:    200       -15925.199             1.602            2.205
Chain 1:    300        -8572.917             1.354            1.000
Chain 1:    400        -8263.243             1.025            1.000
Chain 1:    500        -8776.694             0.832            0.858
Chain 1:    600        -8732.350             0.694            0.858
Chain 1:    700        -7786.804             0.612            0.121
Chain 1:    800        -8132.560             0.541            0.121
Chain 1:    900        -7636.720             0.488            0.065
Chain 1:   1000        -7687.287             0.440            0.065
Chain 1:   1100        -7659.269             0.340            0.059
Chain 1:   1200        -7588.945             0.121            0.043
Chain 1:   1300        -7720.710             0.037            0.037
Chain 1:   1400        -7837.391             0.034            0.017
Chain 1:   1500        -7614.210             0.031            0.017
Chain 1:   1600        -7537.269             0.032            0.017
Chain 1:   1700        -7520.744             0.020            0.015
Chain 1:   1800        -7561.310             0.016            0.010
Chain 1:   1900        -7546.510             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002883 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85966.147             1.000            1.000
Chain 1:    200       -13150.652             3.269            5.537
Chain 1:    300        -9626.928             2.301            1.000
Chain 1:    400       -10513.991             1.747            1.000
Chain 1:    500        -8535.221             1.444            0.366
Chain 1:    600        -8185.230             1.210            0.366
Chain 1:    700        -8560.722             1.044            0.232
Chain 1:    800        -8974.543             0.919            0.232
Chain 1:    900        -8479.603             0.823            0.084
Chain 1:   1000        -8224.680             0.744            0.084
Chain 1:   1100        -8469.770             0.647            0.058   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8183.365             0.097            0.046
Chain 1:   1300        -8380.072             0.063            0.044
Chain 1:   1400        -8371.674             0.054            0.043
Chain 1:   1500        -8271.969             0.032            0.035
Chain 1:   1600        -8361.449             0.029            0.031
Chain 1:   1700        -8457.562             0.026            0.029
Chain 1:   1800        -8071.255             0.026            0.029
Chain 1:   1900        -8172.728             0.021            0.023
Chain 1:   2000        -8142.442             0.019            0.012
Chain 1:   2100        -8281.127             0.017            0.012
Chain 1:   2200        -8062.691             0.017            0.012
Chain 1:   2300        -8204.858             0.016            0.012
Chain 1:   2400        -8214.653             0.016            0.012
Chain 1:   2500        -8179.806             0.015            0.012
Chain 1:   2600        -8177.378             0.014            0.012
Chain 1:   2700        -8087.485             0.014            0.012
Chain 1:   2800        -8067.783             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002595 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400927.430             1.000            1.000
Chain 1:    200     -1584685.464             2.651            4.301
Chain 1:    300      -890396.015             2.027            1.000
Chain 1:    400      -456900.729             1.757            1.000
Chain 1:    500      -357152.499             1.462            0.949
Chain 1:    600      -232261.425             1.308            0.949
Chain 1:    700      -118673.618             1.258            0.949
Chain 1:    800       -85923.688             1.148            0.949
Chain 1:    900       -66296.827             1.053            0.780
Chain 1:   1000       -51116.095             0.978            0.780
Chain 1:   1100       -38616.975             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37791.575             0.482            0.381
Chain 1:   1300       -25784.568             0.451            0.381
Chain 1:   1400       -25504.231             0.357            0.324
Chain 1:   1500       -22101.392             0.345            0.324
Chain 1:   1600       -21319.744             0.294            0.297
Chain 1:   1700       -20198.590             0.204            0.296
Chain 1:   1800       -20143.603             0.166            0.154
Chain 1:   1900       -20469.063             0.138            0.056
Chain 1:   2000       -18984.098             0.117            0.056
Chain 1:   2100       -19222.248             0.085            0.037
Chain 1:   2200       -19447.792             0.084            0.037
Chain 1:   2300       -19065.984             0.040            0.020
Chain 1:   2400       -18838.355             0.040            0.020
Chain 1:   2500       -18640.197             0.026            0.016
Chain 1:   2600       -18271.259             0.024            0.016
Chain 1:   2700       -18228.508             0.019            0.012
Chain 1:   2800       -17945.567             0.020            0.016
Chain 1:   2900       -18226.484             0.020            0.015
Chain 1:   3000       -18212.761             0.012            0.012
Chain 1:   3100       -18297.618             0.011            0.012
Chain 1:   3200       -17988.804             0.012            0.015
Chain 1:   3300       -18193.142             0.011            0.012
Chain 1:   3400       -17668.889             0.013            0.015
Chain 1:   3500       -18279.465             0.015            0.016
Chain 1:   3600       -17587.852             0.017            0.016
Chain 1:   3700       -17973.347             0.019            0.017
Chain 1:   3800       -16935.653             0.023            0.021
Chain 1:   3900       -16931.841             0.022            0.021
Chain 1:   4000       -17049.166             0.023            0.021
Chain 1:   4100       -16963.025             0.023            0.021
Chain 1:   4200       -16779.860             0.022            0.021
Chain 1:   4300       -16917.861             0.022            0.021
Chain 1:   4400       -16875.147             0.019            0.011
Chain 1:   4500       -16777.750             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00149 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12082.766             1.000            1.000
Chain 1:    200        -9041.371             0.668            1.000
Chain 1:    300        -7969.991             0.490            0.336
Chain 1:    400        -8090.925             0.371            0.336
Chain 1:    500        -8084.578             0.297            0.134
Chain 1:    600        -8013.054             0.249            0.134
Chain 1:    700        -7811.434             0.217            0.026
Chain 1:    800        -7797.264             0.190            0.026
Chain 1:    900        -7788.946             0.169            0.015
Chain 1:   1000        -7819.623             0.153            0.015
Chain 1:   1100        -7867.908             0.053            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001695 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -46062.459             1.000            1.000
Chain 1:    200       -15240.953             1.511            2.022
Chain 1:    300        -8537.239             1.269            1.000
Chain 1:    400        -8361.725             0.957            1.000
Chain 1:    500        -8172.016             0.770            0.785
Chain 1:    600        -7828.134             0.649            0.785
Chain 1:    700        -7715.804             0.559            0.044
Chain 1:    800        -7996.641             0.493            0.044
Chain 1:    900        -7916.659             0.439            0.035
Chain 1:   1000        -7709.941             0.398            0.035
Chain 1:   1100        -7717.591             0.298            0.027
Chain 1:   1200        -7633.719             0.097            0.023
Chain 1:   1300        -7578.028             0.019            0.021
Chain 1:   1400        -7891.340             0.021            0.023
Chain 1:   1500        -7591.776             0.023            0.027
Chain 1:   1600        -7510.192             0.020            0.015
Chain 1:   1700        -7492.152             0.018            0.011
Chain 1:   1800        -7534.187             0.015            0.011
Chain 1:   1900        -7563.364             0.015            0.011
Chain 1:   2000        -7574.161             0.012            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002607 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86165.213             1.000            1.000
Chain 1:    200       -13163.473             3.273            5.546
Chain 1:    300        -9618.596             2.305            1.000
Chain 1:    400       -10451.286             1.748            1.000
Chain 1:    500        -8550.629             1.443            0.369
Chain 1:    600        -8202.280             1.210            0.369
Chain 1:    700        -8411.615             1.041            0.222
Chain 1:    800        -8971.172             0.918            0.222
Chain 1:    900        -8457.265             0.823            0.080
Chain 1:   1000        -8213.587             0.744            0.080
Chain 1:   1100        -8454.122             0.646            0.062   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8158.763             0.096            0.061
Chain 1:   1300        -8374.196             0.061            0.042
Chain 1:   1400        -8366.919             0.053            0.036
Chain 1:   1500        -8269.836             0.032            0.030
Chain 1:   1600        -8362.788             0.029            0.028
Chain 1:   1700        -8463.816             0.028            0.028
Chain 1:   1800        -8073.954             0.026            0.028
Chain 1:   1900        -8175.568             0.022            0.026
Chain 1:   2000        -8145.518             0.019            0.012
Chain 1:   2100        -8283.405             0.018            0.012
Chain 1:   2200        -8065.411             0.017            0.012
Chain 1:   2300        -8207.345             0.016            0.012
Chain 1:   2400        -8214.846             0.016            0.012
Chain 1:   2500        -8184.315             0.015            0.012
Chain 1:   2600        -8180.603             0.014            0.012
Chain 1:   2700        -8091.272             0.014            0.012
Chain 1:   2800        -8070.891             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003338 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8400113.823             1.000            1.000
Chain 1:    200     -1585369.166             2.649            4.299
Chain 1:    300      -891808.822             2.025            1.000
Chain 1:    400      -457658.558             1.756            1.000
Chain 1:    500      -357962.677             1.461            0.949
Chain 1:    600      -232828.096             1.307            0.949
Chain 1:    700      -118984.221             1.257            0.949
Chain 1:    800       -86127.434             1.147            0.949
Chain 1:    900       -66458.952             1.053            0.778
Chain 1:   1000       -51233.627             0.977            0.778
Chain 1:   1100       -38692.559             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37862.979             0.482            0.381
Chain 1:   1300       -25818.779             0.451            0.381
Chain 1:   1400       -25534.571             0.357            0.324
Chain 1:   1500       -22121.384             0.345            0.324
Chain 1:   1600       -21336.313             0.295            0.297
Chain 1:   1700       -20211.234             0.205            0.296
Chain 1:   1800       -20155.107             0.167            0.154
Chain 1:   1900       -20480.624             0.139            0.056
Chain 1:   2000       -18993.221             0.117            0.056
Chain 1:   2100       -19231.631             0.086            0.037
Chain 1:   2200       -19457.451             0.085            0.037
Chain 1:   2300       -19075.372             0.040            0.020
Chain 1:   2400       -18847.695             0.040            0.020
Chain 1:   2500       -18649.567             0.026            0.016
Chain 1:   2600       -18280.587             0.024            0.016
Chain 1:   2700       -18237.777             0.019            0.012
Chain 1:   2800       -17954.836             0.020            0.016
Chain 1:   2900       -18235.838             0.020            0.015
Chain 1:   3000       -18222.113             0.012            0.012
Chain 1:   3100       -18306.963             0.011            0.012
Chain 1:   3200       -17998.121             0.012            0.015
Chain 1:   3300       -18202.471             0.011            0.012
Chain 1:   3400       -17678.150             0.013            0.015
Chain 1:   3500       -18288.847             0.015            0.016
Chain 1:   3600       -17597.135             0.017            0.016
Chain 1:   3700       -17982.741             0.019            0.017
Chain 1:   3800       -16944.845             0.023            0.021
Chain 1:   3900       -16941.036             0.022            0.021
Chain 1:   4000       -17058.361             0.023            0.021
Chain 1:   4100       -16972.204             0.023            0.021
Chain 1:   4200       -16788.984             0.022            0.021
Chain 1:   4300       -16927.025             0.022            0.021
Chain 1:   4400       -16884.302             0.019            0.011
Chain 1:   4500       -16786.889             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00134 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13771.249             1.000            1.000
Chain 1:    200       -10364.695             0.664            1.000
Chain 1:    300        -8453.798             0.518            0.329
Chain 1:    400        -8171.127             0.397            0.329
Chain 1:    500        -8333.093             0.322            0.226
Chain 1:    600        -8168.957             0.271            0.226
Chain 1:    700        -8114.731             0.234            0.035
Chain 1:    800        -8034.060             0.206            0.035
Chain 1:    900        -8363.876             0.187            0.035
Chain 1:   1000        -8104.059             0.172            0.035
Chain 1:   1100        -8189.562             0.073            0.032
Chain 1:   1200        -8097.546             0.041            0.020
Chain 1:   1300        -8007.305             0.020            0.019
Chain 1:   1400        -8038.377             0.016            0.011
Chain 1:   1500        -8127.239             0.016            0.011
Chain 1:   1600        -8036.263             0.015            0.011
Chain 1:   1700        -8011.302             0.014            0.011
Chain 1:   1800        -7978.390             0.014            0.011
Chain 1:   1900        -8006.992             0.010            0.011
Chain 1:   2000        -7942.579             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00137 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51600.287             1.000            1.000
Chain 1:    200       -17243.195             1.496            1.993
Chain 1:    300        -9024.509             1.301            1.000
Chain 1:    400        -9936.188             0.999            1.000
Chain 1:    500        -8407.029             0.835            0.911
Chain 1:    600        -8739.131             0.702            0.911
Chain 1:    700        -9275.958             0.610            0.182
Chain 1:    800        -8229.047             0.550            0.182
Chain 1:    900        -8483.612             0.492            0.127
Chain 1:   1000        -8441.159             0.443            0.127
Chain 1:   1100        -7827.729             0.351            0.092
Chain 1:   1200        -7844.795             0.152            0.078
Chain 1:   1300        -7927.348             0.062            0.058
Chain 1:   1400        -7750.931             0.055            0.038
Chain 1:   1500        -7591.835             0.039            0.030
Chain 1:   1600        -7779.232             0.038            0.024
Chain 1:   1700        -7723.452             0.033            0.023
Chain 1:   1800        -7634.119             0.021            0.021
Chain 1:   1900        -7672.240             0.019            0.012
Chain 1:   2000        -7788.267             0.020            0.015
Chain 1:   2100        -7622.027             0.014            0.015
Chain 1:   2200        -7922.659             0.018            0.021
Chain 1:   2300        -7738.259             0.019            0.022
Chain 1:   2400        -7658.060             0.018            0.021
Chain 1:   2500        -7696.005             0.016            0.015
Chain 1:   2600        -7644.106             0.014            0.012
Chain 1:   2700        -7484.963             0.016            0.015
Chain 1:   2800        -7576.894             0.016            0.015
Chain 1:   2900        -7421.889             0.017            0.021
Chain 1:   3000        -7593.694             0.018            0.021
Chain 1:   3100        -7573.566             0.016            0.021
Chain 1:   3200        -7807.557             0.016            0.021
Chain 1:   3300        -7453.050             0.018            0.021
Chain 1:   3400        -7718.984             0.020            0.021
Chain 1:   3500        -7493.322             0.023            0.023
Chain 1:   3600        -7563.289             0.023            0.023
Chain 1:   3700        -7505.999             0.022            0.023
Chain 1:   3800        -7490.380             0.021            0.023
Chain 1:   3900        -7458.657             0.019            0.023
Chain 1:   4000        -7446.072             0.017            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003061 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 30.61 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86541.447             1.000            1.000
Chain 1:    200       -14278.863             3.030            5.061
Chain 1:    300       -10368.385             2.146            1.000
Chain 1:    400       -12669.155             1.655            1.000
Chain 1:    500        -8836.895             1.411            0.434
Chain 1:    600        -8933.920             1.177            0.434
Chain 1:    700        -8589.136             1.015            0.377
Chain 1:    800        -9352.298             0.898            0.377
Chain 1:    900        -8822.294             0.805            0.182
Chain 1:   1000        -9123.191             0.728            0.182
Chain 1:   1100        -9079.667             0.628            0.082   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8452.989             0.130            0.074
Chain 1:   1300        -8915.114             0.097            0.060
Chain 1:   1400        -8830.917             0.080            0.052
Chain 1:   1500        -8763.700             0.037            0.040
Chain 1:   1600        -8801.428             0.037            0.040
Chain 1:   1700        -8895.869             0.034            0.033
Chain 1:   1800        -8398.810             0.032            0.033
Chain 1:   1900        -8527.423             0.027            0.015
Chain 1:   2000        -8534.944             0.024            0.011
Chain 1:   2100        -8677.590             0.025            0.015
Chain 1:   2200        -8395.057             0.021            0.015
Chain 1:   2300        -8483.073             0.017            0.011
Chain 1:   2400        -8576.404             0.017            0.011
Chain 1:   2500        -8477.712             0.017            0.012
Chain 1:   2600        -8528.979             0.017            0.012
Chain 1:   2700        -8432.875             0.018            0.012
Chain 1:   2800        -8397.204             0.012            0.011
Chain 1:   2900        -8487.551             0.012            0.011
Chain 1:   3000        -8414.426             0.012            0.011
Chain 1:   3100        -8371.090             0.011            0.011
Chain 1:   3200        -8331.301             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003278 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.78 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8396892.040             1.000            1.000
Chain 1:    200     -1584512.618             2.650            4.299
Chain 1:    300      -892231.822             2.025            1.000
Chain 1:    400      -459284.129             1.754            1.000
Chain 1:    500      -359683.993             1.459            0.943
Chain 1:    600      -234401.412             1.305            0.943
Chain 1:    700      -120358.123             1.254            0.943
Chain 1:    800       -87468.894             1.144            0.943
Chain 1:    900       -67766.737             1.049            0.776
Chain 1:   1000       -52545.032             0.973            0.776
Chain 1:   1100       -39988.342             0.905            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -39172.776             0.477            0.376
Chain 1:   1300       -27078.223             0.444            0.376
Chain 1:   1400       -26798.244             0.351            0.314
Chain 1:   1500       -23370.621             0.338            0.314
Chain 1:   1600       -22584.290             0.288            0.291
Chain 1:   1700       -21450.947             0.198            0.290
Chain 1:   1800       -21394.213             0.161            0.147
Chain 1:   1900       -21721.615             0.133            0.053
Chain 1:   2000       -20226.451             0.112            0.053
Chain 1:   2100       -20465.340             0.082            0.035
Chain 1:   2200       -20693.176             0.081            0.035
Chain 1:   2300       -20308.819             0.038            0.019
Chain 1:   2400       -20080.379             0.038            0.019
Chain 1:   2500       -19882.423             0.024            0.015
Chain 1:   2600       -19511.258             0.023            0.015
Chain 1:   2700       -19467.816             0.018            0.012
Chain 1:   2800       -19184.081             0.019            0.015
Chain 1:   2900       -19466.012             0.019            0.014
Chain 1:   3000       -19452.149             0.011            0.012
Chain 1:   3100       -19537.311             0.011            0.011
Chain 1:   3200       -19227.123             0.011            0.014
Chain 1:   3300       -19432.512             0.010            0.011
Chain 1:   3400       -18905.859             0.012            0.014
Chain 1:   3500       -19520.137             0.014            0.015
Chain 1:   3600       -18823.730             0.016            0.015
Chain 1:   3700       -19212.850             0.018            0.016
Chain 1:   3800       -18167.719             0.022            0.020
Chain 1:   3900       -18163.736             0.021            0.020
Chain 1:   4000       -18281.065             0.021            0.020
Chain 1:   4100       -18194.578             0.021            0.020
Chain 1:   4200       -18009.750             0.021            0.020
Chain 1:   4300       -18148.906             0.020            0.020
Chain 1:   4400       -18104.876             0.018            0.010
Chain 1:   4500       -18007.254             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001215 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49343.852             1.000            1.000
Chain 1:    200       -15883.634             1.553            2.107
Chain 1:    300       -19119.689             1.092            1.000
Chain 1:    400       -16115.396             0.866            1.000
Chain 1:    500       -17940.928             0.713            0.186
Chain 1:    600       -15437.674             0.621            0.186
Chain 1:    700       -16847.620             0.544            0.169
Chain 1:    800       -14058.442             0.501            0.186
Chain 1:    900       -11976.191             0.465            0.174
Chain 1:   1000       -13739.196             0.431            0.174
Chain 1:   1100       -24076.034             0.374            0.174
Chain 1:   1200       -13940.069             0.236            0.174
Chain 1:   1300       -12918.932             0.227            0.174
Chain 1:   1400       -13065.847             0.209            0.162
Chain 1:   1500       -11947.709             0.209            0.162
Chain 1:   1600       -12687.404             0.198            0.128
Chain 1:   1700       -12925.501             0.192            0.128
Chain 1:   1800       -26931.827             0.224            0.128
Chain 1:   1900       -11288.976             0.345            0.128
Chain 1:   2000       -10197.428             0.343            0.107
Chain 1:   2100       -10354.559             0.302            0.094
Chain 1:   2200        -9865.067             0.234            0.079
Chain 1:   2300        -9656.232             0.228            0.058
Chain 1:   2400       -10131.636             0.232            0.058
Chain 1:   2500       -15432.557             0.257            0.058
Chain 1:   2600       -10046.123             0.304            0.107
Chain 1:   2700       -10116.562             0.303            0.107
Chain 1:   2800       -11851.407             0.266            0.107
Chain 1:   2900       -16632.971             0.156            0.107
Chain 1:   3000       -10062.158             0.211            0.146
Chain 1:   3100       -10095.140             0.209            0.146
Chain 1:   3200        -9994.638             0.206            0.146
Chain 1:   3300       -13775.606             0.231            0.274
Chain 1:   3400        -9355.143             0.273            0.287
Chain 1:   3500       -14478.663             0.274            0.287
Chain 1:   3600       -10458.926             0.259            0.287
Chain 1:   3700       -12017.265             0.272            0.287
Chain 1:   3800        -8939.842             0.291            0.344
Chain 1:   3900        -9570.309             0.269            0.344
Chain 1:   4000       -11843.971             0.223            0.274
Chain 1:   4100        -9376.490             0.249            0.274
Chain 1:   4200       -14211.598             0.282            0.340
Chain 1:   4300        -9138.387             0.310            0.344
Chain 1:   4400        -8769.158             0.267            0.340
Chain 1:   4500        -9144.818             0.236            0.263
Chain 1:   4600       -14362.491             0.234            0.263
Chain 1:   4700        -9362.424             0.274            0.340
Chain 1:   4800        -9601.823             0.242            0.263
Chain 1:   4900       -10058.859             0.240            0.263
Chain 1:   5000        -9964.250             0.222            0.263
Chain 1:   5100        -8829.394             0.208            0.129
Chain 1:   5200        -9667.903             0.183            0.087
Chain 1:   5300       -12148.545             0.148            0.087
Chain 1:   5400        -8777.873             0.182            0.129
Chain 1:   5500        -9325.404             0.184            0.129
Chain 1:   5600       -10349.004             0.157            0.099
Chain 1:   5700        -9618.437             0.112            0.087
Chain 1:   5800        -8971.563             0.116            0.087
Chain 1:   5900       -14382.632             0.149            0.099
Chain 1:   6000       -11724.003             0.171            0.129
Chain 1:   6100        -8933.668             0.190            0.204
Chain 1:   6200        -9335.923             0.185            0.204
Chain 1:   6300       -13141.841             0.194            0.227
Chain 1:   6400        -9533.185             0.193            0.227
Chain 1:   6500        -9984.148             0.192            0.227
Chain 1:   6600       -12326.999             0.201            0.227
Chain 1:   6700        -8578.432             0.237            0.290
Chain 1:   6800        -9285.290             0.237            0.290
Chain 1:   6900       -13803.459             0.233            0.290
Chain 1:   7000       -13560.797             0.212            0.290
Chain 1:   7100        -8915.140             0.233            0.290
Chain 1:   7200        -9716.580             0.237            0.290
Chain 1:   7300       -10941.112             0.219            0.190
Chain 1:   7400        -8486.319             0.210            0.190
Chain 1:   7500        -8794.484             0.209            0.190
Chain 1:   7600       -12048.348             0.217            0.270
Chain 1:   7700        -8898.915             0.209            0.270
Chain 1:   7800       -12406.961             0.229            0.283
Chain 1:   7900        -8539.513             0.242            0.283
Chain 1:   8000        -8641.982             0.241            0.283
Chain 1:   8100        -8622.233             0.189            0.270
Chain 1:   8200        -8681.963             0.182            0.270
Chain 1:   8300        -8833.029             0.172            0.270
Chain 1:   8400       -10494.930             0.159            0.158
Chain 1:   8500        -8568.017             0.178            0.225
Chain 1:   8600        -9103.547             0.157            0.158
Chain 1:   8700       -10014.094             0.131            0.091
Chain 1:   8800        -9468.261             0.108            0.059
Chain 1:   8900        -9666.301             0.065            0.058
Chain 1:   9000       -11577.661             0.080            0.059
Chain 1:   9100        -8463.517             0.117            0.091
Chain 1:   9200        -9392.369             0.126            0.099
Chain 1:   9300        -8448.085             0.135            0.112
Chain 1:   9400        -8603.430             0.121            0.099
Chain 1:   9500       -13162.375             0.134            0.099
Chain 1:   9600        -8969.740             0.174            0.112
Chain 1:   9700        -8609.310             0.170            0.112
Chain 1:   9800        -8837.691             0.166            0.112
Chain 1:   9900       -10695.216             0.182            0.165
Chain 1:   10000        -8615.614             0.189            0.174
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001504 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.04 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57675.395             1.000            1.000
Chain 1:    200       -18073.104             1.596            2.191
Chain 1:    300        -8968.101             1.402            1.015
Chain 1:    400        -8171.898             1.076            1.015
Chain 1:    500        -8848.659             0.876            1.000
Chain 1:    600        -8268.051             0.742            1.000
Chain 1:    700        -8687.822             0.643            0.097
Chain 1:    800        -8158.093             0.570            0.097
Chain 1:    900        -7918.217             0.510            0.076
Chain 1:   1000        -8008.376             0.461            0.076
Chain 1:   1100        -7880.106             0.362            0.070
Chain 1:   1200        -7749.875             0.145            0.065
Chain 1:   1300        -7996.544             0.046            0.048
Chain 1:   1400        -7864.739             0.038            0.031
Chain 1:   1500        -7525.386             0.035            0.031
Chain 1:   1600        -7726.495             0.031            0.030
Chain 1:   1700        -7575.220             0.028            0.026
Chain 1:   1800        -7549.913             0.022            0.020
Chain 1:   1900        -7551.956             0.019            0.017
Chain 1:   2000        -7691.014             0.019            0.018
Chain 1:   2100        -7558.379             0.019            0.018
Chain 1:   2200        -7781.248             0.021            0.020
Chain 1:   2300        -7599.175             0.020            0.020
Chain 1:   2400        -7672.703             0.019            0.020
Chain 1:   2500        -7593.183             0.016            0.018
Chain 1:   2600        -7531.055             0.014            0.018
Chain 1:   2700        -7553.139             0.012            0.010
Chain 1:   2800        -7635.468             0.013            0.011
Chain 1:   2900        -7368.968             0.017            0.018
Chain 1:   3000        -7536.795             0.017            0.018
Chain 1:   3100        -7515.946             0.016            0.011
Chain 1:   3200        -7740.638             0.016            0.011
Chain 1:   3300        -7434.171             0.017            0.011
Chain 1:   3400        -7692.708             0.020            0.022
Chain 1:   3500        -7436.163             0.022            0.029
Chain 1:   3600        -7490.840             0.022            0.029
Chain 1:   3700        -7449.393             0.022            0.029
Chain 1:   3800        -7446.819             0.021            0.029
Chain 1:   3900        -7399.517             0.018            0.022
Chain 1:   4000        -7392.674             0.016            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003112 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87279.244             1.000            1.000
Chain 1:    200       -14030.381             3.110            5.221
Chain 1:    300       -10276.303             2.195            1.000
Chain 1:    400       -11959.135             1.682            1.000
Chain 1:    500        -8997.118             1.411            0.365
Chain 1:    600        -9042.090             1.177            0.365
Chain 1:    700        -9426.508             1.015            0.329
Chain 1:    800        -8531.057             0.901            0.329
Chain 1:    900        -8554.212             0.801            0.141
Chain 1:   1000        -9238.199             0.728            0.141
Chain 1:   1100        -8790.823             0.633            0.105   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9177.863             0.116            0.074
Chain 1:   1300        -8577.895             0.086            0.070
Chain 1:   1400        -8735.248             0.074            0.051
Chain 1:   1500        -8655.973             0.042            0.042
Chain 1:   1600        -8666.032             0.041            0.042
Chain 1:   1700        -8545.527             0.039            0.042
Chain 1:   1800        -8601.908             0.029            0.018
Chain 1:   1900        -8449.074             0.030            0.018
Chain 1:   2000        -8551.178             0.024            0.018
Chain 1:   2100        -8535.598             0.019            0.014
Chain 1:   2200        -8493.446             0.016            0.012
Chain 1:   2300        -8653.039             0.010            0.012
Chain 1:   2400        -8470.550             0.011            0.012
Chain 1:   2500        -8544.017             0.011            0.012
Chain 1:   2600        -8449.654             0.012            0.012
Chain 1:   2700        -8493.888             0.011            0.011
Chain 1:   2800        -8445.787             0.011            0.011
Chain 1:   2900        -8553.458             0.010            0.011
Chain 1:   3000        -8504.744             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003195 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.95 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8417180.685             1.000            1.000
Chain 1:    200     -1587649.873             2.651            4.302
Chain 1:    300      -891653.790             2.027            1.000
Chain 1:    400      -458173.848             1.757            1.000
Chain 1:    500      -358326.034             1.461            0.946
Chain 1:    600      -233130.757             1.307            0.946
Chain 1:    700      -119540.728             1.256            0.946
Chain 1:    800       -86814.290             1.146            0.946
Chain 1:    900       -67208.264             1.051            0.781
Chain 1:   1000       -52060.095             0.975            0.781
Chain 1:   1100       -39574.922             0.907            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38762.840             0.479            0.377
Chain 1:   1300       -26743.480             0.446            0.377
Chain 1:   1400       -26467.758             0.352            0.315
Chain 1:   1500       -23061.450             0.339            0.315
Chain 1:   1600       -22280.938             0.289            0.292
Chain 1:   1700       -21156.753             0.199            0.291
Chain 1:   1800       -21101.856             0.162            0.148
Chain 1:   1900       -21428.695             0.134            0.053
Chain 1:   2000       -19939.784             0.112            0.053
Chain 1:   2100       -20178.145             0.082            0.035
Chain 1:   2200       -20404.984             0.081            0.035
Chain 1:   2300       -20021.708             0.038            0.019
Chain 1:   2400       -19793.605             0.038            0.019
Chain 1:   2500       -19595.537             0.024            0.015
Chain 1:   2600       -19225.145             0.023            0.015
Chain 1:   2700       -19181.930             0.018            0.012
Chain 1:   2800       -18898.484             0.019            0.015
Chain 1:   2900       -19179.999             0.019            0.015
Chain 1:   3000       -19166.134             0.012            0.012
Chain 1:   3100       -19251.242             0.011            0.012
Chain 1:   3200       -18941.485             0.011            0.015
Chain 1:   3300       -19146.567             0.011            0.012
Chain 1:   3400       -18620.685             0.012            0.015
Chain 1:   3500       -19233.712             0.014            0.015
Chain 1:   3600       -18538.901             0.016            0.015
Chain 1:   3700       -18926.799             0.018            0.016
Chain 1:   3800       -17884.156             0.022            0.020
Chain 1:   3900       -17880.236             0.021            0.020
Chain 1:   4000       -17997.555             0.021            0.020
Chain 1:   4100       -17911.207             0.022            0.020
Chain 1:   4200       -17726.934             0.021            0.020
Chain 1:   4300       -17865.703             0.021            0.020
Chain 1:   4400       -17822.101             0.018            0.010
Chain 1:   4500       -17724.551             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001368 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49004.212             1.000            1.000
Chain 1:    200       -19242.838             1.273            1.547
Chain 1:    300       -20360.643             0.867            1.000
Chain 1:    400       -15330.297             0.732            1.000
Chain 1:    500       -17912.100             0.615            0.328
Chain 1:    600       -20243.131             0.531            0.328
Chain 1:    700       -22566.289             0.470            0.144
Chain 1:    800       -11567.873             0.530            0.328
Chain 1:    900       -17447.855             0.509            0.328
Chain 1:   1000       -10978.926             0.517            0.337
Chain 1:   1100       -10058.914             0.426            0.328
Chain 1:   1200       -12679.785             0.292            0.207
Chain 1:   1300       -16877.846             0.311            0.249
Chain 1:   1400       -11851.478             0.321            0.249
Chain 1:   1500       -10898.880             0.315            0.249
Chain 1:   1600       -10324.295             0.309            0.249
Chain 1:   1700       -11100.865             0.306            0.249
Chain 1:   1800       -18111.209             0.250            0.249
Chain 1:   1900        -9589.629             0.305            0.249
Chain 1:   2000       -14058.604             0.278            0.249
Chain 1:   2100        -9643.227             0.314            0.318
Chain 1:   2200       -16062.755             0.334            0.387
Chain 1:   2300        -9070.154             0.386            0.400
Chain 1:   2400       -11660.898             0.366            0.387
Chain 1:   2500       -10701.096             0.366            0.387
Chain 1:   2600        -9233.425             0.376            0.387
Chain 1:   2700       -10696.270             0.383            0.387
Chain 1:   2800        -9325.928             0.359            0.318
Chain 1:   2900        -9301.848             0.270            0.222
Chain 1:   3000        -8802.987             0.244            0.159
Chain 1:   3100        -9077.999             0.201            0.147
Chain 1:   3200       -13801.104             0.196            0.147
Chain 1:   3300       -12016.979             0.133            0.147
Chain 1:   3400        -8810.983             0.148            0.147
Chain 1:   3500       -16115.483             0.184            0.148
Chain 1:   3600       -10344.358             0.224            0.148
Chain 1:   3700       -10403.966             0.211            0.148
Chain 1:   3800        -9037.764             0.211            0.151
Chain 1:   3900        -9426.349             0.215            0.151
Chain 1:   4000       -10180.622             0.217            0.151
Chain 1:   4100        -9518.454             0.221            0.151
Chain 1:   4200       -11375.155             0.203            0.151
Chain 1:   4300        -9366.957             0.209            0.163
Chain 1:   4400        -9119.947             0.176            0.151
Chain 1:   4500       -16367.848             0.175            0.151
Chain 1:   4600        -9738.404             0.187            0.151
Chain 1:   4700       -13214.756             0.213            0.163
Chain 1:   4800       -10289.058             0.226            0.214
Chain 1:   4900        -9113.494             0.235            0.214
Chain 1:   5000       -14815.818             0.266            0.263
Chain 1:   5100       -12253.117             0.280            0.263
Chain 1:   5200        -8697.401             0.304            0.284
Chain 1:   5300       -10544.283             0.301            0.284
Chain 1:   5400       -10065.772             0.303            0.284
Chain 1:   5500       -10000.929             0.259            0.263
Chain 1:   5600        -9471.608             0.196            0.209
Chain 1:   5700        -8542.001             0.181            0.175
Chain 1:   5800        -8827.034             0.156            0.129
Chain 1:   5900       -14417.278             0.182            0.175
Chain 1:   6000        -8901.230             0.205            0.175
Chain 1:   6100       -10612.932             0.200            0.161
Chain 1:   6200       -10383.439             0.162            0.109
Chain 1:   6300       -12539.697             0.161            0.109
Chain 1:   6400       -11507.160             0.166            0.109
Chain 1:   6500        -9940.082             0.181            0.158
Chain 1:   6600        -8528.281             0.192            0.161
Chain 1:   6700        -8137.426             0.186            0.161
Chain 1:   6800        -8724.436             0.189            0.161
Chain 1:   6900       -10139.140             0.164            0.158
Chain 1:   7000       -12538.340             0.121            0.158
Chain 1:   7100        -9321.515             0.140            0.158
Chain 1:   7200        -8927.140             0.142            0.158
Chain 1:   7300       -11502.800             0.147            0.158
Chain 1:   7400       -10884.800             0.144            0.158
Chain 1:   7500        -8108.314             0.162            0.166
Chain 1:   7600        -8433.045             0.150            0.140
Chain 1:   7700        -8606.837             0.147            0.140
Chain 1:   7800       -11880.291             0.168            0.191
Chain 1:   7900        -8041.321             0.202            0.224
Chain 1:   8000        -8617.784             0.189            0.224
Chain 1:   8100       -10581.456             0.173            0.186
Chain 1:   8200        -8129.354             0.199            0.224
Chain 1:   8300       -11161.825             0.204            0.272
Chain 1:   8400        -7987.349             0.238            0.276
Chain 1:   8500        -9229.942             0.217            0.272
Chain 1:   8600       -10184.044             0.222            0.272
Chain 1:   8700       -11688.507             0.233            0.272
Chain 1:   8800        -8272.098             0.247            0.272
Chain 1:   8900        -8595.324             0.203            0.186
Chain 1:   9000       -10082.414             0.211            0.186
Chain 1:   9100        -8342.088             0.213            0.209
Chain 1:   9200        -8959.219             0.190            0.147
Chain 1:   9300        -9347.385             0.167            0.135
Chain 1:   9400        -8103.808             0.143            0.135
Chain 1:   9500        -8155.212             0.130            0.129
Chain 1:   9600       -10671.523             0.144            0.147
Chain 1:   9700        -8538.525             0.156            0.153
Chain 1:   9800        -8820.749             0.118            0.147
Chain 1:   9900       -11111.103             0.135            0.153
Chain 1:   10000        -8033.772             0.159            0.206
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001426 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.26 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -53059.811             1.000            1.000
Chain 1:    200       -16712.516             1.587            2.175
Chain 1:    300        -8636.816             1.370            1.000
Chain 1:    400        -8391.182             1.035            1.000
Chain 1:    500        -7993.124             0.838            0.935
Chain 1:    600        -8555.716             0.709            0.935
Chain 1:    700        -8153.260             0.615            0.066
Chain 1:    800        -8271.406             0.540            0.066
Chain 1:    900        -7985.830             0.484            0.050
Chain 1:   1000        -7872.291             0.437            0.050
Chain 1:   1100        -7717.659             0.339            0.049
Chain 1:   1200        -7614.074             0.123            0.036
Chain 1:   1300        -7631.060             0.029            0.029
Chain 1:   1400        -8010.092             0.031            0.036
Chain 1:   1500        -7654.141             0.031            0.036
Chain 1:   1600        -7792.186             0.026            0.020
Chain 1:   1700        -7574.066             0.024            0.020
Chain 1:   1800        -7629.352             0.023            0.020
Chain 1:   1900        -7636.184             0.020            0.018
Chain 1:   2000        -7642.914             0.019            0.018
Chain 1:   2100        -7632.324             0.017            0.014
Chain 1:   2200        -7734.386             0.017            0.013
Chain 1:   2300        -7849.878             0.018            0.015
Chain 1:   2400        -7674.971             0.015            0.015
Chain 1:   2500        -7649.408             0.011            0.013
Chain 1:   2600        -7562.979             0.010            0.011
Chain 1:   2700        -7622.507             0.008            0.008   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003107 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.07 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86340.907             1.000            1.000
Chain 1:    200       -13421.892             3.216            5.433
Chain 1:    300        -9756.200             2.270            1.000
Chain 1:    400       -10708.712             1.724            1.000
Chain 1:    500        -8732.464             1.425            0.376
Chain 1:    600        -8708.064             1.188            0.376
Chain 1:    700        -8475.974             1.022            0.226
Chain 1:    800        -9123.725             0.903            0.226
Chain 1:    900        -8518.268             0.811            0.089
Chain 1:   1000        -8411.794             0.731            0.089
Chain 1:   1100        -8413.134             0.631            0.071   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8183.658             0.090            0.071
Chain 1:   1300        -8426.622             0.056            0.029
Chain 1:   1400        -8443.367             0.047            0.028
Chain 1:   1500        -8293.849             0.026            0.027
Chain 1:   1600        -8407.828             0.027            0.027
Chain 1:   1700        -8483.374             0.025            0.018
Chain 1:   1800        -8059.000             0.024            0.018
Chain 1:   1900        -8160.743             0.018            0.014
Chain 1:   2000        -8135.257             0.017            0.014
Chain 1:   2100        -8261.378             0.018            0.015
Chain 1:   2200        -8062.575             0.018            0.015
Chain 1:   2300        -8155.635             0.016            0.014
Chain 1:   2400        -8224.150             0.017            0.014
Chain 1:   2500        -8170.352             0.016            0.012
Chain 1:   2600        -8172.088             0.014            0.011
Chain 1:   2700        -8088.679             0.015            0.011
Chain 1:   2800        -8048.024             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002591 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8395753.047             1.000            1.000
Chain 1:    200     -1581880.387             2.654            4.307
Chain 1:    300      -890577.148             2.028            1.000
Chain 1:    400      -457843.097             1.757            1.000
Chain 1:    500      -358207.706             1.461            0.945
Chain 1:    600      -233173.241             1.307            0.945
Chain 1:    700      -119267.213             1.257            0.945
Chain 1:    800       -86445.857             1.147            0.945
Chain 1:    900       -66763.319             1.053            0.776
Chain 1:   1000       -51547.144             0.977            0.776
Chain 1:   1100       -39010.125             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38186.222             0.480            0.380
Chain 1:   1300       -26126.351             0.449            0.380
Chain 1:   1400       -25845.111             0.355            0.321
Chain 1:   1500       -22427.934             0.343            0.321
Chain 1:   1600       -21643.419             0.293            0.295
Chain 1:   1700       -20514.934             0.203            0.295
Chain 1:   1800       -20458.762             0.165            0.152
Chain 1:   1900       -20785.007             0.137            0.055
Chain 1:   2000       -19294.866             0.115            0.055
Chain 1:   2100       -19533.342             0.085            0.036
Chain 1:   2200       -19760.010             0.084            0.036
Chain 1:   2300       -19376.997             0.039            0.020
Chain 1:   2400       -19148.993             0.039            0.020
Chain 1:   2500       -18951.079             0.025            0.016
Chain 1:   2600       -18581.059             0.024            0.016
Chain 1:   2700       -18538.000             0.018            0.012
Chain 1:   2800       -18254.796             0.020            0.016
Chain 1:   2900       -18536.154             0.020            0.015
Chain 1:   3000       -18522.332             0.012            0.012
Chain 1:   3100       -18607.320             0.011            0.012
Chain 1:   3200       -18297.900             0.012            0.015
Chain 1:   3300       -18502.713             0.011            0.012
Chain 1:   3400       -17977.488             0.013            0.015
Chain 1:   3500       -18589.599             0.015            0.016
Chain 1:   3600       -17895.981             0.017            0.016
Chain 1:   3700       -18282.995             0.019            0.017
Chain 1:   3800       -17242.240             0.023            0.021
Chain 1:   3900       -17238.370             0.022            0.021
Chain 1:   4000       -17355.674             0.022            0.021
Chain 1:   4100       -17269.397             0.022            0.021
Chain 1:   4200       -17085.547             0.022            0.021
Chain 1:   4300       -17224.007             0.021            0.021
Chain 1:   4400       -17180.747             0.019            0.011
Chain 1:   4500       -17083.265             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001298 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12436.296             1.000            1.000
Chain 1:    200        -9390.892             0.662            1.000
Chain 1:    300        -8090.005             0.495            0.324
Chain 1:    400        -8281.453             0.377            0.324
Chain 1:    500        -8145.209             0.305            0.161
Chain 1:    600        -8037.948             0.256            0.161
Chain 1:    700        -7950.035             0.221            0.023
Chain 1:    800        -7957.687             0.194            0.023
Chain 1:    900        -7864.037             0.174            0.017
Chain 1:   1000        -8053.956             0.159            0.023
Chain 1:   1100        -8089.894             0.059            0.017
Chain 1:   1200        -7984.478             0.028            0.013
Chain 1:   1300        -7921.346             0.013            0.013
Chain 1:   1400        -7943.576             0.011            0.012
Chain 1:   1500        -8030.638             0.010            0.011
Chain 1:   1600        -7993.012             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001387 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.87 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61802.741             1.000            1.000
Chain 1:    200       -17938.064             1.723            2.445
Chain 1:    300        -8949.887             1.483            1.004
Chain 1:    400        -8422.542             1.128            1.004
Chain 1:    500        -8279.192             0.906            1.000
Chain 1:    600        -8949.337             0.767            1.000
Chain 1:    700        -8388.452             0.667            0.075
Chain 1:    800        -7881.220             0.592            0.075
Chain 1:    900        -8055.646             0.529            0.067
Chain 1:   1000        -7961.210             0.477            0.067
Chain 1:   1100        -7882.697             0.378            0.064
Chain 1:   1200        -7737.978             0.135            0.063
Chain 1:   1300        -7730.402             0.035            0.022
Chain 1:   1400        -7884.952             0.031            0.020
Chain 1:   1500        -7710.716             0.031            0.022
Chain 1:   1600        -7751.822             0.024            0.020
Chain 1:   1700        -7618.511             0.019            0.019
Chain 1:   1800        -7636.827             0.013            0.017
Chain 1:   1900        -7672.147             0.011            0.012
Chain 1:   2000        -7742.515             0.011            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002677 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.77 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86094.596             1.000            1.000
Chain 1:    200       -13551.712             3.177            5.353
Chain 1:    300        -9919.684             2.240            1.000
Chain 1:    400       -10961.012             1.704            1.000
Chain 1:    500        -8886.622             1.410            0.366
Chain 1:    600        -8410.528             1.184            0.366
Chain 1:    700        -8334.525             1.016            0.233
Chain 1:    800        -8658.528             0.894            0.233
Chain 1:    900        -8741.248             0.796            0.095
Chain 1:   1000        -8468.654             0.719            0.095
Chain 1:   1100        -8742.683             0.622            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8555.505             0.089            0.037
Chain 1:   1300        -8566.181             0.053            0.032
Chain 1:   1400        -8622.813             0.044            0.031
Chain 1:   1500        -8490.609             0.022            0.022
Chain 1:   1600        -8603.408             0.018            0.016
Chain 1:   1700        -8684.379             0.018            0.016
Chain 1:   1800        -8271.279             0.019            0.016
Chain 1:   1900        -8367.565             0.019            0.016
Chain 1:   2000        -8340.809             0.016            0.013
Chain 1:   2100        -8463.368             0.015            0.013
Chain 1:   2200        -8283.560             0.015            0.013
Chain 1:   2300        -8362.435             0.015            0.013
Chain 1:   2400        -8432.132             0.016            0.013
Chain 1:   2500        -8377.552             0.015            0.012
Chain 1:   2600        -8376.986             0.013            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003268 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413716.696             1.000            1.000
Chain 1:    200     -1586238.832             2.652            4.304
Chain 1:    300      -892019.007             2.027            1.000
Chain 1:    400      -458553.832             1.757            1.000
Chain 1:    500      -358486.683             1.461            0.945
Chain 1:    600      -233393.443             1.307            0.945
Chain 1:    700      -119412.917             1.257            0.945
Chain 1:    800       -86588.231             1.147            0.945
Chain 1:    900       -66899.492             1.052            0.778
Chain 1:   1000       -51678.732             0.977            0.778
Chain 1:   1100       -39141.968             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38315.359             0.480            0.379
Chain 1:   1300       -26258.456             0.448            0.379
Chain 1:   1400       -25976.514             0.355            0.320
Chain 1:   1500       -22560.439             0.342            0.320
Chain 1:   1600       -21775.989             0.292            0.295
Chain 1:   1700       -20648.060             0.202            0.294
Chain 1:   1800       -20591.805             0.165            0.151
Chain 1:   1900       -20917.852             0.137            0.055
Chain 1:   2000       -19428.314             0.115            0.055
Chain 1:   2100       -19666.716             0.084            0.036
Chain 1:   2200       -19893.294             0.083            0.036
Chain 1:   2300       -19510.405             0.039            0.020
Chain 1:   2400       -19282.489             0.039            0.020
Chain 1:   2500       -19084.607             0.025            0.016
Chain 1:   2600       -18714.786             0.023            0.016
Chain 1:   2700       -18671.718             0.018            0.012
Chain 1:   2800       -18388.651             0.020            0.015
Chain 1:   2900       -18669.875             0.019            0.015
Chain 1:   3000       -18656.091             0.012            0.012
Chain 1:   3100       -18741.078             0.011            0.012
Chain 1:   3200       -18431.760             0.012            0.015
Chain 1:   3300       -18636.464             0.011            0.012
Chain 1:   3400       -18111.469             0.012            0.015
Chain 1:   3500       -18723.252             0.015            0.015
Chain 1:   3600       -18030.030             0.017            0.015
Chain 1:   3700       -18416.771             0.018            0.017
Chain 1:   3800       -17376.680             0.023            0.021
Chain 1:   3900       -17372.826             0.021            0.021
Chain 1:   4000       -17490.127             0.022            0.021
Chain 1:   4100       -17403.913             0.022            0.021
Chain 1:   4200       -17220.181             0.021            0.021
Chain 1:   4300       -17358.551             0.021            0.021
Chain 1:   4400       -17315.401             0.018            0.011
Chain 1:   4500       -17217.948             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001789 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12834.925             1.000            1.000
Chain 1:    200        -9722.372             0.660            1.000
Chain 1:    300        -8170.614             0.503            0.320
Chain 1:    400        -8437.600             0.385            0.320
Chain 1:    500        -8314.877             0.311            0.190
Chain 1:    600        -8162.822             0.263            0.190
Chain 1:    700        -8279.017             0.227            0.032
Chain 1:    800        -8118.930             0.201            0.032
Chain 1:    900        -8029.722             0.180            0.020
Chain 1:   1000        -8043.786             0.162            0.020
Chain 1:   1100        -8234.269             0.064            0.020
Chain 1:   1200        -8093.914             0.034            0.019
Chain 1:   1300        -8020.538             0.016            0.017
Chain 1:   1400        -8049.105             0.013            0.015
Chain 1:   1500        -8149.601             0.013            0.014
Chain 1:   1600        -8061.740             0.012            0.012
Chain 1:   1700        -8027.515             0.011            0.011
Chain 1:   1800        -7998.559             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001379 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.79 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58345.799             1.000            1.000
Chain 1:    200       -18092.752             1.612            2.225
Chain 1:    300        -8919.107             1.418            1.029
Chain 1:    400        -8136.165             1.087            1.029
Chain 1:    500        -8495.208             0.878            1.000
Chain 1:    600        -8663.309             0.735            1.000
Chain 1:    700        -7888.208             0.644            0.098
Chain 1:    800        -7677.815             0.567            0.098
Chain 1:    900        -7996.663             0.509            0.096
Chain 1:   1000        -7836.221             0.460            0.096
Chain 1:   1100        -7914.404             0.361            0.042
Chain 1:   1200        -7783.360             0.140            0.040
Chain 1:   1300        -7765.335             0.037            0.027
Chain 1:   1400        -7992.561             0.031            0.027
Chain 1:   1500        -7670.232             0.030            0.027
Chain 1:   1600        -7855.795             0.031            0.027
Chain 1:   1700        -7647.125             0.024            0.027
Chain 1:   1800        -7754.652             0.022            0.024
Chain 1:   1900        -7806.474             0.019            0.020
Chain 1:   2000        -7695.911             0.019            0.017
Chain 1:   2100        -7568.476             0.019            0.017
Chain 1:   2200        -8002.511             0.023            0.024
Chain 1:   2300        -7659.184             0.027            0.027
Chain 1:   2400        -7712.180             0.025            0.024
Chain 1:   2500        -7713.790             0.021            0.017
Chain 1:   2600        -7605.918             0.020            0.014
Chain 1:   2700        -7615.726             0.017            0.014
Chain 1:   2800        -7713.976             0.017            0.014
Chain 1:   2900        -7464.919             0.020            0.014
Chain 1:   3000        -7614.673             0.020            0.017
Chain 1:   3100        -7605.507             0.019            0.014
Chain 1:   3200        -7815.188             0.016            0.014
Chain 1:   3300        -7528.221             0.015            0.014
Chain 1:   3400        -7763.537             0.018            0.020
Chain 1:   3500        -7515.326             0.021            0.027
Chain 1:   3600        -7581.981             0.021            0.027
Chain 1:   3700        -7531.568             0.021            0.027
Chain 1:   3800        -7528.994             0.020            0.027
Chain 1:   3900        -7494.134             0.017            0.020
Chain 1:   4000        -7485.839             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002599 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.99 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86954.134             1.000            1.000
Chain 1:    200       -13912.642             3.125            5.250
Chain 1:    300       -10165.915             2.206            1.000
Chain 1:    400       -11739.345             1.688            1.000
Chain 1:    500        -8886.400             1.415            0.369
Chain 1:    600        -9643.894             1.192            0.369
Chain 1:    700        -8592.569             1.039            0.321
Chain 1:    800        -8798.508             0.912            0.321
Chain 1:    900        -8975.376             0.813            0.134
Chain 1:   1000        -8993.503             0.732            0.134
Chain 1:   1100        -8865.750             0.633            0.122   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8511.486             0.113            0.079
Chain 1:   1300        -8815.553             0.079            0.042
Chain 1:   1400        -8749.095             0.067            0.034
Chain 1:   1500        -8664.436             0.035            0.023
Chain 1:   1600        -8776.890             0.029            0.020
Chain 1:   1700        -8834.335             0.017            0.014
Chain 1:   1800        -8391.986             0.020            0.014
Chain 1:   1900        -8497.088             0.019            0.013
Chain 1:   2000        -8482.769             0.019            0.013
Chain 1:   2100        -8599.510             0.019            0.013
Chain 1:   2200        -8393.855             0.018            0.013
Chain 1:   2300        -8489.594             0.015            0.012
Chain 1:   2400        -8556.283             0.015            0.012
Chain 1:   2500        -8504.841             0.015            0.012
Chain 1:   2600        -8519.052             0.014            0.011
Chain 1:   2700        -8426.360             0.014            0.011
Chain 1:   2800        -8373.601             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003514 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.14 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8413862.213             1.000            1.000
Chain 1:    200     -1586956.162             2.651            4.302
Chain 1:    300      -891563.446             2.027            1.000
Chain 1:    400      -457875.600             1.757            1.000
Chain 1:    500      -358007.217             1.462            0.947
Chain 1:    600      -233091.142             1.307            0.947
Chain 1:    700      -119498.625             1.256            0.947
Chain 1:    800       -86727.499             1.147            0.947
Chain 1:    900       -67115.468             1.052            0.780
Chain 1:   1000       -51950.322             0.976            0.780
Chain 1:   1100       -39452.847             0.907            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38639.824             0.479            0.378
Chain 1:   1300       -26618.538             0.446            0.378
Chain 1:   1400       -26342.450             0.353            0.317
Chain 1:   1500       -22933.983             0.340            0.317
Chain 1:   1600       -22152.528             0.290            0.292
Chain 1:   1700       -21028.662             0.200            0.292
Chain 1:   1800       -20973.703             0.162            0.149
Chain 1:   1900       -21300.361             0.135            0.053
Chain 1:   2000       -19811.529             0.113            0.053
Chain 1:   2100       -20050.202             0.083            0.035
Chain 1:   2200       -20276.602             0.082            0.035
Chain 1:   2300       -19893.675             0.038            0.019
Chain 1:   2400       -19665.598             0.038            0.019
Chain 1:   2500       -19467.318             0.025            0.015
Chain 1:   2600       -19097.296             0.023            0.015
Chain 1:   2700       -19054.186             0.018            0.012
Chain 1:   2800       -18770.647             0.019            0.015
Chain 1:   2900       -19052.143             0.019            0.015
Chain 1:   3000       -19038.393             0.012            0.012
Chain 1:   3100       -19123.423             0.011            0.012
Chain 1:   3200       -18813.802             0.011            0.015
Chain 1:   3300       -19018.768             0.011            0.012
Chain 1:   3400       -18493.052             0.012            0.015
Chain 1:   3500       -19105.776             0.014            0.015
Chain 1:   3600       -18411.354             0.016            0.015
Chain 1:   3700       -18798.941             0.018            0.016
Chain 1:   3800       -17756.824             0.022            0.021
Chain 1:   3900       -17752.857             0.021            0.021
Chain 1:   4000       -17870.235             0.022            0.021
Chain 1:   4100       -17783.854             0.022            0.021
Chain 1:   4200       -17599.705             0.021            0.021
Chain 1:   4300       -17738.428             0.021            0.021
Chain 1:   4400       -17694.956             0.018            0.010
Chain 1:   4500       -17597.361             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001343 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.43 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49573.852             1.000            1.000
Chain 1:    200       -22743.382             1.090            1.180
Chain 1:    300       -14980.279             0.899            1.000
Chain 1:    400       -21084.531             0.747            1.000
Chain 1:    500       -24747.566             0.627            0.518
Chain 1:    600       -19820.827             0.564            0.518
Chain 1:    700       -14735.897             0.533            0.345
Chain 1:    800       -17364.480             0.485            0.345
Chain 1:    900       -14206.548             0.456            0.290
Chain 1:   1000       -10930.099             0.440            0.300
Chain 1:   1100       -11445.087             0.345            0.290
Chain 1:   1200       -20098.824             0.270            0.290
Chain 1:   1300       -12602.368             0.277            0.290
Chain 1:   1400       -10838.889             0.265            0.249
Chain 1:   1500       -10526.193             0.253            0.249
Chain 1:   1600       -12302.260             0.243            0.222
Chain 1:   1700       -11336.730             0.217            0.163
Chain 1:   1800       -11959.686             0.207            0.163
Chain 1:   1900       -12646.648             0.190            0.144
Chain 1:   2000       -18273.449             0.191            0.144
Chain 1:   2100       -12033.667             0.238            0.163
Chain 1:   2200       -12537.395             0.199            0.144
Chain 1:   2300        -9745.646             0.168            0.144
Chain 1:   2400       -12021.442             0.171            0.144
Chain 1:   2500       -10115.139             0.187            0.188
Chain 1:   2600       -10390.703             0.175            0.188
Chain 1:   2700       -10185.339             0.168            0.188
Chain 1:   2800       -10332.269             0.165            0.188
Chain 1:   2900       -10098.231             0.161            0.188
Chain 1:   3000       -10789.636             0.137            0.064
Chain 1:   3100       -10407.396             0.089            0.040
Chain 1:   3200       -13672.154             0.109            0.064
Chain 1:   3300       -17208.953             0.101            0.064
Chain 1:   3400       -11257.960             0.135            0.064
Chain 1:   3500        -9724.568             0.132            0.064
Chain 1:   3600        -9697.770             0.129            0.064
Chain 1:   3700        -9414.132             0.130            0.064
Chain 1:   3800        -9848.078             0.133            0.064
Chain 1:   3900       -10192.509             0.134            0.064
Chain 1:   4000        -9253.792             0.138            0.101
Chain 1:   4100       -10789.328             0.149            0.142
Chain 1:   4200        -9927.493             0.133            0.101
Chain 1:   4300       -11418.625             0.126            0.101
Chain 1:   4400        -8975.695             0.100            0.101
Chain 1:   4500        -9232.115             0.087            0.087
Chain 1:   4600       -14214.208             0.122            0.101
Chain 1:   4700       -11623.237             0.141            0.131
Chain 1:   4800       -12831.273             0.146            0.131
Chain 1:   4900       -13653.900             0.149            0.131
Chain 1:   5000       -13108.679             0.143            0.131
Chain 1:   5100       -10541.719             0.153            0.131
Chain 1:   5200       -10885.390             0.148            0.131
Chain 1:   5300       -10940.429             0.135            0.094
Chain 1:   5400        -9317.849             0.125            0.094
Chain 1:   5500        -9833.107             0.128            0.094
Chain 1:   5600        -8991.721             0.102            0.094
Chain 1:   5700       -14171.725             0.116            0.094
Chain 1:   5800       -13613.309             0.111            0.060
Chain 1:   5900        -9868.436             0.143            0.094
Chain 1:   6000        -8988.689             0.148            0.098
Chain 1:   6100       -10905.920             0.142            0.098
Chain 1:   6200       -13023.510             0.155            0.163
Chain 1:   6300       -13544.823             0.158            0.163
Chain 1:   6400        -8964.367             0.192            0.163
Chain 1:   6500        -9195.695             0.189            0.163
Chain 1:   6600        -9136.282             0.180            0.163
Chain 1:   6700       -12829.313             0.173            0.163
Chain 1:   6800       -15212.353             0.184            0.163
Chain 1:   6900        -9219.669             0.211            0.163
Chain 1:   7000       -13049.669             0.231            0.176
Chain 1:   7100        -9035.873             0.258            0.288
Chain 1:   7200        -9678.559             0.248            0.288
Chain 1:   7300        -9176.936             0.250            0.288
Chain 1:   7400        -9398.174             0.201            0.157
Chain 1:   7500       -11279.819             0.215            0.167
Chain 1:   7600        -8858.469             0.242            0.273
Chain 1:   7700        -8896.186             0.213            0.167
Chain 1:   7800        -9658.220             0.206            0.167
Chain 1:   7900        -8781.984             0.151            0.100
Chain 1:   8000        -8682.971             0.122            0.079
Chain 1:   8100        -8748.676             0.079            0.066
Chain 1:   8200        -9817.277             0.083            0.079
Chain 1:   8300       -12204.365             0.097            0.100
Chain 1:   8400       -12396.728             0.096            0.100
Chain 1:   8500       -10295.244             0.100            0.100
Chain 1:   8600        -9348.188             0.083            0.100
Chain 1:   8700        -9509.585             0.084            0.100
Chain 1:   8800        -8732.130             0.085            0.100
Chain 1:   8900       -11321.467             0.098            0.101
Chain 1:   9000       -11655.295             0.100            0.101
Chain 1:   9100       -12754.747             0.107            0.101
Chain 1:   9200        -8744.985             0.142            0.101
Chain 1:   9300       -11345.988             0.146            0.101
Chain 1:   9400       -11536.235             0.146            0.101
Chain 1:   9500        -9866.338             0.142            0.101
Chain 1:   9600       -10245.218             0.136            0.089
Chain 1:   9700        -9725.932             0.140            0.089
Chain 1:   9800        -9312.179             0.135            0.086
Chain 1:   9900       -12362.896             0.137            0.086
Chain 1:   10000        -9522.288             0.164            0.169
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.86 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -47048.803             1.000            1.000
Chain 1:    200       -16210.471             1.451            1.902
Chain 1:    300        -9089.223             1.229            1.000
Chain 1:    400        -8335.846             0.944            1.000
Chain 1:    500        -8936.928             0.769            0.783
Chain 1:    600        -9689.139             0.654            0.783
Chain 1:    700        -7975.320             0.591            0.215
Chain 1:    800        -7815.282             0.520            0.215
Chain 1:    900        -8103.081             0.466            0.090
Chain 1:   1000        -8298.550             0.422            0.090
Chain 1:   1100        -7841.062             0.327            0.078
Chain 1:   1200        -8042.077             0.140            0.067
Chain 1:   1300        -7875.992             0.063            0.058
Chain 1:   1400        -8162.162             0.058            0.036
Chain 1:   1500        -7661.387             0.058            0.036
Chain 1:   1600        -7841.506             0.052            0.035
Chain 1:   1700        -7571.222             0.034            0.035
Chain 1:   1800        -7705.571             0.034            0.035
Chain 1:   1900        -7730.870             0.031            0.025
Chain 1:   2000        -7838.885             0.030            0.025
Chain 1:   2100        -7755.866             0.025            0.023
Chain 1:   2200        -8010.187             0.026            0.023
Chain 1:   2300        -7718.388             0.027            0.032
Chain 1:   2400        -7653.846             0.025            0.023
Chain 1:   2500        -7715.269             0.019            0.017
Chain 1:   2600        -7644.497             0.018            0.014
Chain 1:   2700        -7635.891             0.014            0.011
Chain 1:   2800        -7639.541             0.012            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003197 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 31.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -87665.988             1.000            1.000
Chain 1:    200       -14209.389             3.085            5.170
Chain 1:    300       -10467.937             2.176            1.000
Chain 1:    400       -11663.271             1.657            1.000
Chain 1:    500        -9429.978             1.373            0.357
Chain 1:    600        -9214.112             1.148            0.357
Chain 1:    700        -9161.202             0.985            0.237
Chain 1:    800        -8769.130             0.868            0.237
Chain 1:    900        -8780.445             0.771            0.102
Chain 1:   1000        -9090.368             0.698            0.102
Chain 1:   1100        -9232.458             0.599            0.045   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8810.207             0.087            0.045
Chain 1:   1300        -9138.502             0.055            0.036
Chain 1:   1400        -9083.927             0.045            0.034
Chain 1:   1500        -8959.321             0.023            0.023
Chain 1:   1600        -9071.522             0.022            0.015
Chain 1:   1700        -9133.392             0.022            0.015
Chain 1:   1800        -8690.889             0.022            0.015
Chain 1:   1900        -8797.153             0.024            0.015
Chain 1:   2000        -8781.633             0.020            0.014
Chain 1:   2100        -8908.597             0.020            0.014
Chain 1:   2200        -8696.399             0.018            0.014
Chain 1:   2300        -8791.156             0.015            0.012
Chain 1:   2400        -8858.312             0.015            0.012
Chain 1:   2500        -8806.202             0.015            0.012
Chain 1:   2600        -8818.378             0.014            0.011
Chain 1:   2700        -8726.824             0.014            0.011
Chain 1:   2800        -8675.210             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003545 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405651.197             1.000            1.000
Chain 1:    200     -1586031.380             2.650            4.300
Chain 1:    300      -891494.689             2.026            1.000
Chain 1:    400      -458069.580             1.756            1.000
Chain 1:    500      -358406.102             1.461            0.946
Chain 1:    600      -233465.077             1.306            0.946
Chain 1:    700      -119811.914             1.255            0.946
Chain 1:    800       -87055.313             1.145            0.946
Chain 1:    900       -67431.724             1.050            0.779
Chain 1:   1000       -52259.508             0.974            0.779
Chain 1:   1100       -39757.525             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38942.152             0.478            0.376
Chain 1:   1300       -26911.315             0.445            0.376
Chain 1:   1400       -26634.323             0.351            0.314
Chain 1:   1500       -23223.673             0.338            0.314
Chain 1:   1600       -22441.483             0.288            0.291
Chain 1:   1700       -21316.256             0.198            0.290
Chain 1:   1800       -21261.002             0.161            0.147
Chain 1:   1900       -21587.647             0.134            0.053
Chain 1:   2000       -20098.239             0.112            0.053
Chain 1:   2100       -20336.883             0.082            0.035
Chain 1:   2200       -20563.440             0.081            0.035
Chain 1:   2300       -20180.374             0.038            0.019
Chain 1:   2400       -19952.274             0.038            0.019
Chain 1:   2500       -19754.109             0.024            0.015
Chain 1:   2600       -19383.988             0.023            0.015
Chain 1:   2700       -19340.877             0.018            0.012
Chain 1:   2800       -19057.421             0.019            0.015
Chain 1:   2900       -19338.879             0.019            0.015
Chain 1:   3000       -19325.110             0.011            0.012
Chain 1:   3100       -19410.144             0.011            0.011
Chain 1:   3200       -19100.537             0.011            0.015
Chain 1:   3300       -19305.488             0.010            0.011
Chain 1:   3400       -18779.820             0.012            0.015
Chain 1:   3500       -19392.539             0.014            0.015
Chain 1:   3600       -18698.102             0.016            0.015
Chain 1:   3700       -19085.703             0.018            0.016
Chain 1:   3800       -18043.641             0.022            0.020
Chain 1:   3900       -18039.689             0.021            0.020
Chain 1:   4000       -18157.037             0.021            0.020
Chain 1:   4100       -18070.682             0.021            0.020
Chain 1:   4200       -17886.542             0.021            0.020
Chain 1:   4300       -18025.245             0.020            0.020
Chain 1:   4400       -17981.753             0.018            0.010
Chain 1:   4500       -17884.180             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001338 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12268.421             1.000            1.000
Chain 1:    200        -9199.568             0.667            1.000
Chain 1:    300        -8162.790             0.487            0.334
Chain 1:    400        -8157.108             0.365            0.334
Chain 1:    500        -8038.547             0.295            0.127
Chain 1:    600        -7954.819             0.248            0.127
Chain 1:    700        -7861.727             0.214            0.015
Chain 1:    800        -7905.755             0.188            0.015
Chain 1:    900        -8028.689             0.169            0.015
Chain 1:   1000        -7937.206             0.153            0.015
Chain 1:   1100        -7905.527             0.053            0.012
Chain 1:   1200        -7867.701             0.021            0.012
Chain 1:   1300        -7833.773             0.008            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001542 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 15.42 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56825.841             1.000            1.000
Chain 1:    200       -17328.193             1.640            2.279
Chain 1:    300        -8680.491             1.425            1.000
Chain 1:    400        -8456.337             1.076            1.000
Chain 1:    500        -8128.229             0.868            0.996
Chain 1:    600        -8519.205             0.731            0.996
Chain 1:    700        -7830.521             0.639            0.088
Chain 1:    800        -8143.776             0.564            0.088
Chain 1:    900        -7995.440             0.504            0.046
Chain 1:   1000        -7751.910             0.456            0.046
Chain 1:   1100        -7948.790             0.359            0.040
Chain 1:   1200        -7634.277             0.135            0.040
Chain 1:   1300        -7699.379             0.036            0.038
Chain 1:   1400        -7915.964             0.036            0.038
Chain 1:   1500        -7640.311             0.036            0.036
Chain 1:   1600        -7572.080             0.032            0.031
Chain 1:   1700        -7535.815             0.024            0.027
Chain 1:   1800        -7618.318             0.021            0.025
Chain 1:   1900        -7580.343             0.020            0.025
Chain 1:   2000        -7634.727             0.017            0.011
Chain 1:   2100        -7547.811             0.016            0.011
Chain 1:   2200        -7694.060             0.014            0.011
Chain 1:   2300        -7600.402             0.014            0.012
Chain 1:   2400        -7652.916             0.012            0.011
Chain 1:   2500        -7744.564             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003309 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.09 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85848.971             1.000            1.000
Chain 1:    200       -13418.433             3.199            5.398
Chain 1:    300        -9820.681             2.255            1.000
Chain 1:    400       -10675.112             1.711            1.000
Chain 1:    500        -8654.220             1.416            0.366
Chain 1:    600        -8329.868             1.186            0.366
Chain 1:    700        -8373.190             1.017            0.234
Chain 1:    800        -8525.211             0.892            0.234
Chain 1:    900        -8656.012             0.795            0.080
Chain 1:   1000        -8378.753             0.719            0.080
Chain 1:   1100        -8627.850             0.622            0.039   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8284.776             0.086            0.039
Chain 1:   1300        -8505.535             0.052            0.033
Chain 1:   1400        -8507.859             0.044            0.029
Chain 1:   1500        -8399.592             0.022            0.026
Chain 1:   1600        -8503.611             0.019            0.018
Chain 1:   1700        -8591.963             0.020            0.018
Chain 1:   1800        -8184.509             0.023            0.026
Chain 1:   1900        -8281.446             0.023            0.026
Chain 1:   2000        -8253.510             0.020            0.013
Chain 1:   2100        -8374.034             0.018            0.013
Chain 1:   2200        -8184.784             0.016            0.013
Chain 1:   2300        -8321.170             0.015            0.013
Chain 1:   2400        -8328.373             0.016            0.013
Chain 1:   2500        -8294.674             0.015            0.012
Chain 1:   2600        -8292.649             0.013            0.012
Chain 1:   2700        -8206.726             0.013            0.012
Chain 1:   2800        -8171.913             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.004731 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 47.31 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8367944.128             1.000            1.000
Chain 1:    200     -1576627.791             2.654            4.307
Chain 1:    300      -888976.988             2.027            1.000
Chain 1:    400      -456743.348             1.757            1.000
Chain 1:    500      -357901.587             1.461            0.946
Chain 1:    600      -233009.949             1.307            0.946
Chain 1:    700      -119239.911             1.256            0.946
Chain 1:    800       -86458.245             1.147            0.946
Chain 1:    900       -66780.035             1.052            0.774
Chain 1:   1000       -51560.947             0.976            0.774
Chain 1:   1100       -39020.258             0.908            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38193.475             0.480            0.379
Chain 1:   1300       -26125.949             0.449            0.379
Chain 1:   1400       -25841.812             0.355            0.321
Chain 1:   1500       -22423.519             0.343            0.321
Chain 1:   1600       -21638.504             0.293            0.295
Chain 1:   1700       -20509.095             0.203            0.295
Chain 1:   1800       -20452.565             0.165            0.152
Chain 1:   1900       -20778.653             0.137            0.055
Chain 1:   2000       -19288.591             0.116            0.055
Chain 1:   2100       -19526.811             0.085            0.036
Chain 1:   2200       -19753.639             0.084            0.036
Chain 1:   2300       -19370.573             0.039            0.020
Chain 1:   2400       -19142.715             0.039            0.020
Chain 1:   2500       -18944.936             0.025            0.016
Chain 1:   2600       -18575.054             0.024            0.016
Chain 1:   2700       -18532.016             0.018            0.012
Chain 1:   2800       -18249.098             0.020            0.016
Chain 1:   2900       -18530.271             0.020            0.015
Chain 1:   3000       -18516.363             0.012            0.012
Chain 1:   3100       -18601.377             0.011            0.012
Chain 1:   3200       -18292.111             0.012            0.015
Chain 1:   3300       -18496.799             0.011            0.012
Chain 1:   3400       -17971.928             0.013            0.015
Chain 1:   3500       -18583.620             0.015            0.016
Chain 1:   3600       -17890.540             0.017            0.016
Chain 1:   3700       -18277.220             0.019            0.017
Chain 1:   3800       -17237.393             0.023            0.021
Chain 1:   3900       -17233.606             0.022            0.021
Chain 1:   4000       -17350.839             0.022            0.021
Chain 1:   4100       -17264.690             0.022            0.021
Chain 1:   4200       -17081.037             0.022            0.021
Chain 1:   4300       -17219.333             0.021            0.021
Chain 1:   4400       -17176.235             0.019            0.011
Chain 1:   4500       -17078.834             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001421 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12631.213             1.000            1.000
Chain 1:    200        -9502.040             0.665            1.000
Chain 1:    300        -8225.480             0.495            0.329
Chain 1:    400        -8384.796             0.376            0.329
Chain 1:    500        -8063.463             0.309            0.155
Chain 1:    600        -8159.606             0.259            0.155
Chain 1:    700        -8281.810             0.224            0.040
Chain 1:    800        -8123.844             0.199            0.040
Chain 1:    900        -8197.139             0.178            0.019
Chain 1:   1000        -8159.953             0.160            0.019
Chain 1:   1100        -8194.247             0.061            0.019
Chain 1:   1200        -8093.683             0.029            0.015
Chain 1:   1300        -8186.459             0.015            0.012
Chain 1:   1400        -8074.843             0.014            0.012
Chain 1:   1500        -8170.706             0.011            0.012
Chain 1:   1600        -8113.255             0.011            0.012
Chain 1:   1700        -8056.600             0.010            0.011
Chain 1:   1800        -8030.572             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001401 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.01 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58499.393             1.000            1.000
Chain 1:    200       -17950.110             1.629            2.259
Chain 1:    300        -9053.080             1.414            1.000
Chain 1:    400        -8479.558             1.077            1.000
Chain 1:    500        -8601.899             0.865            0.983
Chain 1:    600        -8591.642             0.721            0.983
Chain 1:    700        -7995.405             0.628            0.075
Chain 1:    800        -8584.700             0.559            0.075
Chain 1:    900        -8013.564             0.504            0.071
Chain 1:   1000        -8043.556             0.454            0.071
Chain 1:   1100        -7810.298             0.357            0.069
Chain 1:   1200        -7854.704             0.132            0.068
Chain 1:   1300        -7890.015             0.034            0.030
Chain 1:   1400        -7818.468             0.028            0.014
Chain 1:   1500        -7755.304             0.028            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003345 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.45 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86519.010             1.000            1.000
Chain 1:    200       -13793.809             3.136            5.272
Chain 1:    300       -10112.607             2.212            1.000
Chain 1:    400       -11175.397             1.683            1.000
Chain 1:    500        -9076.297             1.393            0.364
Chain 1:    600        -8516.409             1.171            0.364
Chain 1:    700        -8647.044             1.006            0.231
Chain 1:    800        -9044.230             0.886            0.231
Chain 1:    900        -8818.566             0.790            0.095
Chain 1:   1000        -8716.817             0.712            0.095
Chain 1:   1100        -8948.406             0.615            0.066   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8465.829             0.094            0.057
Chain 1:   1300        -8736.826             0.060            0.044
Chain 1:   1400        -8792.063             0.051            0.031
Chain 1:   1500        -8650.937             0.030            0.026
Chain 1:   1600        -8756.791             0.024            0.026
Chain 1:   1700        -8827.711             0.024            0.026
Chain 1:   1800        -8397.821             0.025            0.026
Chain 1:   1900        -8501.633             0.023            0.016
Chain 1:   2000        -8476.722             0.022            0.016
Chain 1:   2100        -8610.196             0.021            0.016
Chain 1:   2200        -8405.368             0.018            0.016
Chain 1:   2300        -8500.639             0.016            0.012
Chain 1:   2400        -8565.249             0.016            0.012
Chain 1:   2500        -8510.211             0.015            0.012
Chain 1:   2600        -8514.273             0.014            0.011
Chain 1:   2700        -8429.596             0.014            0.011
Chain 1:   2800        -8386.504             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003419 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8416338.256             1.000            1.000
Chain 1:    200     -1583251.342             2.658            4.316
Chain 1:    300      -890767.730             2.031            1.000
Chain 1:    400      -457817.698             1.760            1.000
Chain 1:    500      -357927.277             1.464            0.946
Chain 1:    600      -232983.128             1.309            0.946
Chain 1:    700      -119388.263             1.258            0.946
Chain 1:    800       -86643.116             1.148            0.946
Chain 1:    900       -67016.168             1.053            0.777
Chain 1:   1000       -51837.279             0.977            0.777
Chain 1:   1100       -39337.474             0.909            0.536   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38520.809             0.479            0.378
Chain 1:   1300       -26492.608             0.447            0.378
Chain 1:   1400       -26215.617             0.353            0.318
Chain 1:   1500       -22806.191             0.340            0.318
Chain 1:   1600       -22024.557             0.290            0.293
Chain 1:   1700       -20899.520             0.201            0.293
Chain 1:   1800       -20844.376             0.163            0.149
Chain 1:   1900       -21170.719             0.135            0.054
Chain 1:   2000       -19682.332             0.114            0.054
Chain 1:   2100       -19920.695             0.083            0.035
Chain 1:   2200       -20147.146             0.082            0.035
Chain 1:   2300       -19764.340             0.039            0.019
Chain 1:   2400       -19536.347             0.039            0.019
Chain 1:   2500       -19338.334             0.025            0.015
Chain 1:   2600       -18968.281             0.023            0.015
Chain 1:   2700       -18925.307             0.018            0.012
Chain 1:   2800       -18641.975             0.019            0.015
Chain 1:   2900       -18923.357             0.019            0.015
Chain 1:   3000       -18909.555             0.012            0.012
Chain 1:   3100       -18994.518             0.011            0.012
Chain 1:   3200       -18685.088             0.011            0.015
Chain 1:   3300       -18889.966             0.011            0.012
Chain 1:   3400       -18364.596             0.012            0.015
Chain 1:   3500       -18976.819             0.015            0.015
Chain 1:   3600       -18283.119             0.016            0.015
Chain 1:   3700       -18670.132             0.018            0.017
Chain 1:   3800       -17629.178             0.023            0.021
Chain 1:   3900       -17625.315             0.021            0.021
Chain 1:   4000       -17742.631             0.022            0.021
Chain 1:   4100       -17656.270             0.022            0.021
Chain 1:   4200       -17472.459             0.021            0.021
Chain 1:   4300       -17610.914             0.021            0.021
Chain 1:   4400       -17567.614             0.018            0.011
Chain 1:   4500       -17470.125             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001429 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49127.145             1.000            1.000
Chain 1:    200       -18111.224             1.356            1.713
Chain 1:    300       -16932.863             0.927            1.000
Chain 1:    400       -16788.789             0.698            1.000
Chain 1:    500       -13627.010             0.605            0.232
Chain 1:    600       -14765.275             0.517            0.232
Chain 1:    700       -11495.613             0.483            0.232
Chain 1:    800       -11822.231             0.426            0.232
Chain 1:    900       -11299.200             0.384            0.077
Chain 1:   1000       -27924.591             0.405            0.232
Chain 1:   1100       -16763.625             0.372            0.232
Chain 1:   1200       -12801.542             0.232            0.232
Chain 1:   1300       -12519.873             0.227            0.232
Chain 1:   1400       -10764.146             0.242            0.232
Chain 1:   1500       -10884.744             0.220            0.163
Chain 1:   1600       -10228.406             0.219            0.163
Chain 1:   1700       -10276.955             0.191            0.064
Chain 1:   1800       -15535.294             0.222            0.163
Chain 1:   1900       -11096.849             0.257            0.310
Chain 1:   2000       -10674.553             0.202            0.163
Chain 1:   2100        -9664.550             0.146            0.105
Chain 1:   2200        -9995.113             0.118            0.064
Chain 1:   2300       -10042.639             0.116            0.064
Chain 1:   2400        -9450.819             0.106            0.063
Chain 1:   2500       -11868.492             0.126            0.064
Chain 1:   2600        -9360.629             0.146            0.105
Chain 1:   2700       -10468.937             0.156            0.106
Chain 1:   2800       -12354.339             0.137            0.106
Chain 1:   2900       -12298.243             0.098            0.105
Chain 1:   3000       -10175.892             0.115            0.106
Chain 1:   3100        -9832.314             0.108            0.106
Chain 1:   3200       -16310.893             0.144            0.153
Chain 1:   3300        -9427.998             0.217            0.204
Chain 1:   3400        -9092.050             0.214            0.204
Chain 1:   3500        -9291.490             0.196            0.153
Chain 1:   3600       -10137.234             0.178            0.106
Chain 1:   3700       -16448.972             0.205            0.153
Chain 1:   3800       -11140.755             0.238            0.209
Chain 1:   3900       -11144.754             0.237            0.209
Chain 1:   4000       -13738.971             0.235            0.189
Chain 1:   4100        -8873.954             0.287            0.384
Chain 1:   4200       -13282.697             0.280            0.332
Chain 1:   4300       -14470.516             0.215            0.189
Chain 1:   4400       -10918.609             0.244            0.325
Chain 1:   4500        -8952.907             0.264            0.325
Chain 1:   4600        -8609.615             0.260            0.325
Chain 1:   4700       -13649.458             0.258            0.325
Chain 1:   4800        -8986.516             0.262            0.325
Chain 1:   4900        -8957.993             0.263            0.325
Chain 1:   5000       -19924.284             0.299            0.332
Chain 1:   5100        -8641.368             0.375            0.332
Chain 1:   5200        -8720.095             0.342            0.325
Chain 1:   5300        -9758.374             0.345            0.325
Chain 1:   5400       -11721.650             0.329            0.220
Chain 1:   5500        -8519.886             0.345            0.369
Chain 1:   5600        -8809.557             0.344            0.369
Chain 1:   5700        -8801.615             0.307            0.167
Chain 1:   5800       -11529.267             0.279            0.167
Chain 1:   5900       -12723.048             0.288            0.167
Chain 1:   6000        -9027.684             0.274            0.167
Chain 1:   6100       -13406.076             0.176            0.167
Chain 1:   6200        -8400.596             0.235            0.237
Chain 1:   6300        -9829.478             0.238            0.237
Chain 1:   6400       -11668.680             0.237            0.237
Chain 1:   6500       -10911.032             0.207            0.158
Chain 1:   6600        -8798.768             0.228            0.237
Chain 1:   6700       -14526.033             0.267            0.240
Chain 1:   6800        -8837.733             0.308            0.327
Chain 1:   6900       -12495.135             0.327            0.327
Chain 1:   7000        -9293.674             0.321            0.327
Chain 1:   7100        -8981.304             0.292            0.293
Chain 1:   7200        -8490.036             0.238            0.240
Chain 1:   7300       -11610.258             0.250            0.269
Chain 1:   7400       -10334.291             0.247            0.269
Chain 1:   7500       -11048.789             0.246            0.269
Chain 1:   7600        -8947.373             0.246            0.269
Chain 1:   7700        -8591.138             0.211            0.235
Chain 1:   7800        -9385.309             0.155            0.123
Chain 1:   7900        -8364.596             0.138            0.122
Chain 1:   8000        -8935.971             0.110            0.085
Chain 1:   8100       -11441.335             0.128            0.122
Chain 1:   8200        -8801.773             0.152            0.123
Chain 1:   8300       -11222.859             0.147            0.123
Chain 1:   8400       -10583.073             0.141            0.122
Chain 1:   8500        -8305.196             0.162            0.216
Chain 1:   8600        -9497.967             0.151            0.126
Chain 1:   8700        -8896.173             0.153            0.126
Chain 1:   8800        -8467.684             0.150            0.126
Chain 1:   8900        -9449.929             0.148            0.126
Chain 1:   9000       -11464.276             0.159            0.176
Chain 1:   9100        -8505.671             0.172            0.176
Chain 1:   9200       -12966.049             0.177            0.176
Chain 1:   9300       -12570.092             0.158            0.126
Chain 1:   9400       -11087.309             0.165            0.134
Chain 1:   9500        -8201.220             0.173            0.134
Chain 1:   9600        -8852.884             0.168            0.134
Chain 1:   9700        -8445.136             0.166            0.134
Chain 1:   9800       -10277.426             0.179            0.176
Chain 1:   9900        -9286.604             0.179            0.176
Chain 1:   10000        -9107.894             0.164            0.134
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001435 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.35 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -61975.494             1.000            1.000
Chain 1:    200       -17983.793             1.723            2.446
Chain 1:    300        -8960.628             1.484            1.007
Chain 1:    400        -9197.058             1.120            1.007
Chain 1:    500        -8437.491             0.914            1.000
Chain 1:    600        -8572.838             0.764            1.000
Chain 1:    700        -8071.522             0.664            0.090
Chain 1:    800        -8143.666             0.582            0.090
Chain 1:    900        -8182.674             0.518            0.062
Chain 1:   1000        -7900.941             0.470            0.062
Chain 1:   1100        -7731.589             0.372            0.036
Chain 1:   1200        -7838.495             0.129            0.026
Chain 1:   1300        -7897.524             0.029            0.022
Chain 1:   1400        -7659.998             0.029            0.022
Chain 1:   1500        -7625.890             0.021            0.016
Chain 1:   1600        -7780.024             0.021            0.020
Chain 1:   1700        -7553.959             0.018            0.020
Chain 1:   1800        -7630.571             0.018            0.020
Chain 1:   1900        -7620.044             0.018            0.020
Chain 1:   2000        -7675.443             0.015            0.014
Chain 1:   2100        -7622.364             0.013            0.010
Chain 1:   2200        -7727.553             0.013            0.010
Chain 1:   2300        -7577.008             0.014            0.014
Chain 1:   2400        -7652.777             0.012            0.010
Chain 1:   2500        -7467.998             0.014            0.014
Chain 1:   2600        -7552.786             0.013            0.011
Chain 1:   2700        -7538.933             0.011            0.010
Chain 1:   2800        -7619.473             0.011            0.011
Chain 1:   2900        -7420.862             0.013            0.011
Chain 1:   3000        -7551.556             0.014            0.014
Chain 1:   3100        -7550.756             0.014            0.014
Chain 1:   3200        -7747.870             0.015            0.017
Chain 1:   3300        -7480.000             0.016            0.017
Chain 1:   3400        -7690.554             0.018            0.025
Chain 1:   3500        -7462.162             0.019            0.025
Chain 1:   3600        -7526.779             0.018            0.025
Chain 1:   3700        -7476.092             0.019            0.025
Chain 1:   3800        -7479.293             0.018            0.025
Chain 1:   3900        -7445.024             0.016            0.017
Chain 1:   4000        -7439.903             0.014            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003619 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 36.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85644.961             1.000            1.000
Chain 1:    200       -13681.447             3.130            5.260
Chain 1:    300       -10024.297             2.208            1.000
Chain 1:    400       -10962.812             1.678            1.000
Chain 1:    500        -9000.438             1.386            0.365
Chain 1:    600        -8765.844             1.159            0.365
Chain 1:    700        -8421.775             0.999            0.218
Chain 1:    800        -8767.073             0.879            0.218
Chain 1:    900        -8800.193             0.782            0.086
Chain 1:   1000        -8547.586             0.707            0.086
Chain 1:   1100        -8830.088             0.610            0.041   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8426.192             0.089            0.041
Chain 1:   1300        -8657.497             0.055            0.039
Chain 1:   1400        -8706.209             0.047            0.032
Chain 1:   1500        -8552.398             0.027            0.030
Chain 1:   1600        -8662.825             0.026            0.030
Chain 1:   1700        -8741.925             0.022            0.027
Chain 1:   1800        -8314.874             0.024            0.027
Chain 1:   1900        -8417.854             0.025            0.027
Chain 1:   2000        -8392.635             0.022            0.018
Chain 1:   2100        -8519.957             0.020            0.015
Chain 1:   2200        -8318.608             0.018            0.015
Chain 1:   2300        -8413.200             0.016            0.013
Chain 1:   2400        -8480.937             0.016            0.013
Chain 1:   2500        -8427.109             0.015            0.012
Chain 1:   2600        -8429.671             0.014            0.011
Chain 1:   2700        -8345.817             0.014            0.011
Chain 1:   2800        -8304.231             0.010            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002588 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 25.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8384028.153             1.000            1.000
Chain 1:    200     -1578379.352             2.656            4.312
Chain 1:    300      -890397.729             2.028            1.000
Chain 1:    400      -458067.402             1.757            1.000
Chain 1:    500      -359022.617             1.461            0.944
Chain 1:    600      -234049.772             1.306            0.944
Chain 1:    700      -119877.474             1.256            0.944
Chain 1:    800       -87023.605             1.146            0.944
Chain 1:    900       -67267.490             1.051            0.773
Chain 1:   1000       -51988.394             0.976            0.773
Chain 1:   1100       -39398.430             0.908            0.534   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38566.695             0.478            0.378
Chain 1:   1300       -26442.628             0.447            0.378
Chain 1:   1400       -26155.395             0.354            0.320
Chain 1:   1500       -22722.839             0.341            0.320
Chain 1:   1600       -21934.541             0.292            0.294
Chain 1:   1700       -20797.907             0.202            0.294
Chain 1:   1800       -20739.976             0.164            0.151
Chain 1:   1900       -21066.326             0.136            0.055
Chain 1:   2000       -19572.187             0.115            0.055
Chain 1:   2100       -19810.564             0.084            0.036
Chain 1:   2200       -20038.230             0.083            0.036
Chain 1:   2300       -19654.364             0.039            0.020
Chain 1:   2400       -19426.304             0.039            0.020
Chain 1:   2500       -19228.908             0.025            0.015
Chain 1:   2600       -18858.289             0.023            0.015
Chain 1:   2700       -18815.060             0.018            0.012
Chain 1:   2800       -18532.092             0.019            0.015
Chain 1:   2900       -18813.553             0.019            0.015
Chain 1:   3000       -18799.512             0.012            0.012
Chain 1:   3100       -18884.595             0.011            0.012
Chain 1:   3200       -18575.015             0.012            0.015
Chain 1:   3300       -18779.980             0.011            0.012
Chain 1:   3400       -18254.661             0.012            0.015
Chain 1:   3500       -18867.054             0.015            0.015
Chain 1:   3600       -18173.084             0.016            0.015
Chain 1:   3700       -18560.468             0.018            0.017
Chain 1:   3800       -17519.293             0.023            0.021
Chain 1:   3900       -17515.519             0.021            0.021
Chain 1:   4000       -17632.718             0.022            0.021
Chain 1:   4100       -17546.480             0.022            0.021
Chain 1:   4200       -17362.547             0.021            0.021
Chain 1:   4300       -17500.990             0.021            0.021
Chain 1:   4400       -17457.649             0.018            0.011
Chain 1:   4500       -17360.236             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001311 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12239.831             1.000            1.000
Chain 1:    200        -8926.255             0.686            1.000
Chain 1:    300        -8155.592             0.489            0.371
Chain 1:    400        -8134.628             0.367            0.371
Chain 1:    500        -7929.471             0.299            0.094
Chain 1:    600        -7766.274             0.253            0.094
Chain 1:    700        -7731.355             0.217            0.026
Chain 1:    800        -7743.662             0.190            0.026
Chain 1:    900        -7757.353             0.169            0.021
Chain 1:   1000        -7784.597             0.153            0.021
Chain 1:   1100        -7836.699             0.053            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001729 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.29 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58053.078             1.000            1.000
Chain 1:    200       -17479.939             1.661            2.321
Chain 1:    300        -8564.233             1.454            1.041
Chain 1:    400        -8153.882             1.103            1.041
Chain 1:    500        -8274.111             0.885            1.000
Chain 1:    600        -8608.706             0.744            1.000
Chain 1:    700        -7771.807             0.653            0.108
Chain 1:    800        -7998.221             0.575            0.108
Chain 1:    900        -7618.212             0.517            0.050
Chain 1:   1000        -7832.588             0.468            0.050
Chain 1:   1100        -7761.454             0.369            0.050
Chain 1:   1200        -7675.279             0.138            0.039
Chain 1:   1300        -7665.034             0.034            0.028
Chain 1:   1400        -7793.229             0.030            0.027
Chain 1:   1500        -7554.493             0.032            0.028
Chain 1:   1600        -7569.732             0.029            0.027
Chain 1:   1700        -7472.546             0.019            0.016
Chain 1:   1800        -7513.876             0.017            0.013
Chain 1:   1900        -7510.343             0.012            0.011
Chain 1:   2000        -7532.611             0.009            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002633 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.33 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86235.879             1.000            1.000
Chain 1:    200       -13284.465             3.246            5.491
Chain 1:    300        -9683.918             2.288            1.000
Chain 1:    400       -10716.008             1.740            1.000
Chain 1:    500        -8607.083             1.441            0.372
Chain 1:    600        -8142.625             1.210            0.372
Chain 1:    700        -8443.781             1.042            0.245
Chain 1:    800        -9020.801             0.920            0.245
Chain 1:    900        -8535.275             0.824            0.096
Chain 1:   1000        -8265.666             0.745            0.096
Chain 1:   1100        -8511.673             0.648            0.064   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8147.001             0.103            0.057
Chain 1:   1300        -8381.110             0.069            0.057
Chain 1:   1400        -8368.193             0.059            0.045
Chain 1:   1500        -8261.950             0.036            0.036
Chain 1:   1600        -8367.764             0.032            0.033
Chain 1:   1700        -8455.076             0.029            0.029
Chain 1:   1800        -8046.728             0.028            0.029
Chain 1:   1900        -8143.125             0.023            0.028
Chain 1:   2000        -8115.435             0.020            0.013
Chain 1:   2100        -8236.437             0.019            0.013
Chain 1:   2200        -8086.062             0.016            0.013
Chain 1:   2300        -8183.504             0.015            0.013
Chain 1:   2400        -8194.151             0.015            0.013
Chain 1:   2500        -8153.133             0.014            0.012
Chain 1:   2600        -8153.303             0.013            0.012
Chain 1:   2700        -8068.749             0.013            0.012
Chain 1:   2800        -8033.300             0.008            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002602 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 26.02 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8440400.620             1.000            1.000
Chain 1:    200     -1588839.196             2.656            4.312
Chain 1:    300      -890162.307             2.032            1.000
Chain 1:    400      -457318.413             1.761            1.000
Chain 1:    500      -357114.701             1.465            0.946
Chain 1:    600      -232094.722             1.310            0.946
Chain 1:    700      -118605.876             1.260            0.946
Chain 1:    800       -85951.142             1.150            0.946
Chain 1:    900       -66356.190             1.055            0.785
Chain 1:   1000       -51211.402             0.979            0.785
Chain 1:   1100       -38749.590             0.911            0.539   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37930.698             0.482            0.380
Chain 1:   1300       -25945.234             0.450            0.380
Chain 1:   1400       -25669.827             0.356            0.322
Chain 1:   1500       -22273.565             0.343            0.322
Chain 1:   1600       -21495.322             0.293            0.296
Chain 1:   1700       -20375.693             0.203            0.295
Chain 1:   1800       -20321.569             0.165            0.152
Chain 1:   1900       -20647.590             0.137            0.055
Chain 1:   2000       -19162.824             0.116            0.055
Chain 1:   2100       -19400.784             0.085            0.036
Chain 1:   2200       -19626.769             0.084            0.036
Chain 1:   2300       -19244.455             0.039            0.020
Chain 1:   2400       -19016.655             0.040            0.020
Chain 1:   2500       -18818.631             0.025            0.016
Chain 1:   2600       -18448.980             0.024            0.016
Chain 1:   2700       -18406.011             0.018            0.012
Chain 1:   2800       -18122.935             0.020            0.016
Chain 1:   2900       -18404.043             0.020            0.015
Chain 1:   3000       -18390.196             0.012            0.012
Chain 1:   3100       -18475.203             0.011            0.012
Chain 1:   3200       -18165.967             0.012            0.015
Chain 1:   3300       -18370.640             0.011            0.012
Chain 1:   3400       -17845.731             0.013            0.015
Chain 1:   3500       -18457.282             0.015            0.016
Chain 1:   3600       -17764.318             0.017            0.016
Chain 1:   3700       -18150.834             0.019            0.017
Chain 1:   3800       -17111.110             0.023            0.021
Chain 1:   3900       -17107.257             0.022            0.021
Chain 1:   4000       -17224.575             0.022            0.021
Chain 1:   4100       -17138.372             0.022            0.021
Chain 1:   4200       -16954.741             0.022            0.021
Chain 1:   4300       -17093.048             0.021            0.021
Chain 1:   4400       -17049.944             0.019            0.011
Chain 1:   4500       -16952.501             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001294 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 12.94 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49294.244             1.000            1.000
Chain 1:    200       -22383.228             1.101            1.202
Chain 1:    300       -25754.050             0.778            1.000
Chain 1:    400       -18356.481             0.684            1.000
Chain 1:    500       -12237.379             0.647            0.500
Chain 1:    600       -12928.627             0.548            0.500
Chain 1:    700       -16989.516             0.504            0.403
Chain 1:    800       -13142.690             0.478            0.403
Chain 1:    900       -16986.904             0.450            0.293
Chain 1:   1000       -10962.569             0.460            0.403
Chain 1:   1100       -12237.319             0.370            0.293
Chain 1:   1200       -18332.799             0.283            0.293
Chain 1:   1300       -11198.931             0.334            0.332
Chain 1:   1400       -15053.699             0.319            0.293
Chain 1:   1500       -17978.533             0.285            0.256
Chain 1:   1600       -22263.495             0.299            0.256
Chain 1:   1700        -9774.083             0.403            0.293
Chain 1:   1800       -11035.769             0.385            0.256
Chain 1:   1900       -10107.079             0.372            0.256
Chain 1:   2000       -10105.741             0.317            0.192
Chain 1:   2100       -11103.524             0.315            0.192
Chain 1:   2200       -10634.908             0.287            0.163
Chain 1:   2300        -9523.634             0.235            0.117
Chain 1:   2400       -18555.915             0.258            0.117
Chain 1:   2500        -9438.775             0.338            0.117
Chain 1:   2600        -9426.105             0.319            0.114
Chain 1:   2700       -12484.090             0.216            0.114
Chain 1:   2800       -10150.525             0.227            0.117
Chain 1:   2900        -9135.091             0.229            0.117
Chain 1:   3000       -12383.301             0.255            0.230
Chain 1:   3100       -10142.463             0.268            0.230
Chain 1:   3200       -16323.905             0.302            0.245
Chain 1:   3300        -9265.245             0.366            0.262
Chain 1:   3400        -9789.809             0.323            0.245
Chain 1:   3500       -10783.483             0.236            0.230
Chain 1:   3600       -11041.666             0.238            0.230
Chain 1:   3700       -10295.062             0.221            0.221
Chain 1:   3800        -8755.242             0.215            0.176
Chain 1:   3900        -9345.157             0.210            0.176
Chain 1:   4000        -9887.763             0.190            0.092
Chain 1:   4100        -8746.282             0.181            0.092
Chain 1:   4200        -9929.883             0.155            0.092
Chain 1:   4300       -11030.024             0.088            0.092
Chain 1:   4400        -9555.437             0.099            0.100
Chain 1:   4500        -8664.963             0.100            0.103
Chain 1:   4600        -8592.672             0.098            0.103
Chain 1:   4700        -8765.782             0.093            0.103
Chain 1:   4800        -8337.834             0.080            0.100
Chain 1:   4900        -8769.459             0.079            0.100
Chain 1:   5000        -8937.152             0.075            0.100
Chain 1:   5100       -11706.425             0.086            0.100
Chain 1:   5200        -8944.762             0.105            0.100
Chain 1:   5300       -11259.311             0.116            0.103
Chain 1:   5400        -8591.477             0.131            0.103
Chain 1:   5500       -14421.115             0.161            0.206
Chain 1:   5600        -8857.144             0.223            0.237
Chain 1:   5700        -8803.665             0.222            0.237
Chain 1:   5800        -8540.596             0.220            0.237
Chain 1:   5900       -12387.502             0.246            0.309
Chain 1:   6000        -8478.689             0.290            0.311
Chain 1:   6100       -10526.112             0.286            0.311
Chain 1:   6200       -14151.761             0.281            0.311
Chain 1:   6300       -11896.004             0.279            0.311
Chain 1:   6400        -8590.574             0.287            0.311
Chain 1:   6500       -11308.259             0.270            0.256
Chain 1:   6600       -12164.503             0.214            0.240
Chain 1:   6700        -8933.644             0.250            0.256
Chain 1:   6800        -8222.359             0.256            0.256
Chain 1:   6900        -9538.152             0.238            0.240
Chain 1:   7000       -12021.807             0.213            0.207
Chain 1:   7100        -8534.953             0.234            0.240
Chain 1:   7200        -9736.367             0.221            0.207
Chain 1:   7300       -11345.082             0.216            0.207
Chain 1:   7400        -9023.703             0.203            0.207
Chain 1:   7500        -9902.214             0.188            0.142
Chain 1:   7600       -12028.252             0.199            0.177
Chain 1:   7700       -12129.014             0.164            0.142
Chain 1:   7800       -11105.822             0.164            0.142
Chain 1:   7900        -9218.881             0.171            0.177
Chain 1:   8000        -9850.875             0.157            0.142
Chain 1:   8100        -8454.930             0.132            0.142
Chain 1:   8200       -12399.777             0.152            0.165
Chain 1:   8300        -8611.610             0.182            0.177
Chain 1:   8400        -8315.409             0.159            0.165
Chain 1:   8500        -8291.946             0.151            0.165
Chain 1:   8600        -8923.162             0.140            0.092
Chain 1:   8700        -8281.067             0.147            0.092
Chain 1:   8800        -8437.587             0.140            0.078
Chain 1:   8900        -8459.767             0.120            0.071
Chain 1:   9000       -10619.465             0.133            0.078
Chain 1:   9100       -11680.540             0.126            0.078
Chain 1:   9200        -9149.802             0.122            0.078
Chain 1:   9300        -8118.559             0.091            0.078
Chain 1:   9400        -8554.420             0.092            0.078
Chain 1:   9500        -8413.583             0.093            0.078
Chain 1:   9600        -9874.848             0.101            0.091
Chain 1:   9700        -8586.507             0.108            0.127
Chain 1:   9800       -10419.973             0.124            0.148
Chain 1:   9900        -9056.199             0.139            0.150
Chain 1:   10000       -11425.369             0.139            0.150
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001919 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 19.19 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57063.373             1.000            1.000
Chain 1:    200       -17520.901             1.628            2.257
Chain 1:    300        -8789.365             1.417            1.000
Chain 1:    400        -8179.204             1.081            1.000
Chain 1:    500        -8926.839             0.882            0.993
Chain 1:    600        -8336.513             0.747            0.993
Chain 1:    700        -7990.368             0.646            0.084
Chain 1:    800        -8138.853             0.568            0.084
Chain 1:    900        -7817.420             0.509            0.075
Chain 1:   1000        -7716.496             0.460            0.075
Chain 1:   1100        -7682.814             0.360            0.071
Chain 1:   1200        -7770.096             0.135            0.043
Chain 1:   1300        -7531.327             0.039            0.041
Chain 1:   1400        -7868.836             0.036            0.041
Chain 1:   1500        -7573.385             0.032            0.039
Chain 1:   1600        -7588.250             0.025            0.032
Chain 1:   1700        -7556.497             0.021            0.018
Chain 1:   1800        -7594.001             0.019            0.013
Chain 1:   1900        -7596.730             0.015            0.011
Chain 1:   2000        -7652.320             0.015            0.007   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.006453 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 64.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86972.404             1.000            1.000
Chain 1:    200       -13645.477             3.187            5.374
Chain 1:    300        -9952.126             2.248            1.000
Chain 1:    400       -11117.795             1.712            1.000
Chain 1:    500        -8925.137             1.419            0.371
Chain 1:    600        -8388.284             1.193            0.371
Chain 1:    700        -8413.941             1.023            0.246
Chain 1:    800        -9126.151             0.905            0.246
Chain 1:    900        -8705.016             0.810            0.105
Chain 1:   1000        -8728.828             0.729            0.105
Chain 1:   1100        -8723.905             0.629            0.078   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8217.750             0.098            0.064
Chain 1:   1300        -8603.749             0.065            0.062
Chain 1:   1400        -8569.559             0.055            0.048
Chain 1:   1500        -8469.425             0.032            0.045
Chain 1:   1600        -8575.555             0.027            0.012
Chain 1:   1700        -8638.852             0.027            0.012
Chain 1:   1800        -8204.305             0.025            0.012
Chain 1:   1900        -8308.839             0.021            0.012
Chain 1:   2000        -8284.330             0.021            0.012
Chain 1:   2100        -8242.759             0.022            0.012
Chain 1:   2200        -8226.418             0.016            0.012
Chain 1:   2300        -8362.578             0.013            0.012
Chain 1:   2400        -8210.162             0.014            0.012
Chain 1:   2500        -8279.177             0.014            0.012
Chain 1:   2600        -8197.691             0.014            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003939 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.39 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8423292.900             1.000            1.000
Chain 1:    200     -1584259.289             2.658            4.317
Chain 1:    300      -889850.792             2.032            1.000
Chain 1:    400      -457649.755             1.760            1.000
Chain 1:    500      -357854.206             1.464            0.944
Chain 1:    600      -232777.647             1.310            0.944
Chain 1:    700      -119150.842             1.259            0.944
Chain 1:    800       -86455.309             1.149            0.944
Chain 1:    900       -66828.767             1.054            0.780
Chain 1:   1000       -51663.889             0.978            0.780
Chain 1:   1100       -39179.459             0.910            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38361.806             0.480            0.378
Chain 1:   1300       -26342.086             0.448            0.378
Chain 1:   1400       -26065.671             0.354            0.319
Chain 1:   1500       -22659.849             0.341            0.319
Chain 1:   1600       -21879.636             0.291            0.294
Chain 1:   1700       -20755.211             0.201            0.294
Chain 1:   1800       -20700.347             0.164            0.150
Chain 1:   1900       -21026.844             0.136            0.054
Chain 1:   2000       -19538.822             0.114            0.054
Chain 1:   2100       -19776.934             0.083            0.036
Chain 1:   2200       -20003.614             0.082            0.036
Chain 1:   2300       -19620.553             0.039            0.020
Chain 1:   2400       -19392.544             0.039            0.020
Chain 1:   2500       -19194.633             0.025            0.016
Chain 1:   2600       -18824.309             0.023            0.016
Chain 1:   2700       -18781.228             0.018            0.012
Chain 1:   2800       -18497.975             0.019            0.015
Chain 1:   2900       -18779.333             0.019            0.015
Chain 1:   3000       -18765.431             0.012            0.012
Chain 1:   3100       -18850.499             0.011            0.012
Chain 1:   3200       -18540.930             0.012            0.015
Chain 1:   3300       -18745.883             0.011            0.012
Chain 1:   3400       -18220.391             0.012            0.015
Chain 1:   3500       -18832.892             0.015            0.015
Chain 1:   3600       -18138.717             0.017            0.015
Chain 1:   3700       -18526.113             0.018            0.017
Chain 1:   3800       -17484.558             0.023            0.021
Chain 1:   3900       -17480.689             0.021            0.021
Chain 1:   4000       -17597.966             0.022            0.021
Chain 1:   4100       -17511.673             0.022            0.021
Chain 1:   4200       -17327.670             0.021            0.021
Chain 1:   4300       -17466.229             0.021            0.021
Chain 1:   4400       -17422.798             0.018            0.011
Chain 1:   4500       -17325.318             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001821 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -13547.548             1.000            1.000
Chain 1:    200       -10153.861             0.667            1.000
Chain 1:    300        -8905.107             0.491            0.334
Chain 1:    400        -8280.216             0.387            0.334
Chain 1:    500        -8569.257             0.317            0.140
Chain 1:    600        -8357.496             0.268            0.140
Chain 1:    700        -8268.119             0.231            0.075
Chain 1:    800        -8256.870             0.203            0.075
Chain 1:    900        -8402.105             0.182            0.034
Chain 1:   1000        -8296.820             0.165            0.034
Chain 1:   1100        -8382.439             0.066            0.025
Chain 1:   1200        -8310.338             0.034            0.017
Chain 1:   1300        -8233.777             0.020            0.013
Chain 1:   1400        -8242.028             0.013            0.011
Chain 1:   1500        -8348.987             0.011            0.011
Chain 1:   1600        -8285.551             0.009            0.010   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.0024 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -51555.567             1.000            1.000
Chain 1:    200       -17028.262             1.514            2.028
Chain 1:    300        -8925.550             1.312            1.000
Chain 1:    400        -8774.213             0.988            1.000
Chain 1:    500        -7953.604             0.811            0.908
Chain 1:    600        -9206.450             0.699            0.908
Chain 1:    700        -8640.040             0.608            0.136
Chain 1:    800        -8528.548             0.534            0.136
Chain 1:    900        -8123.135             0.480            0.103
Chain 1:   1000        -8073.043             0.433            0.103
Chain 1:   1100        -7795.009             0.336            0.066
Chain 1:   1200        -7851.563             0.134            0.050
Chain 1:   1300        -8022.657             0.046            0.036
Chain 1:   1400        -7761.671             0.047            0.036
Chain 1:   1500        -7659.029             0.038            0.034
Chain 1:   1600        -7875.084             0.027            0.027
Chain 1:   1700        -7731.148             0.023            0.021
Chain 1:   1800        -7807.115             0.022            0.021
Chain 1:   1900        -7910.201             0.019            0.019
Chain 1:   2000        -7719.469             0.020            0.021
Chain 1:   2100        -7694.645             0.017            0.019
Chain 1:   2200        -7857.548             0.019            0.021
Chain 1:   2300        -7659.726             0.019            0.021
Chain 1:   2400        -7745.454             0.017            0.019
Chain 1:   2500        -7713.142             0.016            0.019
Chain 1:   2600        -7659.743             0.014            0.013
Chain 1:   2700        -7572.498             0.013            0.012
Chain 1:   2800        -7755.917             0.014            0.013
Chain 1:   2900        -7511.273             0.016            0.021
Chain 1:   3000        -7669.503             0.016            0.021
Chain 1:   3100        -7653.715             0.016            0.021
Chain 1:   3200        -7870.151             0.017            0.021
Chain 1:   3300        -7578.908             0.018            0.021
Chain 1:   3400        -7824.943             0.020            0.024
Chain 1:   3500        -7568.324             0.023            0.028
Chain 1:   3600        -7633.510             0.023            0.028
Chain 1:   3700        -7584.869             0.023            0.028
Chain 1:   3800        -7584.756             0.020            0.028
Chain 1:   3900        -7543.643             0.017            0.021
Chain 1:   4000        -7535.674             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003776 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.76 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86300.163             1.000            1.000
Chain 1:    200       -14044.247             3.072            5.145
Chain 1:    300       -10342.356             2.168            1.000
Chain 1:    400       -11478.879             1.650            1.000
Chain 1:    500        -9325.762             1.367            0.358
Chain 1:    600        -8711.981             1.151            0.358
Chain 1:    700        -8832.335             0.988            0.231
Chain 1:    800        -9128.257             0.869            0.231
Chain 1:    900        -9160.950             0.773            0.099
Chain 1:   1000        -9133.949             0.696            0.099
Chain 1:   1100        -8946.868             0.598            0.070   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8737.394             0.086            0.032
Chain 1:   1300        -9011.281             0.053            0.030
Chain 1:   1400        -9004.125             0.043            0.024
Chain 1:   1500        -8853.820             0.022            0.021
Chain 1:   1600        -8969.782             0.016            0.017
Chain 1:   1700        -9035.824             0.015            0.017
Chain 1:   1800        -8601.208             0.017            0.017
Chain 1:   1900        -8705.339             0.018            0.017
Chain 1:   2000        -8681.066             0.018            0.017
Chain 1:   2100        -8626.975             0.016            0.013
Chain 1:   2200        -8623.830             0.014            0.012
Chain 1:   2300        -8761.223             0.013            0.012
Chain 1:   2400        -8605.808             0.014            0.013
Chain 1:   2500        -8675.338             0.013            0.012
Chain 1:   2600        -8593.210             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003366 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8401471.347             1.000            1.000
Chain 1:    200     -1586137.912             2.648            4.297
Chain 1:    300      -891750.540             2.025            1.000
Chain 1:    400      -458469.822             1.755            1.000
Chain 1:    500      -358726.659             1.460            0.945
Chain 1:    600      -233723.220             1.306            0.945
Chain 1:    700      -119902.522             1.255            0.945
Chain 1:    800       -87060.752             1.145            0.945
Chain 1:    900       -67393.878             1.050            0.779
Chain 1:   1000       -52184.451             0.974            0.779
Chain 1:   1100       -39648.068             0.906            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38828.845             0.478            0.377
Chain 1:   1300       -26771.471             0.446            0.377
Chain 1:   1400       -26491.325             0.352            0.316
Chain 1:   1500       -23073.954             0.339            0.316
Chain 1:   1600       -22289.343             0.289            0.292
Chain 1:   1700       -21161.381             0.200            0.291
Chain 1:   1800       -21105.421             0.162            0.148
Chain 1:   1900       -21431.898             0.134            0.053
Chain 1:   2000       -19941.346             0.113            0.053
Chain 1:   2100       -20179.968             0.082            0.035
Chain 1:   2200       -20406.661             0.081            0.035
Chain 1:   2300       -20023.575             0.038            0.019
Chain 1:   2400       -19795.510             0.038            0.019
Chain 1:   2500       -19597.445             0.024            0.015
Chain 1:   2600       -19227.355             0.023            0.015
Chain 1:   2700       -19184.256             0.018            0.012
Chain 1:   2800       -18900.834             0.019            0.015
Chain 1:   2900       -19182.356             0.019            0.015
Chain 1:   3000       -19168.571             0.012            0.012
Chain 1:   3100       -19253.554             0.011            0.012
Chain 1:   3200       -18944.024             0.011            0.015
Chain 1:   3300       -19148.936             0.010            0.012
Chain 1:   3400       -18623.382             0.012            0.015
Chain 1:   3500       -19235.890             0.014            0.015
Chain 1:   3600       -18541.834             0.016            0.015
Chain 1:   3700       -18929.168             0.018            0.016
Chain 1:   3800       -17887.599             0.022            0.020
Chain 1:   3900       -17883.698             0.021            0.020
Chain 1:   4000       -18001.050             0.021            0.020
Chain 1:   4100       -17914.667             0.021            0.020
Chain 1:   4200       -17730.675             0.021            0.020
Chain 1:   4300       -17869.261             0.021            0.020
Chain 1:   4400       -17825.876             0.018            0.010
Chain 1:   4500       -17728.360             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48855.982             1.000            1.000
Chain 1:    200       -17315.212             1.411            1.822
Chain 1:    300       -19252.992             0.974            1.000
Chain 1:    400       -14903.299             0.804            1.000
Chain 1:    500       -16967.728             0.667            0.292
Chain 1:    600       -11002.710             0.646            0.542
Chain 1:    700       -14598.562             0.589            0.292
Chain 1:    800       -14626.296             0.516            0.292
Chain 1:    900       -16818.582             0.473            0.246
Chain 1:   1000       -10811.453             0.481            0.292
Chain 1:   1100       -10751.570             0.382            0.246
Chain 1:   1200       -10399.146             0.203            0.130
Chain 1:   1300       -12043.467             0.207            0.137
Chain 1:   1400        -9672.311             0.202            0.137
Chain 1:   1500       -10998.341             0.202            0.137
Chain 1:   1600       -28717.963             0.209            0.137
Chain 1:   1700        -8916.011             0.407            0.137
Chain 1:   1800       -10410.823             0.421            0.144
Chain 1:   1900       -19199.650             0.454            0.245
Chain 1:   2000       -12327.361             0.454            0.245
Chain 1:   2100       -15658.934             0.475            0.245
Chain 1:   2200        -9596.991             0.534            0.458   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2300       -10752.902             0.531            0.458   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2400       -13177.668             0.525            0.458   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2500        -9913.294             0.546            0.458   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   2600        -9252.288             0.492            0.329
Chain 1:   2700        -9641.295             0.274            0.213
Chain 1:   2800        -9537.695             0.260            0.213
Chain 1:   2900        -8856.740             0.222            0.184
Chain 1:   3000        -8882.083             0.167            0.107
Chain 1:   3100        -8649.305             0.148            0.077
Chain 1:   3200       -11378.712             0.109            0.077
Chain 1:   3300        -9137.239             0.123            0.077
Chain 1:   3400       -11397.331             0.124            0.077
Chain 1:   3500        -8701.356             0.122            0.077
Chain 1:   3600       -11071.404             0.137            0.198
Chain 1:   3700        -8403.104             0.164            0.214
Chain 1:   3800        -9888.294             0.178            0.214
Chain 1:   3900        -8830.756             0.182            0.214
Chain 1:   4000       -10224.605             0.196            0.214
Chain 1:   4100        -9965.825             0.196            0.214
Chain 1:   4200        -9261.475             0.179            0.198
Chain 1:   4300        -9344.142             0.156            0.150
Chain 1:   4400       -10015.624             0.143            0.136
Chain 1:   4500        -8500.427             0.129            0.136
Chain 1:   4600       -10330.240             0.126            0.136
Chain 1:   4700        -9722.655             0.100            0.120
Chain 1:   4800        -8869.743             0.095            0.096
Chain 1:   4900       -10561.704             0.099            0.096
Chain 1:   5000        -9992.687             0.091            0.076
Chain 1:   5100        -8415.769             0.107            0.096
Chain 1:   5200        -8688.155             0.103            0.096
Chain 1:   5300       -11561.794             0.127            0.160
Chain 1:   5400        -9649.311             0.140            0.177
Chain 1:   5500       -14053.942             0.153            0.177
Chain 1:   5600        -8909.280             0.193            0.187
Chain 1:   5700        -8579.580             0.191            0.187
Chain 1:   5800        -9250.886             0.188            0.187
Chain 1:   5900       -11417.277             0.191            0.190
Chain 1:   6000        -8027.847             0.228            0.198
Chain 1:   6100        -8486.316             0.215            0.198
Chain 1:   6200        -8063.792             0.217            0.198
Chain 1:   6300        -9600.032             0.208            0.190
Chain 1:   6400       -10796.199             0.199            0.160
Chain 1:   6500        -8315.847             0.198            0.160
Chain 1:   6600        -8348.727             0.140            0.111
Chain 1:   6700       -12044.807             0.167            0.160
Chain 1:   6800        -9099.691             0.192            0.190
Chain 1:   6900       -11935.529             0.197            0.238
Chain 1:   7000        -9624.772             0.179            0.238
Chain 1:   7100        -8989.872             0.180            0.238
Chain 1:   7200        -8681.983             0.179            0.238
Chain 1:   7300        -8369.799             0.166            0.238
Chain 1:   7400       -10337.197             0.174            0.238
Chain 1:   7500        -8400.337             0.168            0.231
Chain 1:   7600        -8191.348             0.170            0.231
Chain 1:   7700        -9693.239             0.155            0.190
Chain 1:   7800        -9416.477             0.125            0.155
Chain 1:   7900        -7963.011             0.120            0.155
Chain 1:   8000        -8044.265             0.097            0.071
Chain 1:   8100       -10625.672             0.114            0.155
Chain 1:   8200       -10833.896             0.112            0.155
Chain 1:   8300        -7881.783             0.146            0.183
Chain 1:   8400        -9463.644             0.144            0.167
Chain 1:   8500       -11520.797             0.138            0.167
Chain 1:   8600        -8317.547             0.174            0.179
Chain 1:   8700        -8501.036             0.161            0.179
Chain 1:   8800        -8028.696             0.164            0.179
Chain 1:   8900        -8175.112             0.148            0.167
Chain 1:   9000       -10147.184             0.166            0.179
Chain 1:   9100        -8655.578             0.159            0.172
Chain 1:   9200        -7908.549             0.166            0.172
Chain 1:   9300        -8120.076             0.132            0.167
Chain 1:   9400        -8154.690             0.115            0.094
Chain 1:   9500       -10230.870             0.118            0.094
Chain 1:   9600        -8092.422             0.106            0.094
Chain 1:   9700        -8374.952             0.107            0.094
Chain 1:   9800        -8319.806             0.102            0.094
Chain 1:   9900        -9659.424             0.114            0.139
Chain 1:   10000        -8574.068             0.107            0.127
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001757 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.57 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57871.463             1.000            1.000
Chain 1:    200       -17267.829             1.676            2.351
Chain 1:    300        -8450.705             1.465            1.043
Chain 1:    400        -8022.045             1.112            1.043
Chain 1:    500        -8076.202             0.891            1.000
Chain 1:    600        -8219.064             0.745            1.000
Chain 1:    700        -7920.319             0.644            0.053
Chain 1:    800        -7897.344             0.564            0.053
Chain 1:    900        -7709.357             0.504            0.038
Chain 1:   1000        -7658.455             0.454            0.038
Chain 1:   1100        -7572.323             0.356            0.024
Chain 1:   1200        -7499.437             0.121            0.017
Chain 1:   1300        -7561.296             0.018            0.011
Chain 1:   1400        -7634.858             0.013            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003466 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.66 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85358.317             1.000            1.000
Chain 1:    200       -13103.704             3.257            5.514
Chain 1:    300        -9548.472             2.295            1.000
Chain 1:    400       -10475.139             1.744            1.000
Chain 1:    500        -8493.557             1.442            0.372
Chain 1:    600        -8034.250             1.211            0.372
Chain 1:    700        -8361.917             1.044            0.233
Chain 1:    800        -9033.938             0.922            0.233
Chain 1:    900        -8407.274             0.828            0.088
Chain 1:   1000        -8142.234             0.749            0.088
Chain 1:   1100        -8413.255             0.652            0.075   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -7977.708             0.106            0.074
Chain 1:   1300        -8449.736             0.074            0.057
Chain 1:   1400        -8270.080             0.068            0.056
Chain 1:   1500        -8139.684             0.046            0.055
Chain 1:   1600        -8251.979             0.041            0.039
Chain 1:   1700        -8335.710             0.039            0.033
Chain 1:   1800        -7940.669             0.036            0.033
Chain 1:   1900        -8042.315             0.030            0.032
Chain 1:   2000        -8012.840             0.027            0.022
Chain 1:   2100        -8135.044             0.025            0.016
Chain 1:   2200        -7915.915             0.023            0.016
Chain 1:   2300        -8071.004             0.019            0.016
Chain 1:   2400        -8084.669             0.017            0.015
Chain 1:   2500        -8054.333             0.016            0.014
Chain 1:   2600        -8057.043             0.014            0.013
Chain 1:   2700        -7963.241             0.015            0.013
Chain 1:   2800        -7934.212             0.010            0.012   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003964 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8398909.319             1.000            1.000
Chain 1:    200     -1583641.119             2.652            4.304
Chain 1:    300      -889530.043             2.028            1.000
Chain 1:    400      -457015.783             1.758            1.000
Chain 1:    500      -357404.041             1.462            0.946
Chain 1:    600      -232376.848             1.308            0.946
Chain 1:    700      -118693.698             1.258            0.946
Chain 1:    800       -85967.960             1.148            0.946
Chain 1:    900       -66322.790             1.054            0.780
Chain 1:   1000       -51134.047             0.978            0.780
Chain 1:   1100       -38630.635             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37804.673             0.482            0.381
Chain 1:   1300       -25779.781             0.451            0.381
Chain 1:   1400       -25499.401             0.357            0.324
Chain 1:   1500       -22092.483             0.345            0.324
Chain 1:   1600       -21310.743             0.295            0.297
Chain 1:   1700       -20186.639             0.204            0.296
Chain 1:   1800       -20131.288             0.167            0.154
Chain 1:   1900       -20457.127             0.139            0.056
Chain 1:   2000       -18970.302             0.117            0.056
Chain 1:   2100       -19208.379             0.086            0.037
Chain 1:   2200       -19434.565             0.085            0.037
Chain 1:   2300       -19052.108             0.040            0.020
Chain 1:   2400       -18824.346             0.040            0.020
Chain 1:   2500       -18626.470             0.026            0.016
Chain 1:   2600       -18256.916             0.024            0.016
Chain 1:   2700       -18213.999             0.019            0.012
Chain 1:   2800       -17931.065             0.020            0.016
Chain 1:   2900       -18212.094             0.020            0.015
Chain 1:   3000       -18198.280             0.012            0.012
Chain 1:   3100       -18283.245             0.011            0.012
Chain 1:   3200       -17974.163             0.012            0.015
Chain 1:   3300       -18178.706             0.011            0.012
Chain 1:   3400       -17654.085             0.013            0.015
Chain 1:   3500       -18265.325             0.015            0.016
Chain 1:   3600       -17572.796             0.017            0.016
Chain 1:   3700       -17958.992             0.019            0.017
Chain 1:   3800       -16920.021             0.023            0.022
Chain 1:   3900       -16916.221             0.022            0.022
Chain 1:   4000       -17033.489             0.023            0.022
Chain 1:   4100       -16947.352             0.023            0.022
Chain 1:   4200       -16763.890             0.022            0.022
Chain 1:   4300       -16902.058             0.022            0.022
Chain 1:   4400       -16859.091             0.019            0.011
Chain 1:   4500       -16761.707             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001192 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -12006.478             1.000            1.000
Chain 1:    200        -8933.627             0.672            1.000
Chain 1:    300        -7957.927             0.489            0.344
Chain 1:    400        -8053.876             0.370            0.344
Chain 1:    500        -7860.941             0.301            0.123
Chain 1:    600        -7778.450             0.252            0.123
Chain 1:    700        -7716.595             0.217            0.025
Chain 1:    800        -7733.117             0.190            0.025
Chain 1:    900        -7747.478             0.170            0.012
Chain 1:   1000        -7756.388             0.153            0.012
Chain 1:   1100        -7935.735             0.055            0.012
Chain 1:   1200        -7738.127             0.023            0.012
Chain 1:   1300        -7688.830             0.011            0.011
Chain 1:   1400        -7717.601             0.011            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001683 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.83 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -56661.801             1.000            1.000
Chain 1:    200       -17007.188             1.666            2.332
Chain 1:    300        -8543.562             1.441            1.000
Chain 1:    400        -8832.947             1.089            1.000
Chain 1:    500        -8455.736             0.880            0.991
Chain 1:    600        -9158.008             0.746            0.991
Chain 1:    700        -8222.620             0.656            0.114
Chain 1:    800        -8200.216             0.574            0.114
Chain 1:    900        -7932.327             0.514            0.077
Chain 1:   1000        -7759.560             0.465            0.077
Chain 1:   1100        -7736.835             0.365            0.045
Chain 1:   1200        -7559.494             0.134            0.034
Chain 1:   1300        -7720.836             0.037            0.033
Chain 1:   1400        -7814.389             0.035            0.023
Chain 1:   1500        -7617.454             0.033            0.023
Chain 1:   1600        -7513.408             0.027            0.022
Chain 1:   1700        -7486.386             0.016            0.021
Chain 1:   1800        -7525.326             0.016            0.021
Chain 1:   1900        -7578.161             0.014            0.014
Chain 1:   2000        -7569.049             0.012            0.012
Chain 1:   2100        -7581.870             0.011            0.012
Chain 1:   2200        -7652.989             0.010            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00384 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.4 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85719.413             1.000            1.000
Chain 1:    200       -13079.602             3.277            5.554
Chain 1:    300        -9567.241             2.307            1.000
Chain 1:    400       -10432.528             1.751            1.000
Chain 1:    500        -8442.766             1.448            0.367
Chain 1:    600        -8148.118             1.213            0.367
Chain 1:    700        -8415.533             1.044            0.236
Chain 1:    800        -8902.201             0.920            0.236
Chain 1:    900        -8425.984             0.824            0.083
Chain 1:   1000        -8183.767             0.745            0.083
Chain 1:   1100        -8404.421             0.647            0.057   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8168.306             0.095            0.055
Chain 1:   1300        -8330.318             0.060            0.036
Chain 1:   1400        -8263.838             0.053            0.032
Chain 1:   1500        -8208.007             0.030            0.030
Chain 1:   1600        -8204.051             0.026            0.029
Chain 1:   1700        -8142.548             0.024            0.026
Chain 1:   1800        -8023.005             0.020            0.019
Chain 1:   1900        -8136.685             0.016            0.015
Chain 1:   2000        -8098.181             0.013            0.014
Chain 1:   2100        -8238.548             0.012            0.014
Chain 1:   2200        -8023.533             0.012            0.014
Chain 1:   2300        -8165.320             0.012            0.014
Chain 1:   2400        -8170.253             0.011            0.014
Chain 1:   2500        -8139.627             0.011            0.014
Chain 1:   2600        -8133.772             0.011            0.014
Chain 1:   2700        -8045.283             0.011            0.014
Chain 1:   2800        -8027.729             0.010            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003215 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.15 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405352.038             1.000            1.000
Chain 1:    200     -1586043.918             2.650            4.300
Chain 1:    300      -891267.439             2.026            1.000
Chain 1:    400      -458251.210             1.756            1.000
Chain 1:    500      -358417.658             1.461            0.945
Chain 1:    600      -233054.381             1.307            0.945
Chain 1:    700      -118954.757             1.257            0.945
Chain 1:    800       -86141.485             1.148            0.945
Chain 1:    900       -66423.126             1.053            0.780
Chain 1:   1000       -51181.588             0.978            0.780
Chain 1:   1100       -38635.911             0.910            0.538   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -37798.231             0.482            0.381
Chain 1:   1300       -25742.581             0.451            0.381
Chain 1:   1400       -25456.706             0.358            0.325
Chain 1:   1500       -22042.968             0.345            0.325
Chain 1:   1600       -21258.177             0.295            0.298
Chain 1:   1700       -20131.086             0.205            0.297
Chain 1:   1800       -20074.522             0.167            0.155
Chain 1:   1900       -20399.925             0.139            0.056
Chain 1:   2000       -18912.146             0.117            0.056
Chain 1:   2100       -19150.279             0.086            0.037
Chain 1:   2200       -19376.543             0.085            0.037
Chain 1:   2300       -18994.086             0.040            0.020
Chain 1:   2400       -18766.403             0.040            0.020
Chain 1:   2500       -18568.683             0.026            0.016
Chain 1:   2600       -18199.445             0.024            0.016
Chain 1:   2700       -18156.455             0.019            0.012
Chain 1:   2800       -17873.848             0.020            0.016
Chain 1:   2900       -18154.694             0.020            0.015
Chain 1:   3000       -18140.845             0.012            0.012
Chain 1:   3100       -18225.828             0.011            0.012
Chain 1:   3200       -17916.910             0.012            0.015
Chain 1:   3300       -18121.248             0.011            0.012
Chain 1:   3400       -17597.121             0.013            0.015
Chain 1:   3500       -18207.724             0.015            0.016
Chain 1:   3600       -17515.931             0.017            0.016
Chain 1:   3700       -17901.679             0.019            0.017
Chain 1:   3800       -16863.939             0.024            0.022
Chain 1:   3900       -16860.152             0.022            0.022
Chain 1:   4000       -16977.411             0.023            0.022
Chain 1:   4100       -16891.423             0.023            0.022
Chain 1:   4200       -16708.107             0.022            0.022
Chain 1:   4300       -16846.139             0.022            0.022
Chain 1:   4400       -16803.398             0.019            0.011
Chain 1:   4500       -16706.024             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001391 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49059.331             1.000            1.000
Chain 1:    200       -23412.592             1.048            1.095
Chain 1:    300       -18299.574             0.792            1.000
Chain 1:    400       -14098.977             0.668            1.000
Chain 1:    500       -14863.494             0.545            0.298
Chain 1:    600       -12280.166             0.489            0.298
Chain 1:    700       -11763.350             0.426            0.279
Chain 1:    800       -11663.001             0.373            0.279
Chain 1:    900       -11581.871             0.333            0.210
Chain 1:   1000       -13561.066             0.314            0.210
Chain 1:   1100       -14063.786             0.218            0.146
Chain 1:   1200       -14435.281             0.111            0.051
Chain 1:   1300       -11288.964             0.111            0.051
Chain 1:   1400       -10666.931             0.087            0.051
Chain 1:   1500       -10160.737             0.086            0.050
Chain 1:   1600       -10825.728             0.072            0.050
Chain 1:   1700       -12861.360             0.083            0.058
Chain 1:   1800       -12743.526             0.083            0.058
Chain 1:   1900       -10158.673             0.108            0.061
Chain 1:   2000       -10203.757             0.094            0.058
Chain 1:   2100       -12498.504             0.108            0.061
Chain 1:   2200       -10912.019             0.120            0.145
Chain 1:   2300       -10649.138             0.095            0.061
Chain 1:   2400        -9606.785             0.100            0.109
Chain 1:   2500       -10905.939             0.107            0.119
Chain 1:   2600        -9813.426             0.112            0.119
Chain 1:   2700       -17447.656             0.140            0.119
Chain 1:   2800        -9821.844             0.217            0.145
Chain 1:   2900        -9586.667             0.194            0.119
Chain 1:   3000        -9520.290             0.194            0.119
Chain 1:   3100       -10853.946             0.188            0.119
Chain 1:   3200        -9275.810             0.190            0.119
Chain 1:   3300       -14954.173             0.226            0.123
Chain 1:   3400       -12532.107             0.234            0.170
Chain 1:   3500        -9791.876             0.250            0.193
Chain 1:   3600       -10016.744             0.241            0.193
Chain 1:   3700       -20650.659             0.249            0.193
Chain 1:   3800       -11522.298             0.251            0.193
Chain 1:   3900        -9697.650             0.267            0.193
Chain 1:   4000        -8958.012             0.275            0.193
Chain 1:   4100        -9479.632             0.268            0.193
Chain 1:   4200       -10636.292             0.262            0.193
Chain 1:   4300       -13313.274             0.244            0.193
Chain 1:   4400        -9309.118             0.268            0.201
Chain 1:   4500       -16477.051             0.283            0.201
Chain 1:   4600        -9085.312             0.362            0.430
Chain 1:   4700        -9200.703             0.312            0.201
Chain 1:   4800        -9226.191             0.233            0.188
Chain 1:   4900        -9214.540             0.214            0.109
Chain 1:   5000        -9689.260             0.211            0.109
Chain 1:   5100        -8608.712             0.218            0.126
Chain 1:   5200       -14443.584             0.247            0.201
Chain 1:   5300       -10122.554             0.270            0.404
Chain 1:   5400       -14590.592             0.258            0.306
Chain 1:   5500       -10421.134             0.254            0.306
Chain 1:   5600       -14999.499             0.203            0.305
Chain 1:   5700       -12666.306             0.221            0.305
Chain 1:   5800        -8805.199             0.264            0.306
Chain 1:   5900        -9172.975             0.268            0.306
Chain 1:   6000       -11574.796             0.284            0.306
Chain 1:   6100        -8806.992             0.303            0.314
Chain 1:   6200        -9780.649             0.272            0.306
Chain 1:   6300        -8667.631             0.242            0.305
Chain 1:   6400       -10189.246             0.227            0.208
Chain 1:   6500       -12679.195             0.206            0.196
Chain 1:   6600        -9085.927             0.215            0.196
Chain 1:   6700        -9176.398             0.198            0.196
Chain 1:   6800        -9031.955             0.156            0.149
Chain 1:   6900       -12424.243             0.179            0.196
Chain 1:   7000        -8566.291             0.203            0.196
Chain 1:   7100       -10855.079             0.193            0.196
Chain 1:   7200        -9124.776             0.202            0.196
Chain 1:   7300       -11730.429             0.211            0.211
Chain 1:   7400        -9934.049             0.214            0.211
Chain 1:   7500        -8958.376             0.206            0.211
Chain 1:   7600        -8493.176             0.172            0.190
Chain 1:   7700        -8913.611             0.175            0.190
Chain 1:   7800        -9807.415             0.183            0.190
Chain 1:   7900       -11500.992             0.170            0.181
Chain 1:   8000        -8779.328             0.156            0.181
Chain 1:   8100        -9131.619             0.139            0.147
Chain 1:   8200        -8928.844             0.122            0.109
Chain 1:   8300        -9779.538             0.109            0.091
Chain 1:   8400       -14032.256             0.121            0.091
Chain 1:   8500        -9721.685             0.155            0.091
Chain 1:   8600        -8751.512             0.160            0.111
Chain 1:   8700        -8331.403             0.160            0.111
Chain 1:   8800        -8445.619             0.153            0.111
Chain 1:   8900       -10799.711             0.160            0.111
Chain 1:   9000        -9433.583             0.143            0.111
Chain 1:   9100        -8614.512             0.149            0.111
Chain 1:   9200        -8331.825             0.150            0.111
Chain 1:   9300        -9225.799             0.151            0.111
Chain 1:   9400        -8466.536             0.130            0.097
Chain 1:   9500       -12158.764             0.116            0.097
Chain 1:   9600        -9907.937             0.127            0.097
Chain 1:   9700        -8500.666             0.139            0.145
Chain 1:   9800        -8377.724             0.139            0.145
Chain 1:   9900       -10614.055             0.138            0.145
Chain 1:   10000        -9249.427             0.138            0.148
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001655 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.55 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -57522.037             1.000            1.000
Chain 1:    200       -17547.156             1.639            2.278
Chain 1:    300        -8846.776             1.421            1.000
Chain 1:    400        -8243.626             1.084            1.000
Chain 1:    500        -8581.053             0.875            0.983
Chain 1:    600        -8328.761             0.734            0.983
Chain 1:    700        -8467.219             0.632            0.073
Chain 1:    800        -8204.869             0.557            0.073
Chain 1:    900        -8071.224             0.497            0.039
Chain 1:   1000        -7885.897             0.449            0.039
Chain 1:   1100        -7878.359             0.349            0.032
Chain 1:   1200        -7651.350             0.125            0.030
Chain 1:   1300        -7864.330             0.029            0.030
Chain 1:   1400        -7924.166             0.022            0.027
Chain 1:   1500        -7667.563             0.022            0.027
Chain 1:   1600        -7848.669             0.021            0.024
Chain 1:   1700        -7592.711             0.023            0.027
Chain 1:   1800        -7642.838             0.020            0.024
Chain 1:   1900        -7679.348             0.019            0.024
Chain 1:   2000        -7692.593             0.017            0.023
Chain 1:   2100        -7672.009             0.017            0.023
Chain 1:   2200        -7790.950             0.016            0.015
Chain 1:   2300        -7651.557             0.015            0.015
Chain 1:   2400        -7708.721             0.015            0.015
Chain 1:   2500        -7649.705             0.012            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.6 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86243.961             1.000            1.000
Chain 1:    200       -13698.893             3.148            5.296
Chain 1:    300       -10045.296             2.220            1.000
Chain 1:    400       -10904.684             1.685            1.000
Chain 1:    500        -9030.815             1.389            0.364
Chain 1:    600        -8983.190             1.159            0.364
Chain 1:    700        -8573.774             1.000            0.207
Chain 1:    800        -8805.695             0.878            0.207
Chain 1:    900        -8819.369             0.781            0.079
Chain 1:   1000        -8526.977             0.706            0.079
Chain 1:   1100        -8866.009             0.610            0.048   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8452.735             0.085            0.048
Chain 1:   1300        -8722.487             0.052            0.038
Chain 1:   1400        -8690.268             0.044            0.034
Chain 1:   1500        -8583.810             0.025            0.031
Chain 1:   1600        -8692.983             0.026            0.031
Chain 1:   1700        -8773.173             0.022            0.026
Chain 1:   1800        -8349.405             0.024            0.031
Chain 1:   1900        -8450.510             0.025            0.031
Chain 1:   2000        -8424.948             0.022            0.013
Chain 1:   2100        -8550.560             0.020            0.013
Chain 1:   2200        -8353.411             0.017            0.013
Chain 1:   2300        -8445.314             0.015            0.012
Chain 1:   2400        -8514.070             0.016            0.012
Chain 1:   2500        -8460.325             0.015            0.012
Chain 1:   2600        -8461.714             0.014            0.011
Chain 1:   2700        -8378.422             0.014            0.011
Chain 1:   2800        -8338.261             0.009            0.010   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003772 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8380164.729             1.000            1.000
Chain 1:    200     -1584734.594             2.644            4.288
Chain 1:    300      -890593.117             2.022            1.000
Chain 1:    400      -457117.667             1.754            1.000
Chain 1:    500      -357414.093             1.459            0.948
Chain 1:    600      -232770.374             1.305            0.948
Chain 1:    700      -119239.959             1.255            0.948
Chain 1:    800       -86490.138             1.145            0.948
Chain 1:    900       -66892.673             1.050            0.779
Chain 1:   1000       -51723.732             0.975            0.779
Chain 1:   1100       -39223.769             0.907            0.535   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38409.111             0.480            0.379
Chain 1:   1300       -26384.657             0.448            0.379
Chain 1:   1400       -26107.587             0.354            0.319
Chain 1:   1500       -22698.246             0.341            0.319
Chain 1:   1600       -21916.088             0.291            0.293
Chain 1:   1700       -20791.839             0.201            0.293
Chain 1:   1800       -20736.671             0.164            0.150
Chain 1:   1900       -21062.948             0.136            0.054
Chain 1:   2000       -19574.674             0.114            0.054
Chain 1:   2100       -19813.298             0.083            0.036
Chain 1:   2200       -20039.499             0.082            0.036
Chain 1:   2300       -19656.836             0.039            0.019
Chain 1:   2400       -19428.871             0.039            0.019
Chain 1:   2500       -19230.739             0.025            0.015
Chain 1:   2600       -18861.051             0.023            0.015
Chain 1:   2700       -18818.055             0.018            0.012
Chain 1:   2800       -18534.741             0.019            0.015
Chain 1:   2900       -18816.038             0.019            0.015
Chain 1:   3000       -18802.366             0.012            0.012
Chain 1:   3100       -18887.302             0.011            0.012
Chain 1:   3200       -18577.971             0.012            0.015
Chain 1:   3300       -18782.725             0.011            0.012
Chain 1:   3400       -18257.514             0.012            0.015
Chain 1:   3500       -18869.514             0.015            0.015
Chain 1:   3600       -18176.065             0.016            0.015
Chain 1:   3700       -18562.910             0.018            0.017
Chain 1:   3800       -17522.358             0.023            0.021
Chain 1:   3900       -17518.460             0.021            0.021
Chain 1:   4000       -17635.815             0.022            0.021
Chain 1:   4100       -17549.492             0.022            0.021
Chain 1:   4200       -17365.725             0.021            0.021
Chain 1:   4300       -17504.175             0.021            0.021
Chain 1:   4400       -17460.958             0.018            0.011
Chain 1:   4500       -17363.446             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001468 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.68 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -48815.124             1.000            1.000
Chain 1:    200       -17246.689             1.415            1.830
Chain 1:    300       -18871.305             0.972            1.000
Chain 1:    400       -12251.442             0.864            1.000
Chain 1:    500       -14758.319             0.725            0.540
Chain 1:    600       -16334.075             0.621            0.540
Chain 1:    700       -14880.673             0.546            0.170
Chain 1:    800       -20090.411             0.510            0.259
Chain 1:    900       -11093.998             0.543            0.259
Chain 1:   1000       -14889.154             0.515            0.259
Chain 1:   1100       -21346.111             0.445            0.259
Chain 1:   1200       -10616.972             0.363            0.259
Chain 1:   1300       -12588.686             0.370            0.259
Chain 1:   1400       -21971.753             0.359            0.259
Chain 1:   1500       -10242.618             0.456            0.302
Chain 1:   1600        -9802.026             0.451            0.302
Chain 1:   1700        -9997.846             0.443            0.302
Chain 1:   1800       -10041.502             0.418            0.302
Chain 1:   1900       -10170.998             0.338            0.255
Chain 1:   2000       -19126.513             0.359            0.302
Chain 1:   2100       -19781.079             0.332            0.157
Chain 1:   2200       -10620.976             0.317            0.157
Chain 1:   2300       -11606.466             0.310            0.085
Chain 1:   2400        -9449.421             0.290            0.085
Chain 1:   2500        -9562.893             0.177            0.045
Chain 1:   2600        -9262.030             0.176            0.033
Chain 1:   2700        -9256.440             0.174            0.033
Chain 1:   2800       -13516.944             0.205            0.085
Chain 1:   2900        -8972.927             0.254            0.228
Chain 1:   3000       -19939.348             0.263            0.228
Chain 1:   3100        -8952.893             0.382            0.315
Chain 1:   3200        -9616.477             0.303            0.228
Chain 1:   3300       -17445.929             0.339            0.315
Chain 1:   3400        -9429.292             0.401            0.449
Chain 1:   3500        -9682.144             0.403            0.449
Chain 1:   3600        -8899.713             0.408            0.449
Chain 1:   3700        -9555.716             0.415            0.449
Chain 1:   3800       -10199.800             0.390            0.449
Chain 1:   3900        -9587.644             0.345            0.088
Chain 1:   4000       -13490.732             0.319            0.088
Chain 1:   4100        -9909.972             0.233            0.088
Chain 1:   4200       -10828.731             0.234            0.088
Chain 1:   4300       -14263.607             0.214            0.088
Chain 1:   4400       -10544.380             0.164            0.088
Chain 1:   4500        -9562.724             0.172            0.103
Chain 1:   4600       -13725.081             0.193            0.241
Chain 1:   4700       -16509.437             0.203            0.241
Chain 1:   4800       -10833.215             0.249            0.289
Chain 1:   4900       -12550.890             0.256            0.289
Chain 1:   5000       -14294.494             0.240            0.241
Chain 1:   5100        -9785.302             0.250            0.241
Chain 1:   5200       -11795.781             0.258            0.241
Chain 1:   5300        -9441.894             0.259            0.249
Chain 1:   5400       -10252.461             0.232            0.170
Chain 1:   5500       -10170.995             0.222            0.170
Chain 1:   5600       -11492.570             0.203            0.169
Chain 1:   5700        -8807.929             0.217            0.170
Chain 1:   5800        -9465.909             0.172            0.137
Chain 1:   5900        -9027.556             0.163            0.122
Chain 1:   6000        -9378.603             0.154            0.115
Chain 1:   6100        -8612.447             0.117            0.089
Chain 1:   6200        -8472.120             0.102            0.079
Chain 1:   6300        -8487.634             0.077            0.070
Chain 1:   6400       -13026.569             0.104            0.070
Chain 1:   6500        -9567.567             0.139            0.089
Chain 1:   6600        -8720.966             0.137            0.089
Chain 1:   6700       -11926.699             0.134            0.089
Chain 1:   6800        -9130.453             0.158            0.097
Chain 1:   6900       -10584.936             0.166            0.137
Chain 1:   7000        -8542.144             0.187            0.239
Chain 1:   7100        -9022.246             0.183            0.239
Chain 1:   7200        -8264.196             0.191            0.239
Chain 1:   7300        -8693.137             0.195            0.239
Chain 1:   7400       -11895.184             0.187            0.239
Chain 1:   7500        -9564.112             0.176            0.239
Chain 1:   7600        -8644.335             0.177            0.239
Chain 1:   7700        -8602.379             0.150            0.137
Chain 1:   7800       -10444.448             0.137            0.137
Chain 1:   7900        -8310.690             0.149            0.176
Chain 1:   8000       -10346.014             0.145            0.176
Chain 1:   8100        -8464.460             0.162            0.197
Chain 1:   8200       -12580.587             0.185            0.222
Chain 1:   8300        -8752.093             0.224            0.244
Chain 1:   8400        -8907.747             0.199            0.222
Chain 1:   8500        -9389.810             0.180            0.197
Chain 1:   8600       -11701.345             0.189            0.198
Chain 1:   8700       -10579.322             0.199            0.198
Chain 1:   8800        -8717.170             0.203            0.214
Chain 1:   8900       -10042.289             0.190            0.198
Chain 1:   9000       -10847.495             0.178            0.198
Chain 1:   9100       -11406.436             0.161            0.132
Chain 1:   9200        -8648.867             0.160            0.132
Chain 1:   9300        -8074.750             0.123            0.106
Chain 1:   9400        -8350.772             0.125            0.106
Chain 1:   9500       -10410.938             0.139            0.132
Chain 1:   9600       -10255.761             0.121            0.106
Chain 1:   9700       -10113.548             0.112            0.074
Chain 1:   9800        -8324.500             0.112            0.074
Chain 1:   9900        -8365.547             0.099            0.071
Chain 1:   10000        -8216.140             0.094            0.049
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003212 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 32.12 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -50960.257             1.000            1.000
Chain 1:    200       -16239.245             1.569            2.138
Chain 1:    300        -8714.239             1.334            1.000
Chain 1:    400        -8540.136             1.006            1.000
Chain 1:    500        -8299.465             0.810            0.864
Chain 1:    600        -8088.963             0.680            0.864
Chain 1:    700        -7745.738             0.589            0.044
Chain 1:    800        -8250.908             0.523            0.061
Chain 1:    900        -7558.240             0.475            0.061
Chain 1:   1000        -7924.716             0.432            0.061
Chain 1:   1100        -7602.673             0.336            0.046
Chain 1:   1200        -7527.162             0.123            0.044
Chain 1:   1300        -7740.698             0.040            0.042
Chain 1:   1400        -7662.748             0.039            0.042
Chain 1:   1500        -7574.747             0.037            0.042
Chain 1:   1600        -7787.560             0.037            0.042
Chain 1:   1700        -7524.848             0.036            0.035
Chain 1:   1800        -7600.227             0.031            0.028
Chain 1:   1900        -7586.352             0.022            0.027
Chain 1:   2000        -7548.791             0.018            0.012
Chain 1:   2100        -7575.979             0.014            0.010
Chain 1:   2200        -7680.328             0.015            0.012
Chain 1:   2300        -7579.444             0.013            0.012
Chain 1:   2400        -7606.509             0.012            0.012
Chain 1:   2500        -7558.837             0.012            0.010   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003585 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -86076.365             1.000            1.000
Chain 1:    200       -13472.440             3.195            5.389
Chain 1:    300        -9900.103             2.250            1.000
Chain 1:    400       -10638.218             1.705            1.000
Chain 1:    500        -8833.924             1.405            0.361
Chain 1:    600        -8717.928             1.173            0.361
Chain 1:    700        -8560.981             1.008            0.204
Chain 1:    800        -8652.955             0.883            0.204
Chain 1:    900        -8765.272             0.787            0.069
Chain 1:   1000        -8506.071             0.711            0.069
Chain 1:   1100        -8706.922             0.613            0.030   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -8422.179             0.078            0.030
Chain 1:   1300        -8631.333             0.044            0.024
Chain 1:   1400        -8612.364             0.037            0.023
Chain 1:   1500        -8511.861             0.018            0.018
Chain 1:   1600        -8613.397             0.018            0.018
Chain 1:   1700        -8700.651             0.017            0.013
Chain 1:   1800        -8307.209             0.021            0.023
Chain 1:   1900        -8408.825             0.021            0.023
Chain 1:   2000        -8379.351             0.018            0.012
Chain 1:   2100        -8503.629             0.017            0.012
Chain 1:   2200        -8287.545             0.016            0.012
Chain 1:   2300        -8437.604             0.016            0.012
Chain 1:   2400        -8452.435             0.016            0.012
Chain 1:   2500        -8420.371             0.015            0.012
Chain 1:   2600        -8422.605             0.014            0.012
Chain 1:   2700        -8329.132             0.014            0.012
Chain 1:   2800        -8301.250             0.009            0.011   MEAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003305 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 33.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8405352.307             1.000            1.000
Chain 1:    200     -1586659.477             2.649            4.298
Chain 1:    300      -892523.311             2.025            1.000
Chain 1:    400      -458306.263             1.756            1.000
Chain 1:    500      -358293.945             1.460            0.947
Chain 1:    600      -233131.874             1.306            0.947
Chain 1:    700      -119267.920             1.256            0.947
Chain 1:    800       -86418.785             1.147            0.947
Chain 1:    900       -66755.751             1.052            0.778
Chain 1:   1000       -51540.291             0.976            0.778
Chain 1:   1100       -39008.386             0.908            0.537   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38180.097             0.481            0.380
Chain 1:   1300       -26142.194             0.449            0.380
Chain 1:   1400       -25858.764             0.355            0.321
Chain 1:   1500       -22447.353             0.343            0.321
Chain 1:   1600       -21663.096             0.293            0.295
Chain 1:   1700       -20538.632             0.203            0.295
Chain 1:   1800       -20482.717             0.165            0.152
Chain 1:   1900       -20808.405             0.137            0.055
Chain 1:   2000       -19321.043             0.115            0.055
Chain 1:   2100       -19559.341             0.084            0.036
Chain 1:   2200       -19785.320             0.083            0.036
Chain 1:   2300       -19403.086             0.039            0.020
Chain 1:   2400       -19175.382             0.039            0.020
Chain 1:   2500       -18977.198             0.025            0.016
Chain 1:   2600       -18607.987             0.024            0.016
Chain 1:   2700       -18565.128             0.018            0.012
Chain 1:   2800       -18282.104             0.020            0.015
Chain 1:   2900       -18563.142             0.020            0.015
Chain 1:   3000       -18549.445             0.012            0.012
Chain 1:   3100       -18634.326             0.011            0.012
Chain 1:   3200       -18325.322             0.012            0.015
Chain 1:   3300       -18529.791             0.011            0.012
Chain 1:   3400       -18005.186             0.013            0.015
Chain 1:   3500       -18616.274             0.015            0.015
Chain 1:   3600       -17924.066             0.017            0.015
Chain 1:   3700       -18310.017             0.019            0.017
Chain 1:   3800       -17271.333             0.023            0.021
Chain 1:   3900       -17267.511             0.022            0.021
Chain 1:   4000       -17384.842             0.022            0.021
Chain 1:   4100       -17298.648             0.022            0.021
Chain 1:   4200       -17115.270             0.022            0.021
Chain 1:   4300       -17253.424             0.021            0.021
Chain 1:   4400       -17210.554             0.019            0.011
Chain 1:   4500       -17113.138             0.016            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.00147 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 14.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Success! Found best value [eta = 10] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -49379.661             1.000            1.000
Chain 1:    200       -18896.868             1.307            1.613
Chain 1:    300       -16303.767             0.924            1.000
Chain 1:    400       -14367.805             0.727            1.000
Chain 1:    500       -15241.111             0.593            0.159
Chain 1:    600       -16908.975             0.510            0.159
Chain 1:    700       -15864.597             0.447            0.135
Chain 1:    800       -15039.146             0.398            0.135
Chain 1:    900       -11337.170             0.390            0.135
Chain 1:   1000       -13927.884             0.370            0.159
Chain 1:   1100       -12432.211             0.282            0.135
Chain 1:   1200       -12466.110             0.121            0.120
Chain 1:   1300       -12310.132             0.106            0.099
Chain 1:   1400       -17946.076             0.124            0.099
Chain 1:   1500       -12789.458             0.158            0.120
Chain 1:   1600       -10458.158             0.171            0.186
Chain 1:   1700       -10161.175             0.167            0.186
Chain 1:   1800       -14328.807             0.191            0.223
Chain 1:   1900       -10084.517             0.200            0.223
Chain 1:   2000       -10709.781             0.188            0.223
Chain 1:   2100       -11173.714             0.180            0.223
Chain 1:   2200       -11054.354             0.180            0.223
Chain 1:   2300        -9795.397             0.192            0.223
Chain 1:   2400        -9957.789             0.162            0.129
Chain 1:   2500       -17233.371             0.164            0.129
Chain 1:   2600        -9637.838             0.221            0.129
Chain 1:   2700       -11066.207             0.231            0.129
Chain 1:   2800       -12080.980             0.210            0.129
Chain 1:   2900        -9984.758             0.189            0.129
Chain 1:   3000        -9720.816             0.186            0.129
Chain 1:   3100       -10801.880             0.192            0.129
Chain 1:   3200        -9278.013             0.207            0.129
Chain 1:   3300       -14753.109             0.231            0.164
Chain 1:   3400       -19065.587             0.252            0.210
Chain 1:   3500        -9265.432             0.316            0.210
Chain 1:   3600       -18935.737             0.288            0.210
Chain 1:   3700       -10149.384             0.362            0.226
Chain 1:   3800        -9074.816             0.365            0.226
Chain 1:   3900       -10469.585             0.357            0.226
Chain 1:   4000       -10297.134             0.356            0.226
Chain 1:   4100        -9305.987             0.357            0.226
Chain 1:   4200       -12847.417             0.368            0.276
Chain 1:   4300       -12170.113             0.337            0.226
Chain 1:   4400        -9528.071             0.342            0.276
Chain 1:   4500       -10978.052             0.249            0.133
Chain 1:   4600       -14601.847             0.223            0.133
Chain 1:   4700        -9296.301             0.193            0.133
Chain 1:   4800        -9116.368             0.184            0.133
Chain 1:   4900        -8955.596             0.172            0.132
Chain 1:   5000       -10431.496             0.185            0.141
Chain 1:   5100        -9763.933             0.181            0.141
Chain 1:   5200       -13011.584             0.178            0.141
Chain 1:   5300        -9544.834             0.209            0.248
Chain 1:   5400        -8740.622             0.190            0.141
Chain 1:   5500        -8841.042             0.178            0.141
Chain 1:   5600        -9793.009             0.163            0.097
Chain 1:   5700       -10742.877             0.115            0.092
Chain 1:   5800       -10908.877             0.114            0.092
Chain 1:   5900       -16404.658             0.146            0.097
Chain 1:   6000        -9216.843             0.210            0.097
Chain 1:   6100       -11451.119             0.223            0.195
Chain 1:   6200        -9553.605             0.218            0.195
Chain 1:   6300       -13316.111             0.210            0.195
Chain 1:   6400       -11162.787             0.220            0.195
Chain 1:   6500        -9611.566             0.235            0.195
Chain 1:   6600        -9423.745             0.227            0.195
Chain 1:   6700        -8737.979             0.226            0.195
Chain 1:   6800        -9271.512             0.230            0.195
Chain 1:   6900       -12053.157             0.220            0.195
Chain 1:   7000        -9056.151             0.175            0.195
Chain 1:   7100        -8756.386             0.159            0.193
Chain 1:   7200       -11748.882             0.164            0.193
Chain 1:   7300        -8636.937             0.172            0.193
Chain 1:   7400        -8535.564             0.154            0.161
Chain 1:   7500        -8572.894             0.138            0.078
Chain 1:   7600        -8751.236             0.138            0.078
Chain 1:   7700       -12101.093             0.158            0.231
Chain 1:   7800       -11031.340             0.162            0.231
Chain 1:   7900        -8745.660             0.165            0.255
Chain 1:   8000        -9108.509             0.136            0.097
Chain 1:   8100        -8714.034             0.137            0.097
Chain 1:   8200        -8508.578             0.114            0.045
Chain 1:   8300       -11410.821             0.104            0.045
Chain 1:   8400        -8793.690             0.132            0.097
Chain 1:   8500        -9411.270             0.138            0.097
Chain 1:   8600       -12829.230             0.163            0.254
Chain 1:   8700        -9840.535             0.166            0.254
Chain 1:   8800        -9087.733             0.164            0.254
Chain 1:   8900        -9165.649             0.139            0.083
Chain 1:   9000       -11964.431             0.158            0.234
Chain 1:   9100        -8649.179             0.192            0.254
Chain 1:   9200       -11925.584             0.217            0.266
Chain 1:   9300        -8729.299             0.228            0.275
Chain 1:   9400        -9198.859             0.204            0.266
Chain 1:   9500        -8875.349             0.201            0.266
Chain 1:   9600        -8668.548             0.176            0.234
Chain 1:   9700        -8410.101             0.149            0.083
Chain 1:   9800       -12471.160             0.173            0.234
Chain 1:   9900       -11054.299             0.185            0.234
Chain 1:   10000        -8384.370             0.194            0.275
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.001689 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.89 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -58710.319             1.000            1.000
Chain 1:    200       -18247.952             1.609            2.217
Chain 1:    300        -8918.545             1.421            1.046
Chain 1:    400        -8095.467             1.091            1.046
Chain 1:    500        -8345.525             0.879            1.000
Chain 1:    600        -8478.934             0.735            1.000
Chain 1:    700        -8117.253             0.636            0.102
Chain 1:    800        -8440.489             0.562            0.102
Chain 1:    900        -8041.977             0.505            0.050
Chain 1:   1000        -7821.795             0.457            0.050
Chain 1:   1100        -7787.819             0.358            0.045
Chain 1:   1200        -7645.242             0.138            0.038
Chain 1:   1300        -7810.197             0.035            0.030
Chain 1:   1400        -7835.490             0.025            0.028
Chain 1:   1500        -7559.588             0.026            0.028
Chain 1:   1600        -7774.976             0.027            0.028
Chain 1:   1700        -7684.125             0.024            0.028
Chain 1:   1800        -7597.954             0.021            0.021
Chain 1:   1900        -7616.253             0.017            0.019
Chain 1:   2000        -7643.450             0.014            0.012
Chain 1:   2100        -7566.947             0.015            0.012
Chain 1:   2200        -7838.244             0.016            0.012
Chain 1:   2300        -7588.932             0.017            0.012
Chain 1:   2400        -7588.677             0.017            0.012
Chain 1:   2500        -7623.363             0.014            0.011
Chain 1:   2600        -7536.573             0.012            0.011
Chain 1:   2700        -7450.376             0.012            0.011
Chain 1:   2800        -7637.254             0.014            0.012
Chain 1:   2900        -7384.159             0.017            0.012
Chain 1:   3000        -7548.043             0.019            0.022
Chain 1:   3100        -7527.664             0.018            0.022
Chain 1:   3200        -7750.472             0.017            0.022
Chain 1:   3300        -7453.428             0.018            0.022
Chain 1:   3400        -7705.392             0.021            0.024
Chain 1:   3500        -7447.359             0.024            0.029
Chain 1:   3600        -7507.425             0.024            0.029
Chain 1:   3700        -7462.838             0.023            0.029
Chain 1:   3800        -7531.414             0.022            0.029
Chain 1:   3900        -7425.353             0.020            0.022
Chain 1:   4000        -7409.373             0.018            0.014
Chain 1:   4100        -7425.150             0.018            0.014
Chain 1:   4200        -7464.676             0.015            0.009   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.003785 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.85 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100       -85928.778             1.000            1.000
Chain 1:    200       -13991.578             3.071            5.141
Chain 1:    300       -10279.598             2.168            1.000
Chain 1:    400       -11614.235             1.654            1.000
Chain 1:    500        -9277.782             1.374            0.361
Chain 1:    600        -9317.406             1.146            0.361
Chain 1:    700        -9582.024             0.986            0.252
Chain 1:    800        -8801.061             0.874            0.252
Chain 1:    900        -8628.239             0.779            0.115
Chain 1:   1000        -9313.358             0.708            0.115
Chain 1:   1100        -8839.205             0.614            0.089   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200        -9177.911             0.103            0.074
Chain 1:   1300        -8710.506             0.073            0.054
Chain 1:   1400        -8798.073             0.062            0.054
Chain 1:   1500        -8721.299             0.038            0.037
Chain 1:   1600        -8729.201             0.037            0.037
Chain 1:   1700        -8617.514             0.036            0.037
Chain 1:   1800        -8675.096             0.028            0.020
Chain 1:   1900        -8551.363             0.027            0.014
Chain 1:   2000        -8613.579             0.021            0.013
Chain 1:   2100        -8756.949             0.017            0.013
Chain 1:   2200        -8552.581             0.015            0.013
Chain 1:   2300        -8705.882             0.012            0.013
Chain 1:   2400        -8544.767             0.013            0.014
Chain 1:   2500        -8615.353             0.013            0.014
Chain 1:   2600        -8527.153             0.014            0.014
Chain 1:   2700        -8561.345             0.013            0.014
Chain 1:   2800        -8521.209             0.013            0.014
Chain 1:   2900        -8614.617             0.012            0.011
Chain 1:   3000        -8447.807             0.013            0.016
Chain 1:   3100        -8603.782             0.014            0.018
Chain 1:   3200        -8475.757             0.013            0.015
Chain 1:   3300        -8483.456             0.011            0.011
Chain 1:   3400        -8643.882             0.011            0.011
Chain 1:   3500        -8652.640             0.010            0.011
Chain 1:   3600        -8432.256             0.012            0.015
Chain 1:   3700        -8578.356             0.013            0.017
Chain 1:   3800        -8438.748             0.014            0.017
Chain 1:   3900        -8373.249             0.014            0.017
Chain 1:   4000        -8449.143             0.013            0.017
Chain 1:   4100        -8445.574             0.011            0.015
Chain 1:   4200        -8428.535             0.010            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.002497 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 24.97 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Iteration: 250 / 250 [100%]  (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1:    100     -8372609.774             1.000            1.000
Chain 1:    200     -1581496.207             2.647            4.294
Chain 1:    300      -892254.435             2.022            1.000
Chain 1:    400      -458569.949             1.753            1.000
Chain 1:    500      -359346.813             1.458            0.946
Chain 1:    600      -234331.944             1.304            0.946
Chain 1:    700      -120217.111             1.253            0.946
Chain 1:    800       -87284.548             1.144            0.946
Chain 1:    900       -67560.000             1.049            0.772
Chain 1:   1000       -52295.291             0.973            0.772
Chain 1:   1100       -39702.730             0.905            0.533   MAY BE DIVERGING... INSPECT ELBO
Chain 1:   1200       -38877.060             0.478            0.377
Chain 1:   1300       -26759.904             0.446            0.377
Chain 1:   1400       -26474.001             0.352            0.317
Chain 1:   1500       -23040.576             0.339            0.317
Chain 1:   1600       -22251.306             0.290            0.292
Chain 1:   1700       -21116.144             0.200            0.292
Chain 1:   1800       -21058.471             0.163            0.149
Chain 1:   1900       -21385.010             0.135            0.054
Chain 1:   2000       -19890.460             0.113            0.054
Chain 1:   2100       -20129.294             0.083            0.035
Chain 1:   2200       -20356.655             0.082            0.035
Chain 1:   2300       -19972.969             0.038            0.019
Chain 1:   2400       -19744.835             0.039            0.019
Chain 1:   2500       -19546.990             0.025            0.015
Chain 1:   2600       -19176.645             0.023            0.015
Chain 1:   2700       -19133.447             0.018            0.012
Chain 1:   2800       -18850.115             0.019            0.015
Chain 1:   2900       -19131.717             0.019            0.015
Chain 1:   3000       -19117.882             0.012            0.012
Chain 1:   3100       -19202.896             0.011            0.012
Chain 1:   3200       -18893.289             0.011            0.015
Chain 1:   3300       -19098.251             0.011            0.012
Chain 1:   3400       -18572.636             0.012            0.015
Chain 1:   3500       -19185.385             0.014            0.015
Chain 1:   3600       -18491.058             0.016            0.015
Chain 1:   3700       -18878.632             0.018            0.016
Chain 1:   3800       -17836.743             0.022            0.021
Chain 1:   3900       -17832.885             0.021            0.021
Chain 1:   4000       -17950.168             0.022            0.021
Chain 1:   4100       -17863.822             0.022            0.021
Chain 1:   4200       -17679.751             0.021            0.021
Chain 1:   4300       -17818.368             0.021            0.021
Chain 1:   4400       -17774.931             0.018            0.010
Chain 1:   4500       -17677.439             0.015            0.008   MEDIAN ELBO CONVERGED
Chain 1: 
Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
Chain 1: COMPLETED.
Setting 'QR' to TRUE can often be helpful when using one of the variational inference algorithms. See the documentation for the 'QR' argument.
# average over all simulations to output final table
final_table <- as.data.frame(apply(summary_stats, c(2,3), FUN=mean))
row.names(final_table) <-c('Oracle', 'Unadjusted', 'PMF', 'DEF')
names(final_table) <- c('RMSE', 'All', 'Causal', 'Non-causal')
final_table <-round(final_table, 2)
final_table
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